Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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1. Introduction
Although social workers provide diverse assistance, the incidence of suicide is still high in Taiwan [20]. However, due to cultural characteristics, people who own suicidal ideation often reluctant to seek help as well as passively wait for help. The social networking (SN) becomes one of the social tools. Some users utilize it to interact with their friends and express their mood or feelings in the SN.
Several real suicide cases are rescued by notifying from the messages of the SN [1] [2] , however, the evidence is not enough for endorsing amount of the budgets to emerge the suicide prevention (SP) process to the SN. Therefore, it is a problem for decision-makers to decide which user groups are the targets for the SP in the SN, what kind of the messages are keys for the SP and have to be extracted from the SN [10] , when is the best time to emerge the SP process to the SN, which region is the best place for trial, and which SN is the best adopting platform? The decision-making is not only medical-oriented, but also technology-oriented.
This chapter illustrates an explicit decision support process for management of software requirements elicitation and analysis. As Shi, et al. [15] illustrates their research outcomes by utilizing the Unified Modeling Language (UML) as the basis of their decision support system to help decision-makers to distinguish regional environmental risk zones. Similarly, Sutcliffe, et al. [19] tries to visualize the requirements by user-centred design (UCD) methods in their visual decision support tools to support public health professionals in their analysis activities. Our proposed process, Extensible Requirements Elicitation and Analysis Process (XREAP) [5] , is revised from part of the use case driven approach [7] [9]. Therefore, it is necessary for an analyst to understand the UML [8] visualization knowledge.
On the other hand, Perini and Susi extend their decision support system research to the environmental modelling and software field [11]. Their research approach is to hold interviews of producers, technicians anddomain experts as well as acquisition of domain documentation. Meanwhile, they also try to analyse actor roles and strategic dependencies among actors, goal-analysis and plan-analysis. Furthermore, Schlobinski, et al. [13] illustrates the user requirements that are derived from a UCD process to engage diverse user representatives for four cities in Europe.
Based on the knowledge sharing concept, Shafiei [14] and his team members develop a multi-enterprise collaborative decision support system for supply-chain management and show their idea is feasible. This evidence shows that the collaborative knowledge sharing is a possible route to promote the quality of the decision-making. Further, Cercone and his partners predict that their e-Health decision support system can find and verify evidence from multiple sources, lead to cost-effective use of drugs, improve patients’ quality of life, and optimize drug-related health outcomes [3]. That is, a series of the knowledge and evidence can be collected, shared and reused further for related fields as well as promote our health life to next higher e-Health generation.
Our proposed process includes functions to elicit the diverse requirements from users by utilizing the XREAP tool, analyses all requirements on-line, transforms the final requirements into use case diagram, and provides on-demand complexity metric. Essentially, the process can elicit sufficient sources for user requirements and provide enough complexity information for decision makers. In conclusion, we can straightforwardly understand the complexity between the diverse user requirements and even make an appropriate decision, whether it is the right time to move one of the specific SP activities toward one of the SN’s with our proposed process.
2. Background
A definition of suicide from [12] is death from injury, poisoning, or suffocation in which there is evidence that the injury was self-inflicted and that the deceased intended to kill him/her-self. The generation of suicidal behaviour is from suicidal ideation, which means any self-reported thoughts of engaging in suicide-related behaviour. Therefore, everyone who commits suicide will have suicidal ideation before s/he commits suicide;so suicidal ideation can be regarded as the motivation for suicide.
As the official report from the World Health Organization (WHO) [18] said that the world almost one million people die from suicide every year. That is, one death every 40 seconds in 2011. Surprisingly, a global map of suicide rates is drawn by the most recent year available as of 2011, which is also provided by the WHO, discloses that the suicide rate is beyond 16 per 100, 000 people in some countries. That is, one suicides oneself every 40 seconds. These countries, for example, at least include Lithuania (31. 5), South Korea (31. 0), Japan (24. 4), Russia (23. 5), Finland (18. 3), Belgium (17. 6), France (17. 0), Sweden (15. 8), South Africa (15. 4), and Hong Kong (15. 2) [20]. Therefore, the suicide behaviour is one of the implicit social problems for many countries.
Based on the above, it is necessary to reduce the suicidal ideation in order to decrease the occurrence of suicide. Shneidman, et al. [16] proposed a three-level prevention model to do exactly that. The model is divided into three program response categories: prevention, intervention and postvention. Within this three-level prevention model, prevention is to increase the protection factor and decrease the risk factor. The research team tries to focus on the second level of the three-level prevention model and analyses, whether moving SP to SN can elicit the high-risk group so that early detection can lead to early treatment.
3. Decision support process
The mission of the Taiwan Suicide Prevention Centre (TSPC) is tried to decrease the suicide rate. However, it was found that adolescents and young adults, for example, aged 15 to 24, are difficult for the TSPC to intervene to help them from the viewpoint of the TSPC managers. Therefore, the TSPC’s chairman called for a brainstorm meeting to invite a group of enthusiastic scholars and participants to find some feasible solutions to reduce the suicide rate of Taiwanese adolescents and young adults in 2010 [6]. Although there are several alternative solutions for the TSPC to promote the suicide prevention capacity, it is hard for the TSPC to decide which solution is the best one and worthwhile to invest substantial resources. Note that these alternatives are belonging to the preliminary decision, not final decision, in the TSPC meeting.
It is worth mentioning that the social networking, such as the Facebook, is one of the alternatives in the TSPC meeting. Anyhow, the social-networking service includes diverse online social platforms such as the Facebook, the Twitter, and the Google+. Hence it is necessary for us to be carefully considerate whether moving suicide prevention toward social networking, to propose our analysis outcomes, and to assist the TSPC chairman to make a final decision.
This study utilizes a requirements elicitation and analysis process, the XREAP [5] , to explore whether moving the SP to the SN is feasible. Because the XREAP is an exhausted approach to elicit the requirements from the execution domain, the outcomes of the XREAP tool will illustrate the overview of the required requirements. Therefore, the implicit needs will be extracted from the XREAP process, and the decision-makers will own most options and situations for further decision-making.
Furthermore, the XREAP tool is a requirements engineering utility that is based on the XREAP concept and is designed by Java programming language [5]. It is suitable for software-development process and acts as a role for eliciting and analysing the software requirements from users as well as generates a series of use case diagrams for further design [17]. Here our research team tries to adopt the XREAP tool in the decision support process, to generate a complex use case diagram, and to assist the TSPC managers to decide.
In Summary, the research team utilizes the XREAP tool to assist us to elicit, collect, and analyse the all possible requirements from the TSPC managers, users of social networking, information technologies, health promotion concepts, and social environment. That is, the XREAP tool is acted as a decision support process tool.
3.1. Execution procedures
This step utilizes at least two approaches. The first method enhances the requirements analysis integrity by plus-minus-interesting (PMI) and alternative-possibilities-choice (APC) thinking styles. The second one bases on both UML and Extensible Markup Language (XML) standards to cope with all activities. To understand the execution procedures of the XREAP tool, Figure 1 utilizes the UML state diagram to illustrate the execution procedures of the XREAP tool.
Figure 1.
Execution procedures of the XREAP tool
Explicitly, The XREAP tool owns four states and the presenting state, including another four sub-states such as TreeView, GridView, UseCaseDiagram, and XMLView. Meanwhile, the editing state includes two sub-states: TreeEditor and GridEditor. That is, the analyst can maintain the requirements between TreeView and GridView states and then transform to a use case diagram as well as save as the XML text format. The XML text format can also be read as the input file of the XREAP tool for further revising. The following sections illustrate these approaches, respectively.
3.2. Input requirements
Firstly, the PMI thinking style is shown in Figure 2 and categories the requirements by three views of points, including plus, minus, and interesting. This method will not only collect the stakeholder’s requirements, but also elicit the implicit requirements that do not mention by users. The first step of the PMI thinking is concentrated on the plus view of points. That is, the analyst must focus on the positive facet of the user requirements and record all requirements from users, and all possible derived needs. Similarly, the analyst has to utilize the same thinking process to achieve the minus and interesting facets, respectively.
Figure 2.
Graphical user interface for user requirements by categories
On the other hand, the APC thinking includes three parts: alternatives, possibilities, and choice. That is, the analyst has to focus on the requirements, actors, and use cases to consider the specific requirement for alternatives, feasibility, and decision-making. To facilitate the alternative generation, the APC thinking suggests at least ten progressive questions for further analyze and is shown in Figure 3. The illustration of detail processing is also listed as below.
Explanation (E): it asks for an analyst to describe the specific requirement again in order to confirm that the analyst understands the user illustration.
Assumption (A): the analyst has to confirm the specific requirement’s executive constraint.
Viewpoint (V): the analyst has to consider the specific requirement by different view of points.
Problem (P): the analyst might propose any questions for specific requirement.
Review (R): the analyst bases on the E, A, V, and P illustrations to consider again for specific requirement.
Design (D): the analyst summaries the R illustration and proposes a solution to handle the specific requirement.
Figure 3.
Sample collection of use requirements by grid
Note that the APC processing focuses on the specific requirement that is categorized by the PMI method. If an analyst finds any new requirement during the APC’s first five steps, the analyst should insert a fresh requirement to the requirements list. Then the analyst can elicit the actor from the specific requirement. Every actor also needs PMI and APC processing as well as it is possible to find some implicit actors. At last, the analyst can derive the use case from the specific requirements by treating the PMI and APC thinking. Similarly, it is also possible for an analyst to discover some implicit use cases during the whole processing.
This kind of the analysis means prevents an analyst only to elicit the favorable requirements from users and ignores the implicit requirements inadvertently. Ordinarily, most of the exceptions might be disregarded by the analyst during the system analysis phase and be inserted during the programming phase, even maintenance phase. Such a conventional analysis processing might waste a lot of time revising the system architecture and let the system weaker than original version. Accordingly, the PMI and APC processing can compensate the aforementioned drawback, try to elicit all possible requirements from users, and maintain the requirements’ integrity during system analysis phase.
In order to minimize the problem-solving scale, the decision-makers can utilize the divide-and-conquer methodology to decomposite the original problem to several independent sub-problems. That is, decision-makers can integrate all sub-problems’ solutions into one solution and make their final decision. For example, the social networking is a large field and includes several famous social websites such as the Facebook, the Twitter, the Google+, etc. Therefore, we can divide our original problem from “whether moving suicide prevention toward social networking” into “whether moving suicide prevention toward the Facebook social networking”, “whether moving suicide prevention toward the Twitter social networking”, and “whether moving suicide prevention toward the Google+ social networking. ”
3.3. Export use case diagram
As shown in Figure 4, a use case diagram is transformed from the XREAP grid collection format. In order to simplify the decision scope, we utilize the divide-and-conquer method to decompose our original problem and only consider the Facebook social networking part in this chapter. Therefore, Figure 4 shows the use case diagram of “whether moving suicide prevention toward the Facebook social networking. ”Note that the human icon represents an actor, the oval icon means use case, and the line represents the association between actors and use cases. Normally, the use case diagram is one-to-one mapping to the XREAP grid collection phase. Note that the use case diagram also reflects the original requirements listed in the XREAP tree collection phase.
Figure 4.
Use case diagram of whether moving suicide prevention toward the Facebook social networking
The analyst can modify the use case diagram. However, the reverse flow is not allowed by the XREAP tool. That is, the analyst has to roll back to the grid collection phase to revise the specific sources of the requirements’ illustration and then further transform a new use case diagram to replace the original diagram. Although such a modification procedure of the XREAP tool is not so convenience, anyhow, it urges the analysts to reconsider and confirm their requirements carefully, not unceremoniously.
4. Results
This research utilizes the grounded theory to prove the correction rate of the XREAP tool. The success of the XREAP approach can be indirectly proven by the comparison results of traditional method and the XREAP tool. The XREAP tool is a method for requirements elicitation and analysis. Alternatively, it can be adopted to list the problem variables, extract the implicit problems, and analyze the at-hand solutions.
The more association lines among actors and use cases, the more complex relationship with the requirements of the specific problem-solving. For example, a use case diagram with twenty association lines among its actors and use cases is absolutely complex than the other use case diagram with only five association lines.
As the use case diagram shown in Figure 4, the decision-makers can count on the numbers of the association lines among actors and use cases. That is, there are seven use cases and six actors that are associated with eleven directed association lines and five <<include>> dependency lines, one <<extend>> association line, and three generalization relationship linesfor implementing a virtual suicide prevention gatekeeper, Socio-Health, in the Facebook environment. Note that this case study only covers the adolescents and young adults in Taiwan.
The statistical table of shape items is also shown in Table 1 and the final score of the complexity calculation of the Socio-Health problem is 58. Note that the shape item of the use case is categorized as three levels: generic use case(s), included use case(s), and extended use case(s). A generic use case can include and/or extend one more use case. Therefore, the generic use case might own higher complexity weight than the included and extended use case(s). Based on our implementation experiences, the complexity of most included use cases is higher than the one of most extended use cases. Similarly, the shape item of the actor is also categorized into six levels: related to one use case, related to 2~4 use cases, related to 5~8 use cases, related to at least nine use cases, and generalized. The corresponding weights are assigned by their implementation complexities.
Table 2 shows the problem complexity assessment range for the analyst to estimate the final calculation of the XREAP tool. Based on the Table 2, the complexity score is below 100 is categorized as tiny problem and correspondingly easy to handle.
Based on complexity assessment for such a use case diagram, we can decide to execute these implementation tasks. Correspondingly, the generic decision-making by intuition for the same task might be also similar to the result for utilizing the XREAP tool and consider this task is a small task. However, our proposed process provides a visual and standard diagram for decision-makers to make their decision through understanding of their problems.
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
Shape Items
\n\t\t\t
Weight
\n\t\t\t
Number
\n\t\t\t
Calculation
\n\t\t
\n\t\t
\n\t\t\t
Use case
\n\t\t\t
Generic use case(s)
\n\t\t\t
5
\n\t\t\t
1
\n\t\t\t
5
\n\t\t
\n\t\t
\n\t\t\t
Included use case(s)
\n\t\t\t
3
\n\t\t\t
5
\n\t\t\t
15
\n\t\t
\n\t\t
\n\t\t\t
extended use case(s)
\n\t\t\t
2
\n\t\t\t
1
\n\t\t\t
2
\n\t\t
\n\t\t
\n\t\t\t
Actor
\n\t\t\t
Related to one use case
\n\t\t\t
1
\n\t\t\t
2
\n\t\t\t
2
\n\t\t
\n\t\t
\n\t\t\t
Related to 2~4 use cases
\n\t\t\t
2
\n\t\t\t
1
\n\t\t\t
2
\n\t\t
\n\t\t
\n\t\t\t
Related to 5~8 use cases
\n\t\t\t
3
\n\t\t\t
1
\n\t\t\t
3
\n\t\t
\n\t\t
\n\t\t\t
Related to 9+ use cases
\n\t\t\t
5
\n\t\t\t
0
\n\t\t\t
0
\n\t\t
\n\t\t
\n\t\t\t
Generalized
\n\t\t\t
1
\n\t\t\t
3
\n\t\t\t
3
\n\t\t
\n\t\t
\n\t\t\t
Association lines
\n\t\t\t
1
\n\t\t\t
11
\n\t\t\t
11
\n\t\t
\n\t\t
\n\t\t\t
<<include>> dependency line
\n
2
\n
5
\n
10
\n
\n
\n\t
<<extend>> association line
\n
2
\n
1
\n
2
\n
\n
\n\t
Generalization relationship line
\n\t
1
\n\t
3
\n\t
3
\n
\n
\n\t
Calculation of complexity weight
\n\t
\n\t
\n\t
58
\n
\n
Table 1.
Statistical table of shape items for utilizing XREAP tool
\n\t
\n\t
\n\t
\n\t\t
Problem Complexity Score
\n\t\t
Possible Assessment
\n\t
\n\t
\n\t\t
Less than 100
\n\t\t
Tiny problem
\n\t
\n\t
\n\t\t
101~200
\n\t\t
Small problem
\n\t
\n\t
\n\t\t
201~300
\n\t\t
Medium problem
\n\t
\n\t
\n\t\t
300~400
\n\t\t
Large problem
\n\t
\n\t
\n\t\t
Greater than 400
\n\t\t
Huge problem
\n\t
\n
Table 2.
Problem complexity assessment range
5. Discussion
Based on our empirical outcomes, the following arguments will focus on five significant concerns: limitation of the XREAP tool, the ratio of requirements elicitation, divide-and-conquer, complexity assessment, and decision-making guidelines.
5.1. Limitation of the XREAP tool
As the utilization of the XREAP tool to make some decisions for several projects, we found some pros and cons. They are listed in Table 3 for the analyst further reference. Furthermore, the XREAP tool owns some limitations. For example, the mind brainstorm function supports graphical user interface for user requirements by categories. That is, every PMI item can provide a number of the entries. However, the arrangement of the requirements’ map is not so concise that some of the requirements might be overlapped each other, and the screen will be too small to browse while every PMI item is more than 15 entries.
\n\t
\n\t
\n\t
\n\t\t
Pros
\n\t\t
Cons
\n\t
\n\t
\n\t\t
Is cross-platform
\n\t\t
Is a bit slow during execution
\n\t
\n\t
\n\t\t
Is visualization
\n\t\t
Is not beautiful on graphic user interface
\n\t
\n\t
\n\t\t
Supports mind brainstorm function
\n\t\t
Is not easy for utilization
\n\t
\n\t
\n\t\t
Can transfer from requirements to a use case diagram
\n\t\t
Cannot reverse transfer from a use case diagram to requirements
\n\t
\n\t
\n\t\t
Can exchange use case diagram with the XML metadata interchange standard
\n\t\t
Can only exchange with the Star UML tool
\n\t
\n\t
\n\t\t
Can be utilized as a decision support tool
\n\t\t
Does not yet include the calculation function of the complexity assessment
\n\t
\n
Table 3.
Pros and cons of the XREAP tool
5.2. The ratio of requirements elicitation
Fundamentally, the requirements elicitation is the first phase in our decision-making process. As most of the decision-makers known, the higher ratio of requirements elicitation is obtained, the better quality of decision-making will be executed. If decision-makers are eager for the highest quality of their decision-making, it is necessary for them to try to focus on the requirements elicitation phase. Fortunately, our proposed methodology can elicit required information from users by utilizing the XREAP tool. Meanwhile, the implicit information for persons, actions, tenancies, environment and equipment can be elicited by the XREAP tool as possible as it could extract from user requirements by both PMI and APC methods. Furthermore, all requirements are listed within a tabular frame in the XREAP tool, and it is convenient for the decision-makers to browse and review. As compared with other decision-making tools, we believe the XREAP tool can supply the exhaustive capability to elicit user requirements.
5.3. Divide-and-conquer
If the problem is too large to solve, it is feasible for problem-solvers to utilize the divide-and-conquer approach to decompose the problem into several smaller problems. If the smaller problem is still too large to handle, problem-solverscan divide such a problem again until they can cope with the scope of the problem. The divide-and-conquer methodology is widely used in several fields such as computer science. Similarly, the decision-makers are problem-solvers. Therefore, decision-makers can try to analyze the small problems one by one and integrate all solutions into a total solution for original problem.
5.4. Complexity assessment
Generally speaking, the complexity assessment is not an easy task. As our proposed methodology illustration, the complexity can be counted for the numbers of the actors and use cases in the final use case diagram. The more actors and use cases, the more complex interwoven network for requirements will be presented. Although the roughly count of the use cases and actors might be too simple to convey the complexity of the requirements, such a computation method is easy for decision-makers to confirm the existing input requirements quickly and repeatedly. However, it is possible for researchers to propose better complexity assessment for the XREAP tool in the future. Based on the complexity assessment results, decision-makers can conveniently make their decision.
5.5. Decision-making guidelines
Although the XREAP tool is one of the simple software for eliciting requirements, it can become a supplement to improve the decision-making quality for decision-makers. Normally, it is necessary for decision-makers to refer the decision-making guidelines that are gathered by other decision-makers. As the popularity of the Internet, it is possible for decision-makers to share and revise their decision-making guidelines in the cloud. Based on the knowledge management experiences from the healthcare field in 2008 [4] , it is feasible to share, revise and manage the specific knowledge through the network. That is, if the decision-making guidelines are utilized and revised by most decision-makers, then the optimal decision-making process will be generated.
6. Conclusion
It is a smart behaviour for decision-makers to spend more time to realize the whole views of the problems and solutions before they make wise decisions. However, an effective decision analysis tool is hard to obtain. The XREAP software is an optional choice for assisting decision-makers. As the tool results said, the SP service can be spread through SN, and it explores and assists the potential subjects who present the trend of suicide ideation.
Acknowledgements
The authors would like to thank all research colleagues in the National Suicide Prevention Centre, Taipei, Taiwan. The authorsalso express thanks for partial financial support from the National Science Council, Taiwan, under grant number NSC101-2220-E017-001.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/38959.pdf",chapterXML:"https://mts.intechopen.com/source/xml/38959.xml",downloadPdfUrl:"/chapter/pdf-download/38959",previewPdfUrl:"/chapter/pdf-preview/38959",totalDownloads:1843,totalViews:146,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,introChapter:null,impactScore:0,impactScorePercentile:14,impactScoreQuartile:1,hasAltmetrics:0,dateSubmitted:"March 5th 2012",dateReviewed:"July 30th 2012",datePrePublished:null,datePublished:"October 17th 2012",dateFinished:"September 11th 2012",readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/38959",risUrl:"/chapter/ris/38959",book:{id:"3070",slug:"decision-support-systems_2012"},signatures:"Po-Hsun Cheng, Heng-Shuen Chen, Wen-Chen Chiang and Hsin- Ciang Chang",authors:[{id:"3422",title:"Dr.",name:"Heng-Shuen",middleName:null,surname:"Chen",fullName:"Heng-Shuen Chen",slug:"heng-shuen-chen",email:"chenhs@ntu.edu.tw",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"National Taiwan University",institutionURL:null,country:{name:"Taiwan"}}},{id:"7762",title:"Dr.",name:"Po-Hsun",middleName:null,surname:"Cheng",fullName:"Po-Hsun Cheng",slug:"po-hsun-cheng",email:"cph@nknu.edu.tw",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"National Kaohsiung Normal University",institutionURL:null,country:{name:"Taiwan"}}},{id:"152898",title:"Mr.",name:"Hsin-Ciang",middleName:null,surname:"Chang",fullName:"Hsin-Ciang Chang",slug:"hsin-ciang-chang",email:"j86050123456@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"National Kaohsiung Normal University",institutionURL:null,country:{name:"Taiwan"}}},{id:"152899",title:"Mr.",name:"Wen-Chen",middleName:null,surname:"Chiang",fullName:"Wen-Chen Chiang",slug:"wen-chen-chiang",email:"iriairis5@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"National Kaohsiung Normal University",institutionURL:null,country:{name:"Taiwan"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Background",level:"1"},{id:"sec_3",title:"3. Decision support process",level:"1"},{id:"sec_3_2",title:"3.1. Execution procedures",level:"2"},{id:"sec_4_2",title:"3.2. Input requirements",level:"2"},{id:"sec_5_2",title:"3.3. Export use case diagram",level:"2"},{id:"sec_7",title:"4. Results",level:"1"},{id:"sec_8",title:"5. Discussion",level:"1"},{id:"sec_8_2",title:"5.1. Limitation of the XREAP tool",level:"2"},{id:"sec_9_2",title:"5.2. The ratio of requirements elicitation",level:"2"},{id:"sec_10_2",title:"5.3. Divide-and-conquer",level:"2"},{id:"sec_11_2",title:"5.4. Complexity assessment",level:"2"},{id:"sec_12_2",title:"5.5. Decision-making guidelines",level:"2"},{id:"sec_14",title:"6. Conclusion",level:"1"},{id:"sec_15",title:"Acknowledgements",level:"1"}],chapterReferences:[{id:"",body:"Alao AO. , Soderberg M. , Pohl EL. and Alao AL. Cybersuicide: Review of the Role of the Internet on Suicide. Cyberpsychology and Behavior 2006; 9(4) 489-493. "},{id:"",body:"Biddle L. , Donovan J. , Hawton K. , Kapur N. and Gunnell D. Suicide and the Internet. British Medical Journal 2008; 336(7648) 800-802. "},{id:"",body:"Cercone N. , An X. , Li, J. , Gu Z. and An A. Finding Best Evidence for Evidence-based Best Practice Recommendations in Health Care: The Initial Decision Support System Design. Knowledge and Information Systems 2011; 29(1)159-201. "},{id:"",body:"Cheng PH. , Chen SJ. , LaiJS. and Lai F. A Collaborative Knowledge Management Process for Implementing Healthcare Enterprise Information Systems. IEICE Transactions on Information and Systems 2008; E91-D(6) 1664-1672. "},{id:"",body:"Cheng PH. , Chang HC. and Chang FH. Another extensible requirements elicitation and analysis method. In: AI-Dabass D. , Tandayya P. , Yonus J. , Heednacram A. and Ibrahim Z. (eds. ) IEEE CICSyN2012: proceedings of the 4th International Conference on Computational Intelligence, Communication Systems and Networks, IEEE CICSyN2012, 24-26 July, 2012, Phuket, Thailand. Los Alamitos: IEEE Computer Society’s Conference Publishing Services; 2012. "},{id:"",body:"Chiang WC. , Cheng PH. , Su MJ. , Chen HS. , Wu SW. and Lin JK. Socio-Health with personal mental health records: suicidal-tendency observation system on Facebook for Taiwanese adolescents and young adults. Shyu CR. (eds. ) IEEE HEALTHCOM2011: proceedings of the IEEE 13th International Conference on e-Health Networking, Applications and Services, IEEE HEALTHCOM2011, 13-15 June, 2011, Columbia, Missouri, USA. Leonia: EDAS Conference Services; 2011. "},{id:"",body:"Fox CJ. Introduction to Software Engineering Design: Processes, Principles and Patterns with UML2. New York: Addison-Wesley;2006. "},{id:"",body:"Fowler M. UML Distilled: A Brief Guide to the Standard Object Modeling Language, 3rd Edition. New York: Addison-Wesley Professional; 2004. "},{id:"",body:"Ivar J. Object-oriented Software Engineering: A Use Case Driven Approach. New York: ACM Press; 1997. "},{id:"",body:"Manning CD. , Raghavan P. and Schutze H. An Introduction to Information Retrieval. New York: Cambridge University Press; 2008. "},{id:"",body:"Perini A. and Susi A. Understanding the Requirements of a Decision Support System for Agriculture: An Agent-Oriented Approach. Environmental Modelling and Software Journal 2004; 19(9)821-829. "},{id:"",body:"Rudd MD. , Joiner T. and Rajab MH. Treating Suicidal Behavior: An Effective, Time-limited Approach. New York: Guilford Press; 2004. "},{id:"",body:"Schlobinski S. , Denzer R. , Frysinger S. , Güttler R. and Hell T. Vision and Requirements of Scenario-Driven Environmental Decision Support Systems Supporting Automation for End Users. In: Qian Z. , Cao L. , Su W. , Wang T. , and Yang H. (ed. ) Environmental Software Systems, Frameworks of Environment, IFIP Advances in Information and Communication Technology, Vol. 359. New York: Springer;2011. p. 51-63. "},{id:"",body:"Shafiei F. , Sundaram D. and Piramuthu S. Multi-enterprise Collaborative Decision Support System. Expert Systems with Applications 2012; 39(9)7637-7651. "},{id:"",body:"Shi W. and Zeng W. Analysis and Design on Environmental Risk Zoning Decision Support System Based on UML. In: Qian Z. , Cao L. , Su W. , Wang T. , and Yang H. (ed. ) Recent Advances in Computer Science and Information Engineering Vol. 2, Lecture Notes in Electrical Engineering, Vol. 125. New York: Springer; 2012. p. 799-804. "},{id:"",body:"Shneidman ES. , Farberow NL. and Litman RE. The Psychology of Suicide: A Clician's Guide to Evaluation and Treatment. New York: Jason Aronson Inc. Publishers; 1977. "},{id:"",body:"Sommerville I. Software Engineering, 9th Edition. London: Addison Wesley; 2010. "},{id:"",body:"World Health Organizatoin. WHO: Suicide Prevention: SUPRE. http://www. who. int/mental_health/prevention/suicide/suicideprevent/en/ (accessed 17 December 2011). "},{id:"",body:"Sutcliffe A. , Bruijn de O. , Thew S. , Buchan I. , Jarvis P. , McNaugh J. and Procter R. Developing Visualization-based Decision Support Tools for Epidemiology. Information Visualization 2012, DOI: 10. 1177/1473871612445832. (accessed 15 June 2012). "},{id:"",body:"Taiwan Suicide Prevention Center. TSPC. http://www. tspc. doh. gov. tw (accessed 7 Janruary 2012). "}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Po-Hsun Cheng",address:null,affiliation:'
Department of Software Engineering, National Kaohsiung Normal University, Taiwan
As the world population is projected to grow close to 10 billion by 2050, we need to produce about 50% more food compared to 2013 production to meet the global demand [1]. This goal needs to be met while facing the challenges of climate change, the limited scope of arable land expansion, and dwindling water resources. In addition, anticipated food production also needs to incorporate practices for sustainable management of croplands to preserve soil health, conserve water resources, and encompass biodiversity [2]. Considering these challenges and constraints in achieving our food production targets there is an unprecedented need for monitoring of crop growth and health and timely interventions to maintain or improve crop productivity while reducing wastage of inputs and resources. Advances in sensors, communication technologies, computational systems, and powerful data analytics are enabling us to accomplish these tasks. Technologies that can enable efficient use of agricultural inputs and reduce environmental losses while contributing to increased and sustainable production are of great value for achieving food security. Several existing and emerging tools and technologies such as geographic information system (GIS), remote sensing (RS), Global Positioning System (GPS), Artificial Intelligence (AI), Big Data Analytics, and Internet of Things (IoT) are instrumental in achieving this goal through efficient monitoring of crops and soils, and, combined with other pieces of information, are providing data-driven insights for targeted or site-specific management of crops ensuring increased productivity [3]. Geographic Information System (GIS), a key foundational technology, is defined as a powerful system comprising tools for the collection, storage, and retrieval of data at will, as well as analyzing, transforming, and displaying the spatial data for a specific purpose [4, 5, 6, 7]. It plays a critical role as it provides the spatial context and information on several features each of which is available as a data layer. In addition, it provides the tools to manipulate spatial and non-spatial data and presents them through intuitive and illustrative map formats [8]. GIS has been making an impact in diverse domains that include geography, environmental sciences, natural resources, forestry, agriculture, food, manufacturing, banking, and health services [8]. Recent decades have seen a significant increase in the application of GIS tools for diverse applications in agriculture at local, regional, national, or global scales. These applications most often involve the use of GIS along with partner technologies such as remote sensing, GPS, and data analytics towards an in-depth understanding of a given farm or a region and facilitating intervention or corrective measures for the crops and/or the soils. Since the GIS data are linked to a common referencing system, another advantage GIS offers is that the same data can be used for different applications or goals and we can also bring in other data and, combining that with existing data, we can perform a joint analysis for deriving novel insights. Many studies have reported the use of GIS for diverse applications in different crops [9, 10, 11]. To further enable the readers to develop a strong appreciation for the role of this powerful technology in agriculture, here we have reviewed the most widely used and emerging applications of GIS, either by itself or in combination with other partner technologies, and how it has been making major impacts on agricultural productivity and supply chains.
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\n
\n
2. Key partner technologies of GIS
\n
The power and impactful contributions of GIS in diverse domains can be attributed to the combined use of GIS and two other key geospatial technologies: GPS and Remote Sensing. Each of these three partner technologies plays a crucial role in realizing the goals of applications (Section 3), and, therefore, are briefly described below.
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2.1 Geographic information system (GIS)
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Based on its role in supporting the collection, storage, retrieval, and analysis of data on features and location, and its utility for data-driven solutions, especially in site-specific management, GIS is considered the brain of Precision Agriculture [12]. Digital GIS maps differ from conventional maps in that they harbor several layers of information each layer providing information or a map about a given attribute such as soil survey, precipitation, nutrient status, pest infestation, yield, etc. In addition, GIS provides the analytical capability by using statistical tools and geospatial analytics enabling extraction of inter-relationships between attributes, and the insights, thus derived, are valuable for decision making with respect to management practices.
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2.2 Global positioning system (GPS)
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This positioning/navigation system based on a satellite network enables the determination of positional information by providing the latitude, longitude, and elevation of a location. The location information collected by GPS receivers enables farmers and researchers to the reliable identification of fields, mapping of field boundaries, water bodies, infested or problematic areas in the field, and for understanding the relation to several other attributes within and outside the boundaries of a given field. Such a high-fidelity field mapping permits site-specific application of nutrients, pesticides, herbicides, and water, thereby improving productivity and reducing input costs—the essence of precision agriculture.
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2.3 Remote sensing (RS)
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Remote sensing, with its diverse methods and applications in agriculture, has revolutionized crop monitoring and interventions for improving farm productivity [13, 14]. RS, in combination with GPS, GIS, and other tools is critical for implementing the goals of precision agriculture. This combination is crucial for enabling several applications that provide the basis for site-specific management of fields and include soil mapping, crop growth monitoring, estimation of soil moisture and fertility, detection of biotic (pests and diseases) and abiotic (drought and flood) stresses, and yield estimation.
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3. GIS applications
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The onset of digital agriculture, considered the fourth revolution in agriculture, has totally transformed the way farming is done, thanks to advances in geospatial technologies, sensors, artificial intelligence, robotics, and other tools and technologies. The ability to precisely identify the problem areas in cropland and monitoring and management of all steps in the entire agriculture value chain requires image and non-image data along with spatial context. GIS, with its component tools and analytic modules, and the data gathered by its partner technologies like remote sensing and GPS provides intuitive and lucid visualization of information for data-driven decision making for improving crop productivity. While GIS has been used for agricultural applications for quite some time, the number of applications has been growing rapidly in recent years due to technological advances. Several most common and emerging applications are presented and discussed below.
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3.1 Land suitability assessment and land use planning
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We are in an era where we are facing the challenge of feeding billions of people while the fertile land is shrinking, therefore, we need to optimize the use of natural resources to maximize the benefits. GIS provides an excellent platform for assessing the quality of land for suitable applications. Multi-criteria decision-making (MCDM) approach based on GIS is the most popular choice among researchers for land use planning. Researchers use different features offered by GIS such as soil type distribution, soil texture map, buried deep underground water level distribution, soil fertility distribution, soil pollution distribution, hydraulic conductivity of soil (Ks), slope (S), soil texture (ST), depth to water-table (DTW), and electrical conductivity of groundwater (ECw), climate conditions, topography, and satellite data, and identify the variety of interactions, dependencies, and the impact of these interacting factors on sustainable land use.
\n
Chen et al. [15] evaluated weight sensitivity of MCDM model for land suitability assessment for irrigated agriculture. They aimed to examine the sensitivity of changing weights of the input features on the model output. The results suggested a strong influence of sensitivity and, therefore, they have recommended giving special emphasis on this criterion. Zolekar and Bhagat [16] have used GIS-based MCDM model with IRS P6 LISS-IV images as input for the evaluation of agricultural practices in hilly regions. The rank of influential criteria was determined by correlation analysis and recommendations from scientific literature. The combined use of remote sensing and GIS turned out to be beneficial for land suitability evaluation. Pan and Pan [17] applied three scales, two-step analytic hierarchy processes (AHP) for GIS-based crop suitability assessment. They have emphasized the importance of selecting appropriate evaluation factors and suggested the consideration of features with a significant difference and controlling the land use and avoiding causality. Following this approach of feature selection, the AHP output was spatially distinct. The authors have recommended appropriate land use based on land suitability maps. In another study, [18] selected the features based on growth requirements for examining the land suitability for the wheat crop. Analytic Network Process (ANP) model was deployed for assessing the interdependence of strategic input features for site suitability evaluation of citrus crops [19]. The ANP coupled with GIS–MCDM identified critical factors for maximizing yield and minimizing production loss. AHP integrated with geo-statistics had proven its merit for maize cultivation land suitability mapping in calcareous and saline-sodic soils [20]. These powerful GIS tools enable land reclamation planning with suitable conservation practices.
\n
Integrated fuzzy membership and GIS model were used to analyze arable land suitable for farming. Topography and eight soil parameters were utilized for fuzzy membership classification and the important crop productivity-related soil features were accommodated accordingly. Fuzzy membership allowed the consideration of partial memberships which is unlikely in classical approaches for classification. This self-adaptive approach revealed that the land was better suitable for groundnut cultivation contrary to the current practice of Finger millet cultivation. Results of this experiment proved that the GIS-based decision system can surpass the traditional knowledge and, if deployed accurately, can improve the productivity of land [21]. This is the need of the hour technology as land and natural resources are declining, and the demand for food production is increasing rapidly. The fuzzy set model, AHP, and GIS were combined to generate a land suitability map for tobacco production [22]. This study has once again demonstrated the advantage of using Fuzzy membership functions for land suitability analysis. AHP has the power of accurately assigning weights to the input factors in a logical way. The maps were generated by ArcMap. The integrated application of fuzzy, AHP, and GIS helped to circumvent the problems resulting from the uncertainties, subjectivities, and hierarchy characteristics of the traditional land suitability assessment process. GIS is a powerful tool to delineate the study area, manipulate geographic data, process maps, and present results in land suitability assessment. Integration of Fuzzy set and AHP methods with GIS provides a precise and powerful combination in applying for land suitability analysis. Researchers advocate that Fuzzy logic coupled with other decision-making methods is one of the best approaches for land suitability analysis [21, 22, 23]. Scientists are also exploring artificial intelligence along with GIS for efficient land use planning [23].
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3.2 Water resource management
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Abundance of water supply is a primary requirement for meeting the demand for food production by the ever-increasing global population. As indicated earlier, farmers have the responsibility of feeding about 10 billion people in 2050 which demands a 50% increment in food production compared to 2013 level [24, 25]. The availability of clean water is decreasing and dependence solely on rainfall is not a viable option for the farmers [26, 27]. In this challenging scenario, water resource management is the key to success. Irrigation is the best solution for meeting the water requirement in agriculture. GIS technology backed by remote sensing has already proved its merit for the management of water resources [28, 29, 30, 31]. Researchers strongly suggested that remote sensing can supplement the traditional geophysical models for groundwater potential assessment and recharge experiments [32, 33]. Many researchers supported the potential of GIS for groundwater management [34, 35]. Tripathi et al. [36] integrated the MODFLOW groundwater model with the GIS for watershed prioritization. Singh et al. [37, 38, 39] combined GIS and remote sensing for delineating groundwater potential zones. Lineament and hydro- geomorphological maps were prepared from remote sensing images. The delineated groundwater potential zones have been found to show synergy with the well-yield data. When sub-watershed level runoff and sediment yield were assessed using the combination of GIS and remote sensing data it reduced the time of the input data process and produced good results compared to actual runoff and sediment yield [40]. Determining the suitability of irrigation for a given geography is one of the most popular applications of GIS. A study conducted in UAE accounted for non-renewable sources like desalination and treated sewage effluent (TSE) to assess irrigation suitability [41]. This type of water-scarce region needs optimization of water resources management. Land management, topography, climate conditions, soil capabilities, and water potential were used in the analytical hierarchical process (AHP) GIS model to assess crop suitability. The results showed that the land was unsuitable for cereals and vegetables but the cultivation of sorghum, jojoba, fruits, date palm, and forage was recommended. This study unleashed the power of GIS technology for using every acre of fertile land in a geography with a high level of water scarcity. Reduction in clean water resources is motivating researchers and policymakers to identify suitable alternatives for irrigation water. [42] evaluated the scope of using urban treated wastewater as an alternate source of irrigation. They have utilized the MCDM method which was executed in the GIS software environment and the Analytic hierarchy process (AHP) was used. This analysis revealed that the suitability of treated wastewater is subject to suitability for crop cultivation, nitrate contamination burden, and aquifer vulnerability. ISAREG irrigation scheduling model was integrated with GIS with the aim of generating efficient irrigation scheduling advice and identification of practices to account for water savings and salinity control [43]. These results advocated the successful outcome of the model for irrigation scheduling and choosing water saving measures during both wet and dry years. Though the intensification of irrigation is beneficial for food production, it was pointed out that soil salinization and waterlogging are the major drawbacks of irrigated agriculture intensification, and that strong emphasis should be put on leveraging GIS and remote sensing technology for monitoring the problem areas followed by planning conservation and preventive measures [44].
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3.3 Soil health and fertility management
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Soil fertility is directly proportional to productivity. It controls the availability of nutrients and water to the crop. The soil fertility has been degrading due to various factors like pollution, sealing, overgrazing, waterlogging, excessive use of agricultural chemicals, and erosion. It is crucial to determine soil health and fertility status for planning effective practices for site-specific management or precision farming [45, 46, 47]. Soil macronutrients (N, P, and K), micronutrients (Zn, Mn, and Fe), pH, soil organic carbon (SOC), water holding capacity, erosion status, and moisture content are extensively used features for soil fertility status assessment [48, 49, 50, 51]. Spatial interpolation, Multi-Criteria Decision Analysis (MCDA) [52, 53, 54, 55], and Ordered Weighted Averaging (OWA) [56, 57, 58, 59] are the most popular geospatial analysis techniques which provide spatiotemporal variability of soil health and fertility status to the decision-makers.
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Soil erosion status is an essential parameter for soil quality assessment and spatial variation in erosion gives a clear picture for agricultural planning [60]. It was demonstrated that geospatial maps of soil erodibility generated by Inverse Distance Weighted (IDW) method is a great tool for assisting in sub-watershed level land use planning. [61] combined the remote sensing and GIS technology to assess the soil fertility status. They have used the LISS III and IV images for land use classification and the RUSLE method for soil erosion estimation, collected the soil nutrient field data, and applied a geostatistical model to identify the spatial variation of soil erosion and nutrient availability. In another study, [62] used the IDW model for derived soil nutrient maps and applied the OWA method to make the maps homogenized and used those as the input for the fuzzy inference system for soil fertility mapping. Fuzzy mathematics developed with soil organic matter (SOC), total N, total P, total K, available N, available P, available K, pH value, and cation exchange capacity as indicators in ArcGIS showed that the soil fertility of mid- and low- yielding fields were low and are directly correlated with soil profile configuration [63]. The association of crop productivity with the soil fertility is evident and GIS-based soil maps and fertility status give prior information about the field-specific crop suitability.
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Leena et al. [64] proposed GIS-enabled cloud technology for soil fertility management decision support system. This system has the capability to make fertilizer recommendation based on soil test and crop response. This recommendation system helps farmers optimize their fertilizer usage and maximize yield. This system generated spatial nutrient variation works as fantastic e-governance system for the government agencies. GPS- and GIS-based soil fertility maps are great tools for thorough monitoring of the soil health and, based on such maps, [65] recommended application of paper mill sludge to reduce acidity in the soil and cultivate pulses and groundnut to make the best use of the acidic soil. These geospatial soil maps have proven to be an effective decision support system in the context of food production challenges due to soil degradation. [66] applied soil fertility index (SFI) based on the variables of sand, silt, clay, pH, EC, OM, CaCO3, Ntotal, Pavb, Kexc, Caexc, Naexc, Mgexc, and available micronutrients (Feavb, Cuavb, Znavb, Mnavb) and proved the strength of SFI. This study demonstrated the potential of combining Sentinel 2 image-derived crop yield for validation of soil fertility model. Advances in the observatory systems such as remotely sensed data of fine-to-coarse spatiotemporal resolutions, and in the process-based and data-driven modeling techniques have facilitated the collection, storage, analysis, visualization, and interpretation of non-spatial data for soil fertility index (SFI) [67, 68, 69, 70, 71, 72].
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Li et al. [72] applied weighted space fuzzy clustering coupled with the soil nutrient space mutation distribution for soil fertility characterization. This information aids in optimizing the fertilizer recommendation system. Agricultural practices such as crop residue management, nutrient management, soil tillage, and pest management affect ecosystem goods and services and soil quality and fertility [73, 74, 75]. The best management practices, compatible land use/cover changes, and land suitability analysis are required to prevent the degradation and loss of prime farmlands [73, 76, 77]. Soil erosion management, soil biodiversity improvement, and rehabilitative farming systems are some of the best management practices used to improve soil quality and crop yields [78, 79, 80].
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A study that leveraged GIS and fuzzy evaluation method to evaluate the soil fertility status used total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, available potassium, soil organic matter, cation exchange capacity, and pH as indicators for the generation of fertility indices [63]. This fertility index revealed that total nitrogen and soil organic matter are higher for paddy fields. These fertility maps also give an insight into the suitable soil qualities under different types of land use and climatic conditions. Sub-watershed level nutrient mapping revealed that available N, P, S, Zn, and Fe are controlling agents of soil fertility [81]. Thus, fertility maps and their relationship with soil properties and crop yields serve as an information system for precision agriculture.
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3.4 Biotic and abiotic damage assessment and intervention
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Studies have reported that biotic crop damage, caused by insects, fungi, and other pests, can cause 15–70% yield loss [82, 83, 84]. This scenario impacts the demand and supply chain and also affects the economy of farmers. The changing pattern of weather makes the crops susceptible to pests and diseases. The availability of crop protection methods is quite beneficial for tackling crop health, but the lack of timely information about the pests and diseases makes the damage irrepressible. GIS technology holds immense potential for site-specific pest and disease management. Remote sensing and GIS-based forewarning systems are boon to farmers to arrest the yield and economic loss. Ranjan and Vinayak [85] advocated that pest and disease forecasting systems allow farmers to apply the control measures in time to reduce the cost of production. Apart from the forewarning system, the pest population density map also plays a crucial role in identifying the hotspots and extending advisory to farmers. According to [86], information about the geospatial density of oriental fruit moth, Grapholita molesta, had the scope of reducing the crop injury and pest population by applying geographically suitable management measures. The difference in current and predicted geospatial distribution of two polyphagous and invasive Icerya species clearly indicated the impact of climate change in modifying the pest attack patterns [87]. Such pest distribution maps enable farmers, agricultural experts, and policymakers to prepare management strategies to combat pest attack in the future. Tracking the migratory patterns of pests is of utmost importance given the instances of a sudden outbreaks of cutworm, Agrotis ipsilon, and fall armyworm, Spodoptera frugiperda, in different continents than their usual geographical locations [88, 89, 90]. Remote sensing and GIS are important tools for monitoring habitats of pest species such as western tarnished plant bug, Lygus hesperus, and Migratory and Australian Plague locusts [91, 92]. Remote sensing and GIS are rapid and cost-effective technology for assessing the extent of crop damage by pests and diseases [93]. Researchers had demonstrated the feasibility of pest and disease type detection and severity mapping from remote sensing images [94, 95, 96]. These damage assessment maps hold key spatial information about the damaged crop acreage and its trend across multiple years over different geographical units. These maps have the potential to act as an aid for insurance settlement for the farmers and for those seeking government subsidies and benefits.
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Natural calamities cause irreversible damage to agriculture. Rapid mapping and quantification of damage aid in economic loss recovery and act as a decision support system. A geo-spatial model is used in a case study to assess the impacts of extreme flood events on agricultural production in the Quang Nam province of Vietnam. [97] generated the water surface by interpolating flood depth marks by the inverse distance weighting (IDW) and employed a digital elevation model (DEM) to generate the flood inundation map. This map overlaid with the land use map gave an effective estimate of the damaged agricultural area [98, 99, 100]. Drought is another constraint to agricultural productivity and understanding the hotspot and climatology is crucial to strategically minimize the impact. MODIS satellite Normalized Difference Vegetation Index (NDVI) derived drought risk classes were prepared to access the spatial pattern [101]. GIS-based characterization of climate variability and drought zones provides scope for strategic measures adoption to maximize productivity [102, 103].
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3.5 Crop monitoring and yield prediction
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Monitoring of crop growth, health, and accurate or near accurate prediction of yield is crucial not only for estimating economic return but also for assessing the food production thereby helping in the management of food security. Many studies showed that traditional methods of crop yield estimation could lead to poor assessment and inaccurate crop area appraisal [104, 105]. Moreover, these methods require time-consuming, labor-intensive, and expensive crop and yield data collection. This is where technologies like remote sensing (RS), GPS, and GIS provide a huge advantage as they can be used to assess temporal and spatial variability of crop dynamics and yield output [106]. The use of two key partner technologies, RS and GIS, with required input from others can provide an efficient solution for monitoring crop health and developing models for predicting crop yields across diverse spatial scales. While remotely sensed images and associated analytics permit the tracking of crop health and predicting the yield, GIS technology enables the collection, storage, retrieval, and visualization of data that were linked geographically. Remotely sensed geospatial data acquired by satellites, aircrafts, or unmanned aerial vehicles (UAVs) can be used to gather information on several features of the crops and the characteristics of the soils supporting their growth thereby enabling the assessment of crop health. The images gathered can be used for assessing general vigor, disease or pest infestations, or deviations from expected growth due to drought or other abiotic stresses. Geospatial data collected in a spatiotemporal manner and the associated analysis techniques help in assessing the changes in the health of crops thereby permitting management interventions while providing predictions on anticipated yields based on the growth and health of the crops. A commonly used method for assessing crop health is based on the determination of vegetation indices that are calculated based on surface reflectance from crop canopies at two or more wavelengths. Many vegetation indices are available for evaluating the extent and vigor of vegetation, crop growth dynamics, stress due to biotic or abiotic factors, and other useful assessments [107]. Adhav et al. [108] used multiple vegetation indices that included Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Vegetation Index (CVI), and Difference Vegetation Index (DVI) to determine crop health as well as variations in health conditions. To further improve the efficiency of health assessment, they have combined all vegetation indices using ArcMap 10.5 software and reclassified the merged indices which were then used for categorical representation of health scenarios. Such a representation helps farmers to identify areas that need immediate management intervention [108]. Determination of crop health is particularly critical in smallholder farms as the subsistence and livelihood of these farmers depend on the productivity of their crops. As per a recent study, small farms were found to account for 84% of all farms worldwide but they operate only on around 12% of all agricultural land and produce about 35% of world’s food [109]. Use of UAVs for gathering and leveraging data for assessing crop growth and dynamics has proven to be crucial for farmers to take timely and appropriate corrective measures to maintain or increase productivity. A recent study in South Africa [110] evaluated the utility of multispectral UAV imagery and random forest machine learning (ML) algorithm to estimate maize chlorophyll content at various growth stages and created a chlorophyll variation map capturing the spatial heterogeneity of chlorophyll in the field thereby helping the farmers to take management actions. Considering the strategic role of sustainable intensification towards the food production goals of Sub-Saharan Africa [111], such RS- and GIS-based diagnostics and interventions are critical for smallholder farmers. Another study assessed crop health using different chlorophyll indices in addition to modified vegetation index by leveraging data from two different satellites and ArcMap (of ArcGIS) for geospatial analytics [112] resulting in insights that could be used for managing nutrient applications towards improving crop productivity. Two essential prerequisites to implement location-specific management practices and interventions are the availability of an accurate acreage map of crop of interest and the cropping systems of a given area and technologies for predicting yield before the reproductive phase or harvesting of the crop. The use of RS and GIS technologies can help achieve both goals. NDVI, a commonly used vegetation index, serves the dual purpose of assessing crop health and predicting crop yield while GIS tools can provide the spatial context. Several studies abound that leveraged NDVI and GIS for yield predictions and a few examples are discussed here. In a study that measured NDVI values at different growth stages of rice, several linear regression-based yield prediction models were developed using NDVI values and narrowed down to a model that had the highest prediction potential and was also able to predict yield well ahead of harvesting time [113]. Such a model can help the farmers to implement changes to the fertilization, water, pest, and disease management practices towards realizing improved productivity. Using time-series data of SPOT vegetation and two key spectro-agrometeorological variables, rainfall estimate (RFE) and NDVIactual (NDVIa), that are highly correlated to maize yield, [114] have developed an operational model with high predictive ability for yield forecasting in Ethiopia. By leveraging both RS and GIS, this model enabled yield forecast at flowering season which is more than two months earlier than the forecast by conventional method thus providing an advantage of early intervention towards crop productivity and crucial data for the authorities for crop production estimates [113]. In a field study aimed at developing an efficient model for predicting potato tuber yield using RS and GIS techniques two vegetation indices, NDVI and soil adjusted vegetation index (SAVI), generated from images acquired by Landsat-8 and Sentinel-2 satellites were found to be highly effective in yield prediction [115]. In addition, the indices enabled them to create maps of the study area that has clearly shown zones differing in productivity. This is very useful information for both farmers for implementing necessary management practices and for authorities in arriving at accurate production estimates. While manly researchers have used RS-based vegetation indices and GIS for predicting crop yields, several researchers have combined GIS with crop simulation or physiological models and demonstrated their strong performance in yield prediction [116, 117, 118, 119]. Crop simulation models came into prominence due to their utility in designing management practices, assessing the role of climate variations on crop performance, and predicting yields [120, 121]. Similarly, physiological crop models have evolved from their original applications in farm management to measuring the impact of climatic changes on crop productivity. The ability to incorporate spatial variability of the inputs that go into physiological or simulation models makes them even more powerful for determining the interactions between climatic variation and crop productivity while highlighting the spatial heterogeneity. In a study that integrated RS data, crop growth model, and GIS, it was found that yield estimates from RS images were more precise compared to another approach where GIS climate layers and soil attributes were integrated into Oryza 2000 rice crop model highlighting the superiority of combining RS, GIS, and crop model for estimating crop yields [118]. To capture the spatial variability of input variables and their influence on yield estimates, [116] have linked RS and GIS with a growth model of soybean. The results demonstrated spatial variability in simulated yield estimates and the variability was primarily attributed to soil characteristics and rainfall. The availability of such spatial patterns from the simulated yield estimates is very helpful in productivity estimates in areas prone to abiotic stresses, for example, droughts, as well as providing insights into factors contributing to yield. Efforts also exist that have created web-based decision support systems based on a combination of simulation model and GIS towards making agronomic decisions [119]. In yet another approach, the Erosion Productivity Impact Calculator (EPIC), a model for the analysis of the relationship between soil erosion and crop yield at field level, was integrated with GIS and an Inference Engine (IE) towards global estimation of crop productivity [122]. While the integration of GIS expands the application of EPIC to regional or global level, the availability of IE helps in determining potential crop combinations for given growing conditions. This study not only demonstrated the ability of GIS-based EPIC for crop productivity simulations at global level but also delivered predictions for future yields and how they are adversely affected by global climate change underscoring the importance of the development of climate-resilient varieties of crops. Since traditional crop productivity simulations are based on site-specific crop models, [123] developed an operational crop model that can be utilized at the regional level, North China, by integrating USDA EPIC model with NASA MODIS LAI product from Earth Resources Observation System (EROS), ancillary ground data, and GIS [123]. Applications also exist where a combination of GIS and RS was used for assessing damage in some high-value crops. Cranberry is one such crop that exhibits extreme crop yield variations due to soil characteristics which in turn influence water and nutrient availability. Using GIS, GPS, and RS, [124] have created a spatial variation map for the crop enabling the analysis of crop losses within zones in a field or at the whole field level.
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3.6 Precision farming
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Precision Farming, also called Precision Agriculture (PA) or site-specific crop management (SSCM), is the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production [125]. Earl et al. [126] defined it as a system that integrates information with crop production that is designed to increase long-term, site-specific as well as whole farm production efficiency, productivity, and profitability while minimizing unintended impacts on wildlife and the environment [126]. The operational goals of precision farming include better management of inputs such as seeds, fertilizers, pesticides, herbicides, and water using right amounts of inputs at the right place, and at the right time. Several crucial tools and systems such as GPS, GIS, and RS are required for the collection of timely geospatial information on soil-plant-animal requirements towards mining insights followed by leveraging those insights for prescribing and applying site-specific treatments towards improving agricultural productivity while contributing to sustainability and protecting the environment [127, 128, 129]. The role of different tools and technologies as well as the applications of precision farming are described in several review articles and references therein [9, 128, 130]. While GPS, GIS, and RS are vital for obtaining and analyzing the data for deriving insights, a key technology that implements the precision applications by leveraging the input of these three tools is variable rate technology (VRT). VRT systems take all the required information about a field such as soil maps, yield, infestation of pests, diseases, and weeds, and they determine the quantities of fertilizers, pesticides, herbicides, and other inputs and ensure their application at the right place and at the right time saving the input costs. The integration of GIS, GPS, and VRT technologies thus provides farmers an unprecedented ability to view field maps and apply input where and when needed towards ensuring crop productivity. Precision farming can be broadly divided into three steps or stages depending on data collection or site-directed or specific activities happening during, before, and after the crop growth period [131]. These are Preparatory or Pre-planting stage, Crop growth stage, and Harvesting stage. Role of GIS in each of these stages is discussed below.
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\nPreparatory stage: This is essentially a planning stage that encompasses the data collection prior to planting and includes, among other things, gathering data on soil nutrient status, groundwater, previous crops and their residual influence on the next crops, and data on pests, diseases, and problematic weeds in that area that could affect yields of the crop to be planted. Data gathered on all these aspects are stored in a GIS system. Historical crop data from GIS also helps in ‘variable planting’ decisions that determine where to plant what crop/variety and to what extent so that the variable planting plan can be carried out in the field automatically by seeding machines [131]. A major activity in the preparatory stage is soil mapping and accurate prediction of soil properties is critical for precision farming interventions and for sustainable agriculture. The approach used for traditional soil mapping relies on a representative soil property from a location of focus and, therefore, has the limitation of not capturing the variability of soil properties in the maps generated. Moreover, soil sample collection, analysis, interpretation, and map generation are all monotonous, time-consuming, and costly. These methods have been improved greatly with the developments in spatial science and the geospatial modules in ArcGIS and other tools are being used extensively for their ability in spatial interpolation [132, 133, 134].
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\nCrop growth stage: In this stage, the insights and the data gathered in the preparatory stage are retrieved through GIS and used for formulating and implementing management practices addressing irrigation, soil fertility, and protection from biotic and abiotic stresses. The availability of diverse types of imaging sensors, satellite and proximal sensing capabilities, GPS and GIS systems have enabled the assessment of plant characteristics, growth, health, and also in gathering information on the soils and infestation of pests and diseases, all with precise geospatial information. By using RGB, hyper- or multi- spectral remote sensing options for measuring the reflectance, fluorescence, or other useful emissions, it became possible to assess the disease status of the crops and take precise management measures for controlling the diseases at precise locations based on GPS and GIS information, thus improving crop health, and helping to improve productivity [135]. The use of advanced geoinformatics tools and data-driven recommendations are also being used for monitoring and managing pesticides and other plant protection inputs with a particular focus on sustainable practices towards improving productivity while reducing impact on the environment. Geoinformatics-based tools are also being leveraged for accelerating the crop germplasm that is suited for a given location or region towards realizing increased productivity as well as the germplasm that has tolerance to biotic or abiotic resistance needed for a given geographic location [136]. Spectral reflectance and vegetation indices of the crops being monitored combined with GIS are also playing a crucial role in managing nutrients and water stress in precision farming programs. Use of remote sensing and GIS in detecting nutrient stress of the crops enables site-specific correction management of nutrients thereby promoting plant growth while reducing the cost of cultivation due to targeted use of nutrients [137]. Similar site-specific management can also be implemented for addressing water stress and such an approach is especially attractive for croplands with limited water resources. The role of data analytics used in remote sensing, GIS, and other tools for achieving the goals of precision agriculture needs special mention. Early and accurate detection of biotic (pests and diseases) and abiotic (water deficiency) stresses is a prerequisite for taking appropriate management measures. Recent years have seen the use of several machine learning methods for developing models that are enabling both early and accurate detection of biotic stress agents such as diseases and weeds thereby helping precision crop protection [138].
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\nHarvesting stage: This final stage serves the dual purpose of knowing the final output (yield) resulting from the season-long precision practices on the crop and leveraging this yield data to formulate a strategy for the next crop season [131]. The data collected at the harvesting stage is loaded into GIS for analysis towards generation of maps and insights for future use emphasizing the central role of GIS in precision farming. Yield monitoring and mapping is an important component of precision farming. The purpose of a yield monitor is to provide the farmer or a researcher with an accurate assessment of yield variability in the field and when combined with GPS, it can provide the data for creating yield maps [130]. Information on yield measurements and the geospatial context is critical for precision farming as it helps in formulating necessary tweaks to the management decisions for the next crop season.
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3.7 Biomass assessment
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Renewable sources of energy are crucial to achieving climate change and sustainability goals. Agricultural residues are a promising source of biomass-based energy the demand for which is rapidly increasing around the globe. One challenge with agricultural residues for efficiently channeling them for energy production is the fact that their availability is seasonal and is geographically widely distributed. A solution that can address this spatio-temporal variability, seasonal fluctuations in biomass supply levels, and identification and transport of residues to power plants is a critical prerequisite for biomass-based energy generation. GIS, in combination with remote sensing, can be a great tool for precise identification and assessment of the crop residues and for planning a given region’s feedstock material for renewable energy and its economical transportation to power plants. GIS-based estimation of bioenergy potential enables a technologically advanced solution for leveraging the residues from existing cropping practices that promise even more benefits as the farmers shift from conventional to smart farming [139]. Some of the efforts in leveraging GIS and its partner technologies to this end are discussed below. Methods that can predict biomass potentials of a given region containing weather and crop production variations are of high value for enabling an efficient supply chain from biomass to power plants. By using BioSTAR, a carbon-based crop model, [140] have calculated biomass potentials for maize, triticale, and cup plant, and linked them with a GIS map of the soil dataset of Hannover region in Germany and demonstrated the utility of this method for predicting agricultural potentials under diverse environmental and crop management practices and conditions [140]. In a study that mapped rice cropland in a rural area in India, images from WorldView-2 satellite were used and the resulting map along with agricultural production statistics was analyzed in GIS for assessing the availability of rice straw as a feedstock for generating bioenergy [141]. In addition, the study also estimated the annual rice straw availability and the electrical power it could generate, thus providing valuable information for energy developers and policymakers for planning. Since the success and sustainability of a biomass-based energy generation project depend on several factors that include the feedstock resource, logistics, and environmental considerations, the role and value of GIS and key associated tools and technologies need to be understood prior to establishing the supply chain and the power plants. Two tools can help to address this task: GIS and life cycle assessment (LCA). While GIS is critical for assessing the resources dispersed in small or large areas, LCA is useful in evaluating the environmental impacts of bioenergy production projects. A comprehensive review on the application of LCA, especially spatial LCA, in understanding the impact of biomass-based energy generation on different ecosystem services and the value of integrating LCA and GIS to conduct a holistic assessment of environmental benefits in connection with bioenergy production recommended the inclusion of LCA as an essential component in planning bioenergy projects [142]. To assess the spatial and temporal availability of crop residues and to pinpoint locations for ideal power plants along with cost considerations, an integrated GIS-based biomass, site optimization, and logistics cost model was developed by using soil erosion, soil conditioning index (SCI), and crop residue yield indicators [143]. To estimate crop residues, prediction models based on artificial neural networks (ANNs) were developed for each of these indicators and were implemented on a GIS platform. The utility of this model was also demonstrated using a sustainable assessment of cotton stalks (CS) that are used to produce fuel pellets. An advantage of this model is that its use can be extended to assessment of multiple types of crop residues [143]. Models based on GIS and multi-criteria inclusion-exclusion analysis and facility location-allocation were also developed for the identification of sustainable crop biomass at larger spatial and longer temporal scales and to suggest ideal biogas plants along with cost considerations for biomass delivery [144].
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3.8 Supply chain management
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GIS technology has proved to be of great value in understanding and optimizing agricultural supply chains and its use is being extended to diverse crops and locations. For ease of understanding its impact on supply chains can be discussed using the following three categories.
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3.8.1 Improving supply chain management process
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GIS technology has the potential to assist the successful transition of traditional agriculture systems to smart systems. While there are many studies that have thoroughly investigated the role of big data analytics in supply chains of diverse industries, such studies are lacking in the application of big GIS analytics (BGA) in agriculture. To this end, a systematic review of recent literature examined the role of BGA in agricultural applications and has proposed a framework for supply chains where BGA can play even a bigger role in improving the quality of GIS applications in agriculture [145]. The proposed framework serves as a useful reference for scientists and authorities for the successful management of big GIS data and leveraging it for improving productivity. The utility of Geographical Information Technologies (GITs) for improving the complex supply chain management process in the cotton crop was explored and was found to be of great value since the GITs framework enables visualization of current states as well as alternative options and what-if analyses for all steps that require decision making [146]. Another important application for which GIS was used is the analysis of supply chain patterns and description of spatial components of safe crop product (SCP) in China [147]. By using the spatial functions provided by GIS such as representation, location, analysis, traceability coding, and other techniques, tracing and retracing of the quality of safe crop product (SCP) was achieved. This system was also successfully demonstrated in a real supply chain for (re)tracing of SCP. By developing a GIS-based constrained linear programming model for minimizing transportation and storage costs for soybean and its byproducts, and further optimizing this model using General Algebraic Modeling System (GAMS), [148] have identified the lowest cost supply chains. The origin to destination cost matrices and geographic data maps required for the model development and optimization was developed by ArcGIS Network Analyst and ArcMap, respectively. This study demonstrated the combinatorial utility of ArcGIS, ArcMap, and GAMS for developing optimal supply chains that are of value to the players in the process [148].
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3.8.2 Decision support systems
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Production of biofuels from renewable sources such as agricultural residues can reduce the usage of fossil fuels thereby helping in the reduction of greenhouse gases. Identification of ideal locations for establishing biofuel facilities and designing a cost-effective supply chain for transferring biomass to the facility is highly desirable. To this end, in one approach a decision support system (DSS) has been developed by integrating a GIS-based method and two modeling methods, simulation, and optimization [149]. While GIS-based method was used for selecting facility sites, the selected sites were run through simulation and optimization modeling, and together these three methods provided an integrated DSS for assessing the cost, energy use, and emissions for the facility candidates as well as minimizing supply chain costs. In another approach, an intelligent spatial decision support system (ISDSS) was proposed to overcome the drawbacks of GIS in enabling creation of a knowledge base that supports decision making. The ISDSS combines GIS and intelligent systems and has spatial data mining capability through IoT devices [150].
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3.8.3 Locating power plants and developing supply chains
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The sustainability of a biomass-based power plant depends on, among other things, a consistent supply of the feedstock, an economical supply chain, and an optimal location of the facility. The GIS-based analysis enables the identification of an ideal location for the plant and in making valid decisions related to the supply chain development. Using open-source GIS software, Latterini et al. [151], have simulated the identification of suitable locations for a small size power plant in Lazio region of Italy that can use olive prunings as the feedstock. This user-friendly and low-cost procedure, which can also be extended to other feedstocks, also provided supply chain costs for the evaluation of different sites and can serve as a useful tool for stakeholders in the development of economical biomass-based end-to-end supply chains [151]. In another study, an integrated approach combining GIS-based analysis with optimization modeling was developed resulting in a support system for decision-makers in comparing facility candidates and in minimizing supply chain costs [152]. The system developed could also be used for similar supply chains such as low capital biodiesel plants.
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4. Conclusions
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The use of GIS in agriculture has increased at a rapid pace during the recent decades and the number of applications and the prominence of GIS has further amplified in the recent years due to advances in digital technologies that have been leveraging GIS as an essential partner technology for assessing crops, soils, and their environments. As discussed in this chapter, GIS is being used at all stages of agricultural value chain. In addition to the historical, current, and popular uses of GIS in land suitability/use planning and management of water, soil, and biotic and abiotic stresses, the advent of digital agricultural tools and technologies has increasingly leveraged the capabilities of GIS in new and emerging applications in high fidelity crop monitoring, yield prediction, precision farming, and supply chain management for both primary produce and biomass utilization towards energy production. The multitude of capabilities and insights provided by GIS, including the recent enhancements to collect and analyze data in real time, has further elevated its importance in providing location/spatial intelligence needed for improving the productivity and profitability of farms through precision practices. With the current and emerging applications, in combination with existing and newer partner technologies, GIS has a pivotal role in achieving sustainable agricultural productivity.
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Acknowledgments
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We would like to thank Anu Swatantran for the support and helpful discussions, Chris Seifert, Andrea Arias, and Brian Lutz for support and funding, and Jochen Scheel, Steve Callistein, Janae Lehman Bell, and Nicole Janovick for rapid review and approval of the manuscript.
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\n
Conflict of interest
\n
The authors declare no conflict of interest.
\n
\n',keywords:"Geographic Information system (GIS), precision agriculture, remote sensing, Global Positioning System (GPS), resource management",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81685.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81685.xml",downloadPdfUrl:"/chapter/pdf-download/81685",previewPdfUrl:"/chapter/pdf-preview/81685",totalDownloads:33,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"April 4th 2022",datePrePublished:"May 20th 2022",datePublished:null,dateFinished:"May 7th 2022",readingETA:"0",abstract:"Technological innovations during the recent centuries have enabled us to significantly boost agricultural production to feed the rapidly increasing global population. While advances in digital technologies triggered the onset of the fourth revolution in agriculture, we also have several challenges such as limited cropland, diminishing water resources, and climate change, underscoring the need for unprecedented measures to achieve agricultural resilience to support the world population. Geographic information system (GIS), along with other partner technologies such as remote sensing, global positioning system, artificial intelligence, computational systems, and data analytics, has been playing a pivotal role in monitoring crops and in implementing optimal and targeted management practices towards improving crop productivity. Here we have reviewed the diverse applications of GIS in agriculture that cover the entire pipeline from land-use planning to crop-soil-yield monitoring to post-harvest operations. GIS, in combination with digital technologies and through new and emerging areas of applications, is enabling the realization of precision farming and sustainable food production goals.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81685",risUrl:"/chapter/ris/81685",signatures:"Parmita Ghosh and Siva P. Kumpatla",book:{id:"11134",type:"book",title:"Geographic Information System",subtitle:null,fullTitle:"Geographic Information System",slug:null,publishedDate:null,bookSignature:"Prof. Yuanzhi Zhang and Dr. Qiuming Cheng",coverURL:"https://cdn.intechopen.com/books/images_new/11134.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-742-7",printIsbn:"978-1-80355-741-0",pdfIsbn:"978-1-80355-743-4",isAvailableForWebshopOrdering:!0,editors:[{id:"77597",title:"Prof.",name:"Yuanzhi",middleName:null,surname:"Zhang",slug:"yuanzhi-zhang",fullName:"Yuanzhi Zhang"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Key partner technologies of GIS",level:"1"},{id:"sec_2_2",title:"2.1 Geographic information system (GIS)",level:"2"},{id:"sec_3_2",title:"2.2 Global positioning system (GPS)",level:"2"},{id:"sec_4_2",title:"2.3 Remote sensing (RS)",level:"2"},{id:"sec_6",title:"3. GIS applications",level:"1"},{id:"sec_6_2",title:"3.1 Land suitability assessment and land use planning",level:"2"},{id:"sec_7_2",title:"3.2 Water resource management",level:"2"},{id:"sec_8_2",title:"3.3 Soil health and fertility management",level:"2"},{id:"sec_9_2",title:"3.4 Biotic and abiotic damage assessment and intervention",level:"2"},{id:"sec_10_2",title:"3.5 Crop monitoring and yield prediction",level:"2"},{id:"sec_11_2",title:"3.6 Precision farming",level:"2"},{id:"sec_12_2",title:"3.7 Biomass assessment",level:"2"},{id:"sec_13_2",title:"3.8 Supply chain management",level:"2"},{id:"sec_13_3",title:"3.8.1 Improving supply chain management process",level:"3"},{id:"sec_14_3",title:"3.8.2 Decision support systems",level:"3"},{id:"sec_15_3",title:"3.8.3 Locating power plants and developing supply chains",level:"3"},{id:"sec_18",title:"4. 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In: 19th Esri India User Conference. India; 2018\n'},{id:"B113",body:'\nMaloom JM, Saludes RB, Dorado MA, Cruz PC. Development of a GIS-based model for predicting rice yield. Philiphines Journal of Crop Science. 2014;39:8-19\n'},{id:"B114",body:'\nZinna AW, Suryabhagavan KV. Remote sensing and GIS based spectro-agrometeorological maize yield forecast model for South Tigray Zone, Ethiopia. Journal of Geographic Information System. 2016;8(2):282-292\n'},{id:"B115",body:'\nAl-Gaadi KA, Hassaballa AA, Tola E, Kayad AG, Madugundu R, Alblewi B, et al. Prediction of potato crop yield using precision agriculture techniques. PLoS One. 2016;11(9):e0162219\n'},{id:"B116",body:'\nCarbone GJ, Narurnalani S, King M. Application of remote sensing and technologies with physiological crop models. Photogrammetric Engineering and Remote Sensing. 1996;62(2):171-179\n'},{id:"B117",body:'\nHartkamp AD, White JW, Hoogenboom G. Interfacing geographic information systems with agronomic modeling: A review. Agronomy Journal. 1999;91:761-772\n'},{id:"B118",body:'\nSailaja B, Voleti SR, Subrahmanyam D, Nathawat MS, Rao NH. Regional rice yield estimation by integration of spatial technologies and crop model. Journal of Remote Sensing & GIS. 2013;4(2):56-66\n'},{id:"B119",body:'\nKadiyala MD, Nedumaran S, Singh P, Chukka S, Irshad MA, Bantilan MC. An integrated crop model and GIS decision support system for assisting agronomic decision making under climate change. The Science of the Total Environment. 2015;521:123-134\n'},{id:"B120",body:'\nBouman BAM, Van Keulen H, Van Laar HH, et al. The ‘School of de Wit’ Crop Growth Simulation Models: Pedigree and Historical Overview. Agricultural Systems. 1996;52:171-198\n'},{id:"B121",body:'\nJones JW, Hoogenboom G, Porter CH, et al. The DSSAT Cropping System Model. European Journal of Agronomy. 2003;18:235-265\n'},{id:"B122",body:'\nTan G, Shibasaki R. Global estimation of crop productivity and the impacts of global warming by GIS and EPIC integration. Ecological Modelling. 2003;168(3):357-370\n'},{id:"B123",body:'\nYang P, Tan GX, Zha Y, Shibasaki R. Integrating remotely sensed data with an ecosystem model to estimate crop yield in north China. In: Proceedings of XXth ISPRS Congress Proceedings Commission VII, WG VII/2, Istanbul, Turkey; 2004 Jul. pp. 150-156\n'},{id:"B124",body:'\nOudemans PV, Pozdnyakova L, Hughes MG, Rahman F. GIS and remote sensing for detecting yield loss in cranberry culture. Journal of Nematology. 2002;34(3):207\n'},{id:"B125",body:'\nPierce FJ, Nowak P. Aspects of precision agriculture. Advances in Agronomy. 1999 Jan 1;67:1-85\n'},{id:"B126",body:'\nEarl R, Wheeler PN, Blackmore BS, Godwin RJ. Precision farming: The management of variability. The Journal of the Institution of Agricultural Engineers. 1996;51:18-23\n'},{id:"B127",body:'\nStrickland RM, Ess DR, Parsons SD. Precision farming and precision pest management: The power of new crop production technologies. Journal of Nematology. 1998;30(4):431\n'},{id:"B128",body:'\nGoswami SB, Matin S, Saxena A, Bairagi GD. A review: The application of Remote Sensing, GIS and GPS in Precision Agriculture. International Journal of Advanced Technology & Engineering Research. 2012;2(1):50-54\n'},{id:"B129",body:'\nBalafoutis A, Beck B, Fountas S, Vangeyte J, Wal TV, Soto I, et al. Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability. 2017;9(8):1339\n'},{id:"B130",body:'\nBanu S. Precision agriculture: Tomorrow’s technology for today’s farmer. Journal of Food Processing & Technology. 2015;6(8):1\n'},{id:"B131",body:'\nPfister B. What is Precision Agriculture?. 1998. Available from: http://www.directionsmag.com/articles/what-is-precision-agriculture/124210\n\n'},{id:"B132",body:'\nFasina AS, Omolayo FO, Ajayi OS, Falodun AA. Influence of land use on soil properties of three mapping units in Southwestern Nigeria-Implications for sustainable soil management. Research Journal of Applied Sciences. 2007;2(8):879-883\n'},{id:"B133",body:'\nPravat KS, Gouri B, Ramkrishna M. Spatial analysis of soil properties using GIS based geostatistics models. Earth System and Environment. 2016;2:107. DOI: 10.1007/s40808-016-0160-4\n'},{id:"B134",body:'\nKingsley J, Lawani SO, Esther AO, Ndiye KM, Sunday OJ, Penížek V. Predictive mapping of soil properties for precision agriculture using geographic information system (GIS) based geostatistics models. Modern Applied Science. 2019;13(10)\n'},{id:"B135",body:'\nOerke EC. Remote sensing of diseases. Annual Review of Phytopathology. 2020;58:225-252\n'},{id:"B136",body:'\nRoberts DP, Short NM, Sill J, Lakshman DK, Hu X, Buser M. Precision agriculture and geospatial techniques for sustainable disease control. Indian Phytopathology. 2021;74(2):287-305\n'},{id:"B137",body:'\nShanmugapriya P, Rathika S, Ramesh T, Janaki P. Applications of remote sensing in agriculture-A Review. International Journal of Current Microbiology and Applied Sciences. 2019;8(1):2270-2283\n'},{id:"B138",body:'\nBehmann J, Mahlein AK, Rumpf T, Römer C, Plümer L. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture. 2015;16(3):239-260\n'},{id:"B139",body:'\nBharti A, Paritosh K, Mandla VR, Chawade A, Vivekanand V. GIS application for the estimation of bioenergy potential from agriculture residues: An overview. Energies. 2021;14(4):898\n'},{id:"B140",body:'\nBauböck R, Karpenstein-Machan M, Kappas M. Computing the biomass potentials for maize and two alternative energy crops, triticale and cup plant (Silphium perfoliatum L.), with the crop model BioSTAR in the region of Hannover (Germany). Environmental Sciences Europe. 2014;26(1):1-2\n'},{id:"B141",body:'\nHiloidhari M, Baruah DC. GIS mapping of rice straw residue for bioenergy purpose in a rural area of Assam, India. Biomass and Bioenergy. 2014;71:125-133\n'},{id:"B142",body:'\nHiloidhari M, Baruah DC, Singh A, Kataki S, Medhi K, Kumari S, et al. Emerging role of Geographical Information System (GIS), Life Cycle Assessment (LCA) and spatial LCA (GIS-LCA) in sustainable bioenergy planning. Bioresource Technology. 2017;242:218-226\n'},{id:"B143",body:'\nSahoo K, Hawkins GL, Yao XA, Samples K, Mani S. GIS-based biomass assessment and supply logistics system for a sustainable biorefinery: A case study with cotton stalks in the Southeastern US. Applied Energy. 2016;182:260-273\n'},{id:"B144",body:'\nSahoo K, Mani S, Das L, Bettinger P. GIS-based assessment of sustainable crop residues for optimal siting of biogas plants. Biomass and Bioenergy. 2018;110:63-74\n'},{id:"B145",body:'\nSharma R, Kamble SS, Gunasekaran A. Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives. Computers and Electronics in Agriculture. 2018;155:103-120\n'},{id:"B146",body:'\nPerveen S, Arsalan MH. A review of potential use of geo-information technologies for cotton supply chain management. International Journal of Biodiversity and Conservation. 2014;6(10):724-734\n'},{id:"B147",body:'\nQu XH, Zhuang DF, Qiu DS. Studies on GIS based tracing and traceability of safe crop product in China. Agricultural Sciences in China. 2007;6(6):724-731\n'},{id:"B148",body:'\nNardi MG, Sperry SE, Davis TD. Grain supply chain management optimization using ARCGIS in Argentina. In: 2007 ESRI User Conference Proceedings. Redlands, CA; 2007. pp. 1-23\n'},{id:"B149",body:'\nZhang F, Johnson D, Johnson M, Watkins D, Froese R, Wang J. Decision support system integrating GIS with simulation and optimisation for a biofuel supply chain. Renewable Energy. 2016;85:740-748\n'},{id:"B150",body:'\nYusianto R, Hardjomidjojo H. Intelligent spatial decision support system concept in the potato agro-industry supply chain. In: 2020 International Conference on Computer Science and Its Application in Agriculture (ICOSICA). Bogor, Indonesia: IEEE; 2020. pp. 1-7\n'},{id:"B151",body:'\nLatterini F, Stefanoni W, Suardi A, Alfano V, Bergonzoli S, Palmieri N, et al. A GIS approach to locate a small size biomass plant powered by olive pruning and to estimate supply chain costs. Energies. 2020;13(13):3385\n'},{id:"B152",body:'\nZhang F, Wang J, Liu S, Zhang S, Sutherland JW. Integrating GIS with optimization method for a biofuel feedstock supply chain. Biomass and Bioenergy. 2017;98:194-205\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Parmita Ghosh",address:"parmita.ghosh@corteva.com",affiliation:'
Department of Digital Solutions Data Science, Corteva Agriscience™, Telangana, India
'},{corresp:null,contributorFullName:"Siva P. Kumpatla",address:null,affiliation:'
Department of Data Science and Bioinformatics, Corteva Agriscience™, Johnston, United States
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It can occur in different parts of the body. Although, fistulae mostly develop due to untreated chronic infections, traumatic injuries and congenital deformities, specific infections or diseases, and post-surgical healing abnormalities can also cause fistula formation. Although, there is a general classification system made by the World Health Organization to identify fistulae, specifically, in this chapter oral fistulae are divided into four different categories, namely dentoalveolar, oroantral, oronasal and orocutaneous fistulae. The diagnosis and the treatment protocols for oral fistulas are described using this specific classification and with additional new techniques introduced for the correction of the lesions. Conventional surgical methods also are summarized. The importance of the radiological examination is emphasized and the practitioners are informed of possible complications.",signatures:"Erol Cansiz, Alper Gultekin, Melek Koltuk and Sirmahan Cakarer",authors:[{id:"66265",title:"Associate Prof.",name:"Sirmahan",surname:"Cakarer",fullName:"Sirmahan Cakarer",slug:"sirmahan-cakarer",email:"sirmacakar@yahoo.com"},{id:"142533",title:"Dr.",name:"Alper",surname:"Gultekin",fullName:"Alper Gultekin",slug:"alper-gultekin",email:"alpergultekin@hotmail.com"},{id:"177392",title:"Dr.",name:"Erol",surname:"Cansiz",fullName:"Erol Cansiz",slug:"erol-cansiz",email:"erolca@yahoo.com"},{id:"184229",title:"Dr.",name:"Melek",surname:"Koltuk",fullName:"Melek Koltuk",slug:"melek-koltuk",email:"melekkoltuk@gmail.com"}],book:{id:"5112",title:"A Textbook of Advanced Oral and Maxillofacial Surgery",slug:"a-textbook-of-advanced-oral-and-maxillofacial-surgery-volume-3",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"66265",title:"Associate Prof.",name:"Sirmahan",surname:"Cakarer",slug:"sirmahan-cakarer",fullName:"Sirmahan Cakarer",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Istanbul University",institutionURL:null,country:{name:"Turkey"}}},{id:"90148",title:"Dr.",name:"Mohammad Hosein",surname:"Motamedi",slug:"mohammad-hosein-motamedi",fullName:"Mohammad Hosein Motamedi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/90148/images/system/90148.jpg",biography:"Dr. Mohammad Hosein Kalantar Motamedi is a Professor of Oral and Maxillofacial Surgery at the Trauma Research Center, BMSU, and attending faculty of OMF Surgery at the Azad University of Medical Sciences, Tehran. He received his American Diploma from Pennington High School, in Pennington, VA, USA (honor student) and his Iranian Diploma from Hurr High School in Tehran (honor student). After graduation he was accepted at the University of Houston, Texas, USA where he studied predent. Two years later he transferred to the University of Tehran College of dentistry where he received his Doctorate of Dental Surgery Degree (DDS). He completed his Residency in Oral and Maxillofacial Surgery at SBUMS from 1987-1991, and was valedictorian of the National Board Exams in 1991. In 1995 he completed his fellowship at the University of Basel, Switzerland; he obtained full Professorship in 2007. He has published 25 books, 61 international conferences proceedings, supervised 63 doctorate dissertations, and published 185 papers indexed in PUBMED with an h-index of 21. He is Editor-in-Chief of TRAUMA MONTHLY journal and one of the founders of the BMSU Trauma Research Center and more recently the Craniomaxillofacial Research Center. He was granted 1st place prize at the Bagher Alum National Research Festival in 2010 and awarded 2nd Place Prize at the 16th International Razi Research Festival on Medical Sciences. The Iranian Society of OMF Surgeons awarded him “Most Published” in 2009 and 2010, and he was chosen for membership in the National Elite Foundation. In 2013 he was accepted in the Academy of Medical Sciences and he is listed in the Marquis Book \\'Who’s Who in the World” (1999-2020).",institutionString:"BMSU and AUMS",institution:{name:"Baqiyatallah University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}},{id:"142533",title:"Dr.",name:"Alper",surname:"Gultekin",slug:"alper-gultekin",fullName:"Alper Gultekin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Istanbul University",institutionURL:null,country:{name:"Turkey"}}},{id:"171777",title:"Prof.",name:"Abdolreza",surname:"Jamilian",slug:"abdolreza-jamilian",fullName:"Abdolreza Jamilian",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Islamic Azad University Dental Branch of Tehran",institutionURL:null,country:{name:"Iran"}}},{id:"171873",title:"Dr.",name:"Alireza",surname:"Darnahal",slug:"alireza-darnahal",fullName:"Alireza Darnahal",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"177392",title:"Dr.",name:"Erol",surname:"Cansiz",slug:"erol-cansiz",fullName:"Erol Cansiz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Istanbul University",institutionURL:null,country:{name:"Turkey"}}},{id:"177463",title:"Dr.",name:"Michael",surname:"Horan",slug:"michael-horan",fullName:"Michael Horan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Case Western Reserve University",institutionURL:null,country:{name:"United States of America"}}},{id:"178376",title:"Prof.",name:"Serdar",surname:"Yalcin",slug:"serdar-yalcin",fullName:"Serdar Yalcin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Istanbul University",institutionURL:null,country:{name:"Turkey"}}},{id:"179980",title:"MSc.",name:"Saeed",surname:"Khosravi",slug:"saeed-khosravi",fullName:"Saeed Khosravi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Tehran University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}},{id:"185307",title:"Dr.",name:"Alexandra",surname:"Radu",slug:"alexandra-radu",fullName:"Alexandra Radu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Case Western Reserve University",institutionURL:null,country:{name:"United States of America"}}}]},generic:{page:{slug:"open-access-funding",title:"Open Access Funding",intro:"
IntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
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In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\\n\\n
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Does your institution already have a budget for covering Open Access publication costs?
\\n\\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\\n
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If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{},profiles:[{id:"396",title:"Dr.",name:"Vedran",middleName:null,surname:"Kordic",slug:"vedran-kordic",fullName:"Vedran Kordic",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/396/images/7281_n.png",biography:"After obtaining his Master's degree in Mechanical Engineering he continued his education at the Vienna University of Technology where he obtained his PhD degree in 2004. He worked as a researcher at the Automation and Control Institute, Faculty of Electrical Engineering, Vienna University of Technology until 2008. His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. 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This chapter aims to present the main good practices, challenges, and opportunities related to Industry 4.0 paradigm.",book:{id:"6291",slug:"digital-transformation-in-smart-manufacturing",title:"Digital Transformation in Smart Manufacturing",fullTitle:"Digital Transformation in Smart Manufacturing"},signatures:"Antonella Petrillo, Fabio De Felice, Raffaele Cioffi and Federico\nZomparelli",authors:[{id:"161682",title:"Prof.",name:"Fabio",middleName:null,surname:"De Felice",slug:"fabio-de-felice",fullName:"Fabio De Felice"},{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo"},{id:"205141",title:"Dr.",name:"Federico",middleName:null,surname:"Zomparelli",slug:"federico-zomparelli",fullName:"Federico Zomparelli"},{id:"208748",title:"Dr.",name:"Raffaele",middleName:null,surname:"Cioffi",slug:"raffaele-cioffi",fullName:"Raffaele Cioffi"}]},{id:"35715",doi:"10.5772/38693",title:"The Role and Importance of Cultural Tourism in Modern Tourism Industry",slug:"the-role-and-importance-of-cultural-tourism-in-modern-tourism-industry",totalDownloads:41085,totalCrossrefCites:31,totalDimensionsCites:62,abstract:null,book:{id:"2298",slug:"strategies-for-tourism-industry-micro-and-macro-perspectives",title:"Strategies for Tourism Industry",fullTitle:"Strategies for Tourism Industry - Micro and Macro Perspectives"},signatures:"Janos Csapo",authors:[{id:"118766",title:"Dr.",name:"János",middleName:null,surname:"Csapó",slug:"janos-csapo",fullName:"János Csapó"}]},{id:"38973",doi:"10.5772/51460",title:"Risk Management in Construction Projects",slug:"risk-management-in-construction-projects",totalDownloads:102568,totalCrossrefCites:36,totalDimensionsCites:59,abstract:null,book:{id:"2175",slug:"risk-management-current-issues-and-challenges",title:"Risk Management",fullTitle:"Risk Management - Current Issues and Challenges"},signatures:"Nerija Banaitiene and Audrius Banaitis",authors:[{id:"139414",title:"Dr.",name:"Nerija",middleName:null,surname:"Banaitiene",slug:"nerija-banaitiene",fullName:"Nerija Banaitiene"},{id:"149658",title:"Dr.",name:"Audrius",middleName:null,surname:"Banaitis",slug:"audrius-banaitis",fullName:"Audrius Banaitis"}]},{id:"40977",doi:"10.5772/53885",title:"The Emergence of Scientific Reasoning",slug:"the-emergence-of-scientific-reasoning",totalDownloads:4554,totalCrossrefCites:8,totalDimensionsCites:59,abstract:null,book:{id:"654",slug:"current-topics-in-children-s-learning-and-cognition",title:"Current Topics in Children's Learning and Cognition",fullTitle:"Current Topics in Children's Learning and Cognition"},signatures:"Bradley J. Morris, Steve Croker, Amy M. Masnick and Corinne Zimmerman",authors:[{id:"154336",title:"Prof.",name:"Bradley",middleName:null,surname:"Morris",slug:"bradley-morris",fullName:"Bradley Morris"},{id:"154337",title:"Prof.",name:"Steve",middleName:null,surname:"Croker",slug:"steve-croker",fullName:"Steve Croker"},{id:"154338",title:"Prof.",name:"Amy",middleName:null,surname:"Masnick",slug:"amy-masnick",fullName:"Amy Masnick"},{id:"154339",title:"Prof.",name:"Corinne",middleName:null,surname:"Zimmerman",slug:"corinne-zimmerman",fullName:"Corinne Zimmerman"}]}],mostDownloadedChaptersLast30Days:[{id:"58890",title:"Philosophy and Paradigm of Scientific Research",slug:"philosophy-and-paradigm-of-scientific-research",totalDownloads:14074,totalCrossrefCites:9,totalDimensionsCites:17,abstract:"Before carrying out the empirical analysis of the role of management culture in corporate social responsibility, identification of the philosophical approach and the paradigm on which the research carried out is based is necessary. Therefore, this chapter deals with the philosophical systems and paradigms of scientific research, the epistemology, evaluating understanding and application of various theories and practices used in the scientific research. The key components of the scientific research paradigm are highlighted. Theories on the basis of which this research was focused on identification of the level of development of the management culture in order to implement corporate social responsibility are identified, and the stages of its implementation are described.",book:{id:"5791",slug:"management-culture-and-corporate-social-responsibility",title:"Management Culture and Corporate Social Responsibility",fullTitle:"Management Culture and Corporate Social Responsibility"},signatures:"Pranas Žukauskas, Jolita Vveinhardt and Regina Andriukaitienė",authors:[{id:"179629",title:"Prof.",name:"Jolita",middleName:null,surname:"Vveinhardt",slug:"jolita-vveinhardt",fullName:"Jolita Vveinhardt"}]},{id:"74550",title:"School Conflicts: Causes and Management Strategies in Classroom Relationships",slug:"school-conflicts-causes-and-management-strategies-in-classroom-relationships",totalDownloads:2328,totalCrossrefCites:1,totalDimensionsCites:10,abstract:"Conflicts cannot cease to exist, as they are intrinsic to human beings, forming an integral part of their moral and emotional growth. Likewise, they exist in all schools. The school is inserted in a space where the conflict manifests itself daily and assumes relevance, being the result of the multiple interpersonal relationships that occur in the school context. Thus, conflict is part of school life, which implies that teachers must have the skills to manage conflict constructively. Recognizing the diversity of school conflicts, this chapter aimed to present its causes, highlighting the main ones in the classroom, in the teacher-student relationship. It is important to conflict face and resolve it with skills to manage it properly and constructively, establishing cooperative relationships, and producing integrative solutions. Harmony and appreciation should coexist in a classroom environment and conflict should not interfere, negatively, in the teaching and learning process. This bibliography review underscore the need for during the teachers’ initial training the conflict management skills development.",book:{id:"7827",slug:"interpersonal-relationships",title:"Interpersonal Relationships",fullTitle:"Interpersonal Relationships"},signatures:"Sabina Valente, Abílio Afonso Lourenço and Zsolt Németh",authors:[{id:"324514",title:"Ph.D.",name:"Sabina",middleName:"N.",surname:"Valente",slug:"sabina-valente",fullName:"Sabina Valente"},{id:"326375",title:"Ph.D.",name:"Abílio",middleName:"Afonso",surname:"Lourenço",slug:"abilio-lourenco",fullName:"Abílio Lourenço"},{id:"329177",title:"Dr.",name:"Zsolt",middleName:null,surname:"Németh",slug:"zsolt-nemeth",fullName:"Zsolt Németh"}]},{id:"58969",title:"Corruption, Causes and Consequences",slug:"corruption-causes-and-consequences",totalDownloads:27687,totalCrossrefCites:13,totalDimensionsCites:15,abstract:"Corruption is a constant in the society and occurs in all civilizations; however, it has only been in the past 20 years that this phenomenon has begun being seriously explored. It has many different shapes as well as many various effects, both on the economy and the society at large. Among the most common causes of corruption are the political and economic environment, professional ethics and morality and, of course, habits, customs, tradition and demography. Its effects on the economy (and also on the wider society) are well researched, yet still not completely. Corruption thus inhibits economic growth and affects business operations, employment and investments. It also reduces tax revenue and the effectiveness of various financial assistance programs. The wider society is influenced by a high degree of corruption in terms of lowering of trust in the law and the rule of law, education and consequently the quality of life (access to infrastructure, health care). There also does not exist an unambiguous answer as to how to deal with corruption. Something that works in one country or in one region will not necessarily be successful in another. This chapter tries to answer at least a few questions about corruption and the causes for it, its consequences and how to deal with it successfully.",book:{id:"6487",slug:"trade-and-global-market",title:"Trade and Global Market",fullTitle:"Trade and Global Market"},signatures:"Štefan Šumah",authors:[{id:"228073",title:"Mr.",name:"Stefan",middleName:null,surname:"Sumah",slug:"stefan-sumah",fullName:"Stefan Sumah"}]},{id:"55499",title:"Human Resources Management in Nonprofit Organizations: A Case Study of Istanbul Foundation for Culture and Arts",slug:"human-resources-management-in-nonprofit-organizations-a-case-study-of-istanbul-foundation-for-cultur",totalDownloads:2399,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The aim of this study is to investigate the efficiency and importance of human resources management in nonprofit organizations. The understanding was included to the literature as personnel management at the beginning of the twentieth century and it turned into an approach as human resources management in the 1980s. It could be observed that many organizations, which deem the human as the most critical stakeholder, adopt a traditional way of personnel management in operating human resources. The employees play a key role in the success of an organization. For this reason, subjects such as recruitment, training, development, career management, performance appraisal, occupational health, and safety are the fundamental functions of human resources management. The study examines to what extent these roles are evaluated through a case study. The subject matter of the study is the most powerful culture and art foundation in Turkey. Compared to many other nonprofit organizations, the foundation actively performs a variety of services within a year worldwide. The fact that the total number of employees might rise up to 800, including the field personnel, indicates the need of a good functioning human resources management. The human resources practices of the foundation are examined and evaluated within that scope.",book:{id:"5826",slug:"issues-of-human-resource-management",title:"Issues of Human Resource Management",fullTitle:"Issues of Human Resource Management"},signatures:"Beste Gökçe Parsehyan",authors:[{id:"189113",title:"Dr.",name:"Beste",middleName:null,surname:"Gokce Parsehyan",slug:"beste-gokce-parsehyan",fullName:"Beste Gokce Parsehyan"}]},{id:"59152",title:"Marketing Strategies for the Social Good",slug:"marketing-strategies-for-the-social-good",totalDownloads:1669,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Social network sites (SNS) have proven to be a good environment to promote and sell goods and services, but marketing is more than creating commercial strategies. Social marketing strategies can also be used to promote behavioral change and help individuals transform their lives, achieve well-being, and adopt prosocial behaviors. In this chapter, we seek to analyze with a netnographic study, how SNS are being employed by nonprofits and nongovernment organizations (NGOs) to enable citizens and consumers to participate in different programs and activities that promote social transformation and well-being. A particular interest is to identify how organizations are using behavioral economic tactics to nudge individuals and motivate them to engage in prosocial actions. By providing an understanding on how SNS can provide an adequate environment for the design of social marketing strategies, we believe our work has practical implications both for academicians and marketers who want to contribute in the transformation of consumer behavior and the achievement of well-being and social change.",book:{id:"6583",slug:"marketing",title:"Marketing",fullTitle:"Marketing"},signatures:"Alicia De La Pena",authors:[{id:"196878",title:"Dr.",name:"Alicia",middleName:null,surname:"De La Pena",slug:"alicia-de-la-pena",fullName:"Alicia De La Pena"}]}],onlineFirstChaptersFilter:{topicId:"4",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"83040",title:"Corporate Social Responsibility and Social Report: A Case Study in the Basque Country",slug:"corporate-social-responsibility-and-social-report-a-case-study-in-the-basque-country",totalDownloads:1,totalDimensionsCites:null,doi:"10.5772/intechopen.105511",abstract:"This chapter illustrates the case of the Spanish company Telefónica SA, a world leader in the telecommunication industry. The main research question is to understand if social responsibility can play a main role in an uncertain scenario. Or better, understand how Corporate Social Responsibility (CSR) can be organized, managed, and measured to be considered a sustainable choice detached from any greenwashing policy. In terms of the socioeconomic environment with the combination of external social and economic conditions that influence the operation and preformation of an organization, the company should be a system not only capable of generating profits but at the same time contributing to society and environmental protection, integrating social responsibility as a strategic investment in the framework of their competitive strategy, in their management tools and their operations. The analysis of this case study aims to offer a model of CSR analysis and measurement tools such as the social report for managers who will have to face the important challenges of sustainable growth in compliance with the 2030 Agenda and SDGs paradigm (Sustainable Development Goals).",book:{id:"11602",title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg"},signatures:"Vincenzo Basile"},{id:"82771",title:"Making Sense of a Biochemistry Learning Process and Teacher’s Empathy: Computer-Supported Collaborative Learning Using Emoji Symbols",slug:"making-sense-of-a-biochemistry-learning-process-and-teacher-s-empathy-computer-supported-collaborati",totalDownloads:2,totalDimensionsCites:null,doi:"10.5772/intechopen.105927",abstract:"Teaching biochemistry concepts can be a challenging task, as it requires learners and teachers to integrate abstract concepts from chemistry and biology. Students struggle to grasp the molecular processes, as they find it difficult to visualize them. Incorporating Information Communication Technology (ICT) implementations during lessons is known to encourage learners’ involvement in a collaborative learning process and is especially effective when training preservice teachers (PSTs). In the current study, we describe an example in which the teacher plays an important role in creating the Computer-Supported Collaborative Learning (CSCL) in this environment to encourage peer learning while coping with complicated material. We believe that one of the important components in guiding such peer work is the teacher’s ability to sense each group’s progress and to employ empathy in the classroom as a tool for coping with the difficulty and challenge of acquiring new knowledge and for creating a productive dialog between groups that disagree. In this example, the process of Information Communication Technology (ICT) implementation encouraged the preservice teachers (PSTs) to create an alternative set of symbols, which eventually served as a “language” and help them understand the biochemical processes.",book:{id:"11443",title:"Empathy - Advanced Research and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11443.jpg"},signatures:"Dana Sachyani and Ilana Ronen"},{id:"83112",title:"Anomalies in Nigeria Presidential Election Data and the Way Forward",slug:"anomalies-in-nigeria-presidential-election-data-and-the-way-forward",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.106657",abstract:"Nigeria presently runs a presidential system of government and the Independent National Electoral Commission (INEC) is saddled with the responsibility of conducting elections, every four years. A fraud-free and credible election is a necessary ingredient to the growth of democracy. However, election fraud has become a major challenge in the Nigerian political system. Till date, reports show that elections in Nigeria have been marred with vote buying, falsification of results, underage voting, and the use of security forces to intimidate voters, among others. Hence, the authors suggest the need for transparency in the voting process and in the collation of results. There is also a need for an electoral reform to address the issue of electronic voting and electronic transmission of results. Electronic voting should be supported and encouraged by all stakeholders. The INEC, executive and legislative arms of government are advised to work in tandem to provide credible electoral process and improve on the conducts of elections in Nigeria. The chapter concludes with suggestion on the possibility of adopting election forensic techniques to address anomalies in Nigeria electoral results. The authors believe that this chapter contribution will be of great benefit to Nigeria and Africa as a whole.",book:{id:"11435",title:"Election and Democracy in the Digital Age - Status, Challenges, and Trends",coverURL:"https://cdn.intechopen.com/books/images_new/11435.jpg"},signatures:"Sunday Tunmibi and Wole Olatokun"},{id:"83121",title:"Social Media and Democracy",slug:"social-media-and-democracy",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106660",abstract:"The purpose of this chapter is to analyze the implications of freedom of speech regarding political discussions on social media platforms, such as Facebook. The paper will look at the following aspects: the occasion when social media users have to discuss politics as a hobby and as a means to keep their ideas out of the view of face-to-face social circles, to engage in social issues and even be part of protests, to discuss politicians’ public image, and attempt to change some users’ perception about it. Within social media, we witness debates or simple displays of emotions, allowing users to speak their minds and interact with other users, showing empathy toward them. The benefits of this are related to the therapeutic effects of speaking about what upsets them or angers them and finding like-minded users.",book:{id:"11435",title:"Election and Democracy in the Digital Age - Status, Challenges, and Trends",coverURL:"https://cdn.intechopen.com/books/images_new/11435.jpg"},signatures:"Irina-Ana Drobot"},{id:"83113",title:"Agoraphobic Dispositions towards Action Research: Teacher Education Students’ Perceptions and Experiences",slug:"agoraphobic-dispositions-towards-action-research-teacher-education-students-perceptions-and-experien",totalDownloads:3,totalDimensionsCites:0,doi:"10.5772/intechopen.106188",abstract:"One of the contemporary global education thrusts in teacher education is the generation of context-based theory through engagement in action research. While practicing in the classroom, the teacher education student is essentially in the laboratory creating procedural knowledge. Action research in the classroom involves reflective practice, which is indispensable to praxis. Despite the efficacy of action research in facilitating mathetics (learning how to learn), there are some militating situations that are fuelled by diehard traditional perceptions and practices. An exploration of teacher education students’ perceptions and experiences with action research was done with 16 informants who were selected purposively and exposed to in-depth interviews. The data were thematically analyzed and the findings were that some students develop agoraphobic dispositions toward action research due to some miseducative experiences that are largely attributed to traditional educational practices. The teacher education students are exposed to vices like technical rationality instead they should be oriented toward epistemic and pragmatic rationalities that are the linchpins of professional development. The experiences that precipitate agoraphobic dispositions in action research should be known and subsequently obliterated.",book:{id:"11481",title:"Active Learning - Research and Practice for STEAM and social sciences education",coverURL:"https://cdn.intechopen.com/books/images_new/11481.jpg"},signatures:"Davison Zireva"},{id:"83099",title:"Values-Flow in Contextual Psychotherapy: The ‘What’, ‘Why’, and ‘How’ of Sustainable Values-Based Behaviour",slug:"values-flow-in-contextual-psychotherapy-the-what-why-and-how-of-sustainable-values-based-behaviour",totalDownloads:6,totalDimensionsCites:0,doi:"10.5772/intechopen.106594",abstract:"Flow - enjoyed and fully absorbed engagement in meaningful and contextually bounded activities - is widely underutilised in psychotherapy and mental health settings. Two gold standard therapies, Acceptance and Commitment Therapy (ACT) and Dialectical Behaviour Therapy (DBT), while powerful and effective in many ways, would benefit from systematic models that move from initiating positive change to sustaining meaningful change. This chapter introduces ‘Values-Flow’ – an approach aimed at building commitment and sustainable engagement in psychotherapy and values-based behaviour in working adults struggling with sub-optimal functioning. We first introduce Values-Flow and describe how it may benefit psychotherapy skills practice in everyday life. Next, we discuss why Values-Flow is relevant and enhances the practice of ACT and DBT strategies, helping to sustain engagement and creative practice of values-based actions outside of sessions. We then describe the ‘Values-Flow’ framework, which incorporates VIVA (Virtue, Involve, Vital, Accepting) and ARIA (Attend, Reflect, Inform, Act) tools that develop commitment for values-based practice in daily life. We conclude with a case-example of how Values-Flow can build commitment and sustainable engagement in homework completion in psychotherapy.",book:{id:"11444",title:"Happiness - Biopsychosocial and Anthropological Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11444.jpg"},signatures:"Cedomir Ignjatovic, Margaret L. Kern and Lindsay G. Oades"}],onlineFirstChaptersTotal:284},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:11,numberOfPublishedChapters:91,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:333,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:11,numberOfPublishedChapters:143,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:124,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:23,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:12,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"August 17th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:11,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. 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He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. 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He was elected a Yangtze River Scholars Distinguished Professor in 2013, a member of the International Statistical Institute (ISI) in 2016, a member of the board of the International Chinese Statistical Association (ICSA) in 2018, and a fellow of the Institute of Mathematical Statistics (IMS) in 2021. He received the ICSA Outstanding Service Award in 2018 and the National Science Foundation for Distinguished Young Scholars of China in 2012. He serves as a member of the editorial board of Statistics and Its Interface and Journal of Systems Science and Complexity. He is also a field editor for Communications in Mathematics and Statistics. His research interests include biostatistics, empirical likelihood, missing data analysis, variable selection, high-dimensional data analysis, Bayesian statistics, and data science. He has published more than 190 research papers and authored five books.",institutionString:"Yunnan University",institution:{name:"Yunnan University",country:{name:"China"}}},{id:"1177",title:"Prof.",name:"António",middleName:"J. R.",surname:"José Ribeiro Neves",slug:"antonio-jose-ribeiro-neves",fullName:"António José Ribeiro Neves",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1177/images/system/1177.jpg",biography:"Prof. António J. R. Neves received a Ph.D. in Electrical Engineering from the University of Aveiro, Portugal, in 2007. Since 2002, he has been a researcher at the Institute of Electronics and Informatics Engineering of Aveiro. Since 2007, he has been an assistant professor in the Department of Electronics, Telecommunications, and Informatics, University of Aveiro. He is the director of the undergraduate course on Electrical and Computers Engineering and the vice-director of the master’s degree in Electronics and Telecommunications Engineering. He is an IEEE Senior Member and a member of several other research organizations worldwide. His main research interests are computer vision, intelligent systems, robotics, and image and video processing. He has participated in or coordinated several research projects and received more than thirty-five awards. He has 161 publications to his credit, including books, book chapters, journal articles, and conference papers. He has vast experience as a reviewer of several journals and conferences. As a professor, Dr. Neves has supervised several Ph.D. and master’s students and was involved in more than twenty-five different courses.",institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"11317",title:"Dr.",name:"Francisco",middleName:null,surname:"Javier Gallegos-Funes",slug:"francisco-javier-gallegos-funes",fullName:"Francisco Javier Gallegos-Funes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/11317/images/system/11317.png",biography:"Francisco J. Gallegos-Funes received his Ph.D. in Communications and Electronics from the Instituto Politécnico Nacional de México (National Polytechnic Institute of Mexico) in 2003. He is currently an associate professor in the Escuela Superior de Ingeniería Mecánica y Eléctrica (Mechanical and Electrical Engineering Higher School) at the same institute. His areas of scientific interest are signal and image processing, filtering, steganography, segmentation, pattern recognition, biomedical signal processing, sensors, and real-time applications.",institutionString:"Instituto Politécnico Nacional",institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"428449",title:"Dr.",name:"Ronaldo",middleName:null,surname:"Ferreira",slug:"ronaldo-ferreira",fullName:"Ronaldo Ferreira",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428449/images/21449_n.png",biography:null,institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. 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He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. 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Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:null,institution:null},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"417317",title:"Mrs.",name:"Chiedza",middleName:null,surname:"Elvina Mashiri",slug:"chiedza-elvina-mashiri",fullName:"Chiedza Elvina Mashiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"352140",title:"Dr.",name:"Edina",middleName:null,surname:"Chandiwana",slug:"edina-chandiwana",fullName:"Edina Chandiwana",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"342259",title:"B.Sc.",name:"Leonard",middleName:null,surname:"Mushunje",slug:"leonard-mushunje",fullName:"Leonard Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"347042",title:"Mr.",name:"Maxwell",middleName:null,surname:"Mashasha",slug:"maxwell-mashasha",fullName:"Maxwell Mashasha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"2941",title:"Dr.",name:"Alberto J.",middleName:"Jorge",surname:"Rosales-Silva",slug:"alberto-j.-rosales-silva",fullName:"Alberto J. 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This topic is dedicated to biologically plausible descriptions and computational models - at various abstraction levels - of neurons and neural systems. This includes, but is not limited to: single-neuron modeling, sensory processing, motor control, memory, and synaptic plasticity, attention, identification, categorization, discrimination, learning, development, axonal patterning, guidance, neural architecture, behaviors, and dynamics of networks, cognition and the neuroscientific basis of consciousness. Particularly interesting are models of various types of more compound functions and abilities, various and more general fundamental principles (e.g., regarding architecture, organization, learning, development, etc.) found at various spatial and temporal levels.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11419,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"13818",title:"Dr.",name:"Asim",middleName:null,surname:"Bhatti",slug:"asim-bhatti",fullName:"Asim Bhatti",profilePictureURL:"https://mts.intechopen.com/storage/users/13818/images/system/13818.jpg",institutionString:null,institution:{name:"Deakin University",institutionURL:null,country:{name:"Australia"}}},{id:"151889",title:"Dr.",name:"Joao Luis Garcia",middleName:null,surname:"Rosa",slug:"joao-luis-garcia-rosa",fullName:"Joao Luis Garcia Rosa",profilePictureURL:"https://mts.intechopen.com/storage/users/151889/images/4861_n.jpg",institutionString:null,institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",institutionURL:null,country:{name:"Turkey"}}}]},onlineFirstChapters:{paginationCount:6,paginationItems:[{id:"83073",title:"Dental and Orofacial Trauma Impacts on Oral-Health-Related—Quality of Life in Children: Low- and Middle-Income Countries",doi:"10.5772/intechopen.105845",signatures:"Yolanda Malele-Kolisa, Nazia Khan, Mpho P. 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Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 16th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:124,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). 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\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
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In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. 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The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",annualVolume:11402,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",fullName:"Shailendra K. 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