Confusion matrix.
\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"6150",leadTitle:null,fullTitle:"Flight Physics - Models, Techniques and Technologies",title:"Flight Physics",subtitle:"Models, Techniques and Technologies",reviewType:"peer-reviewed",abstract:"The book focuses on the synthesis of the fundamental disciplines and practical applications involved in the investigation, description, and analysis of aircraft flight including applied aerodynamics, aircraft propulsion, flight performance, stability, and control. The book covers the aerodynamic models that describe the forces and moments on maneuvering aircraft and provides an overview of the concepts and methods used in flight dynamics. Computational methods are widely used by the practicing aerodynamicist, and the book covers computational fluid dynamics techniques used to improve understanding of the physical models that underlie computational methods.",isbn:"978-953-51-3808-2",printIsbn:"978-953-51-3807-5",pdfIsbn:"978-953-51-4047-4",doi:"10.5772/intechopen.68297",price:119,priceEur:129,priceUsd:155,slug:"flight-physics-models-techniques-and-technologies",numberOfPages:240,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"fa5828a4ee518adf719c68c1e533f3b7",bookSignature:"Konstantin Volkov",publishedDate:"February 14th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6150.jpg",numberOfDownloads:26262,numberOfWosCitations:16,numberOfCrossrefCitations:13,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:33,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:62,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 14th 2017",dateEndSecondStepPublish:"April 4th 2017",dateEndThirdStepPublish:"July 20th 2017",dateEndFourthStepPublish:"September 29th 2017",dateEndFifthStepPublish:"December 20th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"118184",title:"Dr.",name:"Konstantin",middleName:null,surname:"Volkov",slug:"konstantin-volkov",fullName:"Konstantin Volkov",profilePictureURL:"https://mts.intechopen.com/storage/users/118184/images/system/118184.jpeg",biography:"Dr. Volkov is a senior lecturer in Thermofluids at Kingston University (London, UK). He holds a Ph.D. in fluid mechanics. After completing his Ph.D., Dr. Volkov worked at the Baltic State Technical University (Russia), University of Central Lancashire (UK), University of Surrey (UK). His areas of expertise cover multidisciplinary areas: from design and optimization of energy systems to fundamental problems focused on modeling and simulation of turbulent multiphase flows. He is a Chartered Engineer and member of the Institute of Physics, Institution of Mechanical Engineers, and Combustion Institute in the UK. He is the author of more than 120 scientific papers and a member of the editorial board and the scientific committee of a number of journals and conferences.",institutionString:"Kingston University London",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"6",institution:{name:"Kingston University",institutionURL:null,country:{name:"United Kingdom"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"683",title:"Aeronautics",slug:"aeronautics"}],chapters:[{id:"58784",title:"Learning from Nature: Unsteady Flow Physics in Bioinspired Flapping Flight",doi:"10.5772/intechopen.73091",slug:"learning-from-nature-unsteady-flow-physics-in-bioinspired-flapping-flight",totalDownloads:2006,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:0,abstract:"There are few studies on wing flexibility and the associated aerodynamic performance of insect wings during free flight, which are potential candidates for developing bioinspired microaerial vehicles (MAVs). To this end, this chapter aims at understanding wing deformation and motions of insects through a combined experimental and computational approach. Two sets of techniques are currently being developed to make this integration possible: first, data acquisition through the use of high-speed photogrammetry and accurate data reconstruction to quantify the wing and body motions in free flight with great detail and second, direct numerical simulation (DNS) for force measurements and visualization of vortex structures. Unlike most previous studies that focus on the near-field vortex formation mechanisms of a single rigid flapping wing, this chapter presents freely flying insects with full-field vortex structures and associated unsteady aerodynamics at low Reynolds numbers. Our chapter is expected to lead to valuable insights into the underlying physics about flow mechanisms of low Reynolds number flight in nature, which will have great significance to flapping-wing MAV design and optimization research in the future.",signatures:"Haibo Dong, Ayodeji T. Bode-Oke and Chengyu Li",downloadPdfUrl:"/chapter/pdf-download/58784",previewPdfUrl:"/chapter/pdf-preview/58784",authors:[{id:"208608",title:"Dr.",name:"Haibo",surname:"Dong",slug:"haibo-dong",fullName:"Haibo Dong"},{id:"222158",title:"MSc.",name:"Ayodeji",surname:"Bode-Oke",slug:"ayodeji-bode-oke",fullName:"Ayodeji Bode-Oke"},{id:"222159",title:"Dr.",name:"Chengyu",surname:"Li",slug:"chengyu-li",fullName:"Chengyu Li"}],corrections:null},{id:"57483",title:"Helicopter Flight Physics",doi:"10.5772/intechopen.71516",slug:"helicopter-flight-physics",totalDownloads:8219,totalCrossrefCites:3,totalDimensionsCites:9,hasAltmetrics:1,abstract:"This chapter is dedicated to present the principles that constitute the fundamentals of helicopter flight physics, starting from the basics of the main rotor aerodynamics and of the component parts related to flight control. The chapter opens with a short history of helicopter development, taking the date of 13th November 1907 for a reference point; this is the date when the first helicopter flight occurred, having the French man, Paul Cornu, for a pilot. The main constructive solutions for helicopters are presented and the basic equations of fluid mechanics are applied on a helicopter model with one main rotor and tail rotor. Helicopter hovering, vertical flight, and forward flight are approached, too, one by one. Furthermore, the ground effect, autorotation, stability, and helicopter control are focused on. At the end of the chapter, the main factors that determine the helicopter performances are mentioned.",signatures:"Constantin Rotaru and Michael Todorov",downloadPdfUrl:"/chapter/pdf-download/57483",previewPdfUrl:"/chapter/pdf-preview/57483",authors:[{id:"206857",title:"Prof.",name:"Constantin",surname:"Rotaru",slug:"constantin-rotaru",fullName:"Constantin Rotaru"},{id:"209010",title:"Prof.",name:"Michael",surname:"Todorov",slug:"michael-todorov",fullName:"Michael Todorov"}],corrections:null},{id:"57517",title:"Flight Dynamic Modelling and Simulation of Large Flexible Aircraft",doi:"10.5772/intechopen.71050",slug:"flight-dynamic-modelling-and-simulation-of-large-flexible-aircraft",totalDownloads:2734,totalCrossrefCites:4,totalDimensionsCites:10,hasAltmetrics:0,abstract:"The drive for aircraft efficiency and minimum environmental impact is requiring the aerospace industry to generate technologically innovative and highly integrated aircraft concepts. This has changed the approach towards conceptual design and highlighted the need for modular low fidelity aircraft simulation models that not only capture conventional flight dynamics but also provide insight into aeroservoelasticity and flight loads. The key aspects that drive the need for modularity are discussed alongside integration aspects related to coupling aerodynamic models, flight dynamic equations of motion and structural dynamic models. The details of developing such a simulation framework are presented and the utility of such a tool is illustrated through two test cases. The first case focuses on aircraft response to a gust that has a spanwise varying profile. The second investigates aircraft dynamics during control surface failure scenarios. The Cranfield Accelerated Aeroplane Loads Model (CA2LM) forms the basis of the presented discussion.",signatures:"Gaétan Dussart, Vilius Portapas, Alessandro Pontillo and Mudassir\nLone",downloadPdfUrl:"/chapter/pdf-download/57517",previewPdfUrl:"/chapter/pdf-preview/57517",authors:[{id:"206753",title:"Dr.",name:"Mudassir",surname:"Lone",slug:"mudassir-lone",fullName:"Mudassir Lone"},{id:"206955",title:"Mr.",name:"Vilius",surname:"Portapas",slug:"vilius-portapas",fullName:"Vilius Portapas"},{id:"220349",title:"Mr.",name:"Gaetan",surname:"Dussart",slug:"gaetan-dussart",fullName:"Gaetan Dussart"},{id:"220350",title:"Mr.",name:"Alessandro",surname:"Pontillo",slug:"alessandro-pontillo",fullName:"Alessandro Pontillo"}],corrections:null},{id:"56528",title:"Aerodynamic Characteristics and Longitudinal Stability of Tube Launched Tandem-Scheme UAV",doi:"10.5772/intechopen.70228",slug:"aerodynamic-characteristics-and-longitudinal-stability-of-tube-launched-tandem-scheme-uav",totalDownloads:1715,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Tube launched unmanned aerial vehicles (UAV) are often implemented with aerodynamic scheme with forward and rear wings (so-called tandem-scheme). Specificity of such UAV is that immediately after launch, they have a flight path in which wings are turned from position along the fuselage to flight position in which sweep angles are about zero. UAV aerodynamic characteristics for different wing rotation angles were researched by computational fluid dynamics (CFD) methods (Ansys 16 software). Quantitative results prove that UAV is unstable with wings rotation angles up to 60° because rear wings produce lift ahead of center of gravity. Therefore, low time of wings unfolding is required. For high angles of wings rotation (low sweep angles), UAV model is stable in a wide range of angles of attack. Local aerodynamic defects were found in the area of the rotation units of both wings. Longitudinal vortex along the left side of fuselage was observed, but it does not result in significant roll moment. Further research might include UAV dynamics modelling based on calculated aerodynamics characteristics or flight tests.",signatures:"Illia S. Kryvokhatko and Oleksandr M. Masko",downloadPdfUrl:"/chapter/pdf-download/56528",previewPdfUrl:"/chapter/pdf-preview/56528",authors:[{id:"207157",title:"Ph.D.",name:"Illia",surname:"Kryvokhatko",slug:"illia-kryvokhatko",fullName:"Illia Kryvokhatko"},{id:"208137",title:"Mr.",name:"Oleksandr",surname:"Masko",slug:"oleksandr-masko",fullName:"Oleksandr Masko"}],corrections:null},{id:"57229",title:"Airfoil Boundary Layer Optimization Toward Aerodynamic Efficiency of Wind Turbines",doi:"10.5772/intechopen.70895",slug:"airfoil-boundary-layer-optimization-toward-aerodynamic-efficiency-of-wind-turbines",totalDownloads:1492,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter describes the method of airfoil optimization considering boundary layer for aerodynamic efficiency increment. The advantages of laminar boundary layer expansion in airfoil of horizontal axis wind turbine (HAWT) blades are presented as well. The genetic algorithm (GA) optimization interfaced with the flow solver XFOIL was used with multi-objective function. The power performance of turbine with optimized airfoil was calculated by using blade element method (BEM) in software QBlade. The CFD simulation from OpenFOAM® with Spalart-Allmaras turbulence model showed the visualized airflow. The optimized airfoil shows enlarged laminar boundary layer region in all flow regime with a higher aerodynamic efficiency and the increased gliding ratio (GR). The power velocity and annual energy production (AEP) curves show the performance improvement of wind turbine with the optimized airfoil. The boundary layer thickness and skin-friction coefficient values support the decreased drag of the optimized airfoil. The smaller laminar separation bubbles and reduced stall regime of CFD simulations illustrate the desirable aerodynamics of the resulted airfoil.",signatures:"Youjin Kim, Ali Al-Abadi and Antonio Delgado",downloadPdfUrl:"/chapter/pdf-download/57229",previewPdfUrl:"/chapter/pdf-preview/57229",authors:[{id:"208318",title:"M.Sc.",name:"Youjin",surname:"Kim",slug:"youjin-kim",fullName:"Youjin Kim"},{id:"208320",title:"Dr.",name:"Ali",surname:"Al-Abadi",slug:"ali-al-abadi",fullName:"Ali Al-Abadi"},{id:"208321",title:"Prof.",name:"Antonio",surname:"Delgado",slug:"antonio-delgado",fullName:"Antonio Delgado"}],corrections:null},{id:"57091",title:"LES of Unsteady Aerodynamic Forces on a Long-Span Curved Roof",doi:"10.5772/intechopen.70880",slug:"les-of-unsteady-aerodynamic-forces-on-a-long-span-curved-roof",totalDownloads:1493,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The present paper discusses the unsteady aerodynamic forces on long-span curved roofs by using large eddy simulation (LES). The forced vibration test in a turbulent boundary layer is simulated. The models are force vibrated in the first anti-symmetric mode to investigate the influences of a roof’s vibration on the wind pressure and flow field around a vibrating roof. The characteristics of unsteady aerodynamic forces in a wider range of reduced frequency of vibration are also investigated. A comparison between the wind tunnel experiment and the LES indicates that the LES can be used effectively to evaluate the unsteady aerodynamic force.",signatures:"Wei Ding",downloadPdfUrl:"/chapter/pdf-download/57091",previewPdfUrl:"/chapter/pdf-preview/57091",authors:[{id:"214111",title:"Dr.",name:"Wei",surname:"Ding",slug:"wei-ding",fullName:"Wei Ding"}],corrections:null},{id:"57441",title:"Wake Topology and Aerodynamic Performance of Heaving Wings",doi:"10.5772/intechopen.71517",slug:"wake-topology-and-aerodynamic-performance-of-heaving-wings",totalDownloads:1389,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Simulating the three-dimensional flow features generated by heaving wings constitutes a great challenge due to the computational effort required to compute the complex three-dimensional flow produced as a function of the kinematics parameters, wing geometry, and Reynolds number. Hereafter, we study the wake topology generated by oscillating rigid wings and the validity of the Strouhal number as the fundamental parameters used to assess the aerodynamic performance of heaving wings. The unsteady laminar incompressible Navier-Stokes equations are solved on moving overlapping structured grids using a second-order accurate in space and time finite-difference numerical method. The numerical simulations are performed at a Reynolds number of Re = 250 and at different values of Strouhal number and heaving frequency.",signatures:"Joel E. Guerrero",downloadPdfUrl:"/chapter/pdf-download/57441",previewPdfUrl:"/chapter/pdf-preview/57441",authors:[{id:"61098",title:"Dr.",name:"Joel",surname:"Guerrero",slug:"joel-guerrero",fullName:"Joel Guerrero"}],corrections:null},{id:"56629",title:"Aeroelastic Stability of Turboprop Aircraft: Whirl Flutter",doi:"10.5772/intechopen.70171",slug:"aeroelastic-stability-of-turboprop-aircraft-whirl-flutter",totalDownloads:1924,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"This chapter is focused on a specific type of dynamic aeroelastic stability phenomenon—whirl flutter. Whirl flutter is caused by the effect of rotating parts of a turboprop power plant (propeller and, gas turbine engine rotor). The chapter presents fundamental facts regarding the whirl flutter phenomenon, including a historical overview and information regarding the occurrence of whirl flutter in aerospace practice. After that, the physical principles of whirl flutter are explained using a simple mechanical system with two degrees of freedom. Next, an analytical solution to determine the aerodynamic forces caused by the gyroscopic motion on each of the propeller blades is provided and the influences of the main structural parameters on the whirl flutter stability are discussed. The second, practical part is focused on the experimental research of the whirl flutter phenomenon and on the certification-related issues. The methodology of certification according to the FAR/CS 23 regulation standard is demonstrated on the example of a twin wing mounted tractor engine commuter aircraft.",signatures:"Jiří Čečrdle",downloadPdfUrl:"/chapter/pdf-download/56629",previewPdfUrl:"/chapter/pdf-preview/56629",authors:[{id:"207285",title:"Dr.",name:"Jiri",surname:"Cecrdle",slug:"jiri-cecrdle",fullName:"Jiri Cecrdle"}],corrections:null},{id:"58577",title:"Goal- and Object-Oriented Models of the Aerodynamic Coefficients",doi:"10.5772/intechopen.71419",slug:"goal-and-object-oriented-models-of-the-aerodynamic-coefficients",totalDownloads:1939,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Nowadays, aeronautics discovers new ways of flights near the critical regimes, unconventional aircraft forms, utilizing the micro–electro-mechanical technologies in flow and aircraft control, adaptive and morphing structures, using the structures and controls based on the biological principles, developing highly flexible structures, etc. Before deployment, these new technologies and solutions must be evaluated, tested in wide aerodynamic, flight dynamic simulations that require improved and new type of aerodynamic coefficient models. The chapter overviews the applicable models of the aerodynamic coefficients, introduces some new models and demonstrates how the different models can be applied in different goal- and object-oriented solutions. The following will be shortly explained: (i) how the aerodynamic forces and moments are generating, (ii) how the linear, nonlinear, steady, and nonsteady aerodynamic coefficient structures and forms might be modeled, and (iii) how to harmonize the model with the goal and object of investigations.",signatures:"Jozsef Rohacs",downloadPdfUrl:"/chapter/pdf-download/58577",previewPdfUrl:"/chapter/pdf-preview/58577",authors:[{id:"208115",title:"Prof.",name:"Jozsef",surname:"Rohacs",slug:"jozsef-rohacs",fullName:"Jozsef Rohacs"}],corrections:null},{id:"56231",title:"The Effects of Storage on Turbine Engine Fuels",doi:"10.5772/intechopen.69897",slug:"the-effects-of-storage-on-turbine-engine-fuels",totalDownloads:1676,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Modern aviation requires reliable and safe sources of fuel which means fuel is frequently stored for extended periods. In addition, as fuel is used, new fuel is added which is not always compatible with the fuel in the tank. The incompatibility and long-term storage leads to a number of problems that will be addressed in this chapter. Some of the possible changes over time include formation of biofilms, deposit formation, water incorporation and additive depletion. The chemistry and biochemistry of each of these areas will be discussed along with how they might be prevented. New areas of research on low temperature oxidation of trace fuel components and the prevention of bacterial growth will be presented. Other problems are related to the reaction of trace components in the fuel which can lead to oxidation and deposit formation. Trace components also vary based on fuel source and lead to problems in compatibility of different fuels. In addition some of the reactions of fuel additives will be discussed.",signatures:"David W. Johnson",downloadPdfUrl:"/chapter/pdf-download/56231",previewPdfUrl:"/chapter/pdf-preview/56231",authors:[{id:"178441",title:"Dr.",name:"David",surname:"Johnson",slug:"david-johnson",fullName:"David Johnson"}],corrections:null},{id:"57125",title:"12-Pulse Active Rectifier for More Electric Aircraft Applications",doi:"10.5772/intechopen.70882",slug:"12-pulse-active-rectifier-for-more-electric-aircraft-applications",totalDownloads:1678,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The Aircraft industry is moving very quickly towards what it known as More Electric Aircraft (MEA). In a modern aircraft power technology system instead of using a fixed 400 Hz supply, a variable frequency supply (360 to 800 Hz) is used, which is dependent on the aircraft speed. In MEA electrical energy feeds the aircraft subsystems such as the flight control actuation, environmental control system, and utility function instead of mechanical, hydraulic and pneumatic energy. Although the new technology of MEA goes towards variable frequency supply, one of the essential parts of the power distribution systems require DC power sources to feed different DC loads, and a portion of load may require a fixed 400 Hz supply. 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The evaluation of these patients necessitates effective imaging techniques in both diagnosis and long-term follow-up. Even though Cardiac Magnetic Resonance imaging is currently the imaging modality of choice for tissue characterization, advanced echocardiography represents a modern alternative. Speckle tracking echocardiography can be used to assess myocardial deformation at both segmental and global levels. Since distinct myocardial pathologies affect deformation differently, information about the underlying tissue can be offered by strain imaging. Echocardiography advances also show promising results in the improvement of diagnostic accuracy, management, and follow-up and a major advantage of echocardiography over other imaging modalities is the ability to use it in real-time, in the cardiac catheterization laboratory, allowing for the performance of imaging immediately before, during, and after interventional procedures. Furthermore, the prevalence of adult congenital heart disease continues to grow due to advances in surgical and diagnostic techniques. Echocardiography has proven to be a useful tool in the diagnosis and follow-up of these patients, both after percutaneous and surgical procedures, and its utility has expanded significantly due to the development of better technology. In addition, stress echocardiography could be useful in the evaluation of several cardiac diseases and should be preferred over other imaging modalities due to the lower cost, wider availability, and radiation-free nature.
\r\n\tThis book intends to provide the reader with a comprehensive overview of the current state-of-the-art novel imaging techniques by focusing on the most important evidence-based developments in this area.
Probabilistic latent semantic analysis (PLSA: probabilistic latent semantic analysis) [1] and latent Dirichlet analysis (LDA: latent Dirichlet allocation) [2] are known as topic models to analyze count data such as documents (text data). In a smart city, it is important to collect and analyze citizens’ voices to discover their concerns and issues. Topic modeling is effective for the above analysis because it can extract topics from a collection of documents. However, when estimating parameters (solutions) in topic models, various solutions are reached due to differences in algorithms and initial values. There could exist a lot of local optimal solutions that are distinct but are equally optimized in the objective function (Figure 1). Since each of these solutions presents an interpretation of data, they are meaningful and worth using. In order to select a solution suitable for the purpose from among the various solutions, it is necessary to know what kind of solutions exist. This chapter introduces methods for analyzing diverse solutions and obtaining an overall picture of the solutions.
An objective function for topic model
The solution, which is the set of parameters estimated in topic models, has a topic distribution
The method [3, 4] using topic distribution
Word distribution
The related studies are shown below. As for analyzing multiple solutions, there are studies on clustering. In these studies, the Rand index or NMI is used to define the distance or similarity between solutions. Then, a non-redundant alternative solution for a given solution is found [8], several non-redundant are searched [9], and solutions are visualized by dendrogram [10]. This chapter focuses on topic model which includes hard clustering as a special case. The article [11] is a study of visualizing the solution of a topic model, but for a single solution. Few studies analyze and visualize multiple solutions in topic models.
The experiments deal mainly with text data of news articles and show the distribution of the solutions and the typical topics in the topic model. We expect that the proposed methods will contribute to the discovery of problems in smart cities.
Topic models could be described by a generative probability model as shown in Figure 2. Shaded circles represent observed variables and white circles represent unobserved variables. The square plates represent repetitions, and the number in the lower right corner indicates the number of repetitions. Items inside the plate are conditionally independent given the variable outside the plate. In fact, the observed variables are “words” and the observed data, which is the accumulation of these words, is a document.
Graphical model for topic model.
Assume that there are topics
Given the probability
where
The first term is a constant for the observed value vectors
There are various algorithms for estimating the model parameters, including collapsed Gibbs sampling (CGS) [12], variational Bayesian estimation (VB) [2], collapsed variational Bayesian estimation (CVB) [13], maximum likelihood estimation (ML) [1], maximum a posteriori probability (MAP) estimation [14]. From these, we use MAP estimation, CGS as a sampling approximation method, and CVB0 [15], which uses zero-order approximation of CVB, as a variational approximation method to estimate diverse solutions.
The update formulas used in the iterations in MAP estimation of
where
The updates at each iteration in CGS estimation can be written as
using the sampled topic set. When sampling for the
where
In CVB0 estimation, for the
The expectations of
Estimation in CVB0 proceeds by alternating between estimating
Initial value setting using information-theoretic clustering is applicable in MAP estimation. It was shown that weighted information-theoretic clustering is a special case of topic models (see Appendix A), and it was confirmed that using the clustering results for initial value setting yields a better solution than using random initial value setting [16]. Specifically, the method is to smooth the word distribution obtained from clustering by adding a small value and use it as the initial word distribution
This section presents the method [3, 4] to assign coordinate values to solutions using the topic distribution
The Normalized Mutual Information (NMI) is known as external criterion for evaluation of clustering [5], but can be applied to solutions in topic models as follows.
There are two solution,
which represents the overlap between
Sum | |||||
19.1 | 2.3 | 7.4 | 312.3 | ||
24.2 | 0.2 | 4.9 | 319.5 | ||
1.2 | 4.8 | 19.0 | 293.6 | ||
Sum | 213.3 | 196.7 | 253.2 |
Confusion matrix.
Table 1 is a frequency distribution. Dividing this by the total frequency yields a two-dimensional probability distribution. The simultaneous probability is given by
Using the simultaneous probabilities
where
Since a symmetrical relationship
Let
Let
Since
where
and we obtain
The vectors
This section introduces the method [7] for representing typical solutions and the topics contained therein, as well as possible topics that can be extracted, by means of the word distribution
Information-theoretic clustering [16, 17] based on similarity of probability distributions is used to estimate representative word distributions from word distributions
where
Representative word distributions with similar relationships are then connected to form
In this network, there are groups of representative word distributions that are similar to each other in areas of high edge density. These groups are extracted by the clustering algorithm based on maximizing modularity [18].
Since word distributions belong to one of the groups via the representative word distribution, solutions can be typified by the frequency distribution of the group to which the included word distribution belongs. As a result, several typical solutions can be found by the analysis using word distributions.
We used three text data sets: NYtimes [19], 20News [20], Nips [19]. Stop-words included in 20News and documents with fewer than 40 words were removed. The characteristics of data sets actually used are shown in Table 2.
Each data set was separated into a training set for 90% of its documents and a test set for 10%. The test set were used to evaluate parameter estimation. The parameters
The number of topics was set to
We evaluated a total of 800 solutions
Name | Number of documents (N) | Number of word types (M) |
---|---|---|
NYtimes | 296,829 | 101,631 |
20News | 14,111 | 60,149 |
Nips | 1491 | 12,375 |
Characteristics of data sets used in experiments.
Name | MapRnd | MapCL | Cgs | Cvb0 |
---|---|---|---|---|
NYtimes | 5983.8 | 5959.3 | 5991.7 | 5973.7 |
20News | 5693.3 | 5662.4 | 5772.7 | 5688.9 |
Nips | 1993.3 | 1989.3 | 2006.4 | 1982.3 |
Perplexities archived in the four methods.
We assigned coordinate values to the solutions by the analysis using topic distributions and applied principal component analysis PCA to visualize them in Figure 3.
Visualizations based on principal component analysis of solutions for NYtimes (top), 20News (bottom left), and Nips (bottom right).
In Figure 3, we see that MapCL is biased toward the range of large (20News) and small (Nips) values of the first principal component. This indicates that the solutions are method-dependent. These diagrams are useful to get an overall picture of the distribution of solutions, and by choosing solutions far from each other (e.g., top left, top right, center, bottom left, bottom right), a non-redundant solution set is obtained. However, how they differ is difficult for humans to understand. Therefore, it is important to analyze using word distributions that are easy for humans to understand.
For the analysis using word distributions, 100 representative word distributions were first obtained by information-theoretic clustering (see Eq. (19)) from a total of 8000 word distributions, 10 in each solution. The frequency distributions of JS divergence between word distributions and JS divergence between representative word distributions are shown in Figure 4 (left). Since the two frequency distributions are well matched, and the representative word distributions preserve the relationships among the word distributions,
Frequency distribution of JS divergence between word distributions for all solutions (left) and inside solutions (right).
Figure 4 (right) shows the frequency distribution of the JS divergence between word distributions inside each solution and between representative word distributions corresponding to the word distributions. The word distributions in the solution of topic models are estimated to be different from each other in the sense of optimizing the objective function (Eq. (2)). Therefore, the JS divergence between word distributions inside the solution has a larger value. Considering this figure, we determined that word distributions are similar to each other if the JS divergence between them is equal to 0.1 or less.
We then connected representative word distributions with similar relationships for the NYtimes data set and represented them as a similarity network of representative word distributions in Figure 5. From this network, the clustering algorithm based on maximizing modularity [18] was used to extract groups (g1 to g15) with representative word distributions that were in regions of high edge density. There were 10 large groups with four or more vertices, the same as the number of topics
Similarity network of representative word distributions for NYtimes.
Table 4 shows the high-frequency words in the representative word distributions for each large group (g1 to g10) in NYtimes. For the adjacent and ambiguous groups (g6 to g10) in Figure 5, two representative word distributions were chosen to represent the variation within the group, and the characteristic words representing the differences are shown in bold. The words “school student” appear in g6, g7, and g8, suggesting that there are a variety of topics related to these words. We see that the word “drug” is listed with “doctor” in g6, but with “case” in g7.
gn | fn | High-frequency words |
---|---|---|
g1 | f41 | Team game season player play games point run coach win won right hit left |
g2 | f40 | Company percent million companies market stock business billion money |
g3 | f22 | Com web computer site information www mail online .internet internet |
g4 | f55 | .bush president campaign .george_bush .al_gore election political vote |
g5 | f36 | Official government .united_states .u_s military war attack palestinian leader |
g6 | f2 | |
f70 | School student drug patient percent program doctor women study problem | |
g7 | f16 | |
f51 | ||
g8 | f76 | School student children family women home friend father mother parent |
f61 | ||
g9 | f20 | Home family room building friend night house children town .new_york |
f37 | ||
g10 | f6 | Water food room cup minutes small building hour restaurant add large home |
f72 |
High-frequency words in the representative word distribution for each group in NYtimes.
The representative word distributions within these groups are somewhat different, and it is not easy to select the appropriate one. Therefore, the proposed method of representing relationships in a human-understandable form may be useful for users. If we were to name the groups according to the high-frequency words, they would be, in order,
We typified solutions by the frequency distribution of the group to which the word distribution in the solution belongs. We call the types of frequency distributions
g1 | g2 | g3 | g4 | g5 | g6 | g7 | g8 | g9 | g10 | Number | |
---|---|---|---|---|---|---|---|---|---|---|---|
Pattern 1 | 2 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 125 |
Pattern 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 81 |
Pattern 3 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 67 |
Pattern 4 | 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 66 |
Pattern 5 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 46 |
High-frequently occurring patterns. “Number” indicates the number of solutions belonging to the pattern.
Group name | High-frequency words |
---|---|
Sports | Season team game run games hit player inning play baseball right |
Sports | Team game season player play point games coach win won yard |
Markets | Company percent million companies market business stock billion |
Presidential election | .bush president campaign .george_bush .al_gore election political |
International conflicts | Official government .united_states attack .u_s military war leader |
Health care | Drug patient doctor percent problem cell research study health |
School | School student case law court lawyer children police official family |
Entertainment | Show book film movie music look play women friend character |
Housing | Car building home room water hour house area air town miles |
Food | Cup food minutes add oil tablespoon wine sugar pepper water |
Group name | High-frequency words |
---|---|
Sports | Team game season player play games point run coach win football |
Markets | Percent company million companies market stock business billion |
IT | Com web computer information site www mail online .internet |
Presidential election | .bush campaign president .george_bush .al_gore election political |
International conflicts | Official government .united_states attack .u_s military war leader |
Health care | Drug patient doctor health research study scientist cell problem |
School | School student law case court lawyer official children police family |
Entertainment | Show film book movie look women play music friend character |
Housing | Car building home room water hour house town miles area air |
Food | Cup food minutes add oil wine tablespoon sugar pepper |
High-frequency words in typical solutions for pattern 1 (top) and pattern 2 (bottom).
As Table 6 (top) shows, the pattern 1 has no topics on IT and instead has two topics on sports. The pattern 3 has no topics related to housing in g9 (see Table 5). If we were to have the right set of topics for a smart city, we should choose a solution from the pattern 2 (see Table 6 (bottom)), which includes the all groups, rather than focusing on sports.
Figure 6 shows similarity networks of representative word distributions for the 20News and Nips data sets. As these figures show, for both data sets, the number of large groups was 10, the same as the number of topics.
Similarity networks of representative word distributions for 20News (top) and Nips (bottom).
The 20News and Nips solutions were typified based on the frequency distribution of the groups, and the top five most frequently occurring patterns are shown in Table 7. As the tables show, these patterns also consist of combinations of the large groups (g1 to g10). Since large groups play a role in many solutions, a solution with one word distribution for all large groups would be the solution of interest. However, such a solution is not necessarily the most common solution, nor is it necessarily the optimal solution. For reference, Table 8 shows examples of such solutions. The 20News example belongs to the most common pattern (pattern 1) and could be a candidate for a good solution. In the 20News data set, documents are labeled with the newsgroup to which they belong [20]. Using the label information, we can find the high-frequency words of the documents belonging to each newsgroup (see Table 9). Note that topic modeling does not use label information. Comparing the topics in Table 8 (top) with those in Table 9, many of them are associated. For example, g1 is associated with n11, g3 with n8–9, g4 with n15, g5 with n14, and g9 with n2. This association with the actual newsgroups would supports that the solution in Table 8 (top) is appropriate. The Nips example belongs to the fourth most common pattern, and a solution to the other patterns would be appropriate.
g1 | g2 | g3 | g4 | g5 | g6 | g7 | g8 | g9 | g10 | Number | |
---|---|---|---|---|---|---|---|---|---|---|---|
Pattern 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 215 |
Pattern 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 44 |
Pattern 3 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 39 |
Pattern 4 | 1 | 2 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 29 |
Pattern 5 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 0 | 1 | 1 | 27 |
g1 | g2 | g3 | g4 | g5 | g6 | g7 | g8 | g9 | g10 | Number | |
---|---|---|---|---|---|---|---|---|---|---|---|
Pattern 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 0 | 0 | 1 | 119 |
Pattern 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 81 |
Pattern 3 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 0 | 1 | 67 |
Pattern 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 64 |
Pattern 5 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 56 |
High-frequently occurring patterns in 20News (top) and Nips (bottom).
Group | High-frequency words |
---|---|
g1 | Game team writes year games article hockey play good players season ca time win |
g2 | God people jesus writes article christian bible church christ time good life christians |
g3 | Writes car article good apr bike dod ve time ca cars back engine ll make front thing |
g4 | Space nasa writes earth article gov launch system time orbit science shuttle moon |
g5 | Writes article people health medical disease time cramer patients cancer study doctor |
g6 | People writes article government gun president make fbi time mr state law fire guns |
g7 | People israel armenian jews turkish writes armenians article war israeli jewish arab |
g8 | Key encryption chip writes government information clipper keys system article |
g9 | File image window program ftp windows files graphics version server jpeg display |
g10 | Dos windows drive writes card scsi system article mb pc problem mac disk bit work |
Group | High-frequency words |
---|---|
g1 | Circuit signal analog chip system output current neural input neuron voltage filter |
g2 | Speech word recognition system model context network hmm training sequence set |
g3 | Function learning algorithm point vector result error bound case equation set |
g4 | Image object images recognition feature map features representation set vector point |
g5 | Neuron cell model input pattern network activity synaptic visual stimulus firing |
g6 | Model data distribution parameter gaussian algorithm function method mean |
g7 | Network unit input weight neural output learning training hidden layer error net |
g8 | Set training data error algorithm classifier performance learning classification test |
g9 | Model control system motion direction position movement motor eye learning field |
g10 | Learning action function algorithm problem reinforcement policy system optimal |
High-frequency words in solutions having one word distribution for all large groups for 20News (top) and Nips (bottom).
Newsgroup | High-frequency words |
---|---|
(n1) alt.atheism | God writes people article atheism religion time evidence jesus |
(n2) comp.graphics | Image graphics jpeg file bit images software data files ftp format |
(n3) comp.os.ms-windows.misc | Windows file dos writes article files ms os problem win program |
(n4) comp.sys.ibm.pc.hardware | Drive scsi card mb ide system controller bus pc writes disk dos |
(n5) comp.sys.mac.hardware | Mac apple writes drive system problem article mb monitor mhz |
(n6) comp.windows.x | Window file server windows program dos motif sun display |
(n7) misc.forsale | Sale shipping offer mail price drive condition dos st email |
(n8) rec.autos | Car writes article cars good engine apr ve people time ford speed |
(n9) rec.motorcycles | Writes bike article dod ca apr ve ride good time bmw back riding |
(n10) rec.sport.baseball | Writes year article game team baseball good games time hit players |
(n11) rec.sport.hockey | Game team hockey writes play ca games article season year nhl |
(n12) sci.crypt | Key encryption government chip writes clipper people article keys |
(n13) sci.electronics | Writes article power good ve work ground time circuit ca make |
(n14) sci.med | Writes article people medical health disease time cancer patients |
(n15) sci.space | Space writes nasa article earth launch orbit shuttle time system |
(n16) soc.religion.christian | God people jesus church christ writes christian christians bible |
(n17) talk.politics.guns | Gun people writes article guns fbi government fire time weapons |
(n18) talk.politics.mideast | People israel armenian writes turkish jews article armenians israeli |
(n19) talk.politics.misc | People writes article president government mr stephanopoulos |
(n20) talk.religion.misc | God writes people jesus article bible christian good christ life time |
High-frequency words in the 20News data set.
In order to select an appropriate solution, it is crucial to determine that the solution is consistent with the objective. Selecting a pattern that matches the objective would be the first step in finding a solution. After that, the solution can be obtained by refining the word distributions that the solution should have by selecting representative word distributions for each group, while confirming the objective.
Since determining the degree of consistency is a relative issue, it is essential to know the overall picture of the solutions and possible word distributions in them.
Figure 7 shows a visualization of the solutions using the patterns assigned by the analysis based on word distribution and the coordinate values assigned by the analysis based on topic distribution. Figure 7 (left) is more useful than Figure 7 (right) (equivalent to Figure 3 (top)) in finding a solution, as it provides additional information on the patterns needed in the first step.
Visualization based on principal component analysis of solutions in NYtimes. Patterns 1–5 (left), for all (right).
The solutions for patterns 1–5 in Figure 7 (left) are grouped together in each pattern. It means that the solutions with the same pattern are also similar to each other in topic distribution. It is interesting to confirm that word and topic distributions are related.
As the experimental results show, analysis using word distribution will play a major role in the search for a solution. This is because the results can be presented in a way that is understandable to humans and can be compared to the objective. Analysis using topic distribution will play the role of a “map” that provides another point of view when a decision is not clear. A map that provides a view of all the solutions should be useful.
It has been reported that information-theoretic clustering outperforms spherical clustering when targeting text data [17]. Topic modeling is an extension of information-theoretic clustering (see Appendix A), which is why we apply this technique to document analysis. The solutions obtained through modeling are diverse. In past studies, however, diversity has not been adequately considered. This chapter introduced methods for analyzing diverse solutions and obtaining an overall picture of the solutions. Also, we showed effectiveness of the methods through experiments.
In this study, we found that there are many solutions that are different from each other in topic models. It is difficult to obtain an appropriate solution by chance. Furthermore, problems in the world, not to mention smart cities, are complex and change rapidly, so there is a high risk of missing important topics. The proposed analysis methods should be useful in the search for solutions. There are various extensions of topic models, such as dynamic topic models [21], but even there, a diversity of solutions may exist. The approach presented in this chapter may also be used for analyses using such models.
This work was supported by JSPS KAKENHI Grant Number 18 K11442.
We present the objective function of weighted information-theoretic clustering (ITC) [16, 17] and show that it is a special case of the objective function of topic models.
Assume that there are
where
The interior of Eq. (22) can be transformed as
where the second term is independent of the clustering result. Thus, the function to be minimized can be expressed as
Meanwhile, the function to be maximized in topic models is
which is the second term in Eq. (2). Applying the hard clustering constraint:
we obtain
Since minimization of Eq. (24) is equivalent to maximization of Eq. (27), topic modeling includes weighted ITC as a special case and is an extension of it.
For weighted ITC, the learning algorithm needs to be changed from ITC [17]. Basically, it should treat documents differently based on the number of words they contain. There are two ways to achieve this: one is to select documents with a probability proportional to the number of words they contain, and the other is to increase the learning rate of competitive learning in proportion to the number of words in the documents selected at learning time. In this experiment, we employed the latter [16].
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:null,institution:null},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"417317",title:"Mrs.",name:"Chiedza",middleName:null,surname:"Elvina Mashiri",slug:"chiedza-elvina-mashiri",fullName:"Chiedza Elvina Mashiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"352140",title:"Dr.",name:"Edina",middleName:null,surname:"Chandiwana",slug:"edina-chandiwana",fullName:"Edina Chandiwana",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"342259",title:"B.Sc.",name:"Leonard",middleName:null,surname:"Mushunje",slug:"leonard-mushunje",fullName:"Leonard Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"347042",title:"Mr.",name:"Maxwell",middleName:null,surname:"Mashasha",slug:"maxwell-mashasha",fullName:"Maxwell Mashasha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"2941",title:"Dr.",name:"Alberto J.",middleName:"Jorge",surname:"Rosales-Silva",slug:"alberto-j.-rosales-silva",fullName:"Alberto J. Rosales-Silva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"437913",title:"Dr.",name:"Guillermo",middleName:null,surname:"Urriolagoitia-Sosa",slug:"guillermo-urriolagoitia-sosa",fullName:"Guillermo Urriolagoitia-Sosa",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"435126",title:"Prof.",name:"Joaquim",middleName:null,surname:"José de Castro Ferreira",slug:"joaquim-jose-de-castro-ferreira",fullName:"Joaquim José de Castro Ferreira",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"437899",title:"MSc.",name:"Miguel Angel",middleName:null,surname:"Ángel Castillo-Martínez",slug:"miguel-angel-angel-castillo-martinez",fullName:"Miguel Angel Ángel Castillo-Martínez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"289955",title:"Dr.",name:"Raja",middleName:null,surname:"Kishor Duggirala",slug:"raja-kishor-duggirala",fullName:"Raja Kishor Duggirala",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jawaharlal Nehru Technological University, Hyderabad",country:{name:"India"}}}]}},subseries:{item:{id:"22",type:"subseries",title:"Applied Intelligence",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence",scope:"This field is the key in the current industrial revolution (Industry 4.0), where the new models and developments are based on the knowledge generation on applied intelligence. The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11418,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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