\r\n\tRecent advances regarding pathogenesis, cardiovascular risk assessment, prediction of damage, and recent advances in treatment, including tolerogenic and biological agents, are welcome to be included in this book. Relevant contributions regarding standard therapies and their optimal use, as well as the role of new therapeutic options, either in combination with previous agents or alone are of interest.
",isbn:"978-1-80356-348-0",printIsbn:"978-1-80356-347-3",pdfIsbn:"978-1-80356-349-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"7005f26b225e5923d4ce4cd7c52f6fe9",bookSignature:"M.D. Sophia Lionaki and Dr. Minas Karagiannis",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11726.jpg",keywords:"Epidemiology, Genetics, Clinical Picture, Physical Examination, Pathogenesis, Histopathology, Nomenclature, Clinical Syndromes, Clinical Picture, Evaluation, Pregnancy Planning, Risk Assessment",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 17th 2022",dateEndSecondStepPublish:"March 17th 2022",dateEndThirdStepPublish:"May 16th 2022",dateEndFourthStepPublish:"August 4th 2022",dateEndFifthStepPublish:"October 3rd 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A researcher devoted to clinical research related to autoimmune diseases of the kidney and especially ANCA vasculitis and lupus nephritis. 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\n
1. Introduction
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Fast changing markets, industries, and services require organizational environments capable of constant adaptation with bright new ideas and reduced time-to-market. Under these competitive reality, HRM has a more critical role than ever because new forms of business require new ways of involving people [1].
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HR professionals must analyze social, economic, political-legal, and technological environment opportunities to redesign HRM processes and practices that are key success factors to the organization mission and objectives.
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To respond to these challenges, HRM has been forced to adopt new logics and most HR managers must forget habits and ways of thinking and acting outdated. On the other hand, they should help organizations to define their strategies and build programs to develop their human capital [1].
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In this context, information systems have increased the efficiency of HRM through more effective recruitment methods, organizational communication, employee involvement, and increased skills of HR managers [2]. From there, human resource management areas must relate human capital strategies to the most appropriate technological solutions. It means creating an eHR organization focused on interconnecting people with business strategy to achieve rapid adaptation to changing needs common to people and business. These same needs begin to make eHR necessary.
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However, not all HR professionals work in strategic HRM. Many still find themselves pressured and seeing their time spent with day-to-day concerns. It is critical to get rid of the administrative burden or at least to mitigate it, to optimize your time and your contribution to the organization.
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With this chapter, we intend to contribute to the definition of the role of HRIS in human resources management, as well as the role of HR professionals in this process.
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\n
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2. Information systems in human resources management
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In recent years, information technology has effects on almost every aspect of our society, as well on organizational processes, including HRM processes and practices [3]. From a position associated with administrative management, it has managed to become a strategic partner of organizations, largely because of the use of technologies.
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“Continuous innovations in technology will fundamentally change the way HR work is accomplished” [4]. Information systems have a deep effect on HRM. It transformed human resources processes and practices mainly in terms of how organizations collect, store, use, and disseminate information.
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The quality of HRM is a key success factor for organizations. The HR professional should analyze and consider the environment, social, economic, political-legal, and technological. To respond to these challenges, HRM has been forced to adopt new logics and most HR managers must forget habits and ways of thinking and acting outdated. On the other hand, they should help organizations to define their strategies and build programs to develop their human capital.
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In fact, the principal goals of HR in organizations are to attract, select, motivate, and retain talented employees in their roles, and “technology has transformed the way HR processes are currently managed, essentially how organizations collect, store, use, and disseminate information about their HR” [4].
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Kovach et al. defines human resources information systems (HRIS) as a systematic procedure for collection, storing, maintaining, retrieving, and validating data needed by organizations about HR [5]. Tannenbaum defines it as a technology-based system used to acquire, store, manipulate, analyze, retrieve, and distribute pertinent information regarding HR in the organization [6].
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HRIS shapes an integration between HRM and information technology [7]. HRIS is a management system designed specifically to provide managers with information to make HR decisions. Is a system that lets you keep track of all your employees and information about them. It is usually done in a database, or more often in a series of inter-related databases.
\n
In this context, information systems have increased the efficiency of HRM through more effective recruitment methods, organizational communication, employee involvement, and increased skills of HR managers [2]. From there, human resource management areas must relate human capital strategies to the most appropriate technological solutions. It means creating an eHR organization focused on interconnecting people with business strategy to achieve rapid adaptation to changing needs common to people and business. These same needs begin to make eHR necessary.
\n
\n
2.1. Objectives
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HRIS shapes an integration between HRM and information technology. Therefore, the basic objectives of HRIS are [7] (Figure 1):
To provide accurate information about human resource and their functioning and relevant environmental factors.
To provide relevant information.
To provide timely information.
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Figure 1.
HRIS objectives.
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Organizations require information about their human resources and their functioning, but also require information from their external environment. Thereby, HRIS allows us to collect, store, manipulate, analyze, retrieved, and distribute information from internal and external environment.
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HRIS in an organization should be developed in such a manner that the data stored in it can be used for several outputs. Because of these multiple uses of data, there is a need to develop a complete system of gathering, processing, and flowing of information [7] (Figure 2).
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Figure 2.
Type of information needed in HRIS.
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Chakraborty lists several examples of information that is collected from HR departments or from the surrounding environment and that makes part of HRIS, such as [8]:
Employee information (name, age, qualification, …);
Type of employee recruited during the year;
Training and development offered;
Results of performance appraisal;
Promotion, demotion, transfer, separation of employees;
Compensation packages, both financial and nonfinancial, offered;
Employee absenteeism and turnover;
Maintenance, safety and health services;
Availability of human resources from different sources;
Training and development facilities available outside the organization;
Expectations of human resources from the organization;
Government policies affecting the employment conditions and labor laws;
Trade union movement and its attitudes toward employer organizations;
Benchmark of HRM practices.
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\n
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2.2. Components of HRIS
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HRIS applications allow users to store and track all types of data that are related to HR [9]. In the research carried out by Dorel et al. is notorious that HRM was focused on collect and store personal data (records) of each employee, handle their salaries, benefits, vacations, etc. However, HR function has developed and became a very important function of management. Keeping this in mind, we can identify three major functional components of HRIS [5] (Figure 3).
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Figure 3.
Functional components of HRIS.
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Input function allows us to enter personnel information into the HRIS. The maintenance function allows us to update and add new data into the database. To generate valuable outputs, HRIS needs to make the necessary calculations, and format the data in a way that it could be understood. Therefore, it is important to remember that the most important elements of HRIS is the information, rather the automation of the process or the hardware/software used [7].
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In HRIS we can identify three dimensions of HRIS: operational, tactical, and strategic [10]:
Operational human resource information system – provides data to support routine and repetitive human resource decisions (e.g. workforce, governmental regulations, …);
Tactical human resource information system – provides data for support decisions related with allocation of resources (e.g. recruitment, job analysis, training and development decisions, compensations plans, …);
Strategic human resource information system – provides data for strategic decisions in human resources plan.
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\n
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2.3. Users
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There are several users of a HRIS. So, who uses HRIS and how is it used? Essentially, we can define three groups of people: HR professionals, functional managers, and employees (Figure 4) [11].
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Figure 4.
User of HRIS.
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In the HR professionals’ case, HRIS helps them to fulfill the job functions, even the most elementary job tasks, like reporting and compliance, payroll and compensation analysis, benefits administration, applicant tracking, and skills inventory.
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In the other hand, functional managers expect that HRIS provide data to achieve goals and objectives. They expect that the system provide information for performance appraisal and management, team and project management resume processing, recruitment and retention, training and skills testing, and management development [12].
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Additionally, individual employees become end users of several HRIS applications, such as self-service, benefit options, career planning, or training and development. Boateng draws attention to the importance of web-based access and self-service portals that have simplified the use of the systems for the employees [7].
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\n
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2.4. Costs and benefits
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Information systems represent a major investment by the organizations. Thus, it is necessary to keep in mind the costs and benefits of implementing a HRIS (Figure 5).
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Figure 5.
Costs and benefits of HRIS.
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The common benefits of HRIS referred by different authors are:
Job issues (change of tasks, need for training, change of posts, dismissal, …) [7, 9].
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There are many software solutions offers, the choice it will be dependent on the decision of the user. Many types of computer-based training, internet access to the recruitment world, and the use of certain programs to assess employees in the hiring process are only available for those with technology-rich environments. The reality is that HRIS enable effectiveness and efficiency, and ensure competitiveness.
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According to Hendrickson study, increased efficiency is a benefit of an HRIS [19]. Both time and cost efficiency can be addressed with the ability to do more transactions with fewer fixed resources. This can specifically be seen in areas such as payroll and benefits. In terms of accuracy, the HRIS helps in transactions. Additionally, the technology can be used to simplify processes [19].
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\n
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3. The role of information systems in HRM
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“Continuous innovations in technology is ganging the way HR work is accomplished” [4]. These technology developments made it possible to create a real-time information-based and interactive work environment. Personnel information systems have evolved from the automated employee record keeping into more complex reporting and decision systems [7].
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In a traditional HRIS, practically we could only work administrative issues, like monitor absences, salary structures, training information, recruitment, media response, accessing to current information, medical information, and global administration.
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However, we watched a break with the past and an increase in effectiveness. HRIS allows us to respond more quickly to changes and to needs of decision-making. HRIS allows budget control, tracking and screening, skills matching, appraisals, feedback, manpower planning, succession planning, skills monitoring, training needs analysis, and global analysis [20].
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The main issue is to define what are the real implications and the role of the information systems in HRM. HRIS can be applied in several areas, integrated into the HRIS system [12] (Figure 6).
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Figure 6.
Areas of application of HRIS.
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According to the research of Kavanagh et al., HR professionals spend their time essentially in business process improvements, talent management processes, workforce metrics, HR strategy, workforce management and planning, and competency management [21]. We will now discuss some examples of the application of information systems in the functional areas of HRM [20].
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3.1. Strategic HR management
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Strategic HRM is characterized by the adoption of a dynamic vision of the resources it manages. It covers not only the planning and implementation of actions, but also the control of results, which must be related to the strategy of the organization [22]. In HRIS, we can find information at these levels:
Environmental scanning: monitoring internal and external environments for detecting opportunities and threats that may influence organizational plans;
Quality and productivity improvements: analysis and development to certify the development of HR quality and productivity.
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3.2. Workforce planning and employment
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HR planning of what the organization will need is of great importance to HR professionals, revealing different skills profiles, working schedules, enabling the organization to have the right people, in the right amount, at right time. It reflects the interests and perspectives of the organization as well as the aspirations of the candidates and collaborators [23]. The information that we can collect in this area from HRIS is, for example:
Promotions, transfers, hiring, and termination rates: tracks data to analyze and make decisions about workforce planning and employment needs.
Analysis and definition of work: allowing employees in geographically dispersed locations to work together.
Recruitment and selection: ability to support processes by creating tools that are more agile and enable online work.
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3.3. Human resource development
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In addition to the need for work organization and decision-making, what will allow organizations to have increased levels of productivity will be the preparation of their staff and their motivation? In this sense, the development of HR will be a factor of competitiveness and even, in some cases, of survival. “Organizational development is directly associated with the development of Human Resources” [9, 23]. In these cases, the information that we can gather from HRIS is:
Career development: analysis of careers, their evolution, development of career plans and the achievement of objectives outlined.
Education, skills, and training programs: analysis and identification of competences, identification of training needs, access to training contents remotely.
Evaluate employee performance: definition of performance goals, design of evaluation metrics, performance evaluation, and feedback of results.
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3.4. Total rewards
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Reward systems consist of all material and immaterial counterparts, which employees can receive, depending on the quality of their performance, the contribution to the development of the business and its identification with the values of the organization [9, 23]. HRIS allows us to identify the following information, regarding rewards:
Salary information: salary processing, holiday management, absences and absences, automatic calculations of wage components.
Retirement planning: identification of succession plans, pensions, streamlining of untying programs.
Salary analysis: analysis of salary developments, salary comparisons.
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3.5. Employee and labor relations
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The role of HR professionals in the social relations system is considerable. They appear as a link between the organization, employees and trade unions and workers’ committees [22]. HRIS can help us in different aspects of this area:
Employee discipline records: access and management on disciplinary proceedings, disciplinary proceedings reports.
Union and labor distribution: management of information on trade unions and workers’ committees, work distribution, and analysis of work and labor relations indicators,
Attitude, climate, culture, and commitment: possibility to automatically inquire the entire organization and perform the attitude, climate, culture and commitment analysis.
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3.6. Risk management
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Safety and working conditions improvement are areas with a profitability difficult to evaluate and considered as real investments for the company, being considered a specific domain of HRM. Thus, great consistency must be sought between the actions developed and the other areas of HRM [22]. HRIS can assist the risk management by analyzing the following points:
Accident and illness: analysis of trend on accidents and illness; managing reports.
Safety, insurance and workers’ claims: agility and support to processes, records maintenance, monitoring of high-risk conditions and accidents.
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4. The role of HR professionals in HRIS
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Human resource management provides the guidance for an organization’s workforce. “The use of information technology (IT) in HRM is vital in order to meet organizational demands with well qualified employees and help support the organization” [2].
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Therefore, we can say that information systems have been a valuable tool for HR managers to facilitating HR processes and practices, as we have already seen. However, HR professionals have a special role in this path.
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The Society for Human Resource Management [20] refers that HR professional should recognize that integrating an HRIS is a big project and a major change for the organization. Some employees (staff and even management) will resist changing and it is imperative that they are prepared to deal with the resistance.
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HR professionals must support the change and facilitate the communication [20]. They need to:
Recognize individuals may react negatively to change.
Anticipate resistance and find ways to deal with it.
Show commitment and a positive attitude toward the change.
Involve people in the process.
Ensure top management is visible and supportive.
Remind that change is a process and successful change takes time.
Reinforce change with incentives.
Communicate with employees and management.
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We simply cannot forget that it will take some time before success is apparent. HR professional needs to remember that HRM was limited to employee record keeping and was provided as a service to the organization [17]. The human resource function has undergone dramatic change. With global competitive success relying upon the application of knowledge, information and technology, HR professionals are now committed to engage more significant and strategic roles, improving efficiency. They become strategic business partners relying on the usage of HRIS in their job [17].
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The future trends in HRIS can easily lead to confusion for organizations and HR professionals and it can cause great changes in the operation of HRM and an HRIS in organizations Kavanagh et al. Note the need to be attentive to the particularities of each context. However, they draw attention to the fact that organizations that are most successful are those who are able to leverage the technology and link it to their HR strategy [21]. In this case, HR professionals need to understand that:
the technology of the future will be both collaborative and connected;
there will increase the use of HR scorecards with workforce analytics;
the automation process and the use of online analytical processing will be more used;
will be possible to access to accurate real-time HR information due to advancements in communication tools;
information security should be a top priority; and
the worker of the future will be able to work anywhere, any time, and on any device [21, 24].
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5. Conclusions
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Increasingly, technology has a profound impact on HRM. As technology evolves, it will also force HRM to take on new contours in both its processes and its practices. HRIS emerged in response to the need for this change to be carried out in the most fruitful way possible, considering the improved accuracy, the quick access to information, the increased competitiveness and efficiency and the re engineer of the HR function.
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There are still many questions about the true objectives of HRIS and the responses they allow to the real needs of HRM. It is true that there are still some limitations to its use and its results. However, its role in HRM allows us to respond more quickly to HRM changes and needs, for example, enabling to control budget, tracking and screening, skills matching, appraisals, feedback, manpower planning, succession planning, skills monitoring, training needs analysis and global analysis.
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By focusing on using technology to continuously improve the quality of the work. Technology can improve the information available to HR, facilitating HR processes, and making them faster and more effective.
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One of the biggest allies in HRM, HRIS is adopted to make organizations more accurate and effective. However, we face several challenges are faced. HR professionals need to prepare themselves for the future by gearing up for new roles or find themselves outsourced. HR professionals needs to integrate an HRIS as a big project and as a major change for the organization, assuming its role as business partner, as a data analyst, as an internal consultant, focused on the strategic issues of HRM, necessary for the development of people, business and organizations.
\n
\n\n',keywords:"information systems, human resource management, HRIS, HR professionals",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/62362.pdf",chapterXML:"https://mts.intechopen.com/source/xml/62362.xml",downloadPdfUrl:"/chapter/pdf-download/62362",previewPdfUrl:"/chapter/pdf-preview/62362",totalDownloads:7268,totalViews:7667,totalCrossrefCites:3,totalDimensionsCites:7,totalAltmetricsMentions:1,impactScore:3,impactScorePercentile:84,impactScoreQuartile:4,hasAltmetrics:1,dateSubmitted:"May 18th 2017",dateReviewed:"June 4th 2018",datePrePublished:"December 21st 2017",datePublished:"October 24th 2018",dateFinished:"June 29th 2018",readingETA:"0",abstract:"Over the last years, human resource management (HRM) has experienced significant transformations. The focus has passed from the administrative management tasks to becoming a strategic partner of the overall organization strategy, largely with the strong support of information technologies’ evolution in this field of knowledge area. The extended use of information systems has a deep effect in the way HRM is managed nowadays. It boosted a major transformation of human resources (HR) processes and practices within organizations, namely on how they collect, store, use, and share information. Several HRM processes have become more efficient and the impact of this service level improvement allowed a greater involvement of HR in the business strategy. This new role in business strategy adds significant changes to HR function and to its professionals. Along this chapter, we discuss the effects of information systems in HRM, considering the existing literature on the topic, and describe the benefits and possible limitations of using them. We also provide an overview of some applications of technology in functional areas of HRM, within organizations.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/62362",risUrl:"/chapter/ris/62362",book:{id:"6259",slug:"management-of-information-systems"},signatures:"Marlene Sofia Alves e Silva and Carlos Guilherme da Silva Lima",authors:[{id:"211475",title:"M.D.",name:"Marlene",middleName:"Sofia",surname:"Silva",fullName:"Marlene Silva",slug:"marlene-silva",email:"marlenesilva.rh@gmail.com",position:"Assistant lecturer",profilePictureURL:"https://mts.intechopen.com/storage/users/211475/images/5970_n.jpg",institution:null},{id:"211476",title:"Dr.",name:"Carlos",middleName:null,surname:"Lima",fullName:"Carlos Lima",slug:"carlos-lima",email:"carloslima.rh@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Information systems in human resources management",level:"1"},{id:"sec_2_2",title:"2.1. Objectives",level:"2"},{id:"sec_3_2",title:"2.2. Components of HRIS",level:"2"},{id:"sec_4_2",title:"2.3. Users",level:"2"},{id:"sec_5_2",title:"2.4. Costs and benefits",level:"2"},{id:"sec_7",title:"3. The role of information systems in HRM",level:"1"},{id:"sec_7_2",title:"3.1. Strategic HR management",level:"2"},{id:"sec_8_2",title:"3.2. Workforce planning and employment",level:"2"},{id:"sec_9_2",title:"3.3. Human resource development",level:"2"},{id:"sec_10_2",title:"3.4. Total rewards",level:"2"},{id:"sec_11_2",title:"3.5. Employee and labor relations",level:"2"},{id:"sec_12_2",title:"3.6. Risk management",level:"2"},{id:"sec_14",title:"4. The role of HR professionals in HRIS",level:"1"},{id:"sec_15",title:"5. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Silva M, Gomes R. Contributions for the best practice in social recruitment—pilot study in the Northern Region of Portugal. Book of industry papers, poster papers and abstracts of the CENTERIS 2015—Conference on Enterprise Information Systems/ProjMAN 2015—International Conference on Project MANagement/HCist 2015—International Conference om Health and Social Care Information Systems and Technologies; October, 2015; Portugal. Portugal: SciKA; 2015. ISBN: 978-98997433-6-6\n'},{id:"B2",body:'Wilkerson A. Draft—The Impact of Information technology on HRM [Internet]. 2015. Available from: https://www.academia.edu/8301970/Draft_The_impact_of_information_technology_on_HRM [Accessed: June 14, 2017]\n'},{id:"B3",body:'Stone D, Deadrick D. Challenges and opportunities affecting the future of human resource management. Human Resources Management Review. 2015;25:139-145\n'},{id:"B4",body:'Stone D, Deadrick D, Lukaszewski K, Johnson R. The influence of technology on the future of human resources management. Human Resources Management Review. 2015;25:216-231\n'},{id:"B5",body:'Kovach K, Hughes A, Fagan P, Maggitti P. Administrative and strategic advantages of HRIS. Employment Relations Today. 2002;29(2):43-48\n'},{id:"B6",body:'Tannenbaum S. HRIS Information: User group implications. Journal of System\'s Management. 1990;41(1):27-36\n'},{id:"B7",body:'Boateng A. The Role of Human Resource Information Systems (HRIS) in Strategic Human Resource Management (SHRM) [thesis]. Sweden: Swedish School of Economics and Business Administration; 2007. 112 p. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.473.5116&rep=rep1&type=pdf\n\n'},{id:"B8",body:'Chakraborty A, Mansor N. Adoption of human resource information system: A theoretical analysis. Journal of Social and Behavioral Sciences. 2013;75:473-478\n'},{id:"B9",body:'Dorel D, Bradic-Martinovic A. The role of information systems in human resources management. Munich Personal RePEc Archive, Paper No. 35286; 2011. 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Journal of Management Policy and Practice. 2013;14(3):35-49\n'},{id:"B16",body:'Beckers A, Bsat M. A DSS classification model for research in human resource information systems. Information Systems Management. 2002;19(3):41-50\n'},{id:"B17",body:'Haines B, Lafleur G. Information technology usage and human resource roles and effectiveness. Human Resource Management. 2008;47(3):525-540\n'},{id:"B18",body:'UK Essays. Advantages and Disadvantages of HRIS in Human Resource Management [Internet]. June 13, 2016. Available from: https://www.ukessays.com/essays/information-technology/advantages-and-disadvantages-human-resource-management-systems.php [Accessed: June 14, 2017]\n'},{id:"B19",body:'Hendrickson A. Human resource information systems: Backbone technology of contemporary human resources. Journal of Labor Research. 2003;4(3):381-394\n'},{id:"B20",body:'SHRM. SHRM Learning System—Module One Strategic Management. USA: Society for Human Resource Management; 2008. 41 p\n'},{id:"B21",body:'Kavanagh M, Thite M, Hohnson R. The Future of HRIS: Emerging trends in HRM & IT; 2012. Available from: https://www.researchgate.net/publication/277249739 [Accessed: June 14, 2017]\n'},{id:"B22",body:'Peretti J. Recursos Humanos. 3rd ed. Lisbon: Edições Sílabo; 2001. 600 p\n'},{id:"B23",body:'Camara P, Guerra P, Rodrigues J. Humanator – Recursos Humanos & Sucesso Empresarial. 6th ed. Lisboa: Publicações Dom Quixote; 2005. 585 p\n'},{id:"B24",body:'Bussler L, Davis E. Information systems: The quiet revolution in human resource management. Journal of Computer Information Systems. 2002;42(2):17-20\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Marlene Sofia Alves e Silva",address:"mssilva@iscap.ipp.pt",affiliation:'
CEOS.PP – Centre for Organisational and Social Studies of P.PORTO (Researcher), Portugal
'},{corresp:null,contributorFullName:"Carlos Guilherme da Silva Lima",address:null,affiliation:'
Altran (Senior Consultant), Portugal
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m",slug:"systematic-generation-of-an-irreducible-polynomial-of-an-arbitrary-degree-m-over-fp-such-that-p-gt-m",signatures:"Hiroaki Nasu, Yasuyuki Nogami, Yoshitaka Morikawa, Shigeki Kobayashi and Tatsuo Sugimura",authors:[null]},{id:"10985",title:"Efficient Pairings on Twisted Elliptic Curve",slug:"efficient-pairings-on-twisted-elliptic-curve",signatures:"Yasuyuki Nogami, Masataka Akane, Yumi Sakemi and Yoshitaka Morikawa",authors:[null]},{id:"10970",title:"An Improvement of Twisted Ate Pairing with Barreto-Naehrig Curve by using Frobenius Mapping",slug:"an-improvement-of-twisted-ate-pairing-with-barreto-naehrig-curve-by-using-frobenius-mapping",signatures:"Yumi Sakemi, Hidehiro Kato, Yasuyuki Nogami and Yoshitaka Morikawa",authors:[null]},{id:"10974",title:"An Improvement of Cyclic Vector Multiplication Algorithm",slug:"an-improvement-of-cyclic-vector-multiplication-algorithm",signatures:"Hidehiro Kato, Yasuyuki Nogami, Tomoki Yoshida, Kenta Nekado, Shoichi Takeuchi and Yoshitaka Morikawa",authors:[null]},{id:"10975",title:"A Wireless Sensor Network for Field Hockey Performance Analysis",slug:"a-wireless-sensor-network-for-field-hockey-performance-analysis",signatures:"Shamala Subramaniam, Mohammed Ahmed Almoqry, Sangeetha Thavamani, Roger Canda, Mohamed Othman and Zuriati Zulkarnain",authors:[null]},{id:"10977",title:"Application of Game Theory to Wireless Networks",slug:"application-of-game-theory-to-wireless-networks",signatures:"S. Mehta and K. S. Kwak",authors:[null]},{id:"10981",title:"Comparison of DP Effects in MANET AAPs with Link Error",slug:"comparison-of-dp-effects-in-manet-aaps-with-link-error",signatures:"Sang-Chul Kim",authors:[null]},{id:"10982",title:"Design of Optimum Weighting Functions for LFM Signals",slug:"design-of-optimum-weighting-functions-for-lfm-signals",signatures:"Anatoliy Kononov, Lars Ulander and Leif Eriksson",authors:[null]},{id:"10986",title:"Stress and EEG",slug:"stress-and-eeg",signatures:"Ssang-Hee Seo and Jung-Tae Lee",authors:[null]}]}],publishedBooks:[{type:"book",id:"3788",title:"Convergence and Hybrid Information Technologies",subtitle:null,isOpenForSubmission:!1,hash:"41060ee16becdd7639b6821bb50963ce",slug:"convergence-and-hybrid-information-technologies",bookSignature:"Marius Crisan",coverURL:"https://cdn.intechopen.com/books/images_new/3788.jpg",editedByType:"Edited by",editors:[{id:"4577",title:"Professor",name:"Marius",surname:"Crisan",slug:"marius-crisan",fullName:"Marius Crisan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[]},onlineFirst:{chapter:{type:"chapter",id:"65993",title:"Automatic Speech Emotion Recognition Using Machine Learning",doi:"10.5772/intechopen.84856",slug:"automatic-speech-emotion-recognition-using-machine-learning",body:'\n
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1. Introduction
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Emotion plays a significant role in daily interpersonal human interactions. This is essential to our rational as well as intelligent decisions. It helps us to match and understand the feelings of others by conveying our feelings and giving feedback to others. Research has revealed the powerful role that emotion play in shaping human social interaction. Emotional displays convey considerable information about the mental state of an individual. This has opened up a new research field called automatic emotion recognition, having basic goals to understand and retrieve desired emotions. In prior studies, several modalities have been explored to recognize the emotional states such as facial expressions [1], speech [2], physiological signals [3], etc. Several inherent advantages make speech signals a good source for affective computing. For example, compared to many other biological signals (e.g., electrocardiogram), speech signals usually can be acquired more readily and economically. This is why the majority of researchers are interested in speech emotion recognition (SER). SER aims to recognize the underlying emotional state of a speaker from her voice. The area has received increasing research interest all through current years. There are many applications of detecting the emotion of the persons like in the interface with robots, audio surveillance, web-based E-learning, commercial applications, clinical studies, entertainment, banking, call centers, cardboard systems, computer games, etc. For classroom orchestration or E-learning, information about the emotional state of students can provide focus on the enhancement of teaching quality. For example, a teacher can use SER to decide what subjects can be taught and must be able to develop strategies for managing emotions within the learning environment. That is why learner’s emotional state should be considered in the classroom.
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Three key issues need to be addressed for successful SER system, namely, (1) choice of a good emotional speech database, (2) extracting effective features, and (3) designing reliable classifiers using machine learning algorithms. In fact, the emotional feature extraction is a main issue in the SER system. Many researchers [4] have proposed important speech features which contain emotion information, such as energy, pitch, formant frequency, Linear Prediction Cepstrum Coefficients (LPCC), Mel-frequency cepstrum coefficients (MFCC), and modulation spectral features (MSFs) [5]. Thus, most researchers prefer to use combining feature set that is composed of many kinds of features containing more emotional information [6]. However, using a combining feature set may give rise to high dimension and redundancy of speech features; thereby, it makes the learning process complicated for most machine learning algorithms and increases the likelihood of overfitting. Therefore, feature selection is indispensable to reduce the dimensions redundancy of features. A review for feature selection models and techniques is presented in [7]. Both feature extraction and feature selection are capable of improving learning performance, lowering computational complexity, building better generalizable models, and decreasing required storage. The last step of speech emotion recognition is classification. It involves classifying the raw data in the form of utterance or frame of the utterance into a particular class of emotion on the basis of features extracted from the data. In recent years in speech emotion recognition, researchers proposed many classification algorithms, such as Gaussian mixture model (GMM) [8], hidden Markov model (HMM) [9], support vector machine (SVM) [10, 11, 12, 13, 14], neural networks (NN) [15], and recurrent neural networks (RNN) [16, 17, 18]. Some other types of classifiers are also proposed by some researchers such as a modified brain emotional learning model (BEL) [19] in which the adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) are merged for speech emotion recognition. Another proposed strategy is a multiple kernel Gaussian process (GP) classification [17], in which two similar notions in the learning algorithm are presented by combining the linear kernel and radial basis function (RBF) kernel. The Voiced Segment Selection (VSS) algorithm also proposed in [20] deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification.
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In previous work [21], we present a system for the recognition of «seven acted emotional states (anger, disgust, fear, joy, sadness, and surprise)». To do that, we extracted the MFCC and MS features and used them to train three different machine learning paradigms (MLR, SVM, and RNN). We demonstrated that the combination of both features has a high accuracy above 94% on the Spanish database. All previously published works generally use the Berlin database. To our knowledge, the Spanish emotional database has never been used before. For this reason, we have chosen to compare them. In this chapter, we concentrate to improve accuracy; more experiments have been performed. This chapter mainly makes the following contributions:
The effect of speaker normalization (SN) is also studied, which removes the mean of features and normalizes them to unit variance. Experiments are performed under a speaker-independent condition.
Additionally, a feature selection technique is assessed to obtain good features from the set of features extracted in [21].
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The rest of the chapter is organized as follows. In the next section, we start by introducing the nature of speech emotions. Section 3 describes features we extracted from a speech signal. A feature selection method and machine learning algorithms used for SER are presented. Section 4 reports on the databases we used and presents the simulation results obtained using different features and different machine learning (ML) paradigms. Section 5 closes this chapter by analyses and conclusion.
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2. Emotion and classification
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This section is concerned with defining the term emotion, presenting its different models. Also for recognizing emotions, there are several techniques and inputs that can be used. A brief description of all of the techniques is presented here.
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2.1 Definition
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A definition is both important and difficult because the everyday word “emotion” is a notoriously fluid term in meaning. Emotion is one of the most difficult concepts to define in psychology. In fact, there are different definitions of emotions in the scientific literature. In everyday speech, emotion is any relatively brief conscious experience characterized by intense mental activity and a high degree of pleasure or displeasure [22, 23]. Scientific discourse has drifted to other meanings and there is no consensus on a definition. Emotion is often entwined with temperament, mood, personality, motivation, and disposition. In psychology, emotion is frequently defined as a complex state of feeling that results in physical and psychological changes. These changes influence thought and behavior. According to other theories, emotions are not causal forces but simply syndromes of components such as motivation, feeling, behavior, and physiological changes [24]. In 1884, in What is an emotion? [25], American psychologist and philosopher William James proposed a theory of emotion whose influence was considerable. According to his thesis, the feeling of intense emotion corresponds to the perception of specific bodily changes. This approach is found in many current theories: the bodily reaction is the cause and not the consequence of the emotion. The scope of this theory is measured by the many debates it provokes. This illustrates the difficulty of agreeing on a definition of this dynamic and complex phenomenon that we call emotion. “Emotion” refers to a wide range of affective processes such as moods, feelings, affects, and well-being [26]. The term “emotion” in [6] has been also referred to an extremely complex state associated with a wide variety of mental, physiological, and physical events.
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2.2 Categorization of emotions
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The categorization of emotions has long been a hot subject of debate in different fields of psychology, affective science, and emotion research. It is mainly based on two popular approaches: categorical (termed discrete) and dimensional (termed continuous). In the first approach, emotions are described with a discrete number of classes. Many theorists have conducted studies to determine which emotions are basic [27]. A most popular example is Ekman [28] who proposed a list of six basic emotions, which are anger, disgust, fear, happiness, sadness, and surprise. He explains that each emotion acts as a discrete category rather than an individual emotional state. In the second approach, emotions are a combination of several psychological dimensions and identified by axes. Other researchers define emotions according to one or more dimensions. Wilhelm Max Wundt proposed in 1897 that emotions can be described by three dimensions: (1) strain versus relaxation, (2) pleasurable versus unpleasurable, and (3) arousing versus subduing [29]. PAD emotional state model is another three-dimensional approach by Albert Mehrabian and James Russell where PAD stands for pleasure, arousal, and dominance. Another popular dimensional model was proposed by James Russell in 1977. Unlike the earlier three-dimensional models, Russell’s model features only two dimensions which include (1) arousal (or activation) and (2) valence (or evaluation) [29].
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The categorical approach is commonly used in SER [30]. It characterizes emotions used in everyday emotion words such as joy and anger. In this work, a set of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) plus neutral, corresponding to the six emotions of Ekman’s model, were used for the recognition of emotion from speech using the categorical approach.
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2.3 Sensory modalities for emotion expression
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There is vigorous debate about what exactly individual can express nonverbally. Humans can express their emotions through many different types of nonverbal communication including facial expressions, quality of speech produced, and physiological signals of the human body. In this section, we discuss each of these categories.
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2.3.1 Facial expressions
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The human face is extremely expressive, able to express countless emotions without saying a word [31]. And unlike some forms of nonverbal communication, facial expressions are universal. The facial expressions for happiness, sadness, anger, surprise, fear, and disgust are the same across cultures.
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2.3.2 Speech
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In addition to faces, voices are an important modality for emotional expression. Speech is a relevant communicational channel enriched with emotions: the voice in speech not only conveys a semantic message but also the information about the emotional state of the speaker. Some important voice feature vectors that have been chosen for research such as fundamental frequency, mel-frequency cepstral coefficient (MFCC), prediction cepstral coefficient (LPCC), etc.
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2.3.3 Physiological signals
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The physiological signals related to autonomic nervous system allow to assess objectively emotions. These include electroencephalogram (EEG), heart rate (HR), electrocardiogram (ECG), respiration (RSP), blood pressure (BP), electromyogram (EMG), skin conductance (SC), blood volume pulse (BVP), and skin temperature (ST) [32]. Using physiological signals to recognize emotions is also helpful to those people who suffer from physical or mental illness thus exhibit problems with facial expressions or tone of voice.
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3. Speech emotion recognition (SER) system
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3.1 Block diagram
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Our SER system consists of four main steps. First is the voice sample collection. The second features vector that is formed by extracting the features. As the next step, we tried to determine which features are most relevant to differentiate each emotion. These features are introduced to machine learning classifier for recognition. This process is described in Figure 1.
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Figure 1.
Block diagram of the proposed system.
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3.2. Feature extraction
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The speech signal contains a large number of parameters that reflect the emotional characteristics. One of the sticking points in emotion recognition is what features should be used. In recent research, many common features are extracted, such as energy, pitch, formant, and some spectrum features such as linear prediction coefficients (LPC), mel-frequency cepstrum coefficients (MFCC), and modulation spectral features. In this work, we have selected modulation spectral features and MFCC, to extract the emotional features.
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Mel-frequency cepstrum coefficient (MFCC) is the most used representation of the spectral property of voice signals. These are the best for speech recognition as it takes human perception sensitivity with respect to frequencies into consideration. For each frame, the Fourier transform and the energy spectrum were estimated and mapped into the Mel-frequency scale. The discrete cosine transform (DCT) of the Mel log energies was estimated, and the first 12 DCT coefficients provided the MFCC values used in the classification process. Usually, the process of calculating MFCC is shown in Figure 2.
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Figure 2.
Schema of MFCC extraction [33].
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In our research, we extract the first 12 order of the MFCC coefficients where the speech signals are sampled at 16 KHz. For each order coefficients, we calculate the mean, variance, standard deviation, kurtosis, and skewness, and this is for the other all the frames of an utterance. Each MFCC feature vector is 60-dimensional.
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Modulation spectral features (MSFs) are extracted from an auditory-inspired long-term spectro-temporal representation. These features are obtained by emulating the spectro-temporal (ST) processing performed in the human auditory system and consider regular acoustic frequency jointly with modulation frequency. The steps for computing the ST representation are illustrated in Figure 3. In order to obtain the ST representation, the speech signal is first decomposed by an auditory filterbank (19 filters in total). The Hilbert envelopes of the critical-band outputs are computed to form the modulation signals. A modulation filterbank is further applied to the Hilbert envelopes to perform frequency analysis. The spectral contents of the modulation signals are referred to as modulation spectra, and the proposed features are thereby named modulation spectral features (MSFs) [5]. Lastly, the ST representation is formed by measuring the energy of the decomposed envelope signals, as a function of regular acoustic frequency and modulation frequency. The energy, taken over all frames in every spectral band, provides a feature. In our experiment, an auditory filterbank with \n\nN\n=\n19\n\n filters and a modulation filterbank with \n\nM\n=\n5\n\n filters are used. In total, 95 \n\n\n\n19\n×\n5\n\n\n\n MSFs are calculated in this work from the ST representation.
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Figure 3.
Process for computing the ST representation [5].
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3.3 Feature selection
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As reported by Aha and Bankert [34], the objective of feature selection in ML is to “reduce the number of features used to characterize a dataset so as to improve a learning algorithm’s performance on a given task.” The objective will be the maximization of the classification accuracy in a specific task for a certain learning algorithm; as a collateral effect, the number of features to induce the final classification model will be reduced. Feature selection (FS) aims to choose a subset of the relevant features from the original ones according to certain relevance evaluation criterion, which usually leads to higher recognition accuracy [35]. It can drastically reduce the running time of the learning algorithms. In this section, we present an effective feature selection method used in our work, named recursive feature elimination with linear regression (LR-RFE).
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Recursive feature elimination (RFE) uses a model (e.g., linear regression or SVM) to select either the best- or worst-performing feature and then excludes this feature. These estimators assign weights to features (e.g., the coefficients of a linear model), so the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features, and the predictive power of each feature is measured [36]. Then, the least important features are removed from the current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached. In this work, we implemented the recursive feature elimination method of feature ranking via the use of basic linear regression (LR-RFE) [37]. Other research also uses RFE with another linear model such as SVM-RFE that is an SVM-based feature selection algorithm created by [38]. Using SVM-RFE, Guyon et al. selected key and important feature sets. In addition to improving the classification accuracy rate, it can reduce classification computational time.
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3.4 Classification methods
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Many machine learning algorithms have been used for discrete emotion classification. The goal of these algorithms is to learn from the training samples and then use this learning to classify new observation. In fact, there is no definitive answer to the choice of the learning algorithm; every technique has its own advantages and limitations. For this reason, here we chose to compare the performance of three different classifiers.
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Multivariate linear regression classification (MLR) is a simple and efficient computation of machine learning algorithms, and it can be used for both regression and classification problems. We have slightly modified the LRC algorithm described as follow Algorithm 1 [39]. We calculated (in step 3) the absolute value of the difference between original and predicted response vectors (\n\n∣\ny\n−\n\ny\ni\n\n∣\n\n), instead of the Euclidean distance between them (\n\n‖\ny\n−\n\ny\ni\n\n‖\n\n).
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Support vector machines (SVM) are an optimal margin classifier in machine learning. It is also used extensively in many studies that related to audio emotion recognition which can be found in [10, 13, 14]. It can have a very good classification performance compared to other classifiers especially for limited training data [11]. SVM theoretical background can be found in [40]. A MATLAB toolbox implementing SVM is freely available in [41]. A polynomial kernel is investigated in this work.
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Algorithm 1. Linear Regression Classification (LRC)
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Inputs: Class models \n\n\nX\ni\n\n∈\n\nR\n\nq\n×\n\np\ni\n\n\n\n,\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n and a test speech vector \n\ny\n∈\n\nR\n\nq\n×\n1\n\n\n\n
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Output: Class of \n\ny\n\n
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1. \n\n\n\nβ\n̂\n\ni\n\n∈\n\nR\n\n\np\ni\n\n×\n1\n\n\n\n is evaluated against each class model, \n\n\n\nβ\n̂\n\ni\n\n=\n\n\n\n\nX\ni\nT\n\n\nX\ni\n\n\n\n\n\n−\n1\n\n\n\n\nX\ni\nT\n\ny\n,\n\n\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n
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2. \n\n\n\ny\n̂\n\ni\n\n\n is computed for each \n\n\n\nβ\n̂\n\ni\n\n,\n\n\ny\n̂\n\ni\n\n=\n\nX\ni\n\n\n\nβ\n̂\n\ni\n\n,\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n;
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3. Distance calculation between original and predicted response variables \n\n\nd\ni\n\n\ny\n\n=\n∣\ny\n−\n\ny\ni\n\n∣\n,\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n;
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4. Decision is made in favor of the class with the minimum distance \n\n\nd\ni\n\n\ny\n\n\n
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Recurrent neural networks (RNN) are suitable for learning time series data, and it has shown improved performance for classification task [42]. While RNN models are effective at learning temporal correlations, they suffer from the vanishing gradient problem which increases with the length of the training sequences. To resolve this problem, long short-term memory (LSTM) RNNs were proposed by Hochreiter et al. [43]; it uses memory cells to store information so that it can exploit long-range dependencies in the data [17].
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Figure 4 shows a basic concept of RNN implementation. Unlike traditional neural network that uses different parameters at each layer, the RNN shares the same parameters (U, V, and W are presented in Figure 4) across all steps. The hidden state formulas and variables are as follows:
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Figure 4.
A basic concept of RNN and unfolding in time of the computation involved in its forward computation [18].
where \n\n\nx\nt\n\n\n, \n\n\ns\nt\n\n\n, and \n\n\no\nt\n\n\n are respectively the input, the hidden state, and the output at time step t and \n\nU\n,\nV\n,\nW\n\n are parameters matrices.
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4. Experimental results and analysis
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4.1 Emotional speech databases
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The performance and robustness of the recognition systems will be easily affected if it is not well trained with a suitable database. Therefore, it is essential to have sufficient and suitable phrases in the database to train the emotion recognition system and subsequently evaluate its performance. There are three main types of databases: acted emotions, natural spontaneous emotions, and elicited emotions [27, 44]. In this work, we used an acted emotion databases because they contain strong emotional expressions. The literature on speech emotion recognition [45] shows that the majority of studies have been conducted with emotional acted speech. In this section, we detailed the two emotional speech databases used for classifying discrete emotions in our experiments: Berlin Database and Spanish Database.
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4.2 Berlin database
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The Berlin database [46] is widely used in emotional speech recognition. It contains 535 utterances spoken by 10 actors (5 female, 5 male) in 7 simulated emotions (anger, boredom, disgust, fear, joy, sadness, and neutral). This database was chosen for the following reasons: (i) the quality of its recording is very good, and (ii) it is public [47] and popular database of emotion recognition that is recommended in the literature [19].
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4.3 Spanish database
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The INTER1SP Spanish emotional database contains utterances from two professional actors (one female and one male speaker).The Spanish corpus that we have the right to access (free for academic and research use) [48] was recorded twice in the «six basic emotions plus neutral (anger, sadness, joy, fear, disgust, surprise and neutral/normal)». Four additional neutral variations (soft, loud, slow, and fast) were recorded once. This is preferred to other created database because it is available for researchers use and it contains more data (6041 utterances in total). This paper has focused on only seven main emotions from the Spanish database in order to achieve a higher and more accurate rate of recognition and to make the comparison with the Berlin database detailed above.
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4.4 Results and analysis
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In this section, experimentation results are presented and discussed. We report the recognition accuracy of using MLR, SVM, and RNN classifiers. Experimental evaluation is performed on the Berlin and Spanish databases. All classification results are obtained under tenfold cross-validation. Cross-validation is a common practice used in performance analysis that randomly partitions the data into N complementary subsets, with \n\nN\n−\n1\n\n of them used for training in each validation and the remaining one used for testing. The neural network structure used is a simple LSTM. It consists of two consecutive LSTM layers with hyperbolic tangent activation followed by two classification dense layers. Features from data are scaled to \n\n\n\n−\n1\n\n1\n\n\n before applying classifiers. Scaling features before recognition is important, because when a learning phase is fit on unscaled data, it is possible for large inputs to slow down the learning and convergence and in some cases prevent the used classifier from effectively learning for the classification problem. The effect of speaker normalization (SN) step prior to recognition is investigated, and there are three different SN schemes that are defined in [6]. SN is useful to compensate for the variations due to speaker diversity rather than the change of emotional state. We used in this section the SN scheme that has given the best results in [6]. The features of each speaker are normalized with a mean of \n\n0\n\n and a standard deviation of \n\n1\n\n. Tables 1, 2, 3 show the recognition rate for each combination of various features and classifiers based on Berlin and Spanish databases. These experiments use feature set without feature selection. As shown in Table 1, SVM classifier yields better results above 81%, with feature combination of MFCC and MS for Berlin database. Our results have improved compared to previous results in [21] because we changed the SVM parameters for each type of features to develop a good model.
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Recognition rate (%)
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Test
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Feature
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Method
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SN
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A
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E
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F
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L
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N
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T
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W
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AVG.
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(\n\nσ\n\n)
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\n\n\n
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#1
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MS
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MLR
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No
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45.90
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45.72
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48.78
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77.08
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59.43
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79.91
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75.94
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66.23
\n
(5.85)
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MFCC
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56.55
\n
62.28
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45.60
\n
54.97
\n
57.35
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74.36
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91.37
\n
64.70
\n
(3.20)
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MFCC+SM
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70.26
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73.04
\n
51.95
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82.44
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69.55
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82.49
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76.55
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73.00
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(3.23)
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#2
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MS
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SVM
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No
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56.61
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54.78
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51.17
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70.98
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67.32
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67.50
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73.13
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70.63
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(6.45)
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MFCC
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73.99
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64.14
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64.76
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55.30
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62.28
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84.13
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83.13
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71.70
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(4.24)
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MFCC+SM
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82.03
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68.70
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69.09
\n
79.16
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76.99
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80.89
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80.63
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81.10
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(2.73)
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#3
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MS
\n
MLR
\n
Yes
\n
48.98
\n
35.54
\n
32.66
\n
80.35
\n
55.54
\n
88.79
\n
85.77
\n
64.20
\n
(5.27)
\n
\n
\n
MFCC
\n
59.71
\n
59.72
\n
48.65
\n
67.10
\n
67.98
\n
91.73
\n
87.51
\n
71.00
\n
(4.19)
\n
\n
\n
MFCC+SM
\n
72.32
\n
68.82
\n
51.98
\n
82.60
\n
81.72
\n
91.96
\n
80.71
\n
75.25
\n
(2.49)
\n
\n
\n
#4
\n
MS
\n
SVM
\n
Yes
\n
62.72
\n
49.44
\n
37.29
\n
76.14
\n
71.30
\n
88.44
\n
80.15
\n
71.90
\n
(2.38)
\n
\n
\n
MFCC
\n
70.68
\n
56.55
\n
56.99
\n
59.88
\n
68.14
\n
91.88
\n
85.44
\n
77.60
\n
(4.35)
\n
\n
\n
MFCC+SM
\n
77.37
\n
69.67
\n
58.16
\n
79.87
\n
88.57
\n
98.75
\n
86.64
\n
81.00
\n
(2.45)
\n
\n\n
Table 1.
Recognition results with MS, MFCC features, and their combination on Berlin database; AVG. denotes average recognition rate; \n\nσ\n\n denotes standard deviation of the 10-cross-validation accuracies.
Berlin (a, fear; e, disgust; f, happiness; l, boredom; n, neutral; t, sadness; w, anger).
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
\n
\n
\n
Recognition rate (%)
\n
\n
\n
\n
\n
Test
\n
Feature
\n
Method
\n
SN
\n
A
\n
D
\n
F
\n
J
\n
N
\n
S
\n
T
\n
AVG.
\n
(\n\nσ\n\n)
\n
\n\n\n
\n
#1
\n
MS
\n
MLR
\n
No
\n
67.72
\n
44.04
\n
68.78
\n
46.95
\n
89.58
\n
63.10
\n
78.49
\n
69.22
\n
(1.37)
\n
\n
\n
MFCC
\n
67.85
\n
61.41
\n
75.97
\n
60.17
\n
95.79
\n
71.89
\n
84.94
\n
77.21
\n
(0.76)
\n
\n
\n
MFCC+SM
\n
78.75
\n
78.18
\n
80.68
\n
63.84
\n
96.80
\n
82.44
\n
89.01
\n
83.55
\n
(0.55)
\n
\n
\n
#2
\n
MS
\n
SVM
\n
No
\n
70.33
\n
69.38
\n
78.09
\n
60.97
\n
89.25
\n
69.38
\n
85.95
\n
80.98
\n
(1.09)
\n
\n
\n
MFCC
\n
79.93
\n
79.02
\n
81.81
\n
75.71
\n
93.77
\n
80.15
\n
92.01
\n
90.94
\n
(0.93)
\n
\n
\n
MFCC+SM
\n
84.90
\n
88.26
\n
89.44
\n
80.90
\n
96.58
\n
83.89
\n
95.63
\n
89.69
\n
(0.62)
\n
\n
\n
#3
\n
MS
\n
MLR
\n
Yes
\n
64.76
\n
49.02
\n
66.87
\n
44.52
\n
87.50
\n
58.26
\n
78.70
\n
67.84
\n
(1.27)
\n
\n
\n
MFCC
\n
66.54
\n
57.83
\n
74.56
\n
56.98
\n
94.02
\n
72.32
\n
89.63
\n
76.47
\n
(1.51)
\n
\n
\n
MFCC+SM
\n
77.01
\n
78.45
\n
80.50
\n
64.18
\n
94.42
\n
80.14
\n
91.29
\n
83.03
\n
(0.97)
\n
\n
\n
#4
\n
MS
\n
SVM
\n
Yes
\n
69.81
\n
70.35
\n
75.44
\n
52.60
\n
86.77
\n
66.94
\n
82.57
\n
78.40
\n
(1.64)
\n
\n
\n
MFCC
\n
77.45
\n
77.41
\n
80.99
\n
69.47
\n
91.89
\n
75.17
\n
93.50
\n
87.47
\n
(0.95)
\n
\n
\n
MFCC+SM
\n
85.28
\n
84.54
\n
84.49
\n
73.47
\n
93.43
\n
81.79
\n
94.04
\n
86.57
\n
(0.72)
\n
\n\n
Table 2.
Recognition results with MS, MFCC features, and their combination on Spanish database.
Recognition results using RNN classifier based on Berlin and Spanish databases.
\n
From Table 1, it can be concluded that applying SN improves recognition results for Berlin database. But this is not the case for the Spanish database, as demonstrated in Tables 2 and 3. Results are the same with the three different classifiers. This can be explained by the number of speakers in each database. The Berlin database contains 10 different speakers, compared to the Spanish database that contains only two speakers and probably the language impact. As regarding the RNN method, we found that combining both types of features has the worst recognition rate for the Berlin database, as shown in Table 3. That is because the RNN model has too many parameters (155 coefficients in total) and a poor training data. This is the phenomena of overfitting. This is confirmed by the fact that when we reduced the number of features from 155 to 59 features, the results show an increase of above 13%, as shown in Table 4. To investigate whether a smaller feature space leads to better recognition performance, we repeated all evaluations on the development set by applying a recursive feature elimination (LR-RFE) for each modality combination. The stability of RFE depends heavily on the type of model that is used for feature ranking at each iteration. In our case, we tested the RFE based on an SVM and regression models; we found that using linear regression provides more stable results. We observed from the previous results that the combination of the features gives the best results. So we applied LR-RFE feature selection only for this combination to improve accuracy. In this work, a total of 155 features were used; best features were chosen from feature selection. Fifty-nine features were selected by RFE feature selection method based on LR from the Berlin database and 110 features from the Spanish database. The corresponding results of LR-RFE can be seen in Table 4. For most setting using the Spanish database, LR-RFE does not significantly improve the average accuracy. However, for recognition based on Berlin database using the three classifiers, LR-RFE leads to a remarkable performance gain, as shown in Figure 5. This increases the average of MFCC combined with MS features from 63.67 to 78.11% for RNN classifier. These results are illustrated in Table 4. For the Spanish database, the feature combination of MFCC and MS after applying LR-RFE selection using RNN has the best recognition rate which is above 94.01%.
\n
\n
\n
\n
\n
\n
\n\n
\n
SN
\n
Classifier
\n
LR-RFE
\n
Berlin
\n
Spanish
\n
\n\n\n
\n
No
\n
MLR
\n
No
\n
73.00 (3.23)
\n
83.55 (0.55)
\n
\n
\n
Yes
\n
79.40 (3.09)
\n
84.19 (0.96)
\n
\n
\n
SVM
\n
No
\n
81.10 (2.73)
\n
89.69 (0.62)
\n
\n
\n
Yes
\n
80.90 (3.17)
\n
90.05 (0.80)
\n
\n
\n
RNN
\n
No
\n
63.67 (7.74)
\n
90.05 (1.64)
\n
\n
\n
Yes
\n
78.11 (3.53)
\n
94.01 (0.76)
\n
\n
\n
Yes
\n
MLR
\n
No
\n
75.25 (2.49)
\n
83.03 (0.97)
\n
\n
\n
Yes
\n
83.20 (3.25)
\n
82.27 (1.12)
\n
\n
\n
SVM
\n
No
\n
81.00 (2.45)
\n
86.57 (0.72)
\n
\n
\n
Yes
\n
83.90 (2.46)
\n
86.47 (1.34)
\n
\n
\n
RNN
\n
No
\n
76.98 (4.79)
\n
87.02 (0.36)
\n
\n
\n
Yes
\n
83.42 (0.70)
\n
85.00 (0.93)
\n
\n\n
Table 4.
Recognition results with combination of MFCC and MS features using ML paradigm before and after applying LR-RFE feature selection method (Berlin and Spanish databases).
\n
Figure 5.
Performance comparison of three machine learning paradigms (MLR, SVM, RNN) using speaker normalization (SN) and RFE feature selection (FS), for the Berlin database, is shown.
\n
The confusion matrix for the best recognition of emotions using MFCC and MS features with RNN based on Spanish database is shown in Table 5. The rate column lists per class recognition rates and precision for a class are the number of samples correctly classified divided by the total number of samples classified to the class. It can be seen that Neutral was the emotion that was least difficult to recognize from speech as opposed to Disgust which was the most difficult and it forms the most notable confusion pair with Fear.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Emotion
\n
Anger
\n
Disgust
\n
Fear
\n
Joy
\n
Neutral
\n
Surprise
\n
Sadness
\n
Rate (%)
\n
\n\n\n
\n
Anger
\n
79
\n
1
\n
0
\n
1
\n
2
\n
3
\n
0
\n
91.86
\n
\n
\n
Disgust
\n
0
\n
67
\n
3
\n
0
\n
1
\n
0
\n
1
\n
93.05
\n
\n
\n
Fear
\n
0
\n
3
\n
70
\n
0
\n
1
\n
0
\n
2
\n
93.33
\n
\n
\n
Joy
\n
3
\n
1
\n
1
\n
71
\n
0
\n
0
\n
0
\n
93.42
\n
\n
\n
Neutral
\n
2
\n
0
\n
1
\n
0
\n
156
\n
0
\n
1
\n
97.50
\n
\n
\n
surprise
\n
2
\n
1
\n
0
\n
3
\n
0
\n
60
\n
0
\n
92.30
\n
\n
\n
Sadness
\n
0
\n
0
\n
1
\n
0
\n
2
\n
0
\n
66
\n
95.65
\n
\n
\n
Precision (%)
\n
91.86
\n
91.78
\n
92.10
\n
94.66
\n
96.29
\n
95.23
\n
94.28
\n
\n
\n\n
Table 5.
Confusion matrix for feature combination after LR-RFE selection based on Spanish database.
\n
\n
\n
\n
5. Conclusion
\n
In this current study, we presented an automatic speech emotion recognition (SER) system using three machine learning algorithms (MLR, SVM, and RNN) to classify seven emotions. Thus, two types of features (MFCC and MS) were extracted from two different acted databases (Berlin and Spanish databases), and a combination of these features was presented. In fact, we study how classifiers and features impact recognition accuracy of emotions in speech. A subset of highly discriminant features is selected. Feature selection techniques show that more information is not always good in machine learning applications. The machine learning models were trained and evaluated to recognize emotional states from these features. SER reported the best recognition rate of 94% on the Spanish database using RNN classifier without speaker normalization (SN) and with feature selection (FS). For Berlin database, all of the classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection (FS) are applied to the features. From this result, we can see that RNN often perform better with more data and it suffers from the problem of very long training times. Therefore, we concluded that the SVM and MLR models have a good potential for practical usage for limited data in comparison with RNN .
\n
Enhancement of the robustness of emotion recognition system is still possible by combining databases and by fusion of classifiers. The effect of training multiple emotion detectors can be investigated by fusing these into a single detection system. We aim also to use other feature selection methods because the quality of the feature selection affects the emotion recognition rate: a good emotion feature selection method can select features reflecting emotion state quickly. The overall aim of our work is to develop a system that will be used in a pedagogical interaction in classrooms, in order to help the teacher to orchestrate his class. For achieving this goal, we aim to test the system proposed in this work.
\n
\n\n',keywords:"speech emotion recognition, feature extraction recurrent neural, network SVM, multivariate linear regression, MFCC, modulation spectral features, machine learning",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65993.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65993.xml",downloadPdfUrl:"/chapter/pdf-download/65993",previewPdfUrl:"/chapter/pdf-preview/65993",totalDownloads:4593,totalViews:0,totalCrossrefCites:21,dateSubmitted:"February 21st 2018",dateReviewed:"January 31st 2019",datePrePublished:"March 25th 2019",datePublished:"February 19th 2020",dateFinished:"March 6th 2019",readingETA:"0",abstract:"This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65993",risUrl:"/chapter/ris/65993",signatures:"Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Kosai Raoof, Mohamed Ali Mahjoub and Catherine Cleder",book:{id:"8141",type:"book",title:"Social Media and Machine Learning",subtitle:null,fullTitle:"Social Media and Machine Learning",slug:"social-media-and-machine-learning",publishedDate:"February 19th 2020",bookSignature:"Alberto Cano",coverURL:"https://cdn.intechopen.com/books/images_new/8141.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78984-028-5",printIsbn:"978-1-78984-027-8",pdfIsbn:"978-1-83880-616-3",isAvailableForWebshopOrdering:!0,editors:[{id:"200724",title:"Dr.",name:"Alberto",middleName:null,surname:"Cano",slug:"alberto-cano",fullName:"Alberto Cano"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"247090",title:"Ph.D. Student",name:"Leila",middleName:null,surname:"Kerkeni",fullName:"Leila Kerkeni",slug:"leila-kerkeni",email:"kerkeni.leila@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Emotion and classification",level:"1"},{id:"sec_2_2",title:"2.1 Definition",level:"2"},{id:"sec_3_2",title:"2.2 Categorization of emotions",level:"2"},{id:"sec_4_2",title:"2.3 Sensory modalities for emotion expression",level:"2"},{id:"sec_4_3",title:"2.3.1 Facial expressions",level:"3"},{id:"sec_5_3",title:"2.3.2 Speech",level:"3"},{id:"sec_6_3",title:"2.3.3 Physiological signals",level:"3"},{id:"sec_9",title:"3. Speech emotion recognition (SER) system",level:"1"},{id:"sec_9_2",title:"3.1 Block diagram",level:"2"},{id:"sec_10_2",title:"3.2. Feature extraction",level:"2"},{id:"sec_11_2",title:"3.3 Feature selection",level:"2"},{id:"sec_12_2",title:"3.4 Classification methods",level:"2"},{id:"sec_14",title:"4. Experimental results and analysis",level:"1"},{id:"sec_14_2",title:"4.1 Emotional speech databases",level:"2"},{id:"sec_15_2",title:"4.2 Berlin database",level:"2"},{id:"sec_16_2",title:"4.3 Spanish database",level:"2"},{id:"sec_17_2",title:"4.4 Results and analysis",level:"2"},{id:"sec_19",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Ali H, Hariharan M, Yaacob S, Adom AH. Facial emotion recognition using empirical mode decomposition. Expert Systems with Applications. 2015;42(3):1261-1277\n'},{id:"B2",body:'Liu ZT, Wu M, Cao WH, Mao JW, Xu JP, Tan GZ. Speech emotion recognition based on feature selection and extreme learning machine decision tree. Neurocomputing. 2018;273:271-280\n'},{id:"B3",body:'Ragot M, Martin N, Em S, Pallamin N, Diverrez JM. Emotion recognition using physiological signals: Laboratory vs. wearable sensors. In: International Conference on Applied Human Factors and Ergonomics. Springer; 2017. pp. 15-22\n'},{id:"B4",body:'Surabhi V, Saurabh M. 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Linear regression for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010;32:2106-2112\n'},{id:"B40",body:'Gunn SR. Support vector machines for classification and regression [PhD thesis]. 1998\n'},{id:"B41",body:'SVM and Kernel Methods MATLAB Toolbox. Available from: http://asi.insa-rouen.fr/enseignants/\n∼arakoto/toolbox/\n\n'},{id:"B42",body:'Parthasarathy S, Tashev I. Convolutional neural network techniques for speech emotion recognition. In: 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE; 2018. pp. 121-125\n'},{id:"B43",body:'Sepp H, Jurgen S. Long Short-term Memory. Neural Computation. 1997;9:1735-1780\n'},{id:"B44",body:'Vaudable C. Analyse et reconnaissance des émotions lors de conversations de centres d’appels [PhD thesis]. Université Paris Sud-Paris XI; 2012\n'},{id:"B45",body:'Swain M, Routray A, Kabisatpathy P. Databases, features and classifiers for speech emotion recognition: A review. International Journal of Speech Technology. 2018;21:1-28\n'},{id:"B46",body:'Burkhardt F, Paeschke A, Rolfes M, Sendlmeier W, Weiss B. A Database of German Emotional Speech. INTERSPEECH; 2005\n'},{id:"B47",body:'Berlin Database of Emotional Speech. Available from: http://emodb.bilderbar.info/start.html\n\n'},{id:"B48",body:'Berlin Database of Emotional Speech. Available from: http://www.elra.info/en/catalogues/ catalogue-language-resources/\n\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Leila Kerkeni",address:"kerkeni.leila@gmail.com",affiliation:'
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
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\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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Cárdenas-Aguayo, M. del C. Silva-Lucero, M. Cortes-Ortiz,\nB. Jiménez-Ramos, L. Gómez-Virgilio, G. Ramírez-Rodríguez, E. Vera-\nArroyo, R. Fiorentino-Pérez, U. García, J. Luna-Muñoz and M.A.\nMeraz-Ríos",authors:[{id:"42225",title:"Dr.",name:"Jose",middleName:null,surname:"Luna-Muñoz",slug:"jose-luna-munoz",fullName:"Jose Luna-Muñoz"},{id:"114746",title:"Dr.",name:"Marco",middleName:null,surname:"Meraz-Ríos",slug:"marco-meraz-rios",fullName:"Marco Meraz-Ríos"},{id:"169616",title:"Dr.",name:"Maria del Carmen",middleName:null,surname:"Cardenas-Aguayo",slug:"maria-del-carmen-cardenas-aguayo",fullName:"Maria del Carmen Cardenas-Aguayo"},{id:"169857",title:"Dr.",name:"Maria del Carmen",middleName:null,surname:"Silva-Lucero",slug:"maria-del-carmen-silva-lucero",fullName:"Maria del Carmen Silva-Lucero"},{id:"169858",title:"Dr.",name:"Maribel",middleName:null,surname:"Cortes-Ortiz",slug:"maribel-cortes-ortiz",fullName:"Maribel Cortes-Ortiz"},{id:"169859",title:"Dr.",name:"Berenice",middleName:null,surname:"Jimenez-Ramos",slug:"berenice-jimenez-ramos",fullName:"Berenice Jimenez-Ramos"},{id:"169860",title:"Dr.",name:"Laura",middleName:null,surname:"Gomez-Virgilio",slug:"laura-gomez-virgilio",fullName:"Laura Gomez-Virgilio"},{id:"169861",title:"Dr.",name:"Gerardo",middleName:null,surname:"Ramirez-Rodriguez",slug:"gerardo-ramirez-rodriguez",fullName:"Gerardo Ramirez-Rodriguez"},{id:"169862",title:"Dr.",name:"Eduardo",middleName:null,surname:"Vera-Arroyo",slug:"eduardo-vera-arroyo",fullName:"Eduardo Vera-Arroyo"},{id:"169863",title:"Dr.",name:"Rosana Sofia",middleName:null,surname:"Fiorentino-Perez",slug:"rosana-sofia-fiorentino-perez",fullName:"Rosana Sofia Fiorentino-Perez"},{id:"169864",title:"Dr.",name:"Ubaldo",middleName:null,surname:"Garcia",slug:"ubaldo-garcia",fullName:"Ubaldo Garcia"}]},{id:"58070",doi:"10.5772/intechopen.72427",title:"MRI Medical Image Denoising by Fundamental Filters",slug:"mri-medical-image-denoising-by-fundamental-filters",totalDownloads:2564,totalCrossrefCites:17,totalDimensionsCites:30,abstract:"Nowadays Medical imaging technique Magnetic Resonance Imaging (MRI) plays an important role in medical setting to form high standard images contained in the human brain. MRI is commonly used once treating brain, prostate cancers, ankle and foot. The Magnetic Resonance Imaging (MRI) images are usually liable to suffer from noises such as Gaussian noise, salt and pepper noise and speckle noise. So getting of brain image with accuracy is very extremely task. An accurate brain image is very necessary for further diagnosis process. During this chapter, a median filter algorithm will be modified. Gaussian noise and Salt and pepper noise will be added to MRI image. A proposed Median filter (MF), Adaptive Median filter (AMF) and Adaptive Wiener filter (AWF) will be implemented. The filters will be used to remove the additive noises present in the MRI images. The noise density will be added gradually to MRI image to compare performance of the filters evaluation. The performance of these filters will be compared exploitation the applied mathematics parameter Peak Signal-to-Noise Ratio (PSNR).",book:{id:"6144",slug:"high-resolution-neuroimaging-basic-physical-principles-and-clinical-applications",title:"High-Resolution Neuroimaging",fullTitle:"High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications"},signatures:"Hanafy M. Ali",authors:[{id:"213318",title:"Dr.",name:"Hanafy",middleName:"M.",surname:"Ali",slug:"hanafy-ali",fullName:"Hanafy Ali"}]},{id:"41589",doi:"10.5772/50323",title:"The Role of the Amygdala in Anxiety Disorders",slug:"the-role-of-the-amygdala-in-anxiety-disorders",totalDownloads:9671,totalCrossrefCites:4,totalDimensionsCites:28,abstract:null,book:{id:"2599",slug:"the-amygdala-a-discrete-multitasking-manager",title:"The Amygdala",fullTitle:"The Amygdala - A Discrete Multitasking Manager"},signatures:"Gina L. Forster, Andrew M. Novick, Jamie L. Scholl and Michael J. Watt",authors:[{id:"145620",title:"Dr.",name:"Gina",middleName:null,surname:"Forster",slug:"gina-forster",fullName:"Gina Forster"},{id:"146553",title:"BSc.",name:"Andrew",middleName:null,surname:"Novick",slug:"andrew-novick",fullName:"Andrew Novick"},{id:"146554",title:"MSc.",name:"Jamie",middleName:null,surname:"Scholl",slug:"jamie-scholl",fullName:"Jamie Scholl"},{id:"146555",title:"Dr.",name:"Michael",middleName:null,surname:"Watt",slug:"michael-watt",fullName:"Michael Watt"}]},{id:"26258",doi:"10.5772/28300",title:"Excitotoxicity and Oxidative Stress in Acute Ischemic Stroke",slug:"excitotoxicity-and-oxidative-stress-in-acute-ischemic-stroke",totalDownloads:7157,totalCrossrefCites:6,totalDimensionsCites:25,abstract:null,book:{id:"931",slug:"acute-ischemic-stroke",title:"Acute Ischemic Stroke",fullTitle:"Acute Ischemic Stroke"},signatures:"Ramón Rama Bretón and Julio César García Rodríguez",authors:[{id:"73430",title:"Prof.",name:"Ramon",middleName:null,surname:"Rama",slug:"ramon-rama",fullName:"Ramon Rama"},{id:"124643",title:"Prof.",name:"Julio Cesar",middleName:null,surname:"García",slug:"julio-cesar-garcia",fullName:"Julio Cesar García"}]},{id:"62072",doi:"10.5772/intechopen.78695",title:"Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment",slug:"brain-computer-interface-and-motor-imagery-training-the-role-of-visual-feedback-and-embodiment",totalDownloads:1439,totalCrossrefCites:13,totalDimensionsCites:23,abstract:"Controlling a brain-computer interface (BCI) is a difficult task that requires extensive training. Particularly in the case of motor imagery BCIs, users may need several training sessions before they learn how to generate desired brain activity and reach an acceptable performance. A typical training protocol for such BCIs includes execution of a motor imagery task by the user, followed by presentation of an extending bar or a moving object on a computer screen. In this chapter, we discuss the importance of a visual feedback that resembles human actions, the effect of human factors such as confidence and motivation, and the role of embodiment in the learning process of a motor imagery task. Our results from a series of experiments in which users BCI-operated a humanlike android robot confirm that realistic visual feedback can induce a sense of embodiment, which promotes a significant learning of the motor imagery task in a short amount of time. We review the impact of humanlike visual feedback in optimized modulation of brain activity by the BCI users.",book:{id:"6610",slug:"evolving-bci-therapy-engaging-brain-state-dynamics",title:"Evolving BCI Therapy",fullTitle:"Evolving BCI Therapy - Engaging Brain State Dynamics"},signatures:"Maryam Alimardani, Shuichi Nishio and Hiroshi Ishiguro",authors:[{id:"11981",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Ishiguro",slug:"hiroshi-ishiguro",fullName:"Hiroshi Ishiguro"},{id:"231131",title:"Dr.",name:"Maryam",middleName:null,surname:"Alimardani",slug:"maryam-alimardani",fullName:"Maryam Alimardani"},{id:"231134",title:"Dr.",name:"Shuichi",middleName:null,surname:"Nishio",slug:"shuichi-nishio",fullName:"Shuichi Nishio"}]}],mostDownloadedChaptersLast30Days:[{id:"29764",title:"Underlying Causes of Paresthesia",slug:"underlying-causes-of-paresthesia",totalDownloads:192666,totalCrossrefCites:3,totalDimensionsCites:7,abstract:null,book:{id:"1069",slug:"paresthesia",title:"Paresthesia",fullTitle:"Paresthesia"},signatures:"Mahdi Sharif-Alhoseini, Vafa Rahimi-Movaghar and Alexander R. Vaccaro",authors:[{id:"91165",title:"Prof.",name:"Vafa",middleName:null,surname:"Rahimi-Movaghar",slug:"vafa-rahimi-movaghar",fullName:"Vafa Rahimi-Movaghar"}]},{id:"63258",title:"Anatomy and Function of the Hypothalamus",slug:"anatomy-and-function-of-the-hypothalamus",totalDownloads:4558,totalCrossrefCites:6,totalDimensionsCites:12,abstract:"The hypothalamus is a small but important area of the brain formed by various nucleus and nervous fibers. Through its neuronal connections, it is involved in many complex functions of the organism such as vegetative system control, homeostasis of the organism, thermoregulation, and also in adjusting the emotional behavior. The hypothalamus is involved in different daily activities like eating or drinking, in the control of the body’s temperature and energy maintenance, and in the process of memorizing. It also modulates the endocrine system through its connections with the pituitary gland. Precise anatomical description along with a correct characterization of the component structures is essential for understanding its functions.",book:{id:"6331",slug:"hypothalamus-in-health-and-diseases",title:"Hypothalamus in Health and Diseases",fullTitle:"Hypothalamus in Health and Diseases"},signatures:"Miana Gabriela Pop, Carmen Crivii and Iulian Opincariu",authors:null},{id:"57103",title:"GABA and Glutamate: Their Transmitter Role in the CNS and Pancreatic Islets",slug:"gaba-and-glutamate-their-transmitter-role-in-the-cns-and-pancreatic-islets",totalDownloads:3478,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"Glutamate and gamma-aminobutyric acid (GABA) are the major neurotransmitters in the mammalian brain. Inhibitory GABA and excitatory glutamate work together to control many processes, including the brain’s overall level of excitation. The contributions of GABA and glutamate in extra-neuronal signaling are by far less widely recognized. In this chapter, we first discuss the role of both neurotransmitters during development, emphasizing the importance of the shift from excitatory to inhibitory GABAergic neurotransmission. The second part summarizes the biosynthesis and role of GABA and glutamate in neurotransmission in the mature brain, and major neurological disorders associated with glutamate and GABA receptors and GABA release mechanisms. The final part focuses on extra-neuronal glutamatergic and GABAergic signaling in pancreatic islets of Langerhans, and possible associations with type 1 diabetes mellitus.",book:{id:"6237",slug:"gaba-and-glutamate-new-developments-in-neurotransmission-research",title:"GABA And Glutamate",fullTitle:"GABA And Glutamate - New Developments In Neurotransmission Research"},signatures:"Christiane S. Hampe, Hiroshi Mitoma and Mario Manto",authors:[{id:"210220",title:"Prof.",name:"Christiane",middleName:null,surname:"Hampe",slug:"christiane-hampe",fullName:"Christiane Hampe"},{id:"210485",title:"Prof.",name:"Mario",middleName:null,surname:"Manto",slug:"mario-manto",fullName:"Mario Manto"},{id:"210486",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Mitoma",slug:"hiroshi-mitoma",fullName:"Hiroshi Mitoma"}]},{id:"35802",title:"Cross-Cultural/Linguistic Differences in the Prevalence of Developmental Dyslexia and the Hypothesis of Granularity and Transparency",slug:"cross-cultural-linguistic-differences-in-the-prevalence-of-developmental-dyslexia-and-the-hypothesis",totalDownloads:3601,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"673",slug:"dyslexia-a-comprehensive-and-international-approach",title:"Dyslexia",fullTitle:"Dyslexia - A Comprehensive and International Approach"},signatures:"Taeko N. 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Testosterone affects nitric oxide (NO) production and phosphodiesterase type 5 (PDE-5) expression in the corpus cavernosum through molecular pathways, preserves smooth muscle contractility by regulating both contraction and relaxation, and maintains the structure of the corpus cavernosum. Interestingly, testosterone deficiency has relationship to neurological diseases, which leads to ED. Testosterone replacement therapy is widely used to treat patients with testosterone deficiency; however, this treatment might also induce some problems. Basic research suggests that PDE-5 inhibitors, L-citrulline, and/or resveratrol therapy might be effective therapeutic options for testosterone deficiency-induced ED. Future research should confirm these findings through more specific experiments using molecular tools and may shed more light on endocrine-related ED and its possible treatments.",book:{id:"5994",slug:"sex-hormones-in-neurodegenerative-processes-and-diseases",title:"Sex Hormones in Neurodegenerative Processes and Diseases",fullTitle:"Sex Hormones in Neurodegenerative Processes and Diseases"},signatures:"Tomoya Kataoka and Kazunori Kimura",authors:[{id:"219042",title:"Ph.D.",name:"Tomoya",middleName:null,surname:"Kataoka",slug:"tomoya-kataoka",fullName:"Tomoya Kataoka"},{id:"229066",title:"Prof.",name:"Kazunori",middleName:null,surname:"Kimura",slug:"kazunori-kimura",fullName:"Kazunori Kimura"}]}],onlineFirstChaptersFilter:{topicId:"18",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81998",title:"Understanding the Neuropathophysiology of Psychiatry Disorder Using Transcranial Magnetic Stimulation",slug:"understanding-the-neuropathophysiology-of-psychiatry-disorder-using-transcranial-magnetic-stimulatio",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.103748",abstract:"Transcranial magnetic stimulation (TMS) is a safe and non-invasive tool that allows researchers to probe and modulate intracortical circuits. The most important aspect of TMS is its ability to directly stimulate the cortical neurons, generating action potentials, without much effect on intervening tissue. This property can be leveraged to provide insight into the pathophysiology of various neuropsychiatric disorders. Using multiple patterns of stimulations (single, paired, or repetitive), different neurophysiological parameters can be elicited. Various TMS protocol helps in understanding the neurobiological basis of disorder and specific behaviors by allowing direct probing of the cortical areas and their interconnected networks. While single-pulse TMS can provide insight into the excitability and integrity of the corticospinal tract, paired-pulse TMS (ppTMS) can provide further insight into cortico-cortical connections and repetitive TMS (rTMS) into cortical mapping and modulating plasticity.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Jitender Jakhar, Manish Sarkar and Nand Kumar"},{id:"81646",title:"Cortical Plasticity under Ketamine: From Synapse to Map",slug:"cortical-plasticity-under-ketamine-from-synapse-to-map",totalDownloads:15,totalDimensionsCites:0,doi:"10.5772/intechopen.104787",abstract:"Sensory systems need to process signals in a highly dynamic way to efficiently respond to variations in the animal’s environment. For instance, several studies showed that the visual system is subject to neuroplasticity since the neurons’ firing changes according to stimulus properties. This dynamic information processing might be supported by a network reorganization. Since antidepressants influence neurotransmission, they can be used to explore synaptic plasticity sustaining cortical map reorganization. To this goal, we investigated in the primary visual cortex (V1 of mouse and cat), the impact of ketamine on neuroplasticity through changes in neuronal orientation selectivity and the functional connectivity between V1 cells, using cross correlation analyses. We found that ketamine affects cortical orientation selectivity and alters the functional connectivity within an assembly. These data clearly highlight the role of the antidepressant drugs in inducing or modeling short-term plasticity in V1 which suggests that cortical processing is optimized and adapted to the properties of the stimulus.",book:{id:"11374",title:"Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex",coverURL:"https://cdn.intechopen.com/books/images_new/11374.jpg"},signatures:"Ouelhazi Afef, Rudy Lussiez and Molotchnikoff Stephane"},{id:"81582",title:"The Role of Cognitive Reserve in Executive Functioning and Its Relationship to Cognitive Decline and Dementia",slug:"the-role-of-cognitive-reserve-in-executive-functioning-and-its-relationship-to-cognitive-decline-and",totalDownloads:24,totalDimensionsCites:0,doi:"10.5772/intechopen.104646",abstract:"In this chapter, we explore how cognitive reserve is implicated in coping with the negative consequences of brain pathology and age-related cognitive decline. Individual differences in cognitive performance are based on different brain mechanisms (neural reserve and neural compensation), and reflect, among others, the effect of education, occupational attainment, leisure activities, and social involvement. These cognitive reserve proxies have been extensively associated with efficient executive functioning. We discuss and focus particularly on the compensation mechanisms related to the frontal lobe and its protective role, in maintaining cognitive performance in old age or even mitigating the clinical expression of dementia.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Gabriela Álvares-Pereira, Carolina Maruta and Maria Vânia Silva-Nunes"},{id:"81488",title:"Aggression and Sexual Behavior: Overlapping or Distinct Roles of 5-HT1A and 5-HT1B Receptors",slug:"aggression-and-sexual-behavior-overlapping-or-distinct-roles-of-5-ht1a-and-5-ht1b-receptors",totalDownloads:20,totalDimensionsCites:0,doi:"10.5772/intechopen.104872",abstract:"Distinct brain mechanisms for male aggressive and sexual behavior are present in mammalian species, including man. However, recent evidence suggests a strong connection and even overlap in the central nervous system (CNS) circuitry involved in aggressive and sexual behavior. The serotonergic system in the CNS is strongly involved in male aggressive and sexual behavior. In particular, 5-HT1A and 5-HT1B receptors seem to play a critical role in the modulation of these behaviors. The present chapter focuses on the effects of 5-HT1A- and 5-HT1B-receptor ligands in male rodent aggression and sexual behavior. Results indicate that 5-HT1B-heteroreceptors play a critical role in the modulation of male offensive behavior, although a definite role of 5-HT1A-auto- or heteroreceptors cannot be ruled out. 5-HT1A receptors are clearly involved in male sexual behavior, although it has to be yet unraveled whether 5-HT1A-auto- or heteroreceptors are important. Although several key nodes in the complex circuitry of aggression and sexual behavior are known, in particular in the medial hypothalamus, a clear link or connection to these critical structures and the serotonergic key receptors is yet to be determined. This information is urgently needed to detect and develop new selective anti-aggressive (serenic) and pro-sexual drugs for human applications.",book:{id:"10195",title:"Serotonin and the CNS - New Developments in Pharmacology and Therapeutics",coverURL:"https://cdn.intechopen.com/books/images_new/10195.jpg"},signatures:"Berend Olivier and Jocelien D.A. Olivier"},{id:"81093",title:"Prehospital and Emergency Room Airway Management in Traumatic Brain Injury",slug:"prehospital-and-emergency-room-airway-management-in-traumatic-brain-injury",totalDownloads:49,totalDimensionsCites:0,doi:"10.5772/intechopen.104173",abstract:"Airway management in trauma is critical and may impact patient outcomes. Particularly in traumatic brain injury (TBI), depressed level of consciousness may be associated with compromised protective airway reflexes or apnea, which can increase the risk of aspiration or result in hypoxemia and worsen the secondary brain damage. Therefore, patients with TBI and Glasgow Coma Scale (GCS) ≤ 8 have been traditionally managed by prehospital or emergency room (ER) endotracheal intubation. However, recent evidence challenged this practice and even suggested that routine intubation may be harmful. This chapter will address the indications and optimal method of securing the airway, prehospital and in the ER, in patients with traumatic brain injury.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Dominik A. Jakob, Jean-Cyrille Pitteloud and Demetrios Demetriades"},{id:"81011",title:"Amino Acids as Neurotransmitters. The Balance between Excitation and Inhibition as a Background for Future Clinical Applications",slug:"amino-acids-as-neurotransmitters-the-balance-between-excitation-and-inhibition-as-a-background-for-f",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.103760",abstract:"For more than 30 years, amino acids have been well-known (and essential) participants in neurotransmission. They act as both neuromediators and metabolites in nervous tissue. Glycine and glutamic acid (glutamate) are prominent examples. These amino acids are agonists of inhibitory and excitatory membrane receptors, respectively. Moreover, they play essential roles in metabolic pathways and energy transformation in neurons and astrocytes. Despite their obvious effects on the brain, their potential role in therapeutic methods remains uncertain in clinical practice. In the current chapter, a comparison of the crosstalk between these two systems, which are responsible for excitation and inhibition in neurons, is presented. The interactions are discussed at the metabolic, receptor, and transport levels. Reaction-diffusion and a convectional flow into the interstitial fluid create a balanced distribution of glycine and glutamate. Indeed, the neurons’ final physiological state is a result of a balance between the excitatory and inhibitory influences. However, changes to the glycine and/or glutamate pools under pathological conditions can alter the state of nervous tissue. Thus, new therapies for various diseases may be developed on the basis of amino acid medication.",book:{id:"10890",title:"Recent Advances in Neurochemistry",coverURL:"https://cdn.intechopen.com/books/images_new/10890.jpg"},signatures:"Yaroslav R. Nartsissov"}],onlineFirstChaptersTotal:18},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:99,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:290,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:104,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:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:1,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:12,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. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 27th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:52,paginationItems:[{id:"80761",title:"Extractions Methods and Biological Applications of Essential Oils",doi:"10.5772/intechopen.102955",signatures:"Sonu Kumar Mahawer, Himani, Sushila Arya, Ravendra Kumar and Om Prakash",slug:"extractions-methods-and-biological-applications-of-essential-oils",totalDownloads:1,totalCrossrefCites:null,totalDimensionsCites:0,authors:null,book:{title:"Essential Oils - Advances in Extractions and Biological Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11332.jpg",subseries:{id:"15",title:"Chemical Biology"}}},{id:"81545",title:"Physiochemical Properties of Essential Oils and Applications",doi:"10.5772/intechopen.104112",signatures:"Sunil Kumar Yadav",slug:"physiochemical-properties-of-essential-oils-and-applications",totalDownloads:1,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Essential Oils - Advances in Extractions and Biological Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11332.jpg",subseries:{id:"15",title:"Chemical Biology"}}},{id:"81927",title:"Purinergic System in Immune Response",doi:"10.5772/intechopen.104485",signatures:"Yerly Magnolia Useche Salvador",slug:"purinergic-system-in-immune-response",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"80495",title:"Iron in Cell Metabolism and Disease",doi:"10.5772/intechopen.101908",signatures:"Eeka Prabhakar",slug:"iron-in-cell-metabolism-and-disease",totalDownloads:9,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Iron Metabolism - Iron a Double‐Edged Sword",coverURL:"https://cdn.intechopen.com/books/images_new/10842.jpg",subseries:{id:"17",title:"Metabolism"}}}]},overviewPagePublishedBooks:{paginationCount:27,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science and Technology from the Department of Chemistry, National University of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013. She relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the National Institute of Fundamental Studies from April 2013 to October 2016. She was a senior lecturer on a temporary basis at the Department of Food Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is currently Deputy Principal of the Australian College of Business and Technology – Kandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI) Ambassador to Sri Lanka.",institutionString:"Australian College of Business & Technology",institution:null}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University, Kuwait. His research interests include optimization, computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, and intelligent systems. Prof. Sarfraz has been a keynote/invited speaker at various platforms around the globe. He has advised/supervised more than 110 students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He has authored and/or edited around seventy books. Prof. Sarfraz is a member of various professional societies. He is a chair and member of international advisory committees and organizing committees of numerous international conferences. He is also an editor and editor in chief for various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:"Beijing University of Technology",institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Lakhno Igor Victorovich was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPhD – 1999, Kharkiv National Medical Univesity.\nDSc – 2019, PL Shupik National Academy of Postgraduate Education \nLakhno Igor has been graduated from an international training courses on reproductive medicine and family planning held in Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor of the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s a professor of the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education . He’s an author of about 200 printed works and there are 17 of them in Scopus or Web of Science databases. Lakhno Igor is a rewiever of Journal of Obstetrics and Gynaecology (Taylor and Francis), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for DSc degree \\'Pre-eclampsia: prediction, prevention and treatment”. Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: obstetrics, women’s health, fetal medicine, cardiovascular medicine.",institutionString:"V.N. Karazin Kharkiv National University",institution:{name:"Kharkiv Medical Academy of Postgraduate Education",country:{name:"Ukraine"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRZkkQAG/Profile_Picture_2022-05-09T12:55:18.jpg",biography:null,institutionString:null,institution:null},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems. \r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.
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\r\n\tPollution is caused by a wide variety of human activities and occurs in diverse forms, for example biological, chemical, et cetera. In recent years, significant efforts have been made to ensure that the environment is clean, that rigorous rules are implemented, and old laws are updated to reduce the risks towards humans and ecosystems. However, rapid industrialization and the need for more cultivable sources or habitable lands, for an increasing population, as well as fewer alternatives for waste disposal, make the pollution control tasks more challenging. Therefore, this topic will focus on assessing and managing environmental pollution. It will cover various subjects, including risk assessment due to the pollution of ecosystems, transport and fate of pollutants, restoration or remediation of polluted matrices, and efforts towards sustainable solutions to minimize environmental pollution.
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\r\n\tThe environment is subject to severe anthropic effects. Among them are those associated with pollution, resource extraction and overexploitation, loss of biodiversity, soil degradation, disorderly land occupation and planning, and many others. These anthropic effects could potentially be caused by any inadequate management of the environment. However, ecosystems have a resilience that makes them react to disturbances which mitigate the negative effects. It is critical to understand how ecosystems, natural and anthropized, including urban environments, respond to actions that have a negative influence and how they are managed. It is also important to establish when the limits marked by the resilience and the breaking point are achieved and when no return is possible. The main focus for the chapters is to cover the subjects such as understanding how the environment resilience works, the mechanisms involved, and how to manage them in order to improve our interactions with the environment and promote the use of adequate management practices such as those outlined in the United Nations’ Sustainable Development Goals.
",annualVolume:11967,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/39.jpg",editor:{id:"137040",title:"Prof.",name:"Jose",middleName:null,surname:"Navarro-Pedreño",fullName:"Jose Navarro-Pedreño",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRAXrQAO/Profile_Picture_2022-03-09T15:50:19.jpg",institutionString:"Miguel Hernández University of Elche, Spain",institution:null},editorTwo:null,editorThree:null,editorialBoard:[{id:"177015",title:"Prof.",name:"Elke Jurandy",middleName:null,surname:"Bran Nogueira Cardoso",fullName:"Elke Jurandy Bran Nogueira Cardoso",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRGxzQAG/Profile_Picture_2022-03-25T08:32:33.jpg",institutionString:"Universidade de São Paulo, Brazil",institution:null},{id:"211260",title:"Dr.",name:"Sandra",middleName:null,surname:"Ricart",fullName:"Sandra Ricart",profilePictureURL:"https://mts.intechopen.com/storage/users/211260/images/system/211260.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}}]},{id:"40",title:"Ecosystems and Biodiversity",keywords:"Ecosystems, Biodiversity, Fauna, Taxonomy, Invasive species, Destruction of habitats, Overexploitation of natural resources, Pollution, Global warming, Conservation of natural spaces, Bioremediation",scope:"
\r\n\tIn general, the harsher the environmental conditions in an ecosystem, the lower the biodiversity. Changes in the environment caused by human activity accelerate the impoverishment of biodiversity.
\r\n
\r\n\tBiodiversity refers to “the variability of living organisms from any source, including terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; it includes diversity within each species, between species, and that of ecosystems”.
\r\n
\r\n\tBiodiversity provides food security and constitutes a gene pool for biotechnology, especially in the field of agriculture and medicine, and promotes the development of ecotourism.
\r\n
\r\n\tCurrently, biologists admit that we are witnessing the first phases of the seventh mass extinction caused by human intervention. It is estimated that the current rate of extinction is between a hundred and a thousand times faster than it was when man first appeared. The disappearance of species is caused not only by an accelerated rate of extinction, but also by a decrease in the rate of emergence of new species as human activities degrade the natural environment. The conservation of biological diversity is "a common concern of humanity" and an integral part of the development process. Its objectives are “the conservation of biological diversity, the sustainable use of its components, and the fair and equitable sharing of the benefits resulting from the use of genetic resources”.
\r\n
\r\n\tThe following are the main causes of biodiversity loss:
\r\n
\r\n\t• The destruction of natural habitats to expand urban and agricultural areas and to obtain timber, minerals and other natural resources.
\r\n
\r\n\t• The introduction of alien species into a habitat, whether intentionally or unintentionally which has an impact on the fauna and flora of the area, and as a result, they are reduced or become extinct.
\r\n
\r\n\t• Pollution from industrial and agricultural products, which devastate the fauna and flora, especially those in fresh water.
\r\n
\r\n\t• Global warming, which is seen as a threat to biological diversity, and will become increasingly important in the future.
",annualVolume:11968,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/40.jpg",editor:{id:"209149",title:"Prof.",name:"Salustiano",middleName:null,surname:"Mato",fullName:"Salustiano Mato",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLREQA4/Profile_Picture_2022-03-31T10:23:50.png",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},editorTwo:{id:"60498",title:"Prof.",name:"Josefina",middleName:null,surname:"Garrido",fullName:"Josefina Garrido",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRj1VQAS/Profile_Picture_2022-03-31T10:06:51.jpg",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},editorThree:{id:"464288",title:"Dr.",name:"Francisco",middleName:null,surname:"Ramil",fullName:"Francisco Ramil",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003RI7lHQAT/Profile_Picture_2022-03-31T10:15:35.png",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},editorialBoard:[{id:"220987",title:"Dr.",name:"António",middleName:"Onofre",surname:"Soares",fullName:"António Soares",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNtzQAG/Profile_Picture_1644499672340",institutionString:null,institution:{name:"University of the Azores",institutionURL:null,country:{name:"Portugal"}}}]},{id:"41",title:"Water Science",keywords:"Water, Water resources, Freshwater, Hydrological processes, Utilization, Protection",scope:"
\r\n\tWater is not only a crucial substance needed for biological life on Earth, but it is also a basic requirement for the existence and development of the human society. Owing to the importance of water to life on Earth, early researchers conducted numerous studies and analyses on the liquid form of water from the perspectives of chemistry, physics, earth science, and biology, and concluded that Earth is a "water polo". Water covers approximately 71% of Earth's surface. However, 97.2% of this water is seawater, 21.5% is icebergs and glaciers, and only 0.65% is freshwater that can be used directly by humans. As a result, the amount of water reserves available for human consumption is limited. The development, utilization, and protection of freshwater resources has become the focus of water science research for the continued improvement of human livelihoods and society.
\r\n
\r\n\tWater exists as solid, liquid, and gas within Earth’s atmosphere, lithosphere, and biosphere. Liquid water is used for a variety of purposes besides drinking, including power generation, ecology, landscaping, and shipping. Because water is involved in various environmental hydrological processes as well as numerous aspects of the economy and human society, the study of various phenomena in the hydrosphere, the laws governing their occurrence and development, the relationship between the hydrosphere and other spheres of Earth, and the relationship between water and social development, are all part of water science. Knowledge systems for water science are improving continuously. Water science has become a specialized field concerned with the identification of its physical, chemical, and biological properties. In addition, it reveals the laws of water distribution, movement, and circulation, and proposes methods and tools for water development, utilization, planning, management, and protection. Currently, the field of water science covers research related to topics such as hydrology, water resources and water environment. It also includes research on water related issues such as safety, engineering, economy, law, culture, information, and education.
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