The 25 sets of samples, from the 18-th level WPT of size 262,144, is used to mimic the bark scale.
\r\n\tAn update on clinical manifestations, their assessment, monitoring, and imagiology, including peripheral arthritis, enthesopathy, and extra-articular findings, and, the differential diagnosis with other diseases which evolves with axial and peripheral calcifications will be provided.
\r\n\r\n\t
\r\n\tAn important component of this book must be dedicated to the more recent treatments namely with biologic therapies but focusing also on new small molecule inhibitors and experimental therapies.
Since human population has been intensively increasing, the need for food and other products from agricultural fields also grows. To guarantee the production, monoculture is carried out at extensive areas, which intensifies the appearance of pests. Generally, pest control in agriculture is performed with the use of chemical pesticides, often applied even in areas without pest incidence, which raises the cost of production and may cause environmental impacts that affect human health. The development of specific hardware and software for pest detection in agriculture can provide support for distinct production forms which reduce negative impacts. In this sense, the capture, recording, and analysis of acoustic signals emitted by insects can be an alternative to optimize the production of certain crops [1].
The cicada (Hemiptera: Cicadidae) is a good example of an insect capable of emitting acoustic signals. In Brazil, the coffee plantation can be attacked by several arthropods and among them,
Cicadas attack crops in the search for sap, their main food. The impacts on the plants occur in the nymphal phase of the cicada when it sucks sap from the host plant root [3]. The
Males usually sound from October to December. In the 1970s, since there was no efficient method for cicadas controlling, many coffee growers had no choice than to eradicate infested crops. Many of them even abandoned their cultivation. However, lately the recommended control has been made through systemic chemical defenses and more recently by the use of a sound trap that attracted
There are few technological devices used in coffee plantations to monitor and control the cicadas, and maybe, because of this, the most used device is the chemical one. Considering that the methods currently used for mapping and monitoring the populations of cicadas consist basically of the nonautomated counting of the individuals through direct observation, this paper aims to present some applications using digital signal processing and support vector machines (SVMs) as techniques for detecting and monitoring cicadas in crops and forests, reducing time and control costs.
Machine learning is a branch of artificial intelligence that seeks to develop algorithms capable of learning certain behaviors or patterns through examples, being able to generalize from training. SVMs, for instance, are supervised machine learning methods with superior results compared to other pattern classification procedures, considering binary problems [5].
Thus, one possibility for improving agricultural procedures would be the development of a device capable of capturing the sound of the adult cicada in order to detect its presence, thus monitoring and quantifying crop insects. This device would be spread across the coffee fields to capture sounds within the widest possible area coverage. That allows for the manager to have more input for decision-making, adopting the most appropriate management technique based on concrete information on population density separated by crop region.
Cicadas emit particular frequency components which characterize certain patterns. To detect them, both Fourier and discrete wavelet (DWT) transforms, which convert a time-domain signal to the frequency domain, can be used. Nevertheless, in the latter case, it is still possible to obtain the time support of frequencies [6]. Then, an SVM can be used to refine the results, pointing out the existence of the important patterns from the wavelet-transformed signals. This is just how the proposed approach was implemented. Related works, such as [1, 7, 8, 9, 10, 11, 12, 13, 14, 15], perform automated data collection for monitoring and corroborate the present work.
This solution consists of a system that, from an input
Based on our assumptions and intending to assess it, we carried out the following preprocessing procedure to convert each acoustic input signal of variable length into a 25-sample long feature vector:
AP1 PRE-PROCESSING PHASE:
BEGINNING.
STEP 1: the raw data from the input signal
STEP 2:
END.
Barka | Band range (Hz Hz) | Initial WPT sample | Final WPT sample | Energy rangeb |
---|---|---|---|---|
0 | 0–100 | 0 | 0–99.9949 | |
1 | 100–200 | 1190 | 100.0790–199.9898 | |
2 | 200–300 | 2379 | 200.0739–299.9847 | |
3 | 300–400 | 3568 | 300.0688–399.9796 | |
4 | 400–510 | 4757 | 400.0637–509.9824 | |
5 | 510–630 | 6065 | 510.0605–629.9931 | |
6 | 630–770 | 7492 | 630.0772–769.9355 | |
7 | 770–920 | 9156 | 770.0196–919.9699 | |
8 | 920–1080 | 10,940 | 920.054–1079.9281 | |
9 | 1080–1270 | 12,842 | 1080.0122–1269.9941 | |
10 | 1270–1480 | 15,102 | 1270.0782–1479.9918 | |
11 | 1480–1720 | 17,599 | 1480.0759–1719.9291 | |
12 | 1720–2000 | 20,452 | 1720.0132–1999.9821 | |
13 | 2000–2320 | 23,782 | 2000.0662–2319.9826 | |
14 | 2320–2700 | 27,587 | 2320.0667–2699.9464 | |
15 | 2700–3150 | 32,105 | 2700.0305–3149.9655 | |
16 | 3150–3700 | 37,456 | 3150.0496–3699.9795 | |
17 | 3700–4400 | 43,996 | 3700.0636–4399.9438 | |
18 | 4400–5300 | 52,319 | 4400.0279–5299.9820 | |
19 | 5300–6400 | 63,021 | 5300.0661–6399.9259 | |
20 | 6400–7700 | 76,100 | 6400.0100–7699.9437 | |
21 | 7700–9500 | 91,558 | 7700.0278–9499.9360 | |
22 | 9500–12,000 | 112,961 | 9500.0201–11999.9767 | |
23 | 12,000–15,500 | 142,688 | 12000.0608–15499.9664 | |
24 | 15,500–22,050 | 184,305 | 15500.0505–22050.0000 |
The 25 sets of samples, from the 18-th level WPT of size 262,144, is used to mimic the bark scale.
Bark band.
Range in fi[Bark band] Hz.
At that level, which is the maximum, and considering the original signal sampling rate of 44,100 samples/s, each sample of the transformed signal has a resolution of
We apply here the technique described in [17], which is based on paraconsistent logic, to analyze the behavior and suitability from the obtained data, i.e., the feature vector. Thus, the data vectors were represented in the paraconsistent plane as point
The paraconsistent plane where the axes G1 and G2 represent the degrees of certainty and contradiction, respectively.
Proceeding, we estimate that an SVM could be a proper classifier to interpret the feature vectors we selected during the preprocessing step, because of its excellence in terms of binary classifications [5]. Accordingly, Figure 3 shows the complete setup for the proposed approach. It is divided into two phases, i.e., training and testing, with four steps each. As discussed ahead, a total of
The experimental setup for the proposed application one.
The SVM has been implemented, as described in [5], in such a way that it receives the input vectors defined in the preprocessing step. In Figure 3, we illustrate the proposed setup, which is divided into two phases, training and testing, with four steps each. As we will discuss later, a total of
The detailed procedures are as follows:
AP1 TRAINING PHASE:
BEGINNING
All the
The SVM structure used in application one approach. The weights determined during the supervised part of the training are
implying that the
END.
Once the training procedures are over, the system is ready for testing, as follows.
AP1 TESTING PHASE:
BEGINNING.
Each one of the
END.
Visually, cicadas are quite noticeable in the farming environment, so a management hypothesis would be the inclusion of a camera to permit visual detection of pests, adjusting the data capture interval for sending to a web server.
The tests were performed by using images captured in the coffee crop and divided into three classes that represent the pest insidious density: high, medium, and none. The submitted images have been converted to grayscale and have a size of 320 × 240 pixels, as in Figure 5.
Examples of images used: high, low, and zero density, respectively.
Similar to AP1, an SVM is used to classify the preprocessed input energies; however, in this case we use the
The experimental setup for the proposed application two.
In Figure 7, we have the SVM illustration with
The SVM structure used in application two. Similar to AP1, the weights determined during the supervised part of the training are
The implementation of the algorithms proposed here were performed using Java programming language.
Thirty-five files from each class were collected and used. The tests were performed with those files not used for training, where two to five training files were experimented. For each set of training files, the DWT maximum level and mean decomposition level were tested with each of the 46 wavelet filters presented in Table 2. The best result, i.e., 96.88% accuracy, was obtained with Haar filter, showing excellent results and confirming our hypothesis of viability of using this system in coffee crop.
Wavelet | Train files/class | Test files | Percentage |
---|---|---|---|
DAUB6 | 2 | 99 | 94.95/100 |
DAUB8 | 3 | 96 | 96.88/100 |
DAUB4 | 4 | 93 | 96.77/100 |
DAUB4 | 5 | 90 | 96.67/100 |
Test results from AP1.
The cross-validation procedure was performed to present the best result in Table 2. The algorithm developed for cross-validation is illustrated in Figure 8.
Cross-validation algorithm.
To develop AP2, an application that uses digital image processing to estimate cicada density in a coffee crop was adopted, where 35 high-density class files, 35 low-density class files, and 35 files considered by an expert as zero density were used. In Tables 3 and 4, we present the results of the tests performed in the laboratory, which demonstrate the viability of future implantation in that system.
Wavelet | Level | Train files/class | Test files | Percentage |
---|---|---|---|---|
COIF6 | 1 | 2 | 99 | 68.69/100 |
COIF6 | 1 | 3 | 96 | 81.25/100 |
DAUB68 | 1 | 4 | 93 | 81.72/100 |
DAUB32 | 1 | 5 | 90 | 83.33/100 |
DAUB12 | 2 | 2 | 99 | 86.87/100 |
DAUB52 | 2 | 3 | 96 | 87.50/100 |
DAUB16 | 2 | 4 | 93 | 87.10/100 |
DAUB38 | 2 | 5 | 90 | 87.78/100 |
DAUB18 | 3 | 2 | 99 | 86.87/100 |
DAUB68 | 3 | 3 | 96 | 89.58/100 |
DAUB24 | 3 | 4 | 93 | 90.32/100 |
DAUB16 | 3 | 5 | 90 | 90.00/100 |
COIF24 | 4 | 2 | 99 | 90.91/100 |
DAUB24 | 4 | 3 | 96 | 92.71/100 |
COIF6 | 4 | 4 | 93 | 92.47/100 |
DAUB22 | 4 | 5 | 90 | 92.22/100 |
Test results from AP2.
Both systems are being implemented in hardware for real-time coffee crop deployment using ESP8266 devices and their derivatives, integrating with cloud server for storing and organizing data to aid farmer decision-making. In future work, we must present practical results of their implementations.
High density | Low density | Null density | Total | |
---|---|---|---|---|
High density | 30 | 3 | 0 | 33 |
Low density | 1 | 31 | 1 | 33 |
Null density | 0 | 0 | 33 | 33 |
Total | 31 | 34 | 34 | 99 |
Best confusion matrix from AP2.
Both AP1 and AP2 are systems that can be used as an additional form of coffee crop pest control and management. However, one possibility to be studied by the present research group is the combined use of the modalities in a single system in order to obtain even more improved results.
An important feature that was used for the laboratory experiments was the Java [18] Serialization class, which allowed for the wavelet transform in both AP1 and AP2 to be performed only once, storing its result on disk, making it possible to recover its value in repetitive cross-validation testing, substantially reducing equipment processing time.
In recent decades, the application of geographic information systems (GIS) in solid waste management (SWM) has been widely adopted in many cities around the world, from developed to developing nations. Moreover, the utilization of GIS is not limited to the management of solid waste. It has been widely applied in agriculture, natural resource management, planning and economic development, disaster management and mitigation, public health, and related areas. In solid waste management, the primary objective for the adoption of GIS is to reduce cost and time (feasibility) and also to help planners make better decisions in designing solid waste management.
Essentially, planning a sustainable waste management approach is complex, tedious, and time-consuming, and decision-makers are frequently subjected to conflicting factors in SWM planning. Sustainable waste management, according to the United Nations, is aimed at the integration of SWM at the national and local level, utilizing a life-cycle approach for resource efficiency and environmentally safe management of solid waste [1]. Waste reduction, resource reuse, recycling, and recovery are all part of sustainable waste management, which helps to reduce pollution while also extending the life of resources to be wasted. As a result, sustainable waste management should be economically feasible, socially acceptable, and environmentally effective upon implementation.
The geographic information system can be used to simplify the ease of implementation of sustainable SWM. Most often, due to the complex nature of SWM, there is poor performance in its technical functions and management functions (policy and legislation, financial, and stakeholders). Owing to this fact, there are different optimization techniques developed and implemented in different areas. Among the techniques used for spatial and non-spatial data information, GIS is common. In most cases, GIS is used with the integration of remote sensing and/or multicriteria decision-making analysis (MCDA). In spatial multicriteria analysis, the combination of GIS and MCDA capabilities is crucial. GIS allows for the acquisition, storage, retrieval, manipulation, and analysis of data to obtain information for decision-making. Whereas MCDA techniques provide tools for aggregating geographic data and decision-maker preferences into a single-dimensional value or utility of alternative decisions [2].
A brief description of how GIS is used in solid waste management was presented in this chapter. Our main goal was to show the fundamentals of GIS in SWM to individuals who have little or no experience with SWM planning, especially in developing countries. Furthermore, experts who specialized in GIS or SWM and have more experience with one than the other should understand the fundamentals of integrating the two to maximize efficiency. In addition, this chapter covers the concepts of sustainable waste management, GIS-based SWM, and MCDA. The application of GIS in solid waste collection optimization and waste disposal planning is discussed also.
Solid waste is defined as any waste discarded by households, commercial, mining, and agricultural operations, as well as the residue sludge from wastewater treatment plants, water supply treatment plants, and air pollution control centers, and it is other than liquids and gases [3]. Solid wastes can be categorized based on sources, reusable potential, degree of biodegradability, and potential impact on the environment, as shown in Table 1. Solid waste management (SWM) is a discipline related to the proper management of solid waste generation, storage, collection, transfer, transport, processing, and disposal of solid waste [5].
Classification | Type | Description | Composition |
---|---|---|---|
Source (based on [4]) | Residential | From Single and multifamily dwellings | Food wastes, paper, cardboard, plastics, textiles, leather, yard wastes, wood, glass, metals, ashes, special wastes (e.g., bulky items, consumer electronics, white goods, batteries, oil, tires), and household hazardous wastes.). |
Commercial | From stores, hotels, restaurants, markets, office buildings, etc. | Paper, cardboard, plastics, wood, food wastes, glass, metals, special wastes, hazardous wastes. | |
Industrial | From light and heavy manufacturing, fabrication, construction sites, power, and chemical plants. | Housekeeping wastes, packaging, food wastes, construction and demolition materials, hazardous wastes, ashes, special wastes. | |
Institutional | From schools, hospitals, prisons, government centers. | Same as the commercial wastes | |
Construction and demolition | New construction sites, road repair, renovation sites, demolition of buildings | Wood, steel, concrete, dirt, etc. | |
Agriculture | Crops, orchards, vineyards, dairies, feedlots, farms. | Spoiled food wastes, agricultural wastes, hazardous wastes (e.g., pesticides). | |
Degree of biodegradability | Biodegradable | Can be decomposed easily by bacteria or any other natural organisms | Food wastes, garden wastes, paper, cardboard |
Nonbiodegradable | Cannot be decomposed or degraded by the biological process | Plastics | |
Potential impact | Hazardous | Wastes whose uses or disposal pose a threat to human health or the environment | Toxic, corrosive explosive, and/ or inflammable Pesticides, herbicides, paints, industrial solvents, fluorescent light bulbs, and mercury-containing batteries |
Nonhazardous | Wastes that are considered less harmful to the environment or human health | Paper, plastics, glass, metals |
The categories and classification of solid wastes.
Solid waste management (SWM) consists of six functional elements, which include solid waste generation, collection, transfer, storage, processing, and disposal. It is a discipline related to the proper management of each of these functional elements. Waste collection is the process of gathering solid waste and recyclable materials and transporting them to a destination where the collection vehicle is emptied, such as a materials-processing facility, a transfer station, or a landfill, once they have been collected [6]. Following the collection of these wastes, the wastes are sorted, processed, and reused to recover, recycle, or reuse them. Additionally, the waste could be treated to recover energy. After the recovery of products, the majority of the waste is compacted to reduce volume, weight, and size. Finally, the remaining waste is either disposed of through regulated methods such as landfilling or thrown in the open.
Integrated solid waste management (ISWM) is a strategic approach to sustainable solid waste management in which solid waste generation, segregation, transportation, sorting, treatment, recovery, and disposal are all combined with the goal of resource efficiency. The US Environmental Protection Agency defines ISWM as the prevention, recycling, composting, and disposal of solid waste to protect public health and the environment, with a focus on reducing recycling, and managing waste [7]. Solid waste management that is integrated can provide both environmental and economic sustainability. No single waste management technology can deal with all waste products in an environmentally sustainable manner. A wide range of management options is ideal. As a result, any waste management system is made up of several interconnected processes. This method examines the entire waste management system and offers methods for estimating overall environmental and economic costs [8].
To bring about a sustainable world for present and future generations, besides the ISWM, there are many approaches to SWM today, including the “zero waste” approach and the life cycle inventory approach. The “Zero waste” approach has been defined in various ways by different individuals. Literally, zero waste is defined as the complete elimination and absence of waste. However, the generation of waste is inevitable as it is a result of extraction and manufacture, distribution, consumption, and other daily activities of human beings. As per the US Environmental Protection Agency, “zero waste” is defined as “the conservation of all resources through responsible production, consumption, reuse, and recovery of products, packaging, and materials without burning or discharges to land, water, or air that endanger the environment or human health”. It is an approach that aims to optimize waste recycling and reduction; products are designed in a manner that can be reused, mended, or recycled back into nature [9].
The collection, transportation, treatment, and disposal of diverse types of waste in a manner that does not damage the environment, human health, or future generations is referred to as sustainable waste management. It encompasses all aspects of waste management organization, from production to final disposal [10]. To minimize irreversible negative impacts on human health and the environment, the sustainability concept suggests that industries and their operations be encouraged to become more efficient in terms of resource utilization, production of less pollution and process waste, and use of nonrenewable resources [11]. The waste management hierarchy, on the other hand, frequently necessitates more effort, invention, and creativity, as well as good regulations, stakeholders, and financial support, to achieve the “zero waste” goal.
Solid waste management (SWM) has become a tough task for many cities around the world due to a significant increase in waste generation as a result of demo-technic growth, population growth combined with technological advancement [12, 13]. Waste collection, treatment, and disposal, among other issues, are crucial due to their costs [13]. Poor SW management has serious environmental implications and puts public health at risk. Moreover, the problem is worse in developing countries that lack basic infrastructure and are known for their less organized waste management practices coupled with a high waste generation rate [14]. According to Tan [12], in these countries, the collection rate is as low as 30–50%, and even the collected waste ends up in unmanaged landfills. Another major challenge is inadequate policy and legislation, lack of public commitment and awareness, lack of technical capacity, and poor financing in these countries [15].
Furthermore, gas and leachate generation are unavoidable outcomes of solid waste disposal in landfills, owing to microbial decomposition, climatic circumstances, refuse properties, and landfilling processes. The migration of gas and leachate away from landfill limits and their release into the surrounding ecosystem pose major environmental concerns at both existing and new sites. These concerns include but are not limited to, fires and explosions, vegetation damage, foul odors, landfill settlement, groundwater pollution, air pollution, and global warming, among others [16].
Recently, the implementation of integrated solid waste management (ISWM) has become popular for better management of growing MSW. It ideally proposes the waste reduction at source, before even generation of wastes utilizing different techniques, innovations, and optimal management practices [17]. In ISWM, there is effective management of waste at all levels, from generation to disposal. Despite its popularity, little is known about its technical operation, and it is also subjective from a management perspective.
Integrated solid waste management compromises the complex multi-objective criteria in each management element. This resulted in making the ISWM more subjective for decision-makers as uncertain solid waste generation, conflicting factors, and economic constraints are dealt with. To overcome these problems, there are some innovations in ISWM, including mathematical modeling, computer-based modeling, geographic information systems, and remote sensing, to name a few. Besides, Gaeta et al. [18] divided the innovations in ISWM grouping into four main typologies of innovations in the solid waste market system as follows: a traditional landfill-oriented system; a modern waste-to-energy incinerator-oriented system; a light recycling system; and a hard recycling system. In this chapter, the focus is on GIS, also detailed discussions on other approaches are presented by [14, 18, 19, 20, 21].
To optimize solid waste management, there were some early attempts. For example, Anderson & Nigam [22] were the first to propose mathematical modeling in SWM. Followed by the development of models for technology selection, siting, and sizing of waste processing facilities in SWM [23, 24, 25]. On the other hand, geographic information systems combined with remote sensing are becoming widely common in the area of SWM. GIS is preferred because of its simplicity, easy access, and low cost [26]. The use of GIS in solid waste management supports capturing, handling, and transmitting the required information promptly and properly, and it is a well-known, innovative technology that has contributed a lot to SWM in a short period.
Historically, early society relied heavily on spatial data to represent geographical locations. In the American War of Independence, at the Battle of Yorktown, the French cartographer prepared the map overlays of troop movements [27]. Also, in 1854, Dr. John Snow, in providing evidence for a water-borne cholera outbreak, mapped the incidence of cases with water supply sources. Even though there was a huge reliance on spatial data in early societies, the breakthrough was witnessed with the introduction of GIS after a century. The precise articulation of GIS’s brief history is muddled at best. This is because most organizations involved in the use of GIS at the time of its inception refused to give up their data, and the early writers of GIS history were not practical users of the technology [28]. Even though the book did not give any description of the overlay processing with defined methodology, the publication of a book named “Design with Nature” by McHarg [29] is known to have been a crucial factor in the evolution of GIS. Many research articles, reviews, and books concur that the evolution of GIS over time is due to improvements in geography, environmental awareness, technology advancements, and the enhanced practical and technical skills of GIS users.
Geographic information arose as integrating and powerful technology in the context of these breakthroughs because it allowed researchers and geographers to include their methods and information in a variety of ways that supported traditional kinds of geographic analysis. For example, map overlay analysis and other forms of analysis and modeling that were previously impossible to achieve using manual approaches [30]. Researchers can now map, model, query, and analyze large volumes of data in a single database thanks to GIS [31]. GIS can play a part in SWM because it is complicated and the components are interconnected. As a result, the planning and monitoring operations are based on spatial data. Customer service, analyzing optimal transfer station locations, planning routes for vehicles transporting waste from residential, commercial, and industrial customers to transfer stations and from transfer stations to landfills, locating new landfills, and monitoring the landfill are all important aspects of SWM that GIS can help with.
In the management of solid waste, a geographic information system is an excellent instrument as it is used in the planning of technical elements. Many researchers have used GIS principles in SWM to optimize the practice of SWM. For example, Chang et al. [32] used GIS in conjunction with a mathematical programming model to develop a multi-objective, mixed-integer programming model for collection vehicle routing and scheduling for solid waste management systems synthesized within a GIS environment and concluded the effectiveness of the model, as well as recommendations for application to other environmental planning and management problems. In recent decades, efforts have been undertaken to shorten the distance between waste collection stations and landfills, hence reducing the number of trucks involved in waste collection and disposal [33, 34, 35, 36].
GIS can be used to save costs and improve waste collection and transportation efficiency. Many elements influence route optimization, including the location of waste bins, collection details, vehicle kinds, trip impedances, and the road network’s integrity [37]. Chang and Lin [38] used a GIS and a mixed-integer programming model to locate proper waste bin locations and waste transfer stations, which resulted in lower direct costs and more manageable operational programs. Even though the process varies, the technique has recently become popular. El-Hallaq & Mosabeh [39] used the GIS integrated location-allocation methodology to rebuild the existing waste bin sites and were successful in finding misplaced bins and recommending an equitable waste bin distribution.
Another key application of GIS in SWM is the selection of disposal sites. Muttiah et al. [40] used a GIS and a Markov-chain-based simulated annealing algorithm to find prospective waste disposal sites, and the simulated annealing method saved order of magnitude of time over an exhaustive search strategy. Multi-criteria coupled with GIS have recently become popular in SWM for assessing conflicting criteria. Asefa et al. [41] used GIS in combination with the multicriteria decision-making method and the analytical hierarchy process to find the best landfill location by balancing competing environmental and socioeconomic concerns. In addition, Rahimi et al. [42] used GIS techniques and fuzzy Multi-Criteria Decision-Making methods in landfill site selection problems, where the criteria weights are determined using the group fuzzy Best-Worst Method, suitability maps are generated using GIS analysis, and the sites are analyzed and ranked using the group fuzzy MULTIMOORA method.
A geographic information system (GIS) has a broad area of application and is a computer-based system that helps in the manipulation of data about specific geographic areas. GIS, as presented by Rolf and Deby [43], is a georeferenced data entry, analysis, and presentation tool based on the computer interface. Data preparation and entry is the initial phase in GIS processing, which entails acquiring, preparing, and entering the data needed for information production into the GIS database system. The second stage is data analysis, which entails going over and analyzing the information that has been gathered and uploaded into the GIS system. Finally, during the data presentation phase, the analytical results are displayed and/or saved appropriately. Data input, data management, analysis, and final output are usually common processes in most GIS technique development for various purposes and applications, depending on the basic concept of the above-mentioned stages. Table 2 summarizes and presents these stages, as well as their fundamental principles and instances.
Stages | Description | Principles/basics | Examples |
---|---|---|---|
Input | Identifying and gathering data related to SWM. Acquisition, reformatting, georeferencing, compiling and documenting these data. | Digitizing, scanning, remote sensing, GPS, internet | Daily waste generation rate, waste collection routes, landfill site location, type of waste, land use map, the elevation of the area. |
Data storage and management | Includes those functions needed to store and retrieve data from the database can be thought of as a representation or model of real-world geographical systems | Geographical entity (towns, road network, and town boundary represented by point, line, and polygon). Object (spatial and nonspatial data) | Data model location of the existing landfill site (spatial) and types of waste dumped with the rate of dumping (nonspatial data). |
Data manipulation and storage | To obtain information useful for waste collection optimization or waste disposal planning. | Fundamental analysis (measurement, classification, overlay operations, and neighborhood and connectivity operations) and advanced analysis (statistical modeling and mathematical modeling) | Vehicle route optimization, suitability analysis of landfill, optimal waste bin location |
Data output | A way to see the analyzed SWM-related data or information in the form of maps, tables, diagrams. | Display monitors, pen plotters, electrostatic plotters, laser printers, line printers, and dot matrix printers and plotters | Optimal route for waste collection (map), schedule for waste collection (table), thematic map of a suitable landfill site. |
Basic principles of GIS.
SWM: solid waste management; GPS: global positioning service.
Geospatial analysis is the process of calculating data that has been entered or saved to generate new information that can be used to improve SWM decisions. The decision on which geographical analysis to use is based on the decision maker’s needs and objectives. For example, when deciding where to build a new landfill, various conflicting environmental, social, and political variables must be considered. When these criteria are entered into the GIS interface, other geographical analyses, such as reclassification, overlaying, buffering, and so on, can be performed. As a result, GIS can assist in the computation of such cases using up-to-date criteria that are examined cost-effectively. The following are some of the most often used spatial analysis methodologies as discussed in [2, 43, 44].
It is usually used at the beginning of any analysis as it allows data exploration without making a significant change. In the measurement function, distances between features or along their perimeters, the counting frequency of features, and the computation of area size features are computed. The retrieval functions allow the selection of specific features based on logical functions and user preferences. Classification is the re-arrangement of specific features into a common data value layer. Usually, measurement, retrieval, and classification are performed using a single vector or raster data layer combined with non-spatial data, sometimes.
The overlay capability, which allows disparate data layers to be joined to create new data, is considered the most important feature of GIS interfaces. It can be used for both vector and raster data types, however, it is most commonly employed for raster overlay computations. It includes operations of intersection, union, difference, and complement using sets of positions. Many GISs allow overlays using an algebraic language, which expresses an overlay function as a formula with data layers as parameters. Arithmetic, relational, and conditional operators, as well as a variety of functions, can be used to combine different layers [2].
Neighborhood functions allow the evaluation of the surrounding areas of the location of the features and operate on the neighboring features of a given feature or set of features. It includes search functions to allow the retrieval of features, line-in-polygon and point-in-polygon functions to compute a given linear or point feature is located within a given polygon, and buffering functions which allow determining a fixed-width environment surrounding a feature. Network analysis is concerned with network computations in the GIS interface. A network is a series of interconnected lines that depict a geographic phenomenon, most commonly transportation. People, cars, and other vehicles can be transported along with a road network; commercial goods can be transported along with a logistic network; phone calls can be transported along with a telephone network, and water pollution can be transported along with a stream or river network. There are two types of network analysis functions: optimal pathfinding and network partitioning. Optimal path finding generates the least cost path on a network between two predefined locations using both geometric and attributes data. Network partitioning assigns network elements (nodes or line segments) to different locations based on predefined criteria.
We have seen that the capability of GIS is reliant on spatial analysis functions such as overlay, connectivity, and proximity. These functions, however, always do not provide the best decision alternatives when there are complex and conflicting sets of criteria presents. For instance, in siting a new waste disposal site, the overlay function can be used to combine different factors such as proximity to a road, surface water, and groundwater, or site slope, elevation, and soil type. However, this function does not provide enough analytical support because of the limited capabilities for incorporating decision makers’ preferences into the GIS-based decision-making process. Thus, the combination of geographic data and the decision maker’s preferences into analysis for better output is required.
As a result, GIS can assist in minimizing value conflicts among conflicting interest parties by giving more and better information, whereas multicriteria decision-making analysis (MCDA) methodologies can aid in lowering factual disagreements [2]. MCDA is a strategy for assisting decision-makers through the essential process of establishing evaluation criteria and determining relevant values in a choice circumstance. Based on literature MCDA has six components as a primary goal, the decision-maker(s), set of criteria, decision alternatives, decision environment, and outcome. Furthermore, the components of MCDA can be achieved through the steps shown in Figure 1. As it is shown in the figure, any spatial decision problem can be structured into three major phases according to [45]; intelligence which examines the existence of a problem or the opportunity for change, design which determines the alternatives, and choice which decides the best alternative. The components and steps of MCDA were discussed in detail by [2, 46, 47].
Framework for spatial multicriteria decision analysis based on [
According to a recent study aimed to present a literature review of MCDA applications used in SWM, the top five and most commonly used MCDA methods are Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), Elimination and choice expressing the reality (ELECTRE), and Preference Ranking Organization Method for Enrichment (PROMETHEE) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Table 3 shows the commonly used MCDA methodologies in SWM, their description, advantages, and disadvantages of these methodologies.
MCDA method | Description | Advantage | Disadvantage |
---|---|---|---|
SAW | Value based method Use of measurement of the utility of an alternative | Easy to use and well understandable. Applicable when exact and total information is collected. Well-proven technique. Good performance when compared with more sophisticated methods. | Normalization is required to solve multidimensional problems |
AHP | Use of value-based, compensatory, and pairwise comparison approaches. Use of Hierarchical structure to present complex decision problem | Applicable when exact and total information is collected. A decision problem can be fragmented into its smallest elements, making evidence of each criterion applied. Applicable for either single or multiple problems, since it incorporates qualitative and quantitative criteria. Generation of inconsistency index to assure decision-makers | Due to aggregation, compensation between good scores on some criteria and bad scores on other criteria can occur Implementation is quite inconvenient due to the complexity Complex computation is required Time-consuming |
TOPSIS | Use of the value-based compensatory method Measures the distances of the alternatives from the ideal solution Selection of the one closest to the ideal solution | Easy to implement the understandable principle Applicable when exact and total information is collected Consideration of both the positive and negative ideal solutions Provision of a well-structured analytical framework for alternatives ranking Use of fuzzy numbers to deal with uncertainty problems | • Normalization is required to solve multidimensional problems |
ELECTRE | Use of outranking method Use of pairwise comparison, compensatory Use of indirect method that ranks alternatives utilizing pairwise comparison | Applicable even when there is missing information Applicable even when there are incomparable alternatives Applicable even when the incorporation of uncertainties is required | Time-consuming without using specific software due to complex computational procedure May or may not reach the preferred alternative |
PROMETHEE | Use of outranking method, pairwise comparison, and compensatory method Use of positive and negative preference flows for each alternative in the valued outranking Applicable even when simple and efficient information is needed Generation of ranking with decision weights | Applicable even when there is missing information | Time-consuming without using specific software When using many criteria, it becomes difficult for decision-makers to obtain a clear view of the problem |
Comparison of MCDA methodologies applied to SWM based on [48].
As mentioned in Table 3, we have to understand that each MCDA available has its strength and drawback, and also there is no general rule in adopting one. Therefore, in SWM the decision-maker decides to depend on the criteria and alternatives available. This also depends on the decision-maker’s previous experience and the availability of adequate software [47].
As discussed in previous sections, GIS-based MCDA is used to overcome complex and conflicting criteria in SWM. In fact, in MCDA methodology development there are goals and alternatives where decision outputs are made based on the decision-maker preferences. In addition, in sustainable waste management or ISWM, the aim is to reduce waste generation, improve reuse, recycle, and recovery, and otherwise properly manage these wastes. So, without any compromises, there are always environmental, economic, political, and social considerations in ISWM. Due to these facts, ISWM planning has been a challenge for the decision-makers as different criteria are taken into consideration under the specific goal of sustainable solid waste management. For instance, [49] used sixteen criteria for landfill site selection, including topography, urban and rural settlements, highways and village roads, railways, airports, wetlands, pipelines, and power line infrastructure, slope, geology, land use, floodplains, aquifers, and surface water. In general, the criteria used in ISWM planning can be categorized as environmental criteria, political criteria, financial and economic criteria, hydrologic and hydrogeologic criteria, topographical criteria, geological criteria, availability of construction materials, and other criteria as shown in Figure 2.
Criteria used for decision making in SWM.
Routing in solid waste collection entails planning and specifying routes for trucks to follow during the collection process [50]. When it comes to SWM optimization, routing is crucial. The rate of solid waste collection is frequently determined by the efficiency of transportation in the SWM component. Due to their complicated structures, transportation of solid waste to the final disposal or treatment facility is a major problem in many cities. As a result, more emphasis should be placed on optimizing waste collection routes, as failure to do so would result in exorbitant costs. As a result, defining the routes reduces waste collection costs while delivering the best service to the community. Routing is essentially the process of choosing a path for traffic within a network, as well as between or across various networks. The five basic steps of route planning are as follows, and they are accomplished in GIS utilizing the previously mentioned principles: (a) Identifying the potential location, (b) Identifying the storage capacity and volume to be collected, (c) Grouping the potential locations for a single truck cover, (d) Planning the shortest route between different groups, (e) Choosing the optimum route that is a shorter distance, less traffic volume, and less expensive.
Over time, routing has been applied in varying ways in solid waste management, giving rise to many models and strategies. According to EPA categorization, these models can be divided into macro-routing, districting and route balancing, and micro-routing [51]. The major difference is that macro-routing aims to optimize the use of the waste collection in daily and long-term capacity by minimizing round trip and haul time. While districting and route balancing divide the workload between the workers, micro-routing considers the details of each daily waste collection so that unnecessary truck movements will be reduced [51]. Among the many identified strategies for route optimization, heuristic routing, right turns, onboard computers, and round trips were presented in Table 4.
Strategies | Description |
---|---|
Heuristic Routing | Heuristic routing is a system used to describe how deliveries are made when problems in a network topology arise. In heuristic routing, routes should not be fragmented or overlapping |
Right Turns | Using only right turn during the collection of solid waste. This helped not only reduce cost by reducing fuel consumption but promoted the safety of drivers. |
Computer-based | This allows the collectors to track routes in realtime. It is also GPS-based and every detail of the route is presented. |
Dump trips | Scheduling the collection trips for the full containers twice per week. This also involves when the containers are full, they should be taken by the nearby truck. |
Strategies to optimize the routing in the solid waste management.
Residential collections are often routed using arc routing or side-of-street routing in high-density routing. This enables the software to arrange automatic collection with two passes on a street segment, as well as a semi-automated collection to serve both sides of a street segment at the same time. Point-to-point routing can be done on a variety of software and web-based platforms. When using point-to-point, the collected side of the roadway is usually ignored. Point-to-point routing can be used to develop commercial routes for solid waste collection as well as for routing calls for services like cart or bin delivery.
For waste to be collected efficiently, proper waste bin allocation and distribution are critical. It simplifies waste sorting, recycling, and transportation at the source. As a result, the second functional element of solid waste management is waste storage at the source. The waste is usually placed in bins on both sides of streets, close to buildings and other sources of waste generation. As a result, due to health concerns, attention should be exercised when storing hazardous material near residential areas. Wastes should also be stripped away regularly. GIS can be used to appropriately place waste storage containers or waste bins so that they are no longer a menace, are evenly distributed among the households, and the cost is decreased.
Several criteria were used to determine where the waste bins should be placed, including proximity to a road and a waste-producing source, land usage, sensitive areas, and so on. Using GIS, the criteria are integrated with the preferences of the decision-makers to obtain optimized waste bin sites using spatial analysis such as buffering. For example, a decision-maker could choose a buffer zone that is 20 meters away from a waste-producing source, 10 meters away from roadways, but 100 meters away from sensitive places such as hospitals, historical sites, and schools. The number of waste bins allocated can also be decided based on the waste generation rate and dispersed evenly. Furthermore, one of the most common strategies for arranging waste bins is using Location-Allocation models to determine the best position. The p-median model is used in several investigations. Because it averages the locations of multiple points, this model decreases the distance and expense of the facility from the source.
In waste disposal planning, a geographic information system plays an important role. GIS is used in waste disposal for a variety of reasons, including decision support for locating suitable landfills and temporal monitoring of disposal locations, including landfills. Due to the competing criteria illustrated in Figure 2, solid waste landfill siting is a complex and time-consuming operation in the traditional method of SWM. Furthermore, the primary purpose of the landfill site selection procedure is to ensure that the disposal facility is located in the best possible area, with the least amount of detrimental influence on the environment and population. Furthermore, a thorough review process is required to determine the best possible disposal place that complies with government standards while also minimizing costs.
Typically, landfilling is the least preferred method of ISWM according to the waste management hierarchy. But proper management of residue from reuse, recycling, and recovery, as well as the ashes from incineration, is a must. Therefore, the GIS-based MCDA approach is common in landfill site selection analysis. The basics and principles of GIS-based MCDA have been discussed in previous sections, so in this section, an overview of how GIS can be applied to landfill suitability analysis is presented. Figure 3 depicts the methodology for adopting GIS, MCDA, and waste disposal site analysis. Also, as presented in Figure 2, the process always starts with setting a goal (suitable landfill site). Then, after defining a set of criteria, GIS functions are combined with decision makers’ preferences to produce the final, most suitable landfill site.
Framework of the study to select a suitable sanitary landfill site, adopted from [
This chapter deals with the application of geographic information systems to solid waste management. GIS is a powerful tool that can assist in minimizing value conflicts among conflicting interest parties by giving more and better information. Essentially, planning a sustainable waste management approach is complex, tedious, and time-consuming, and decision-makers are frequently subjected to conflicting factors in SWM planning. There is an increasing trend of waste generation worldwide, and the situation is worse in developing countries owing to poor infrastructure, finance, and political reasons. To cope with the high waste generation and different problems in SWM, integrated solid waste management is widely used as a sustainable waste management practice. ISWM is a complex and tedious process to implement. Following the challenges in ISWM, many researchers came up with many innovations, like mathematical modeling and computer-based modeling, to mention a few. GIS is a computer-based spatial analysis method applied to SWM, enabling decision-makers to make better judgments by combining the alternatives and their preferences. In this chapter, the basics, and principles of how GIS works, what multicriteria decision making is, how to apply it to GIS, and the utilization of the GIS-based MCDA method in SWM was discussed. Also, information on how to apply GIS to waste collection optimization (routing and waste bin allocation) and waste disposal planning (landfill) was presented. Furthermore, the key principles of GIS-based MCDA method development were supported with references for further reading. Finally, we hope the readers will get some insights and, with some digging, be able to solve problems with SWM in their area easily.
The authors declare no conflict of interest.
Geographic information system
Solid waste management
Integrated waste management
Multicriteria decision making analysis
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Techniques, Applications and Commercialization"},signatures:"F. L. Motta, C. C. P. Andrade and M. H. A. 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Chandel, Silvio Silvério da Silva and Om V. Singh",authors:[{id:"25160",title:"Prof.",name:"Om",middleName:"V",surname:"Singh",slug:"om-singh",fullName:"Om Singh"},{id:"51540",title:"Dr.",name:"Silvio",middleName:null,surname:"Silva",slug:"silvio-silva",fullName:"Silvio Silva"},{id:"76898",title:"Dr.",name:"Anuj",middleName:null,surname:"Chandel",slug:"anuj-chandel",fullName:"Anuj Chandel"}]}],mostDownloadedChaptersLast30Days:[{id:"59269",title:"Gasification of Municipal Solid Waste",slug:"gasification-of-municipal-solid-waste",totalDownloads:3635,totalCrossrefCites:18,totalDimensionsCites:30,abstract:"Gasification of municipal solid waste (MSW) is an attractive alternative fuel production process for the treatment of solid waste as it has several potential benefits over traditional combustion of MSW. Syngas produced from the gasification of MSW can be utilized as a gas fuel being combusted in a conventional burner or in a gas engine to utilize the heat or produce electricity. Also, it can be used as a building block for producing valuable products such as chemicals and other forms of fuel energy. This book chapter covers the properties of MSW, gasification mechanism, chemistry, operating conditions, gasification technologies, processes, recovery system, and most importantly by reviewing the environmental impacts of MSW gasification. As one of recent advanced technologies, a case study of pilot-scale MSW gasification is introduced, which could be one of the most efficient pathways to utilize the technology to produce electricity with a newly developed gasification process by reducing tar and pollutant emission.",book:{id:"6349",slug:"gasification-for-low-grade-feedstock",title:"Gasification for Low-grade Feedstock",fullTitle:"Gasification for Low-grade Feedstock"},signatures:"Yong-Chil Seo, Md Tanvir Alam and Won-Seok Yang",authors:[{id:"213854",title:"Prof.",name:"Yong-Chil",middleName:null,surname:"Seo",slug:"yong-chil-seo",fullName:"Yong-Chil Seo"},{id:"213857",title:"Mr.",name:"Md Tanvir",middleName:null,surname:"Alam",slug:"md-tanvir-alam",fullName:"Md Tanvir Alam"},{id:"222321",title:"Dr.",name:"Won-Seok",middleName:null,surname:"Yang",slug:"won-seok-yang",fullName:"Won-Seok Yang"}]},{id:"52687",title:"Methods of Estimating Forest Biomass: A Review",slug:"methods-of-estimating-forest-biomass-a-review",totalDownloads:3440,totalCrossrefCites:9,totalDimensionsCites:14,abstract:"Forest plays a special role in carbon sequestration and thus mitigating climate change. However, the large uncertainty in biomass estimation is unable to meet the requirement of the accurate carbon accounting. The use of a suitable and rigor method to accurately estimate forest biomass is significant. Moreover, the world is increasingly facing the conflicting pressures of economic growth and environmental protection. Improving energy structure and vigorously developing biomass energy has become the development trend of energy utilization in the future. As energy plant is characterized by a large net accumulation of biomass. Therefore, the scientific evaluation of the size and potential of energy from plant also requires a suitable method for estimating biomass. Here, we reviewed the estimate methods, including allometric equation, mean biomass density, biomass expansion factor, geostatistics, etc. For each method, we will present background, rational, applicability, as well as estimation procedure by exemplifying a case. In this chapter, we argued that the new developed technique such as geo-statistics and remote sensing technique (e.g. LIDAR) would be the key tools to improve forest biomass estimation accuracy. However, prior to this, spatial variation of forest biomass at various levels should be explored using multi-source data and multi-approaches.",book:{id:"5393",slug:"biomass-volume-estimation-and-valorization-for-energy",title:"Biomass Volume Estimation and Valorization for Energy",fullTitle:"Biomass Volume Estimation and Valorization for Energy"},signatures:"Lei Shi and Shirong Liu",authors:[{id:"187616",title:"Associate Prof.",name:"Lei",middleName:null,surname:"Shi",slug:"lei-shi",fullName:"Lei Shi"},{id:"194544",title:"Prof.",name:"Shirong",middleName:null,surname:"Liu",slug:"shirong-liu",fullName:"Shirong Liu"}]},{id:"52751",title:"Biomass Compositional Analysis for Conversion to Renewable Fuels and Chemicals",slug:"biomass-compositional-analysis-for-conversion-to-renewable-fuels-and-chemicals",totalDownloads:2871,totalCrossrefCites:14,totalDimensionsCites:30,abstract:"As the world continues to deplete its nonrenewable resources, there has begun a shift toward using renewable materials for the production of fuels and chemicals. Terrestrial biomass, as well as municipal solid wastes, provides renewable feedstocks for fuel and chemical production. However, one of the major challenges to using biomass as a feedstock for fuel and chemical production is the great amount of innate variability between different biomass types and within individual biomass species. This inconsistency arises from varied growth and harvesting conditions and presents challenges for conversion processes, which frequently require physically and chemically uniform materials. This chapter will examine intrinsic biomass compositional characteristics including cellulose, hemicellulose, lignin, extractives/volatiles, and ash for a wide array of biomass types. Additionally, extrinsic properties, such as moisture content and particle grind size, will be examined for their effect on biomass conversion to fuels using four major conversion processes: direct combustion, pyrolysis, hydrothermal liquefaction, and fermentation. A brief discussion on recent research for the production of building block chemicals from biomass will also be presented.",book:{id:"5393",slug:"biomass-volume-estimation-and-valorization-for-energy",title:"Biomass Volume Estimation and Valorization for Energy",fullTitle:"Biomass Volume Estimation and Valorization for Energy"},signatures:"C. Luke Williams, Rachel M. Emerson and Jaya Shankar Tumuluru",authors:[{id:"95803",title:"Dr.",name:"Jaya Shankar",middleName:null,surname:"Tumuluru",slug:"jaya-shankar-tumuluru",fullName:"Jaya Shankar Tumuluru"},{id:"187715",title:"Dr.",name:"C. Luke",middleName:null,surname:"Williams",slug:"c.-luke-williams",fullName:"C. Luke Williams"},{id:"194456",title:"MSc.",name:"Rachel M.",middleName:null,surname:"Emerson",slug:"rachel-m.-emerson",fullName:"Rachel M. 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From studies focused on biodiesel, bioethanol, biomass and biofuel in general, different spectroscopic techniques have also been applied in the area. The focus of this chapter is to elucidate what has been published in the last few decades over the subject, detailing the basic concepts of the main spectroscopic techniques applied and showing the results and developments over biofuel. The aim of the chapter is to achieve a set of information that can be used as a bigger compile of information of the state of the art regarding the theme.",book:{id:"5509",slug:"frontiers-in-bioenergy-and-biofuels",title:"Frontiers in Bioenergy and Biofuels",fullTitle:"Frontiers in Bioenergy and Biofuels"},signatures:"João Cajaiba Da Silva, Alex Queiroz, Alline Oliveira and Vinícius\nKartnaller",authors:[{id:"107106",title:"Prof.",name:"João",middleName:null,surname:"Cajaiba Da Silva",slug:"joao-cajaiba-da-silva",fullName:"João Cajaiba Da Silva"},{id:"192636",title:"MSc.",name:"Alex",middleName:null,surname:"Queiroz",slug:"alex-queiroz",fullName:"Alex Queiroz"},{id:"192637",title:"MSc.",name:"Alline",middleName:null,surname:"Oliveira",slug:"alline-oliveira",fullName:"Alline Oliveira"},{id:"192638",title:"M.Sc.",name:"Vinicius",middleName:null,surname:"Kartnaller",slug:"vinicius-kartnaller",fullName:"Vinicius Kartnaller"}]},{id:"62959",title:"Biogas for Clean Energy",slug:"biogas-for-clean-energy",totalDownloads:1685,totalCrossrefCites:2,totalDimensionsCites:8,abstract:"This chapter demonstrates a biogas renewable energy resource potential study for electric power generation from easily available biogas feedstock materials in four selected case study sites. Under this study, the site used in the model is a rural Kebele in Jama Woreda at 10.548° N, 39.33° E. The common biogas feedstocks considered under this study are animal slurry, human feces and jatropha byproducts whereas the biodiesel is considered from jatropha seed.",book:{id:"6839",slug:"anaerobic-digestion",title:"Anaerobic Digestion",fullTitle:"Anaerobic Digestion"},signatures:"Demsew Mitiku Teferra and Wondwosen Wubu",authors:[{id:"259485",title:"Mr.",name:"Demsew Mitiku",middleName:null,surname:"Teferra",slug:"demsew-mitiku-teferra",fullName:"Demsew Mitiku Teferra"}]}],onlineFirstChaptersFilter:{topicId:"763",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},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:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,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:17,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,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. 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By addressing hot topics in veterinary sciences, we aim to gather authoritative texts within each issue of this series, providing in-depth overviews and analysis for graduates, academics, and practitioners and foreseeing a deeper understanding of the subject. Forthcoming texts, written and edited by experienced researchers from both industry and academia, will also discuss scientific challenges faced today in Veterinary Medicine and Science. 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After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:0,paginationItems:[]},overviewPageOFChapters:{paginationCount:0,paginationItems:[]},overviewPagePublishedBooks:{paginationCount:0,paginationItems:[]},openForSubmissionBooks:{paginationCount:1,paginationItems:[{id:"11478",title:"Recent Advances in the Study of Dyslexia",coverURL:"https://cdn.intechopen.com/books/images_new/11478.jpg",hash:"26764a18c6b776698823e0e1c3022d2f",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"June 30th 2022",isOpenForSubmission:!0,editors:[{id:"294281",title:"Prof.",name:"Jonathan",surname:"Glazzard",slug:"jonathan-glazzard",fullName:"Jonathan Glazzard"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{paginationCount:13,paginationItems:[{id:"82285",title:"Parvovirus Vectors: The Future of Gene Therapy",doi:"10.5772/intechopen.105085",signatures:"Megha Gupta",slug:"parvovirus-vectors-the-future-of-gene-therapy",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"81793",title:"Canine parvovirus-2: An Emerging Threat to Young Pets",doi:"10.5772/intechopen.104846",signatures:"Mithilesh Singh, Rajendran Manikandan, Ujjwal Kumar De, Vishal Chander, Babul Rudra Paul, Saravanan Ramakrishnan and Darshini Maramreddy",slug:"canine-parvovirus-2-an-emerging-threat-to-young-pets",totalDownloads:15,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"81271",title:"The Diversity of Parvovirus Telomeres",doi:"10.5772/intechopen.102684",signatures:"Marianne Laugel, Emilie Lecomte, Eduard Ayuso, Oumeya Adjali, Mathieu Mével and Magalie Penaud-Budloo",slug:"the-diversity-of-parvovirus-telomeres",totalDownloads:38,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"79209",title:"Virtual Physiology: A Tool for the 21st Century",doi:"10.5772/intechopen.99671",signatures:"Carmen Nóbrega, Maria Aires Pereira, Catarina Coelho, Isabel Brás, Ana Cristina Mega, Carla Santos, Fernando Esteves, Rita Cruz, Ana I. 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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. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. 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. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{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:"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:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{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:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{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:null},{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:"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. 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He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"24",type:"subseries",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11420,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. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. 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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,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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