Arabic–Roman letters mapping table
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\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:"9984",leadTitle:null,fullTitle:"Geophysics and Ocean Waves Studies",title:"Geophysics and Ocean Waves Studies",subtitle:null,reviewType:"peer-reviewed",abstract:"The book “Geophysics and Ocean Waves Studies” presents the collected chapters in two sections named “Geophysics” and “Ocean Waves Studies”. The first section, “Geophysics”, provides a thorough overview of using different geophysical methods including gravity, self-potential, and EM in exploration. Moreover, it shows the significance of rock physics properties and enhanced oil recovery phases during oil reservoir production. The second section, “Ocean Waves Studies”, is intended to provide the reader with a strong description of the latest developments in the physical and numerical description of wind-generated and long waves, including some new features discovered in the last few years. The section is organized with the aim to introduce the reader from offshore to nearshore phenomena including a description of wave dissipation and large-scale phenomena (i.e., storm surges and landslide-induced tsunamis). This book shall be of great interest to students, scientists, geologists, geophysicists, and the investment community.",isbn:"978-1-78985-445-9",printIsbn:"978-1-78985-372-8",pdfIsbn:"978-1-78985-446-6",doi:"10.5772/intechopen.87807",price:119,priceEur:129,priceUsd:155,slug:"geophysics-and-ocean-waves-studies",numberOfPages:184,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"271d086381f9ba04162b0dc7cd57755f",bookSignature:"Khalid S. Essa, Marcello Di Risio, Daniele Celli and Davide Pasquali",publishedDate:"March 17th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/9984.jpg",numberOfDownloads:5113,numberOfWosCitations:0,numberOfCrossrefCitations:9,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:16,numberOfDimensionsCitationsByBook:1,hasAltmetrics:0,numberOfTotalCitations:25,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 16th 2019",dateEndSecondStepPublish:"March 24th 2020",dateEndThirdStepPublish:"May 23rd 2020",dateEndFourthStepPublish:"August 11th 2020",dateEndFifthStepPublish:"October 10th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"102766",title:"Prof.",name:"Khalid S.",middleName:null,surname:"Essa",slug:"khalid-s.-essa",fullName:"Khalid S. Essa",profilePictureURL:"https://mts.intechopen.com/storage/users/102766/images/system/102766.jpg",biography:"Dr. Khalid S. Essa obtained his B.Sc. with honors (1997), M.Sc. (2001) and Ph.D. (2004) in Geophysics from the Faculty of Science, Cairo University. He joined the staff of Cairo University (1997) and was appointed a research Professor of potential field methods in the Department of Geophysics (2014). He has undertaken affiliated post-doctoral visits to Strasbourg University, France (2018-2019), Charles University in Prague, Czech (2014-2015) and Western Michigan University, USA (2006-2007). He has authored more than 70 technical papers and served as an Editor and external reviewer for many top journals. He attended several International Geophysical Conferences in USA, Australia and France. He was a member in SEG, AGU, AAPG, EAGE and EGS. Also, he is a member of the National committee for Geodesy and Geophysics, Academy of Scientific Research and Technology, Egypt (2020-2023) and member of the Petroleum and Mineral Resources Research Council, Sector of Quality Councils, Academy of Scientific Research and Technology, Egypt (2018-2021). He has been awarded the Award of Prof. Nasry Matari Shokry in Applied Geology, Academy of Scientific Research & Technology (2017) and the Award of Cairo University for Scientific Excellence in Interdisciplinary, Multidisciplinary and Future Sciences (2017).",institutionString:"Cairo University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Cairo University",institutionURL:null,country:{name:"Egypt"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"15209",title:"Prof.",name:"Marcello",middleName:null,surname:"Di Risio",slug:"marcello-di-risio",fullName:"Marcello Di Risio",profilePictureURL:"https://mts.intechopen.com/storage/users/15209/images/system/15209.jpg",biography:"Marcello Di Risio is a full professor (academic discipline: Hydraulic Structures, Maritime Engineering, and Hydrology) at the Civil, Construction-Architectural and Environmental Engineering Department of the University of L'Aquila, Italy. He is the Head of the Laboratory of Environmental and Maritime Hydraulics (LIam) at the same department. He received his Ph.D. in 2005 from the Roma Tre University, Italy and held a research grant at the Tor Vergata University of Rome, Italy. From 2008-2020, he served as a researcher and associate professor at the University of L'Aquila, Italy. He has acted as chief scientist and coordinator of several research projects, funded by public and private bodies. With many years of experience in the field of hydraulic and maritime construction, his main research topics are: mathematical and experimental modeling of coastal morphodynamic and hydrodynamic phenomena, landslide-generated waves, maritime and hydraulic works, hydraulic and coastal risk analysis, real-time identification systems of tidal waves, real-time forecasting systems of wave motion and water levels, and development of devices for energy extraction from waves.",institutionString:"University of L'Aquila",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of L'Aquila",institutionURL:null,country:{name:"Italy"}}},coeditorTwo:{id:"309494",title:"Dr.",name:"Daniele",middleName:null,surname:"Celli",slug:"daniele-celli",fullName:"Daniele Celli",profilePictureURL:"https://mts.intechopen.com/storage/users/309494/images/system/309494.jpg",biography:"Daniele Celli is currently a Post Doctorate student at the Department of Civil, Construction-Architectural and Environmental Engineering (DICEAA) of the University of L’Aquila. In 2015, he received his Master’s Degree (cum laude) in Civil Engineering and in 2019 he received his Ph.D. (cum laude) in Risk and Environmental, Territorial and Building Development at the Technical University of Bari. His research interests are focused on coastal structures, water wave generation and propagation, physical and numerical modeling of wave-structure interaction and wave-soil-structure interaction, wave energy assessment and extraction, coastal hydrodynamics, and morphodynamics.",institutionString:"University of L'Aquila",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of L'Aquila",institutionURL:null,country:{name:"Italy"}}},coeditorThree:{id:"309493",title:"Dr.",name:"Davide",middleName:null,surname:"Pasquali",slug:"davide-pasquali",fullName:"Davide Pasquali",profilePictureURL:"https://mts.intechopen.com/storage/users/309493/images/system/309493.jpg",biography:"Davide Pasquali is currently a Research Fellow in the Department of Civil, Construction-Architectural and Environmental Engineering (DICEAA) at the University of L’Aquila. In 2011, he received his Master’s Degree (cum laude) in Civil Engineering and in 2015 he received his Ph.D. in Civil Engineering at the University of L’Aquila. His research interests are focused on water wave generation and propagation, coastal hydrodynamic and morphodynamic, physical and numerical modeling of wave-structure interaction, wave energy assessment and extraction, risk analysis, and marine sediments transport.",institutionString:"University of L'Aquila",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of L'Aquila",institutionURL:null,country:{name:"Italy"}}},coeditorFour:null,coeditorFive:null,topics:[{id:"225",title:"Geophysics",slug:"geophysics"}],chapters:[{id:"71855",title:"Combined Gravity or Self-Potential Anomaly Formula for Mineral Exploration",doi:"10.5772/intechopen.92139",slug:"combined-gravity-or-self-potential-anomaly-formula-for-mineral-exploration",totalDownloads:335,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"A combined gravity and/or self-potential anomaly formula is utilized to estimate the model parameters of the buried geologic structures represented by simple geometric. The simple geometric shapes (spheres, cylinders, and sheets) are not really found but often applied to reduce the nonuniqueness in interpreting the gravity and self-potential data. Numerous approaches through the combined formula such as least squares, Werner deconvolution, and the particle swarm optimization method are used. The application of these methods was demonstrated by applying a synthetic gravity and self-potential example without and with 10% random noise to compare their efficiency in estimating the model parameters of the buried structures. Besides, they were applied to two field data for mineral exploration. The appraised model parameter values from each method were compared together and with those published in literature.",signatures:"Khalid S. Essa and Mahmoud Elhussein",downloadPdfUrl:"/chapter/pdf-download/71855",previewPdfUrl:"/chapter/pdf-preview/71855",authors:[{id:"102766",title:"Prof.",name:"Khalid S.",surname:"Essa",slug:"khalid-s.-essa",fullName:"Khalid S. Essa"},{id:"208891",title:"Dr.",name:"Mahmoud",surname:"Elhussein",slug:"mahmoud-elhussein",fullName:"Mahmoud Elhussein"}],corrections:null},{id:"72198",title:"Long Wire Electromagnetic Measurements (Turam EM)",doi:"10.5772/intechopen.91387",slug:"long-wire-electromagnetic-measurements-turam-em-",totalDownloads:532,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In Scandinavia, EM measurements have traditionally been popular in sulfide ore exploration. The EM methods using a stationary cable loop or a long wire on the ground surface were called Turam. The wire was grounded by electrodes at the ends. The name, Turam meaning two coils, got the name after the measurement system using two coils measuring the quotient and the phase difference of the vertical field. The measurements were performed in the frequency domain, with frequencies around 400 Hz. Using a large cable loop or a long wire grounded at both ends has advantages as energizing transmitter, which should be utilized in deep exploration. The fall-off rate for the primary field is small, and the electric field can be directed in line with strike direction or the direction of the axis of the mineralization. Examples of the interaction between the energizing cable and the conducting half-space are illustrated by computed models. The grounding points can be shifted with repeated measurements for each grounding position. Both man-made and geological noise can be reduced in this way. Field examples are given in the chapter.",signatures:"Ole Bernt Lile",downloadPdfUrl:"/chapter/pdf-download/72198",previewPdfUrl:"/chapter/pdf-preview/72198",authors:[{id:"313912",title:"Prof.",name:"Ole",surname:"Bernt Lile",slug:"ole-bernt-lile",fullName:"Ole Bernt Lile"}],corrections:null},{id:"72011",title:"Rock Physics: Recent History and Advances",doi:"10.5772/intechopen.92161",slug:"rock-physics-recent-history-and-advances",totalDownloads:1002,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,abstract:"This chapter presents the basics of rock physics, the science exploring quantitative relations between various properties (attributes) of the holistic object we call natural rock. This chapter includes several sections, starting with the history and basics; proceeding to the effects of the pore fluid on rock properties; discussing several variables that influence the elastic properties of rocks; presenting selected theories that relate the elastic properties to the porosity, mineralogy, and texture of rocks; and introducing the latest development, digital rock physics. Data examples shown here illustrate qualitative reasoning. Equations are presented as well to mathematically express the conceptual theories discussed. Most importantly, rock physics references are listed to help the reader become willing to delve deeper into the topic and start applying rock physics theories, concepts, and ideas to field data.",signatures:"Jack Dvorkin",downloadPdfUrl:"/chapter/pdf-download/72011",previewPdfUrl:"/chapter/pdf-preview/72011",authors:[{id:"315484",title:"Prof.",name:"Jack",surname:"Dvorkin",slug:"jack-dvorkin",fullName:"Jack Dvorkin"}],corrections:null},{id:"70319",title:"Enhanced Oil Recovery: Chemical Flooding",doi:"10.5772/intechopen.90335",slug:"enhanced-oil-recovery-chemical-flooding",totalDownloads:725,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:0,abstract:"The enhanced oil recovery phase of oil reservoirs production usually comes after the water/gas injection (secondary recovery) phase. The main objective of EOR application is to mobilize the remaining oil through enhancing the oil displacement and volumetric sweep efficiency. The oil displacement efficiency enhances by reducing the oil viscosity and/or by reducing the interfacial tension, while the volumetric sweep efficiency improves by developing a favorable mobility ratio between the displacing fluid and the remaining oil. It is important to identify remaining oil and the production mechanisms that are necessary to improve oil recovery prior to implementing an EOR phase. Chemical enhanced oil recovery is one of the major EOR methods that reduces the residual oil saturation by lowering water-oil interfacial tension (surfactant/alkaline) and increases the volumetric sweep efficiency by reducing the water-oil mobility ratio (polymer). In this chapter, the basic mechanisms of different chemical methods have been discussed including the interactions of different chemicals with the reservoir rocks and fluids. In addition, an up-to-date status of chemical flooding at the laboratory scale, pilot projects and field applications have been reported.",signatures:"Ahmed Ragab and Eman M. Mansour",downloadPdfUrl:"/chapter/pdf-download/70319",previewPdfUrl:"/chapter/pdf-preview/70319",authors:[{id:"277274",title:"Dr.",name:"Eman M.",surname:"Mansour",slug:"eman-m.-mansour",fullName:"Eman M. Mansour"}],corrections:null},{id:"72024",title:"High-Resolution Numerical Simulation of Surface Wave Development under the Action of Wind",doi:"10.5772/intechopen.92262",slug:"high-resolution-numerical-simulation-of-surface-wave-development-under-the-action-of-wind",totalDownloads:677,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The paper describes the numerical experiments with a three-dimensional phase-resolving model based on the initial potential equation of motion with free surface at deep water in the periodic domain written in the surface-following nonstationary curvilinear nonorthogonal coordinate system. The numerical scheme is based on Fourier-transform method. The vertical velocity on surface is calculated by solving the three-dimensional Poisson equation for the velocity potential. The velocity potential is represented as a sum of linear and nonlinear components. The linear component is described by Laplace equation. The nonlinear component is calculated by solution of the three-dimensional Poisson equation with the iterated right-hand side. The model includes some algorithms for calculation of the energy input from wind as well as for calculation of breaking and high-frequency dissipation. Initially, the conditions are assigned as a set of small waves corresponding to JONSWAP spectrum at high wave number. In response to waves’ growth, the spectrum shifts to lower wave numbers. The evolution of spectrum is generally in an agreement with the observed data. The wave spectrum and the spectra of different rates of energy transformation as well as the statistical characteristics of wave field for different stages of development are described.",signatures:"Dmitry Chalikov",downloadPdfUrl:"/chapter/pdf-download/72024",previewPdfUrl:"/chapter/pdf-preview/72024",authors:[{id:"316767",title:"Prof.",name:"Dmitry",surname:"Chalikov",slug:"dmitry-chalikov",fullName:"Dmitry Chalikov"}],corrections:null},{id:"72908",title:"Surface Gravity Wave Modeling in Tropical Cyclones",doi:"10.5772/intechopen.93275",slug:"surface-gravity-wave-modeling-in-tropical-cyclones",totalDownloads:419,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Tropical cyclones are among the deadliest geophysical phenomena on earth. Tropical cyclone-generated wave fields are of interest both scientifically for understanding wind–wave-ocean interaction physics and operationally for predicting potentially hazardous conditions for ship navigation and coastal regions. This chapter briefly reviews the development of third generation wave models, the improvements of their input/dissipation source functions, and their applications in tropical cyclone generated surface wave predictions. Discussion on the status of coupled atmosphere-wave-ocean modeling in tropical cyclone predictions are given at the end of the chapter prompted by the growing scientific evidence on the importance of sea state on air-sea fluxes under extreme wind conditions.",signatures:"Yalin Fan, Paul Hwang and John Yu",downloadPdfUrl:"/chapter/pdf-download/72908",previewPdfUrl:"/chapter/pdf-preview/72908",authors:[{id:"320023",title:"Dr.",name:"Yalin",surname:"Fan",slug:"yalin-fan",fullName:"Yalin Fan"},{id:"323382",title:"Dr.",name:"Paul",surname:"Hwang",slug:"paul-hwang",fullName:"Paul Hwang"},{id:"323383",title:"Mr.",name:"John",surname:"Yu",slug:"john-yu",fullName:"John Yu"}],corrections:null},{id:"72288",title:"Simplified Methods for Storm Surge Forecast and Hindcast in Semi-Enclosed Basins: A Review",doi:"10.5772/intechopen.92171",slug:"simplified-methods-for-storm-surge-forecast-and-hindcast-in-semi-enclosed-basins-a-review",totalDownloads:600,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"It is widely known that small and semi-enclosed basins could be inclined to storm surge events. This is mainly due to either the meteorological exposition, to the presence of a continental shelf or to their shape. These storm surges can induce coastal flooding and consequent problems in terms of infrastructure stability and damage to touristic activities or, in some cases, threaten human life. Therefore, in order to manage the risk, coastal managers or policymakers need to have forecast or hindcast tools. They must help to take preventive actions that may be done previously to the occurrence of natural phenomena and to carry out simultaneous actions useful during the occurrence of the event. This work aims at answering these necessities presenting a review of two methods for storm surge forecast and hindcast in semi-enclosed basins.",signatures:"Davide Pasquali",downloadPdfUrl:"/chapter/pdf-download/72288",previewPdfUrl:"/chapter/pdf-preview/72288",authors:[{id:"309493",title:"Dr.",name:"Davide",surname:"Pasquali",slug:"davide-pasquali",fullName:"Davide Pasquali"}],corrections:null},{id:"73416",title:"Physical and Numerical Modeling of Landslide-Generated Tsunamis: A Review",doi:"10.5772/intechopen.93878",slug:"physical-and-numerical-modeling-of-landslide-generated-tsunamis-a-review",totalDownloads:411,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Landslide-generated tsunamis represent a serious source of hazard for many coastal and lacustrine communities. The understanding of the complex physical phenomena that govern the tsunami generation, propagation and interaction with the coast is essential to reduce and mitigate the tsunamis risk. Experimental, analytical, and numerical models have been extensively used (both as separated tools and in conjunction) to shed light on these complicated natural events. In this work, a non-exhaustive update of the state of the art related to the physical and numerical modeling techniques of landslide-generated tsunamis, with a special focus on those studies published in the last ten years, is provided. As far as numerical models are concerned, a special attention is paid to the most recently developed Computational Fluid Dynamics (CFD) techniques, whose development and application have experienced a boost up the last decade.",signatures:"Alessandro Romano",downloadPdfUrl:"/chapter/pdf-download/73416",previewPdfUrl:"/chapter/pdf-preview/73416",authors:[{id:"321581",title:"Dr.",name:"Alessandro",surname:"Romano",slug:"alessandro-romano",fullName:"Alessandro Romano"}],corrections:null},{id:"73801",title:"Hydrodynamics of Regular Breaking Wave",doi:"10.5772/intechopen.94449",slug:"hydrodynamics-of-regular-breaking-wave",totalDownloads:413,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Turbulence and undertow currents play an important role in surf-zone mixing and transport processes; therefore, their study is fundamental for the understanding of nearshore dynamics and the related planning and management of coastal engineering activities. Pioneering studies qualitatively described the features of breakers in the outer region of the surf zone. More detailed information on the velocity field under spilling and plunging breakers can be found in experimental works, where single-point measurement techniques, such as Hot Wire Anemometry and Laser Doppler Anemometry (LDA), were used to provide maps of the flow field in a time-averaged or ensemble-averaged sense. Moreover, the advent of non-intrusive measuring techniques, such as Particle Image Velocimetry (PIV) provided accurate and detailed instantaneous spatial maps of the flow field. However, by correlating spatial gradients of the measured velocity components, the instantaneous vorticity maps could be deduced. Moreover, the difficulties of measuring velocity due to the existence of air bubbles entrained by the plunging jet have hindered many experimental studies on wave breaking encouraging the development of numerical model as useful tool to assisting in the interpretation and even the discovery of new phenomena. Therefore, the development of an WCSPH method using the RANS equations coupled with a two-equation k–ε model for turbulent stresses has been employed to study of the turbulence and vorticity distributions in in the breaking region observing that these two aspects greatly influence many coastal processes, such as undertow currents, sediment transport and action on maritime structures.",signatures:"Diana De Padova and Michele Mossa",downloadPdfUrl:"/chapter/pdf-download/73801",previewPdfUrl:"/chapter/pdf-preview/73801",authors:[{id:"320394",title:"Prof.",name:"Michele",surname:"Mossa",slug:"michele-mossa",fullName:"Michele Mossa"},{id:"328068",title:"Dr.",name:"Diana",surname:"De Padova",slug:"diana-de-padova",fullName:"Diana De Padova"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"7315",title:"Minerals",subtitle:null,isOpenForSubmission:!1,hash:"f0d5c2a9a5f37e6effcb8486c661d217",slug:"minerals",bookSignature:"Khalid S. 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\r\n\tThe book explains and educates the reader regarding normal sexual function, sexual dysfunction, and sexual dysfunction disorders both in males and females. The objective of the book will be to highlight the importance of sex education and explain normal human sexuality. With the growing number of males and females reporting sexual dysfunction the need for a ready reckoner of sexual dysfunction may be relevant and necessary.
\r\n\r\n\tThe book will have chapters on normal human sexuality, sexual health, Sexual dysfunction in the male and female, sexual dysfunction disorders related to libido, orgasm, ejaculation, erection, and genetic or hormonal or developmental or sexuo-erotic orientation defects.
\r\n\r\n\tThe book will also highlight the importance of sex counselors and therapists.
\r\n\tThere will be a chapter on secondary causes of sexual dysfunction disorders related to diabetes, cardiovascular disease, and obesity. A chapter on remedial measures to enhance sexual activity and maintain human relationships will be discussed. As there is a growing number of cancer survivors a chapter on cancer-related sexual dysfunction will be welcomed for including it.
Speech recognition is often used as the front-end for many natural language processing (NLP) applications. Some of these applications include machine translation, information retrieval and extraction, voice dialing, call routing, speech synthesis/recognition, data entry, dictation, control, etc. Thus, much research work has been done to improve the speech recognition and the related NLP applications. However, speech recognition has some obstacles that should be considered. Pronunciation variations and small words misrecognition are two major problems that lead to performance reduction. Pronunciation variations problem can be divided into two parts: within-word variations and cross-word variations. These two types of pronunciation variations have been tackled by many researchers using different approaches. For example, cross-word problem can be solved using phonological rules and/or small-word merging. (AbuZeina et al., 2011a) used the phonological rules to model cross-word variations for Arabic. For English, (Saon & Padmanabhan, 2001) demonstrated that short words are more frequently misrecognized, they also had achieved a statistically significant enhancement using small-word merging approach.
An automatic speech recognition (ASR) system uses a decoder to perform the actual recognition task. The decoder finds the most likely words sequence for the given utterance using Viterbi algorithm. The ASR decoder task might be seen as an alignment process between the observed phonemes and the reference phonemes (dictionary phonemic transcription). Intuitively, to have a better accuracy in any alignment process, long sequences are highly favorable instead of short ones. As such, we expect enhancement if we merge words (short or long). Hence fore, a thorough investigation was performed on Arabic speech to discover a suitable merging cases. We found that Arabic speakers usually augment two consecutive words; a noun that is followed by an adjective and a preposition that is followed by a word. Even though we believe that other cases are found in Arabic speech, we chose two cases to validate our proposed method. Among the ASR components, the pronunciation dictionary and the language model were used to model our above mentioned objective. This means that the acoustic models for the baseline and the enhanced method are the same.
This research work is conducted for Modern Standard Arabic (MSA). So, the work will necessarily contain many examples in Arabic. Therefore, it would be appropriate for the reader if we start first by providing a Romanization (Ryding, 2005) of the Arabic letters and diacritical marks. Table 1 shows the Arabic–Roman letters mapping table. The diacritics Fatha, Damma, and Kasra are represented using a, u, and i, respectively.
Arabic | Roman | Arabic | Roman | Arabic | Roman | Arabic | Roman |
ء (hamza) | ’ | د (daal) | d | ض (Daad) | D | ك (kaaf) | k |
ب (baa’) | b | ذ (dhaal) | dh | ط (Taa’) | T | ل (laam) | l |
ت (taa’) | t | ر (raa’) | r | ظ (Zaa’) | Z | م (miim) | m |
ث (thaa’) | th | ز (zaay) | z | ع (‘ayn) | ‘ | ن (nuun) | n |
ج (jiim) | j | س (siin) | s | غ (ghayn) | gh | ه (haa’) | h |
ح (Haa’) | H | ش (shiin) | sh | ف (faa’) | f | و (waaw) | w or u |
خ (khaa’) | kh | ص (Saad) | S | ق (qaaf) | q | ي (yaa’) | y or ii |
Arabic–Roman letters mapping table
To validate the proposed method, we used Carnegie Mellon University (CMU) Sphinx speech recognition engine. Our baseline system contains a pronunciation dictionary of 14,234 words from a 5.4 hours pronunciation corpus of MSA broadcast news. For tagging, we used the Arabic module of Stanford tagger. Our results show that part of speech (PoS) tagging is considered a promising track to enhance Arabic speech recognition systems.
The rest of this chapter is organized as follows. Section 2 presents the problem statement. Section 3 demonstrates the speech recognition components. In Section 4, we differentiate between within-word and cross-word pronunciation variations followed by the Arabic speech recognition in Section 5. The proposed method is presented in Section 6 and the results in Section 7. The discussion is provided in Section 8. In Section 9, we highlight some of the future directions. We conclude the work in Section 10.
Continuous speech is characterized by augmenting adjacent words, which do not occur in isolated speech. Therefore, handling this phenomenon is a major requirement in continuous speech recognition systems. Even though Hidden Markov Models (HMMs) based ASR decoder uses triphones to alleviate the negative effects of cross-word phenomenon, more effort is still needed to model some cross-word cases that could not be avoided using triphones. In continuous ASR systems, the dictionary is usually initiated using corpus transcription words, i.e. each word is considered as an independent entity. In this case, speech cross-word merging will reduce the performance. Two main methods are usually used to model the cross-word problem, phonological rules and small-word merging. Even though the phonological rules and small-word merging methods enhance the performance, we believe that generating compound words is also possible using PoS tagging.
Initially, there are two reasons why cross-word modeling is an effective method in speech recognition system: First, the speech recognition problem appears as an alignment process, hence for, having long sequences is better than short ones as demonstrated by (Saon and Padmanabhan, 2001). To illustrate the effect of co-articulation phenomenon (merging of words in continuous speech), let us examine Figure 1 and Figure 2. Figure 1 shows the words to be considered with no compound words, while Figure 2 shows the words with compound words. In both figures we represented the hypotheses words using bold black lines. During decoding, the ASR decoder will investigate many words and hypotheses. Intuitively, the ASR decoder will choose the long words instead of two short words. The difference between the two figures is the total number of words that will be considered during the decoding process. Figure 2 shows that the total number of words for the hypotheses is less than the total words in Figure 1 (Figure 1 contains 34 words while Figure 2 contains 18 words). Having less number of total words during decoding process means having less decoding options (i.e. less ambiguity), which is expected to enhance the performance.
Second, compounding words will lead to more robust language model. the compound words which are represented in the language model will provide better representations of words relations. Therefore, enhancement is expected as correct choice of a word will increase the probability of choosing a correct neighbor words. The effect of compounding words was investigated by (Saon & Padmanabhan, 2001). They mathematically demonstrated that compound words enhance the language model performance, therefore, enhancing the overall recognition output. They showed that the compound words have the effect of incorporating a trigram dependency in a bigram language model. In general, the compound words are most likely to be correctly recognized more than two separated words. Consequently, correct recognition of a word might lead to another correct word through the enhanced N-grams language model. In contrast, misrecognition of a word may lead to another misrecognition in the adjacent words and so on.
For more clarification, we present some cases to show the short word misrecognition, and how is the long word is much likely to be recognized correctly. Table 2 shows three speech files that were tested in the baseline and the enhanced system. Of course, it is early to show some results, but we see that it is worthy to support our motivation claim. In Table 2, it is clear that the misrecognitions were mainly occurred in the short words (the highlighted short words were misrecognized in the baseline system).
In this chapter, the most noticeable Arabic ASRs performance reduction factor, the cross-word pronunciation variations, is investigated. To enhance speech recognition accuracy, a knowledge-based technique was utilized to model the cross-word pronunciation variation at two ASR components: the pronunciation dictionary and the language model. The proposed knowledge-based approach method utilizes the PoS tagging to compound consecutive words according to their tags. We investigated two pronunciation cases, a noun that is followed by an adjective, and a preposition that is followed by a word. the proposed method showed a significant enhancement.
A list of hypotheses without compounding words
A list of hypotheses with compounding words
Modern large vocabulary, speaker-independent, continuous speech recognition systems have three knowledge sources, also called linguistic databases: acoustic models, language model, and pronunciation dictionary (also called lexicon). Acoustic models are the HMMs of the phonemes and triphones (Hwang, 1993). The language model is the module that provides the statistical representations of the words sequences based on the transcription of the text corpus. The dictionary is the module that serves as an intermediary between the acoustic model and the language model. The dictionary contains the words available in the language and the pronunciation of each word in terms of the phonemes available in the acoustic models.
Figure 3 illustrates the sub-systems that are usually found in a typical ASR system. In addition to the knowledge sources, an ASR system contains a Front-End module which is used to convert the input sound into feature vectors to be usable by the rest of the system. Speech recognition systems usually use feature vectors that are based on Mel Frequency Cepstral Coefficients (MFCCs), (Rabiner and Juang, 2004).
The speech files to be tested | سَيَتَقَابَلانِ وَجهًا لِوَجه فِي المُبَارَاةِ النِّهَائِيَّة sayataqabalani wajhan liwajh fy ’lmubarah ’lniha’iya وَمُمَثِّلِينَ عَن عَدَدٍ مِن الدُّوَلِ الأُورُوبِيَّة wamumathilyna ‘an ‘adadin mina ’lduwali ’l’wrubiya |
The baseline system results | سَيَتَقَابَلانِ وَجهًا لِوَجه المُبَارَاةِ النِّهَائِيَّة sayataqabalani wajhan liwajh ’lmubarah ’lniha’iya وَمُمَثِّلِين عَن إِنَّ الدُّوَلِ الأُورُوبِيَّة wamumathilyna ‘an ’ inna ’lduwali ’l’wrubiya |
The enhance system results | سَيَتَقَابَلانِ وَجهًا لِوَجه فِي المُبَارَاةِ النِّهَائِيَّة sayataqabalani wajhan liwajh fy ’lmubarah ’lniha’iya وَمُمَثِّلِينَ عَن عَدَدٍ مِن الدُّوَلِ الأُورُوبِيَّة wamumathilyna ‘an ‘adadin mina ’lduwali ’l’wrubiya |
Illustrative cross-word misrecognition results
An ASR architecture
The following is a brief introduction to typical ASR system components. The reader can find more elaborate discussion in (Jurafsky and Martin, 2009).
The purpose of this sub-system is to extract speech features which play a crucial role in speech recognition performance. Speech features includes Linear Predictive Cepstral Coefficients (LPCC), MFCCs and Perceptual Linear Predictive (PLP) coefficients. The Sphinx engine used in this work is based on MFCCs.
The feature extraction stage aims to produce the spectral properties (features vectors) of speech signals. The feature vector consists of 39 coefficients. A speech signal is divided into overlapping short segments that are represented using MFCCs. Figure 4 shows the steps to extract the MFCCs of a speech signal (Rabiner & Juang, 2004). These steps are summarized below.
Feature vectors extraction
Sampling and Quantization: Sampling and quantization are the two steps for analog-to-digital conversion. The sampling rate is the number of samples taken per second, the sampling rate used in this study is 16 k samples per seconds. The quantization is the process of representing real-valued numbers as integers. The analysis window is about 25.6 msec (410 samples), and consecutive frames overlap by 10 msec.
Preemphasis: This stage is to boost the high frequency part that was suppressed during the sound production mechanism, so making the information more available to the acoustic model.
Windowing: Each analysis window is multiplied by a Hamming window.
Discrete Fourier Transform: The goal of this step is to obtain the magnitude frequency response of each frame. The output is a complex number representing the magnitude and phase of the frequency component in the original signal.
Mel Filter Bank: A set of triangular filter banks is used to approximate the frequency resolution of the human ear. The Mel frequency scale is linear up to 1000 Hz and logarithmic thereafter. For 16 KHz sampling rate, Sphinx engine uses a set of 40 Mel filters.
Log of the Mel spectrum values: The range of the values generated by the Mel filter bank is reduced by replacing each value by its natural logarithm. This is done to make the statistical distribution of spectrum approximately Gaussian.
Inverse Discrete Fourier Transform: This transform is used to compress the spectral information into a set of low order coefficients which is called the Mel-cepstrum. Thirteen MFCC coefficients are used as a basic feature vector,
Deltas and Energy: For continuous models, the 13 MFCC parameters along with computed delta and delta-deltas parameters are used as a single stream 39 parameters feature vector. For semi-continuous models, x(0) represents the log Mel spectrum energy, and is used separately to derive other feature parameters, in addition to the delta and double delta parameters. Figure 5 shows part of the feature vector of a speech file after completing the feature extraction process. Each column represents the basic 13 features of a 25.6 milliseconds frame.
snapshot of the MFCCs of a speech file
This part contains the modifications required for a particular language. It contains three parts: acoustic models, language model, and pronunciation dictionary. Acoustic models contain the HMMs used in recognition process. The language model contains language’s words and their combinations, each combination has two or three words. A pronunciation dictionary contains the words and their pronunciation phonemes.
Acoustic models are statistical representations of the speech phones. Precise acoustic model is a key factor to improve recognition accuracy as it characterizes the HMMs of each phone. Sphinx uses 39 English phonemes (The CMU Pronunciation Dictionary, 2011). The acoustic models use a 3- to 5-state Markov chain to represent the speech phone (Lee, 1988). Figure 6 shows a representation of a 3-state phone’s acoustic model. In Figure 6, S1 is the representation of phone at the beginning, while S2 and S3 represent of the phone at the middle and the end states, respectively. Associated with S1, S2, and S3 are state emission probabilities,
state phone acoustic model
In continuous speech, each phoneme is influenced in different degrees by its neighboring phonemes. Therefore, for better acoustic modeling, Sphinx uses triphones. Triphones are context dependent models of phonemes; each triphone represents a phoneme surrounded by specific left and right phonemes (Hwang, 1993).
The N-gram language model is trained by counting N-gram occurrences in a large transcription corpus to be then smoothed and normalized. In general, an N-gram language model is used to calculate the probability of a given sequence of words as follows:
Where n is limited to include the words’ history as bigram (two consequent words), trigram (three consequent words), 4-gram (four consequent words), etc. for example, by assigning n=2, the probability of a three word sequence using bigram is calculated as follows:
The CMU statistical language tool is described in (Clarkson & Rosenfeld, 1997). The CMU statistical language tool kit has been used to generate our Arabic statistical language model. Figure 7 shows the steps for creation and testing the language model, the steps are:
Compute the word unigram counts.
Convert the word unigram counts into a vocabulary list.
Generate bigram and trigram tables based on this vocabulary.
Steps for creating and testing language model
The CMU language modeling tool comes with a tool for evaluating the language model. The evaluation measures the perplexity as indication of the convenient (goodness) of the language model. For more information of the perplexity, please refer to Section 7.
Both training and recognition stages require a pronunciation dictionary which is a mapping table that maps words into sequences of phonemes. A pronunciation dictionary is basically designed to be used with a particular set of words. It provides the pronunciation of the vocabulary for the transcription corpus using the defined phoneme set. Like acoustic models and language model, the performance of a speech recognition system depends critically on the dictionary and the phoneme set used to build the dictionary. In decoding stage, the dictionary serves as intermediary between the acoustic model and the language model.
There are two types of dictionaries: closed vocabulary dictionary and open vocabulary dictionary. In closed vocabulary dictionary, all corpus transcription words are listed in the dictionary. In contrast, it is possible to have non-corpus transcription words in the open vocabulary dictionary. Typically, the phoneme set, that is used to represent dictionary words, is manually designed by language experts. However, when human expertise is not available, the phoneme set is possible to be selected using data-driven approach as demonstrated by (Singh et al. 2002). In addition to providing phonemic transcriptions of the words of the target vocabulary, the dictionary is the place where alternative pronunciation variants are added such as in (Ali et al., 2009) for Arabic.
With help from the linguistic part, the decoder is the module where the recognition process takes place. The decoder uses the speech features presented by the Front-End to search for the most probable words and, then, sentences that correspond to the observed speech features. The recognition process starts by finding the likelihood of a given sequence of speech features based on the phonemes HMMs.
The speech recognition problem is to transcribe the most likely spoken words given the acoustic observations. If
Where
The main goal of ASRs is to enable people to communicate more naturally and effectively. But this ultimate dream faces many obstacles such as different speaking styles which lead to “pronunciation variation” phenomenon. This phenomenon appears in the form of insertions, deletions, or substitutions of phoneme(s) relative to the phonemic transcription in the pronunciation dictionary. (Benzeghiba et al., 2007) presented the speech variability sources: foreign and regional accents, speaker physiology, spontaneous speech, rate of speech, children speech, emotional state, noises, new words, and more. Accordingly, handling these obstacles is a major requirement to have better ASR performance.
There are two types of pronunciation variations: cross-word variations and within-word variations. A within-word variation causes alternative pronunciation(s) of the same word. In contrast, a cross-word variation occurs in continuous speech in which a sequence of words forms a compound word that should be treated as a one entity. The pronunciation variation can be modeled in two approaches: knowledge-based and data-driven. Knowledge-based depends on linguistic studies that lead to the phonological rules which are called to find the possible alternative variants. On the other hand, data-driven methods depend solely on the pronunciation corpus to find the pronunciation variants (direct data-driven) or transformation rules (indirect data-driven). In this chapter, we will use the knowledge-based approach to model the cross-word pronunciation variation problem.
As pros and cons of both approaches, the knowledge-based approach is not exhaustive; not all of the variations that occur in continuous speech have been described. Whereas obtaining reliable information using data-driven is difficult. However, (Amdal & Fossler-Lussier 2003) mentioned that there is a growing interest in data-driven methods over the knowledge-based methods due to lack of domains expertise. Figure 8 displays these two techniques. Figure 8 also distinguishes between the types of variations and the modeling techniques by a dashed line. The pronunciation variation types are above the dashed line whereas the modeling techniques are under the dashed line.
Pronunciation variations and modeling techniques
This work focuses on Arabic speech recognition, which has gained increasing importance in the last few years. Arabic is a Semitic language spoken by more than 330 million people as a native language (Farghaly & Shaalan, 2009). While Arabic language has many spoken dialects, it has a standard written language. As a result, more challenges are introduced to speech recognition systems as the spoken dialects are not officially written. The same country could contain different dialects and a dialect itself can vary from region to another according to different factors such as religion, gender, urban/rural, etc. Speakers with different dialects usually use modern standard Arabic (MSA) to communicate.
In this chapter, we consider the modern standard Arabic (MSA) which is currently used in writing and in most formal speech. MSA is also the major medium of communication for public speaking and news broadcasting (Ryding, 2005) and is considered to be the official language in most Arabic-speaking countries (Lamel et al., 2009). Arabic language challenges will be presented in the next section. Followed by the literature review and recent results efforts in Arabic speech recognition. For more information about modern standard Arabic, (Ryding, 2005) is a rich reference.
Arabic speech recognition faces many challenges. First, Arabic has many dialects where same words are pronounced differently. In addition, the spoken dialects are not officially written, it is very costly to obtain adequate corpora, which present a training problem for the Arabic ASR researchers (Owen et al., 2006). Second, Arabic has short vowels (diacritics), which are usually ignored in text. The lack of diacritical marks introduces another serious problem to Arabic speech recognition. Consequently, more hypotheses’ words will be considered during decoding process which may reduce the accuracy. (Elmahdy et al., 2009) summarized some of the problems raised in Arabic speech recognition. They highlighted the following problems: Arabic phonetics, diacritization problem, grapheme-to-phoneme, and morphological complexity. Although foreign phoneme sounds as /v/ and /p/ are used in Arabic speech in foreign names, the standard Arabic letters do not have standard letter assigned for foreign sounds. Second, the absence of the diacritical marks in modern Arabic text creates ambiguities for pronunciations and meanings. For example, the non-diacritized Arabic word (كتب) could be read as one of several choices, some of which are: (كَتَبَ,he wrote), (كُتِب, it was written), and (كُتُب, books). Even though, an Arabic reader can interpret and utter the correct choice, it is hard to embed this cognitive process in current speech recognition and speech synthesis systems. The majority of Arabic corpora available for the task of acoustic modeling have non-diacritized transcription. (Elmahdy et al., 2009) also showed that grapheme-to-phoneme relation is only true for diacritized Arabic script. Hence fore, Arabic speech recognition has an obstacle because the lack of diacritized corpora. Arabic morphological complexity is demonstrated by the large number of affixes (prefixes, infixes, and suffixes) that can be added to the three consonant radicals to form patterns. (Farghaly& Shaalan, 2009) provided a comprehensive study of Arabic language challenges and solutions. The mentioned challenges include: the nonconcatenative nature of Arabic morphology, the absence of the orthographic representation of Arabic diacritics from contemporary Arabic text, and the need for an explicit grammar of MSA that defines linguistic constituency in the absence of case marking. (Lamel et al., 2009) presented a number of challenges for Arabic speech recognition such as no diacritics, dialectal variants, and very large lexical variety. (Alotaibi et al., 2008) introduced foreign-accented Arabic speech as a challenging task in speech recognition. (Billa et al., 2002) discussed a number of research issues for Arabic speech recognition, e.g., absence of diacritics in written text and the presence of compound words that are formed by the concatenation of certain conjunctions, prepositions, articles, and pronouns, as prefixes and suffixes to the word stem.
A number of researchers have recently addressed development of Arabic speech recognition systems. (Abushariah et al., 2012) proposed a framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. Their method reduced the WER to 9.81% for a diacritized transcription corpus, as they have reported. (Hyassat & Abu Zitar, 2008) described an Arabic speech recognition system based on Sphinx 4. Three corpora were developed, namely, the Holy Qura’an corpus of about 18.5 hours, the command and control corpus of about 1.5 hours, and the Arabic digits corpus of less than 1 hour of speech. They also proposed an automatic toolkit for building pronunciation dictionaries for the Holy Qur’an and standard Arabic language. (Al-Otaibi, 2001)] provided a single-speaker speech dataset for MSA. He proposed a technique for labeling Arabic speech. using the Hidden Markov Model Toolkit (HTK), he reported a recognition rate for speaker dependent ASR of 93.78%. (Afify et al., 2005) compared grapheme-based recognition system with explicitly modeling diacritics (short vowels). They found that a diacritic modeling improves recognition performance. (Satori et al., 2007) used CMU Sphinx tools for Arabic speech recognition. They demonstrated the use of the tools for recognition of isolated Arabic digits. They achieved a digits recognition accuracy of 86.66% for data recorded from six speakers. (Alghamdi et al., 2009) developed an Arabic broadcast news transcription system. They used a corpus of 7.0 h for training and 0.5 h for testing. The WER they obtained was 14.9%. (Lamel et al., 2009) described the incremental improvements to a system for the automatic transcription of broadcast data in Arabic, highlighting techniques developed to deal with specificities (no diacritics, dialectal variants, and lexical variety) of the Arabic language. (Billa et al., 2002) described the development of audio indexing system for broadcast news in Arabic. Key issues addressed in their work revolve around the three major components of the audio indexing system: automatic speech recognition, speaker identification, and named entity identification. (Soltau et al., 2007) reported advancements in the IBM system for Arabic speech recognition as part of the continuous effort for the Global Autonomous Language Exploitation (GALE) project. The system consisted of multiple stages that incorporate both diacritized and non-diacritized Arabic speech model. The system also incorporated a training corpus of 1,800 hours of unsupervised Arabic speech. (Azmi et al., 2008) investigated using Arabic syllables for speaker-independent speech recognition system for Arabic spoken digits. The pronunciation corpus used for both training and testing consisted of 44 Egyptian speakers. In a clean environment, experiments showed that the recognition rate obtained using syllables outperformed the rate obtained using monophones, triphones, and words by 2.68%, 1.19%, and 1.79%, respectively. Also in noisy telephone channel, syllables outperformed the rate obtained using monophones, triphones, and words by 2.09%, 1.5%, and 0.9%, respectively. (Elmahdy et al., 2009) used acoustic models trained with large MSA news broadcast speech corpus to work as multilingual or multi-accent models to decode colloquial Arabic. (Khasawneh et al., 2004) compared the polynomial classifier that was applied to isolated-word speaker-independent Arabic speech and dynamic time warping (DTW) recognizer. They concluded that the polynomial classifier produced better recognition performance and much faster testing response than the DTW recognizer. (Shoaib et al., 2003) presented an approach to develop a robust Arabic speech recognition system based on a hybrid set of speech features. The hybrid set consisted of intensity contours and formant frequencies. (Alotaibi, 2004) reported achieving high-performance Arabic digits recognition using recurrent networks. (Choi et al., 2008) presented recent improvements to their English/Iraqi Arabic speech-to-speech translation system. The presented system-wide improvements included user interface, dialog manager, ASR, and machine translation components. (Nofal et al., 2004) demonstrated a design and implementation of stochastic-based new acoustic models for use with a command and control system speech recognition system for the Arabic. (Mokhtar & El-Abddin, 1996) represented the techniques and algorithms used to model the acoustic-phonetic structure of Arabic speech recognition using HMMs. (Park et al., 2009) explored the training and adaptation of multilayer perceptron (MLP) features in Arabic ASRs. They used MLP features to incorporate short-vowel information into the graphemic system. They also used linear input networks (LIN) adaptation as an alternative to the usual HMM-based linear adaptation. (Imai et al.,1995) presented a new method for automatic generation of speaker-dependent phonological rules in order to decrease recognition errors caused by pronunciation variability dependent on speakers. (Muhammad et al., 2011) evaluated conventional ASR system for six different types of voice disorder patients speaking Arabic digits. MFCC and Gaussian mixture models (GMM)/HMM were used as features and classifier, respectively. Recognition result was analyzed for recognition for types of diseases. (Bourouba et al., 2006) presented a HMM/support vectors machine (SVM) (k-nearest neighbor) for recognition of isolated spoken Arabic words. (Sagheer et al., 2005) presented a visual speech features representation system. They used it to comprise a complete lip-reading system. (Taha et al., 2007) demonstrated an agent-based design for Arabic speech recognition. They defined the Arabic speech recognition as a multi-agent system where each agent had a specific goal and deals with that goal only. (Elmisery et al., 2003) implemented a pattern matching algorithm based on HMM using field programmable gate array (FPGA). The proposed approach was used for isolated Arabic word recognition. (Gales et al., 2007) described the development of a phonetic system for Arabic speech recognition. (Bahi & Sellami, 2001) presented experiments performed to recognize isolated Arabic words. Their recognition system was based on a combination of the vector quantization technique at the acoustic level and markovian modeling. (Essa et al., 2008) proposed a combined classifier architectures based on Neural Networks by varying the initial weights, architecture, type, and training data to recognize Arabic isolated words. (Emami & Mangu, 2007) studied the use of neural network language models (NNLMs) for Arabic broadcast news and broadcast conversations speech recognition. (Messaoudi et al., 2006) demonstrated that by building a very large vocalized vocabulary and by using a language model including a vocalized component, the WER could be significantly reduced. (Vergyri et al., 2004) showed that the use of morphology-based language models at different stages in a large vocabulary continuous speech recognition (LVCSR) system for Arabic leads to WER reductions. To deal with the huge lexical variety, (Xiang et al., 2006) concentrated on the transcription of Arabic broadcast news by utilizing morphological decomposition in both acoustic and language modeling in their system. (Selouani & Alotaibi, 2011) presented genetic algorithms to adapt HMMs for non-native speech in a large vocabulary speech recognition system of MSA. (Saon et al., 2010) described the Arabic broadcast transcription system fielded by IBM in the GALE project. they reported improved discriminative training, the use of subspace Gaussian mixture models (SGMM), the use of neural network acoustic features, variable frame rate decoding, training data partitioning experiments, unpruned n-gram language models, and neural network based language modeling (NNLMs). The achieved WER was 8.9% on the evaluation test set. (Kuo et al., 2010) studied various syntactic and morphological context features incorporated in an NNLM for Arabic speech recognition.
Since the ASR decoder works better with long words, our method focuses on finding a way to merge transcription words to increase the number of long words. For this purpose, we consider to merge words according to their tags. That is, merge a noun that is followed by an adjective, and merge a preposition that is followed by a word. we utilizes PoS tagging approach to tag the transcription corpus. the tagged transcription is then used to find the new merged words.
A tag is a word property such as noun, pronoun, verb, adjective, adverb, preposition, conjunction, interjection, etc. Each language has its own tags. Tags may be different from language to language. In our method, we used the Arabic module of Stanford tagger (Stanford Log-linear Part-Of-Speech Tagger, 2011). The total number of tags of this tagger is 29 tags, only 13 tags were used in our method as listed in Table 3. As we mentioned, we focused on three kinds of tags: noun, adjectives, and preposition. In Table 3, DT is a shorthand for the determiner article (ال التعريف) that corresponds to "the" in English.
# | Tag | Meaning | Example |
1 | ADJ_NUM | Adjective, Numeric | السابع، الرابعة |
2 | DTJJ | DT + Adjective | النفطية، الجديد |
3 | DTJJR | Adjective, comparative | الكبرى، العليا |
4 | DTNN | DT + Noun, singular or mass | المنظمة، العاصمة |
5 | DTNNP | DT + Proper noun, singular | العراق، القاهرة |
6 | DTNNS | DT + Noun, plural | السيارات، الولايات |
7 | IN | Preposition subordinating conjunction | حرف جر مثل : في حرف مصدري مثل :أنْ |
8 | JJ | Adjective | جديدة، قيادية |
9 | JJR | Adjective, comparative | أدنى، كبرى |
10 | NN | Noun, singular or mass | إنتاج، نجم |
11 | NNP | Proper noun, singular | أوبك، لبنان |
12 | NNS | Noun, plural | توقعات، طلبات |
13 | NOUN_QUANT | Noun, quantity | الربع، ثلثي |
A partial list of Stanford Tagger’s tag with examples
In this work, we used the Noun-Adjective as shorthand for a compound word generated by merging a noun and an adjective. We also used Preposition-Word as shorthand for a compound word generated by merging a preposition with a subsequent word. The prepositions used in our method include:
(من ، الى ، عن ، على ، في ، حتى ، منذ) (mundhu, Hata, fy, ‘ala, ‘an, ’ila, min), Other prepositions were not included as they are rarely used in MSA. Table 4 shows the tagger output for a simple non-diacritized sentence.
An input sentence to the tagger | وأوضح عضو لجنة المقاولين في غرفة الرياض بشير العظم wa ’wdaHa ‘udwu lajnata ’lmuqawilyna fy ghurfitu ’lriyaD bashyru ’l ‘aZm |
Tagger output (read from left to right) | وأوضح/VBD عضو/NN لجنة/NN المقاولين/DTNNS في/IN غرفة/NN الرياض/DTNNP بشير/NNP العظم/DTNN |
An Arabic sentence and its tags
Thus, the tagger output is used to generate compound words by searching for Noun-Adjective and Preposition-Word sequences. Figure 9 shows two possible compound words: (بَرنَامِجضَخم) and (فِيالأُردُن) for Noun-Adjective case and for Preposition-Word case, respectively. These two compound words are, then, represented in new sentences as illustrated in Figure 9. Therefore, the three sentences (the original and the new ones) will be used, with all other cases, to produce the enhanced language model and the enhanced pronunciation dictionary.
The compound words representations
Figure 10 shows the process of generating a compound word. It demonstrates that a noun followed by an adjective will be merged to produce a one compound word. similarly, the preposition followed by a word will be merged to perform a one compound word. It is noteworthy to mention that our method is independent from handling pronunciation variations that may occur at words junctures. That is, our method does not consider the phonological rules that could be implemented between certain words.
The steps for modeling cross-word phenomenon can be described by the algorithm (pseudocode) shown in Figure 11. In the figure, the Offline stage means that the stage is implemented once before decoding, while Online stage means that this stage needs to be repeatedly implemented after each decoding process.
A Noun-Adjective compound word generation
Cross-word modeling algorithm using PoS tagging
The proposed method was investigated on a speaker-independent modern standard Arabic speech recognition system using Carnegie Mellon University Sphinx speech recognition engine. Three performance metrics were used to measure the performance enhancement: the word error rate (WER), out of vocabulary (OOV), and perplexity (PP).
WER is a common metric to measure performance of ASRs. WER is computed using the following formula:
Where:
S is the number of substituted words,
D is the number of deleted words,
I is the number of inserted words,
N is the total number of words in the testing set.
The word accuracy can also be measured using WER as the following formula:
Word Accuracy = 1 – WER
OOV is a metric to measure the performance of ASRs. OOV is known as a source of recognition errors, which in turn could lead to additional errors in the words that follow (Gallwitz et al., 1996). Hence fore, increasing OOVs plays a significant role in increasing WER and deteriorating performance. In this research work, the baseline system is based on a closed vocabulary. The closed vocabulary assumes that all words of the testing set are already included in the dictionary. (Jurafsky & Martin, 2009) explored the differences between open and closed vocabulary. In our method, we calculate OOV as the percentage of recognized words that are not belonging to the testing set, but to the training set. The following formula is used to find OOV:
The perplexity of the language model is defined in terms of the inverse of the average log likelihood per word (Jelinek, 1999). It is an indication of the average number of words that can follow a given word, a measure of the predictive power of the language model, (Saon & Padmanabhan,2001). Measuring the perplexity is a common way to evaluate N-gram language model. It is a way to measure the quality of a model independent of any ASR system. Of course, The measurement is performed on the testing set. A lower perplexity system is considered better than one of higher perplexity. The perplexity formula is:
PP(W) =
Where PP is the perplexity, P is the probability of the word set to be tested W=w1, w2, …, wN, and N is the total number of words in the testing set.
The performance detection method proposed by Plötz in (Plötz,2005) is used to investigate the achieved recognition results. A 95% is used as a level of confidence. The WER of the baseline system (12.21 %) and the total number of words in the testing set (9288 words ) are used to find the confidence interval [εl, εh]. The boundaries of the confidence interval are found to be [12.21 – 0.68, 12.21 + 0.68] [11.53,12.89]. If the changed classification error rate is outside this interval, this change can be interpreted as statistically significant. Otherwise, It is most likely caused by chance.
Table 5 shows the enhancements for different experiments. Since the enhanced method (in Noun-Adjective case) achieved a WER of (9.82%) which is out of the above mentioned confidence interval [11.53,12.89], it is concluded that the achieved enhancement is statistically significant. The other cases are similar, i.e. (Preposition-Word, and Hybrid cases also achieved a significant improvement).
# | Experiment | Accuracy (%) | WER (%) | Enhancement (%) |
Baseline system | 87.79 | 12.21 | ---------- | |
1 | Noun-Adjective | 90.18 | 9.82 | 2.39 |
2 | Preposition-Word | 90.04 | 9.96 | 2.25 |
3 | Hybrid (1 & 2) | 90.07 | 9.93 | 2.28 |
Accuracy achieved and WERs for different cases
Table 5 shows that the highest accuracy achieved is in Noun-Adjective case. The reduction in accuracy in the hybrid case is due to the ambiguity introduced in the language model. For more clarification, our method depends on adding new sentences to the transcription corpus that is used to build the language model. Therefore, adding many sentences will finally cause the language model to be biased to some n-grams (1-grams, 2-grams, and 3-grams) on the account of others.
The common way to evaluate the N-gram language model is using perplexity. The perplexity for the baseline is 34.08. For the proposed cases, the language models’ perplexities are displayed in Table 6. The measurements were taken based on the testing set, which contains 9288 words. The enhanced cases are clearly better as their perplexities are lower. The reason for the low perplexities is the specific domains that we used in our corpus, i.e. economics and sports.
# | Experiment | Perplexity | OOV (%) |
Baseline System | 34.08 | 328/9288 = 3.53% | |
1 | Noun-Adjective | 3.00 | 287/9288 = 3.09% |
2 | Preposition-Word | 3.22 | 299/9288 = 3.21% |
3 | Hybrid (1 & 2) | 2.92 | 316/9288 = 3.40% |
Perplexities and OOV for different experiments
The OOV was also measured for the performed experiments. Our ASR system is based on a closed vocabulary, so we assume that there are no unknown words. The OOV was calculated as the percentage of recognized words that do not belong to the testing set, but to the training set. Hence,
which is equal to 328/9288*100= 3.53%. For the enhanced cases, Table 6 shows the resulting OOVs. Clearly, the lower the OOV the better the performance is, which was achieved in all three cases.
Table 7 shows some statistical information collected during experiments. The “Total compound words” is the total number of Noun-Adjective cases found in the corpus transcription. The “unique compound words” indicates the total number of Noun-Adjective cases after removing duplicates. The last column, “compound words replaced” is the total number of compound words that were replaced back to their original two disjoint words after the decoding process and prior to the evaluation stage.
# | Experiment | Total compound words | unique compound words | compound words replaced |
1 | Noun-Adjective | 3328 | 2672 | 377 |
2 | Preposition-Word | 3883 | 2297 | 409 |
3 | Hybrid (1 & 2) | 7211 | 4969 | 477 |
Statistical information for compound words
Despite the claim that the Stanford Arabic tagger accuracy is more than 96%, a comprehensive manual verification and correction were made on the tagger output. It was reasonable to review the collected compound words as our transcription corpus is small (39217 words). For large corpora, the accuracy of the tagger is crucial for the results. Table 8 shows an error that occurred in the tagger output. The word, for example, “وقال”( waqala) should be VBD instead of NN.
Sentence to be tagged | هذا وقال رئيس لجنة الطاقة بمجلس النواب ورئيس الرابطة الروسية للغاز إن الاحتكارات الأوروبية hadha waqala ra’ysu lajnati ’lTaqa bimajlisi ’lnuwab wa ra’ysu ’lrabiTa ’lrwsiya llghaz ’ina ’l’iHtikarati ’liwrobiya |
Stanford Tagger output (read from left to right) | هذا/DT وقال/NN رئيس/NN لجنة/NN الطاقة/DTNN بمجلس/NN النواب/DTNN ورئيس/NN الرابطة/DTNN الروسية/DTJJ للغاز/NNP إن/NNP الاحتكارات/DTNNS الأوروبية/DTJJ |
Example of Stanford Arabic Tagger Errors
Table 9 shows an illustrative example of the enhancement that was achieved in the enhanced system. It shows that the baseline system missed one word “من”( min) while it appears in the enhanced system. Introducing a compound word in this sentence avoided the misrecognition that occurred in the baseline system.
The text of a speech file to be tested | فِي المَرحَلَةِ السَّابِعَةِ وَالثَّلاثِين مِن الدَّورِيِّ الإِسبَانِيِّ لِكُرَةِ القَدَم fy ’lmarHalati ’lsabi ‘a wa ’lthalathyn mina ’ldawry ’l’sbany likurati ’lqadam |
As recognized by the baseline system | فِي المَرحَلَةِ السَّابِعَةِ وَالثَّلاثِين الدَّورِيِّ الإِسبَانِيِّ لِكُرَةِ القَدَم fy ’lmarHalati ’lsabi ‘a wa ’lthalathyn mina ’ldawry ’l’sbany likurati ’lqadam |
As recognized by the enhanced system | فِي المَرحَلَةِ السَّابِعَةِ وَالثَّلاثِين مِن الدَّورِيِّالإِسبَانِيِّ لِكُرَةِ القَدَم fy ’lmarHalati ’lsabi ‘a wa ’lthalathyn mina ’ldawry ’l’sbany likurati ’lqadam |
Final output after decomposing the merging | فِي المَرحَلَةِ السَّابِعَةِ وَالثَّلاثِين مِن الدَّورِيِّ الإِسبَانِيِّ لِكُرَةِ القَدَم fy ’lmarHalati ’lsabi ‘a wa ’lthalathyn mina ’ldawry ’l’sbany likurati ’lqadam |
An example of enhancement in the enhanced system
According to the proposed algorithm, each sentence in the enhanced transcription corpus can have a maximum of one compound word, since sentences are added to the enhanced corpus once a compound word is formed. Finally, After the decoding process, the results are scanned in order to decompose the compound words back to their original form (two separate words). This process is performed using a lookup table such as:
الكُوَيتالدُّوَلِيِّ الكُوَيت الدُّوَلِيِّ (’lkuwaytldawly ’lkuwayt ’ldawly)
فِيمَطَارِ فِي مَطَارِ (fymatari fy matari)
Table 10 shows comparison results of the suggested methods for cross-word modeling. It shows that PoS tagging approach outperform the other methods ( i.e. the phonological rules and small word merging) which were investigated on the same pronunciation corpus. The use of phonological rules was demonstrated in (AbuZeina et al. 2011a) while merging of small-words method was presented in (AbuZeina et al. 2011b). even though PoS tagging seems to be better than the other methods, more research should be carried out for more confidence. So, the comparison demonstrated in Table 10 is subject to change as more cases need to be investigated for both techniques. That is, cross-word was modeled using only two Arabic phonological rules, while only two compounding schemes were applied in PoS tagging approach.
The recognition time is compared with the baseline system. The comparison includes the testing set which includes 1144 speech files. The specifications of the machine where we conducted the experiments were as follows: a desktop computer which contains a single processing chip of 3.2GHz and 2.0 GB of RAM. We found that the recognition time for the enhanced method is almost the same as the recognition time of the baseline system. This means that the proposed method is almost equal to the baseline system in term of time complexity.
# | System | Accuracy (%) | Execution Time (minutes) |
Baseline system | 87.79 | 34.14 | |
1 | phonological rules | 90.09 | 33.49 |
2 | PoS tagging | 90.18 | 33.05 |
3 | small word merging | 89.95 | 34.31 |
4 | Combined system (1,2,and3) | 88.48 | 30.31 |
Comparison between cross-word modeling techniques
As future work, we propose investigating more word-combination cases. In particular, we expect that the construct phrases Idafa (الإضافة) make a good candidate. Examples include: (سلسلة جبال, silsilt jibal), (مطار بيروت, maTaru bayrwt), ( مدينة القدس, madynatu ’lquds). Another suggested candidate is the Arabic "and" connective (واو العطف), such as: (مواد أدبية ولغوية, mawad ’dabiyah wa lughawiyah ), (yata‘allaqu biqaDaya ’l ‘iraqi wa ‘lsudan يتعلق بقضايا العراق والسودان،). A hybrid system could also be investigated. It is possible to use the different cross-word modeling approaches in a one ASR system. It is also worthy to investigate how to model the compound words in the language model. In our method, we create a new sentence for each compound word. we suggest to investigate representing the compound word exclusively with its neighbors. for example, instead of having two complete sentences to represent the compound words (بَرنَامِجضَخم, barnamijDakhm) and (فِيالأُردُن, fy’l’urdun) as what we proposed in our method:
أَمَّا فِي الأُردُن فَقَد تَمَّ وَضعُ بَرنَامِجضَخم لِتَطوِير مَدِينَةِ العَقَبَة
’mma fy ’l’urdun faqad tamma wad‘u barnamijDakhm litaTwyru madynati ’l ’aqabati
أَمَّا فِيالأُردُن فَقَد تَمَّ وَضعُ بَرنَامِج ضَخم لِتَطوِير مَدِينَةِ العَقَبَة
’ mma fy’l’urdun faqad tamma wad‘u barnamijDakhm litaTwyru madynati ’l ’aqabati
We propose to add the compound words only with their adjacent words like:
وَضعُ بَرنَامِجضَخم لِتَطوِير
waD‘u barnamijDakhm litaTwyr
أَمَّا فِيالأُردُن فَقَد
’ mma fy ’l’urdun faqad
A comprehensive research work should be made to find how to effectively represent the compound words in the language model. In addition, we highly recommend further research in PoS tagging for Arabic.
The proposed knowledge-based approach to model cross-word pronunciation variations problem achieved a feasible improvement. Mainly, PoS tagging approach was used to form compound words. The experimental results clearly showed that forming compound words using a noun and an adjective achieved a better accuracy than merging of a preposition and its next word. The significant enhancement we achieved has not only come from the cross-word pronunciation modeling in the dictionary, but also indirectly from the recalculated n-grams probabilities in the language model. We also conclude that Viterbi algorithm works better with long words. Speech recognition research should consider this fact when designing dictionaries. We found that merging words based on their types (tags) leads to significant improvement in Arabic ASRs. We also found that the proposed method outperforms the other cross-word methods such as phonological rules and small-words merging.
The authors would like to thank King Fahd University of Petroleum and Minerals for providing the facilities to write this chapter. We also thank King Abdulaziz City for Science and Technology (KACST) for partially supporting this research work under Saudi Arabia Government research grant NSTP # (08-INF100-4).
Nature has provided innumerable examples of very efficient solutions to complex problems with seemingly simple rules. With these as inspiration, many engineering problems are tackled using bioinspired techniques. A few of bioinspired techniques are evolutionary and genetic algorithms, stigmergy, hidden Markov models, belief networks, neural networks, etc. These are applicable in a wide variety of domains from robotics [1], communication systems, routing [2], building construction [3], scheduling, optimization, machine intelligence, etc. The brain is a very efficient computing element capable of performing complex tasks. This is possible due to massively parallel computation being performed by the vast number of cells called neurons in the brain while consuming very little energy. This has inspired a domain of algorithms and techniques called artificial intelligence (AI) where machines are programmed to learn and then solve complex tasks. The recent advances in high performance computing and theoretical advances into statistical learning methodologies have enabled a widespread use of AI techniques for tasks such as pattern recognition, natural language understanding, speech recognition, computer vision, odor recognition, machine translation, medical diagnosis, gaming, autonomous driving, path planning, autonomous robots, financial market modeling and the list goes on. Solving these kinds of problems with efficiency is not possible with the traditional computing paradigms. These algorithms are mimicking biology or are inspired from biology to tackle the above problems. For example, it is not humanly possible to have traditional software program coded to classify an image of a simple object such as a cup with reasonable accuracy, considering the innumerable variations available in terms of shape, size, color, etc. However, this is a trivial task for a human being as our brains learn to identify the salient features of an object. The inner working of the brains, especially the way it processes information is the inspiration behind a class of AI techniques called neural networks.
\nAI requires a large amount of compute power while churning through massive amounts of data. Today’s real-world tasks require different sets of AI models with different modalities to interact with each other, hence needing a large pipeline with complex data dependencies. Training is time-consuming, while needing efficient multi-accelerator parallelization. Even with such advances we are nowhere close to the compute power or the efficiency of a human brain. Human brain is still a mystery and is a very actively researched topic. Several neuron models are proposed to mimic various aspects of how the brain works with the limited understand we have up till now.
\nSpiking neural networks (SNNs) are networks made up of interconnected computing elements called neurons. SNNs try to mimic biology to incorporate the efficiencies found in nature. These neurons use spikes to communicate with each other. SNNs are third generation of neural networks [4] and are gaining popularity due to its potential for very low energy dissipation due to their event-driven and asynchronous operation. SNNs are also interesting because of their ability learn in a distributed way using a technique called Spike Timing Dependent Plasticity (STDP) learning [5]. STDP relies on sparsely encoded spiking information among local neurons. SNNs are capable of learning rich spatio-temporal information [6]. In principle, SNNs can be fault tolerant due to its ability to re-learn and adapt the connections with other neurons, akin to how the brains learn. Also SNNs can natively interface with specialized hardware sensors which mimic biological vision (Dynamic Vision Sensor) and hearing (Dynamic Audio Sensor) [7] as they directly transduce sensory information to spikes.
\nIn the rest of the chapter, a brief introduction on neuron biology and artificial neuron models is presented, followed by discussion on information representation as spikes, different learning methodologies, tools, and platforms available for simulating and implementing SNNs and finally few case studies as examples of SNN usage.
\nIn this section, a brief overview of the biological neuron processes is provided to understand the inference and learning dynamics of SNNs. A few popular neuron models are discussed at a high level to make the reader aware of the diversity of such research and its use in SNNs.
\nComplex living organisms have specialized cells called neurons, which are the fundamental unit of central nervous system. Neurons can transmit and receive signals in the form of electrical impulses. In a human brain, there are an estimated 200 billion neurons. Also, there are several different types of neurons in the body. In general, a neuron consists of a cell body or soma consisting of cell machinery, nucleus, dendrites, and an axon as shown in \nFigure 1\n.
\nNeurons (by unknown author, licensed under CC BY-SA
The dendrites receive information from other neurons, and this causes a voltage buildup on the cell body. When this membrane potential reaches a certain threshold, an electrical impulse is generated, and the axon transmits this spike away from the cell body to other neurons. After a spike is generated, the neuron returns to a lower potential called resting potential. Also, immediately after a spike is generated, the neuron cannot generate another spike for a short duration called the refractory period. The axon terminates at axon terminals which interface with dendrites of other neurons; this is called as a synapse. A synapse is connection between a pre synaptic neuron (which generates electrical impulse) and a postsynaptic neuron (receives the spike information) as shown in \nFigure 1\n. The synapse is not a direct connection, instead it consists of a gap called synaptic cleft as shown in \nFigure 2\n. Discussion about astrocyte cells is presented later in Section 4.5.
\nNeuronal synapse along with astrocyte cells (author created).
When an electrical impulse reaches the synapse, the presynaptic neuron releases certain chemicals called neurotransmitters into the synaptic cleft. The postsynaptic neuron picks up these neurotransmitters eventually causing the postsynaptic neurons membrane potential to either increase or decrease. The brain learns by strengthening or weakening the existing synaptic connections or by making new synaptic connections or dissolving those which are no longer needed. In this way, the synapses make the brain plastic and provide the ability to learn. Also, the strength of the synapse also matters for learning as it can modulate the amount of neurotransmitters released in the synaptic cleft resulting in a stronger or weaker synapse and depending on the type of neurotransmitters released, the synapse can be excitatory or inhibitory. An excitatory synapse is one which would increase the membrane potential of the post synaptic neuron; conversely, an inhibitory synapse would decrease the membrane potential. Based on these fundamental concepts, several researchers have proposed various neuron models over the decades. We do not yet fully understand the inner workings of brains and is still an active field of research. New neuron models are being proposed frequently as our understanding of biology increases. A few neuron models are listed below, followed by an overview of select models.
\nSome of the models proposed try to mimic biology for the purpose of understanding and modeling neuro-physiological processes and some models more oriented toward computing purposes. A few of neuron models to consider are McCulloch and Pitts [8], Hodgkin-Huxley [9], Perceptron [10], Izhikevich [11] Integrate and fire [12], Leaky integrate-and-fire [13], Quadratic integrate-and-fire [14], Exponential integrate-and-fire [15], Generalized integrate-and-fire [16], Time-varying integrate-and-fire model [17], Integrate-and-fire or burst [18], Resonate-and-fire [19], and Bayesian neuron model [20].
\nHodgkin and Huxley [9] studied the giant axon of the squid and found currents induced by different types of ions namely sodium ions, potassium ions, and leakage current due to calcium ions. The cell consists of voltage-dependent ion channels which regulate the concentration of these ions across the cell membrane. For the sake of simplicity, at a high level, the total membrane current is the sum of current induced by membrane capacitance and the ion channel currents as shown in Eq. (1), where \n
They also describe gating variables to control the ion channels and the resting potential of the cell. When the membrane potential increases significantly above the resting potential, the gating variable activates and then deactivates the channels resulting in a spike. This is a very simplified model and has several limitations [21].
\nIzhikevich neuron model [11] is more biologically plausible as shown in equations below.
\nWhere \n
Izhikevich neuron model [
Over time, if a biological neuron does not spike, then any potential builtup would dissipate. This phenomenon is modeled by several variations of Leaky Integrate and Fire (LIF) models. LIF neuron model is very popular due to its ease of implementation as a software model and for developing dedicated hardware models. Digital hardware implementation is more popular than the analog variants, again due to its simplicity of design, fabrication, and scalability.
\nA typical generic LIF model adapted for discrete implementation [22] is represented as:
\nSynaptic integration
\nLeak integration
\nThreshold, fire and reset
\nWhere \n
Bayesian neuron (BN) model is proposed in [20]. BN model is a stochastic neuron model. When the membrane potential reaches the threshold a BN model fires a spike stochastically. It generates a spike based on a Poisson process where neuron \n
Where the weight of the synapse between
To generate a Poisson process with time-varying rate \n
\n\n
SNNs understand the language of spikes, and it is necessary to decide what is the best possible way to represent real-world data to achieve best possible training of the network and efficient inference. Different coding techniques model different aspects of input spectrum. Some of the spike coding techniques are described below to get an intuition of signal representation using spikes.
\nWith rate coded spike trains, the information is encoded in the number of spikes over a specified temporal window. The firing rate \n
Evidence of rate coding is experimentally shown in sensory and motor systems [24]. The number of spikes emitted by the receptor neuron increases with the force applied to the muscle.
\nIf the rate \n
Therefore, the instantaneous firing rate is
\nThe expected number of spikes for the temporal window \n
To summarize, the experimental procedure of counting spikes over a time \n
The Peri-stimulus-time histogram and the average time-dependent firing rate [
A spike train \n
The instantaneous firing rate is the expectation over trials.
\nAn empirical estimate of the instantaneous firing rate can be deduced as shown in Eq. (18). It implies that the PSTH as described above represents the instantaneous firing rate.
\nThe average firing rate can be computed for a single neuron, or for a population of neurons representing a class over a single run or over several trials. Rate coding over a time window is suitable for representing the strength of stimulation. On the other hand, population-based rate coding could convey the same information by employing several neurons in a shorter temporal window. The latter trades quick response over a number of neurons. There is evidence of Purkinje neurons demonstrating information coding which is not just firing rate but also the timing and duration of nonfiring, quiescent periods [25, 26].
\nIf the time of spike occurrence in a temporal window carries information, then such coding is referred to as temporal coding. In such coding schemes the quiescent periods and the spiking time both carry information. There are several evidences in biology demonstrating this behavior [27, 28]. A typical temporal code is shown in \nFigure 5A\n, where the time interval of spike to start of stimulus caries information. These are sometimes referred to as pulse codes. Another variation is Rank Order Coding, which uses the relative timing of spikes across a population of cells. Rank order codes look at time to spike across the neuron population and a rank order can be implied from the firing order among the neurons in the population as described in \nFigure 5B\n.
\nDifferent strategies for information coding with spikes (refer to [
There is evidence suggesting that simple temporal averaging of firing rate is too simplistic to model neuronal circuits in the brain [30]. To address some of the shortcomings, several derivations of coding schemes based on different combinations of above concepts are widely used. Few of the common schemes and some task specific coding schemes are Rate code, Time to spike code, Time-to-first-spike: Latency code [31], Reverse time to spike code, Weighted spike code [32], Burst code [33], Population code, Population rate, Rank order code [34], Phase-of-firing code [35, 36], Place code [37], etc. \nFigure 5\n summarizes a few coding strategies. These coding schemes require appropriate algorithms for converting real-world data to spikes and vice versa. A few common conversion techniques are discussed in the next section.
\nSNNs understand the language of spikes; therefore, we must transform the real-world data to appropriate spike representation and subsequently transform the output spikes to real-world formats for human consumption. There are several encoding and decoding algorithms available to achieve this goal. Several heuristics are also employed. Some of the coding techniques mentioned above infer a specific coding/decoding scheme. Based on the nature of application (such as images, audio, video, financial data, user activity data), one must choose which is the best approach.
\nImage pixel values are binned and proportional firing rates are assigned to different neurons in the receptive fields for each pixel neuron, hence generating random process with rate coding [38]. Since spikes have no polarity positive and negative spike, subchannels can be used to represent richer encoding of data. In threshold-based schemes, a spike is generated when the input signal intensity crosses a threshold. Real numbers are compared against different thresholds, and positive and negative spikes are produced accordingly which are rate coded [39]. BSA algorithm for encoding and decoding [40] is used for modeling brain-machine interfaces and neurological processes in the brain. The work presented by the authors of [41] provides details on step-forward (SF), and moving-window (MW) encoding schemes. In SF scheme, a baseline \n
Hebb postulated that synaptic efficacy increases from a presynaptic neuron if it repeatedly assists the post synaptic neuron [42]. This forms the fundamentals of STDP rule for learning. STDP mimics biology where a synapse is strengthened when a presynaptic spike occurs before a post synaptic spike in close intervals, this is called Long-Term Potentiation (LTP). On the other hand, the synapse is weakened if the post synaptic neuron fires before the presynaptic neuron in close intervals. This is called as Long-Term Depression (LTD). In biology neurons are highly selective due to lateral inhibition. This allows for them to learn discriminatory and unique features in an unsupervised manner leading to an emergent Winner Take All (WTA) behavior. Apart from this the biological system demonstrates homeostasis to maintain overall stability. These are key principles in SNN modeling. There are several ways to achieve WTA and homeostasis behavior, some directly modify the neuron state, others use neural circuits. One such example with a scalable neural circuit [43] is shown in \nFigure 6\n. A WTA network consists of inhibitor neurons suppressing the activation of other lateral symbol neurons as shown in \nFigure 6(a)\n. To assist in homeostasis a normalization of the excitations of one neural circuit compared to others can be achieved using a Normalized Winner Take All (NWTA) network as shown in \nFigure 6(b). Where an upper limit (UL) neuron uniformly inhibits all symbol neurons if they are firing beyond a desirable high threshold. On the contrary if the symbol neurons are firing below a desired low threshold, then the lower limit (LL) neuron triggers an excitor (Ex) neuron to uniformly boost the firing rate of all symbol neurons. In this manner all independent neural circuits within an SNN fire in the dynamic range of excitations of the overall network. Both hard and soft WTA behavior can be achieved based on the amount of inhibition generated. In Hard WTA only one symbol neuron is active whereas in soft WTA more than one symbol neuron is active providing richer context.
\n(a) Winner take all network (b) normalized winner take all network [
SNNs can learn in both unsupervised and supervised modes. WTA concepts are essential part of unsupervised learning as the neuron with highest excitation inhibits the lateral neurons the strongest hence enabling it to preferentially pick up unique features. Unsupervised learning is possible by employing a teacher signal which excites the specific neurons to fire thereby allowing it to learn the features represented by the input signal. STDP based learning has its advantages of being able to model spatio-tempotal dynamics. Where the spatial component refers to localized activity/learning and temporal component refers to additional information representation by the spike intervals along the time axis. With the constant advances in SNN research, native STDP based rules are catching up to the more popular backpropagation-based learning methods used in Artificial Neural Networks (ANN). STDP lends itself for efficient localized and distributed learning, which is a huge advantage over other learning methods. Also SNNs can be adapted to model memories in the form of Long Short-Term Memory networks [39] which shows that recurrent learning behavior is also possible. The following sub-sections discus few learning rules used in training SNNs along with a brief introduced to backpropagation-based learning.
\nA classic STDP rule [44] is shown in \nFigure 7\n. The STDP curve tries to approximate experimentally observed behavior.
\nClassic STDP curve [
Here \n
Where \n
There are two broad categorizations of STDP rules, additive and multiplicative STDP [38]. Multiplicative rule tends to be more stable than additive rule. In additive rules the weight changes are independent of current weight and requires additional constraints to keep the values in operating bounds. These weight changes however produce bimodal distribution resulting in strong competition. In multiplicative rule presented in [38], the weight change is inversely proportional to the current weight making it inherently stable and resulting in a unimodal distribution. This distribution lacks synaptic competition which is desirable for learning discriminative features. For such rules, competition must be introduced in a different method. The stable multiplicative rule is further explored below and simplified for efficient implementation. Here the STDP rule is modeled such that weight change of a synapse has an exponential dependence on its current weight as shown in \nFigure 8\n (a). Update for the weight \n
(a) Current weight vs weight change for learning rates (b) STDP windows (c) Comparison of Exp, 2P and Q2PS STDP rules [
If
\nthen,
\nIf
\nthen,
\nWhere \n
The Exp STDP rule requires an exponential and a multiplication operation for both LTP and LTD for each synapse. From the perspective of efficient digital hardware implementation these are expensive operations in terms of circuit area and computation time. Quantized 2-power shift rule (Q2PS), which approximates the Exp rule in Eq. (20) and Eq. (21) by removing both multiplication and exponential. The approximation is summarized in Eq. (22) and Eq. (23).
\nIf
\nIf
\nwhere \n
where \n
With the tremendous advances in the field of ANNs, a growing body of research is available on various statistical learning algorithms. ANNs are inspired by biology but they do not mimic it. ANNs are made up of artificial neuron models specifically tuned for compute purposes and model a biological neuron at a very abstract level. An artificial neuron computes weighted sum of input signals and then an activation function computes the neuron output. In these networks’ neurons transmit signals as real numbers. ANNs compute inference by transmitting the neuron signals in the forward direction. The learning happens usually via a method called Backpropagation. This algorithm computes the gradients based on the error signal produced by a cost function and propagates it back for each layer of neurons in the neural network. The weight updates are usually made using gradient descent algorithms. There are many flavors of gradient descent algorithms available in the literature. For back propagation to work the activation function must be differentiable. Unlike SNNs, where a spike is not differentiable. In general, ANNs have proven to be very effective in tackling a wide variety of problems. Using these algorithms as inspiration several modified STDP rules have been researched, one among them is discussed below. This overview is a very high-level introduction to some of the terminology required to understand the following section. The reader is encouraged to explore further on this topic.
\nThe Backpropagation-STDP (BP STDP) [45] algorithm uses the number of spikes in a spike trains as an approximation for the real value of an artificial neurons excitation. They also divide the time interval into sub-intervals such that each sub-interval contains zero or one spike.
\nIn supervised training, the weight adjustment is governed by the STDP model shown in Eq. (25) and Eq. (26), in conjunction with a teacher signal. The teacher signal when applied to target neurons undergo weight change based on STDP and non-target neurons undergo weight changes based on anti-STDP. Anti-STDP is the opposite of STDP where LTP and LTD equations are swapped. Target neurons are identified by spike trains with maximum spike frequency (\n
A target neuron would generate a spike \n
Stigmergy is a methodology where several independent agents produce an emergent behavior through indirect interaction among themselves. This is facilitated with the help of asynchronous communication through traces left in the environment by individual agents. Stigmergy has been observed in nature and widely researched upon especially in insect colonies, these principles have been applied towards solving various engineering problems. Recent advances in neuroscience have shown evidence of another type of cells called astrocytes working in tandem with neurons to regulate the behavior of the central nervous system [46]. Astrocytes are star shaped cells with several branches called as processes. The end of these branches called as end feet interface with a synapse by wrapping around it creating a region around the synaptic cleft called as microdomain as shown in \nFigure 2\n. Astrocytes also interface the neurons apart from the synapse providing a closed loop feedback mechanism. They also interface with other astrocytes like a synapse, instead this is called as a gap junction. Gap junction facilitates communication between astrocyte cells only through chemical means. Astrocytes are functionally very diverse and play a very important role, only a high-level concept with limited detail is introduced for understanding of relevant discussion. With the help of calcium ions as a signaling mechanism along with the help of neurotransmitters the astrocytes help regulate the efficiency of synaptic transmission. These cells play a critical role in maintaining homeostasis, modulating LTP, LTD and structural plasticity in the brain.
\nSpiking activity results in release of neurotransmitters and change in concentration among different ions in the microdomain and extra cellular space. These changes are monitored as traces for indirect communication by astrocytes. Astrocytes themselves behaving like an environment with calcium ion concentration gradients within the cell acting as a medium for other neuron agents to indirectly infer these changes. This interaction creates a feedback mechanism in an asynchronous and distributed manner [47]. \nFigure 9\n shows the emergent stigmergy pattern in the brain. Short term activity and long-term activity gets communicated over a distance to other synapses over a spatial domain. Greater the distance, lower would be the influence. The details about the stigmergy based brain plasticity is presented in [47], interested readers are encouraged to explore further. This is a relatively new discovery and extensive research is underway to understand the role of astrocytes in overall brain mechanics.
\nStigmergic interactions between astrocytes and neurons (modified from [
There are several spiking neural network simulation tools available which support biologically realistic neuron models for large scale networks. Some of the popular ones are:
\nBrian [48], is a free, open source simulator for spiking neural networks. This simulator is capable of running on several different platforms and is implemented in python making it extendable and easy to use.
\nNEST [49] is another simulator focusing on the dynamics, size and structure of neural systems both large and small. This tool is not intended for modeling the intricate biological details of a neuron.
\nNEURON [50] is simulation environment best suited for modeling individual neurons and their networks. This is popular among neuroscientists for its ability to handle complex models in a computationally efficient manner. Unlike above simulator, NEURON can handle morphological details of a neuron and is used to validate theoretical models with experimental data.
\nThe above tools are commonly used in modeling biologically realistic neuron modes. They have their own unique interfaces and low-level semantics. An effort is made to smooth things out with a tool independent API package developed on Python programming language called PyNN [51]. The PyNN framework provides API support to model SNNs at a high level of abstraction of all aspects of neuron modeling and SNN representation, including populations of neurons, connections, layers etc. Though this provides high level abstraction, it also provides the ability to program at a low level such as adjusting individual parameters at the neuron and synapse level. To make things easy PyNN provides a set of library implementation for neurons, synapses, STDP models etc. They also provide easy interfaces to model various connectivity patterns among neurons like; all-to-all, small-world, random distance-dependent etc. These APIs are simulator independent making the code portable across different supported simulation tools and neuromorphic hardware platforms. It is relatively straightforward to add support to any custom simulation tool. PyNN officially supports BRIAN, NEST and NEURON SNN simulation tools. It is also supported on SpiNNaker [52] and BrainScaleS-2 [53] neuromorphic hardware systems. There are several more simulation tools which work with PyNN.
\nCypress [54] is a C++ based SNN Simulation tool. This provides a C++ wrapper around PyNN APIs. Hence, extending the multi-platform reach of Cypress using C++ interface. It is also capable of executing networks remotely on neuromorphic compute platforms.
\nThe BrainScaleS-2 [53] is a mixed-signal accelerated neuromorphic system with analog neural core, digital connectivity along with embedded SIMD microprocessor. It is efficient for emulations of neurons, synapses, plasticity models etc. This hardware based system is capable of evaluating models up to ten thousand times faster than real time.
\nThe SpiNNaker [52] is another neuromorphic system custom built with digital multicore ARM processors. The SpiNNaker system (NM-MC-1) consists of custom chips each with eighteen cores sharing a local 128 MB RAM. The overall system scales to more than a million cores.
\nApart from the above tools and platforms the are many custom SNN tools available to model SNNs easily for machine learning purposes. ANNarchy (Artificial Neural Networks architect) [55] is a custom simulator for evaluating SNNs. This is implemented in C++ language, along with acceleration support provided using OpenMP/CUDA. The network definitions are provided using python interface.
\nNeuCube [6] is a development environment for creation of Brain-Like Artificial Intelligence. The computational architecture is suited for modeling SNN applications across several domain areas. This tool supports the latest neural network models for AI purpose. It supports PyNN interface, hence extending its versatility. This tool can run on CPU, GPU and SpiNNaker platforms, also a cloud version of the tool is available.
\nTrueNorth [56] is another neuromorphic platform capable of evaluating SNNs at faster than real time and at very low power. They demonstrate running state of the art neural networks on the hardware platform scaling up to 64 million neurons and 16 billion synapses while the system consumes only 70 W of power out of which only 15 W is consumed by the neuromorphic hardware components. The hardware supports inference only, with learning performed off chip.
\nLoihi [57] is the latest offering in the neuromorphic SNN hardware. This hardware approach gets rid of crossbar architecture, which is prevalent in most previous neuromorphic implementations, lending itself to greater amount of flexibility. Loihi is also capable of on-chip learning which is a huge advantage in terms of online learning of synapses.
\nOther simulators capable of modeling software based models and models for custom neuromorphic hardware are presented in [20, 58, 59, 60]. This is still an ongoing field of research and there are several more accelerator-based simulators available hence the reader is encouraged to explore further. Neuromorphic hardware using more exotic hardware devices like memristors and phase change memories are also an active area of research, they are yet to make it to mainstream consumption hence they are only mentioned here.
\nIn this section few case studies are presented to bolster the concepts discussed in this chapter. The topics covered here include STDP learning dynamics, probabilistic graphical models as SNNs, SNN with BP-STDP based learning and SNNs on Neuromorphic Hardware.
\nA SNN is trained [38] to classify handwritten digits from the MNIST dataset using the STDP based learning rules Exp, Q2PS and 2P presented in Section 4.2. The authors build a three-layer SNN as shown in \nFigure 10\n. The MNIST images are of 28x28 pixel dimensions, hence the input layer contains 784 neurons, one per image pixel. The second/hidden layer contains neurons for learning the features of the input images. The number of neurons in this layer is varied over different trials to evaluate the effectiveness of the learning rule. Finally, the third layer consists of 10 neurons for classifying the input with one neuron per class. The input layer encodes the pixel intensities with varying firing rate in the range of 0 Hz – 300 Hz. Each input neuron is fully connected to the hidden layer neurons similarly each hidden layer neuron is fully connected to the output/classification layer neurons. In this network all synapses are plastic with soft WTA connectivity implemented between input layer and hidden layer neurons to facilitate different neurons to pick up shared features. On the other hand, a hard WTA connectivity exists between hidden layer and the classification layer.
\nMNIST SNN architecture showing connectivity, input, learnt features, labels and t-SNE visualizations, along with accuracy results [
A qualitative analysis of the learning rule is depicted by the t-distributed stochastic neighbor embedding (t-SNE) [61] visualizations in \nFigure 10\n. The t-SNE algorithm maps high dimensional data points lying on different but related low-dimensional manifolds to lower dimensions by capturing local structure present in high dimensional data. The input layer firing rate visualizations show the clustering of digit classes in 2 dimensions based on raw pixel data which has 784 dimensions. Similarly, the second visualization is made using the firing rate based on the learnt features of hidden layer as input to the t-SNE algorithm with 100 dimensions. It can be clearly seen that the STDP rule produces tight clustering of input space which is projected on to the feature space. The classification layer further groups these features to its respective classes. Networks with different number of hidden layer neurons are experimented with and the results are shown in the bottom right side of \nFigure 10\n. The robustness of the learning method is also demonstrated with experiments yielding similar accuracies with additive Gaussian white noise along with the use of NWTA network.
\nAn inference network based on a probabilistic graphical model for sentence construction is created using Bayesian neurons. It consists of lexicons representing words and phrases. Here each lexicon is a WTA sub network.
\nThe network consists of two functional sections: word sub network and phrase sub network. Each symbol neuron in word sub network represents a possible word occurrence and each symbol neuron in phrase sub network represents a possible pair of words co-occurring. The synapses between the symbol neurons represent the log conditional probabilities of words and phrases co-occurring. This network is initialized to have same intrinsic potential across all symbol neurons resulting in same initial firing rate. Based on the synaptic weights the strongly connected neurons resonate and enhance each other while laterally inhibiting other symbol neurons within the lexicon WTA network. These winning neurons proportionally excite other symbol neurons across different lexicons. In this manner the network settles on a steady state firing rate which represents a contextually correct behavior. From each lexicon of the word sub network a symbol neuron is picked with highest firing rate representing a grammatically correct semantically meaningful sentence. The WTA connections in this network perform soft WTA action there by the facilitating the retention of contextual information. \nFigure 11\n (a) shows the network topology. For the experiments, random documents images are picked, and fuzzy character recognition is performed. Due to the fuzzy nature, each character position will result in several possible matches hence, multiple possible matches for each word position is possible as described in [62]. An example of lexicon set is [{we, wo, fe, fo, ne, no, ns, us} {must, musk, oust, onst, ahab, bust, chat} {now, noa, non, new, how, hew, hen, heu} {find, rind, tina} {the, fac, fro, kho} {other, ether}]. The SNN after evaluating the lexicons settles on a grammatically correct sentence as [we must now find the other] as seen in \nFigure 11\n (b).
\n(a) Sentence confabulation network, (b) confabulation results spike plot [
Using the learning rule presented in Section 4.4, the authors of [45] train SNNs to evaluate BP-STDP rule on the XOR problem, the iris dataset and the MNIST dataset. They show that the network can model the linearly inseparable XOR problem using an SNN with 2 input, 20 hidden and 2 output neurons. For the iris dataset they create a SNN with 4 input, 30 hidden and 3 output neurons. With this network they were able to achieve 96% accuracy which is comparable to ANN trained with traditional backpropagation with an accuracy of 96.7%. The SNN for MNIST dataset consists of 784 input neurons, 100 through 1500 hidden neurons and 10 output neurons. With this network they were able to achieve 97.2% classification accuracy.
\nDeep networks achieve higher accuracy in recognition tasks and in some cases outperform humans. Eedn framework is proposed in [63], which enables SNNs to be trained using backpropagation with batch normalization [64] and implement them on TrueNorth neuromorphic hardware. The Eedn trained networks are capable of achieving state-of-the-art accuracy across eight standard datasets of vision and speech. In this implementation the inference on hardware can be run at up to 2600 frames/s which is faster than real time while consuming very low power of at most 275 mW across their experiments. The network uses low precision ternary weights +1, 0 and − 1 for its synapses. A binary activation function with an approximate derivative is modeled to enable backpropagation. A hysteresis parameter is introduced in the weight update rule to avoid rapid oscillations of weights during learning. The input images are transduced by applying 12 different convolutional filter operators with binary outputs to get 12 channel input to the network as shown in \nFigure 12\n.
\nExample image from CIFAR10 (column 1) and the corresponding output of 12 typical transduction filters (columns 2–13) [
Experiments were performed on eight datasets using five different network sizes spanning across several TrueNorth chips. The results of the experiments are summarized in \nFigure 13\n.
\nAccuracy of different sized networks on eight datasets. For comparison, accuracy of state-of-the-art unconstrained approaches are shown as bold horizontal lines [
This chapter discussed several concepts and techniques, all of which are bio inspired. The case studies presented provide a strong basis to grasp the immense potential these algorithms provide in tackling the very complex problems of today, which were unimaginable without the advances in this field. This chapter specifically provided a beginner’s guide to the field of spiking neural networks. It presented a brief overview of neuron biology and notes on popular artificial neuron models. Information representation as spikes and how to transduce real world data to spikes and vice-versa was discussed which is similar to how brain represents information. Several tools for spiking neural network modeling and evaluation were provided for wholistic understanding and for experimental evaluation of one’s network models. A few case study examples are presented to understand the presented concepts and the scope of information presented in this chapter. This is an ongoing research and a very hot topic with substantially new concepts and discoveries being published every week. The motivation being the ability for machines to autonomously and efficiently perform tasks which were previously delegated to humans only along every aspect of our lives. This is a paradigm shift and research will continue to not only develop machine intelligence but also to understand the inner workings of our brains, our thoughts and advance the field of neuroscience.
\nThis chapter represents fundamental knowledge for understanding spiking neural networks. Some of the text and images are adopted from the available research literature. Rest of the work represents authors original contributions along with the co-authors of the following research contributions [20, 38, 39, 43, 60, 62]. I am thankful for the support of Dr. Qinru Qiu from Syracuse University and her research group members specifically Amar Shrestha in contributing during the original research.
\nartificial intelligence
\nartificial neural networks
\nBayesian neuron
\nbackpropagation-STDP
\nexcitor neuron
\nlower limit neuron
\nlong-term depression
\nlong-term potentiation
\nmoving-window
\nnormalized winner take all
\nperi-stimulus-time histogram
\nstep-forward
\nspiking neural network
\nspike timing dependent plasticity
\nt-distributed stochastic neighbor embedding
\nupper limit neuron
\nwinner take all
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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\n\nIntechOpen - Registered publisher with office at 5 Princes Gate Court, London, SW7 2QJ - UNITED KINGDOM
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\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{},profiles:[{id:"396",title:"Dr.",name:"Vedran",middleName:null,surname:"Kordic",slug:"vedran-kordic",fullName:"Vedran Kordic",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/396/images/7281_n.png",biography:"After obtaining his Master's degree in Mechanical Engineering he continued his education at the Vienna University of Technology where he obtained his PhD degree in 2004. He worked as a researcher at the Automation and Control Institute, Faculty of Electrical Engineering, Vienna University of Technology until 2008. His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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Morris, Steve Croker, Amy M. Masnick and Corinne Zimmerman",authors:[{id:"154336",title:"Prof.",name:"Bradley",middleName:null,surname:"Morris",slug:"bradley-morris",fullName:"Bradley Morris"},{id:"154337",title:"Prof.",name:"Steve",middleName:null,surname:"Croker",slug:"steve-croker",fullName:"Steve Croker"},{id:"154338",title:"Prof.",name:"Amy",middleName:null,surname:"Masnick",slug:"amy-masnick",fullName:"Amy Masnick"},{id:"154339",title:"Prof.",name:"Corinne",middleName:null,surname:"Zimmerman",slug:"corinne-zimmerman",fullName:"Corinne Zimmerman"}]}],mostDownloadedChaptersLast30Days:[{id:"58890",title:"Philosophy and Paradigm of Scientific Research",slug:"philosophy-and-paradigm-of-scientific-research",totalDownloads:14027,totalCrossrefCites:9,totalDimensionsCites:17,abstract:"Before carrying out the empirical analysis of the role of management culture in corporate social responsibility, identification of the philosophical approach and the paradigm on which the research carried out is based is necessary. Therefore, this chapter deals with the philosophical systems and paradigms of scientific research, the epistemology, evaluating understanding and application of various theories and practices used in the scientific research. The key components of the scientific research paradigm are highlighted. Theories on the basis of which this research was focused on identification of the level of development of the management culture in order to implement corporate social responsibility are identified, and the stages of its implementation are described.",book:{id:"5791",slug:"management-culture-and-corporate-social-responsibility",title:"Management Culture and Corporate Social Responsibility",fullTitle:"Management Culture and Corporate Social Responsibility"},signatures:"Pranas Žukauskas, Jolita Vveinhardt and Regina Andriukaitienė",authors:[{id:"179629",title:"Prof.",name:"Jolita",middleName:null,surname:"Vveinhardt",slug:"jolita-vveinhardt",fullName:"Jolita Vveinhardt"}]},{id:"74550",title:"School Conflicts: Causes and Management Strategies in Classroom Relationships",slug:"school-conflicts-causes-and-management-strategies-in-classroom-relationships",totalDownloads:2308,totalCrossrefCites:1,totalDimensionsCites:10,abstract:"Conflicts cannot cease to exist, as they are intrinsic to human beings, forming an integral part of their moral and emotional growth. Likewise, they exist in all schools. The school is inserted in a space where the conflict manifests itself daily and assumes relevance, being the result of the multiple interpersonal relationships that occur in the school context. Thus, conflict is part of school life, which implies that teachers must have the skills to manage conflict constructively. Recognizing the diversity of school conflicts, this chapter aimed to present its causes, highlighting the main ones in the classroom, in the teacher-student relationship. It is important to conflict face and resolve it with skills to manage it properly and constructively, establishing cooperative relationships, and producing integrative solutions. Harmony and appreciation should coexist in a classroom environment and conflict should not interfere, negatively, in the teaching and learning process. This bibliography review underscore the need for during the teachers’ initial training the conflict management skills development.",book:{id:"7827",slug:"interpersonal-relationships",title:"Interpersonal Relationships",fullTitle:"Interpersonal Relationships"},signatures:"Sabina Valente, Abílio Afonso Lourenço and Zsolt Németh",authors:[{id:"324514",title:"Ph.D.",name:"Sabina",middleName:"N.",surname:"Valente",slug:"sabina-valente",fullName:"Sabina Valente"},{id:"326375",title:"Prof.",name:"Abílio Afonso",middleName:"Afonso",surname:"Lourenço",slug:"abilio-afonso-lourenco",fullName:"Abílio Afonso Lourenço"},{id:"329177",title:"Dr.",name:"Zsolt",middleName:null,surname:"Németh",slug:"zsolt-nemeth",fullName:"Zsolt Németh"}]},{id:"58969",title:"Corruption, Causes and Consequences",slug:"corruption-causes-and-consequences",totalDownloads:27675,totalCrossrefCites:13,totalDimensionsCites:13,abstract:"Corruption is a constant in the society and occurs in all civilizations; however, it has only been in the past 20 years that this phenomenon has begun being seriously explored. It has many different shapes as well as many various effects, both on the economy and the society at large. Among the most common causes of corruption are the political and economic environment, professional ethics and morality and, of course, habits, customs, tradition and demography. Its effects on the economy (and also on the wider society) are well researched, yet still not completely. Corruption thus inhibits economic growth and affects business operations, employment and investments. It also reduces tax revenue and the effectiveness of various financial assistance programs. The wider society is influenced by a high degree of corruption in terms of lowering of trust in the law and the rule of law, education and consequently the quality of life (access to infrastructure, health care). There also does not exist an unambiguous answer as to how to deal with corruption. Something that works in one country or in one region will not necessarily be successful in another. This chapter tries to answer at least a few questions about corruption and the causes for it, its consequences and how to deal with it successfully.",book:{id:"6487",slug:"trade-and-global-market",title:"Trade and Global Market",fullTitle:"Trade and Global Market"},signatures:"Štefan Šumah",authors:[{id:"228073",title:"Mr.",name:"Stefan",middleName:null,surname:"Sumah",slug:"stefan-sumah",fullName:"Stefan Sumah"}]},{id:"55499",title:"Human Resources Management in Nonprofit Organizations: A Case Study of Istanbul Foundation for Culture and Arts",slug:"human-resources-management-in-nonprofit-organizations-a-case-study-of-istanbul-foundation-for-cultur",totalDownloads:2387,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The aim of this study is to investigate the efficiency and importance of human resources management in nonprofit organizations. The understanding was included to the literature as personnel management at the beginning of the twentieth century and it turned into an approach as human resources management in the 1980s. It could be observed that many organizations, which deem the human as the most critical stakeholder, adopt a traditional way of personnel management in operating human resources. The employees play a key role in the success of an organization. For this reason, subjects such as recruitment, training, development, career management, performance appraisal, occupational health, and safety are the fundamental functions of human resources management. The study examines to what extent these roles are evaluated through a case study. The subject matter of the study is the most powerful culture and art foundation in Turkey. Compared to many other nonprofit organizations, the foundation actively performs a variety of services within a year worldwide. The fact that the total number of employees might rise up to 800, including the field personnel, indicates the need of a good functioning human resources management. The human resources practices of the foundation are examined and evaluated within that scope.",book:{id:"5826",slug:"issues-of-human-resource-management",title:"Issues of Human Resource Management",fullTitle:"Issues of Human Resource Management"},signatures:"Beste Gökçe Parsehyan",authors:[{id:"189113",title:"Dr.",name:"Beste",middleName:null,surname:"Gokce Parsehyan",slug:"beste-gokce-parsehyan",fullName:"Beste Gokce Parsehyan"}]},{id:"59152",title:"Marketing Strategies for the Social Good",slug:"marketing-strategies-for-the-social-good",totalDownloads:1651,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Social network sites (SNS) have proven to be a good environment to promote and sell goods and services, but marketing is more than creating commercial strategies. Social marketing strategies can also be used to promote behavioral change and help individuals transform their lives, achieve well-being, and adopt prosocial behaviors. In this chapter, we seek to analyze with a netnographic study, how SNS are being employed by nonprofits and nongovernment organizations (NGOs) to enable citizens and consumers to participate in different programs and activities that promote social transformation and well-being. A particular interest is to identify how organizations are using behavioral economic tactics to nudge individuals and motivate them to engage in prosocial actions. By providing an understanding on how SNS can provide an adequate environment for the design of social marketing strategies, we believe our work has practical implications both for academicians and marketers who want to contribute in the transformation of consumer behavior and the achievement of well-being and social change.",book:{id:"6583",slug:"marketing",title:"Marketing",fullTitle:"Marketing"},signatures:"Alicia De La Pena",authors:[{id:"196878",title:"Dr.",name:"Alicia",middleName:null,surname:"De La Pena",slug:"alicia-de-la-pena",fullName:"Alicia De La Pena"}]}],onlineFirstChaptersFilter:{topicId:"4",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82982",title:"The Well-Being in the Children and Adolescents with ADHD: Possible Influencing Factors and How to Improve It",slug:"the-well-being-in-the-children-and-adolescents-with-adhd-possible-influencing-factors-and-how-to-imp",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.106596",abstract:"In recent years, academics have increasingly emphasized the importance of research into the well-being of children and adolescents. This is because well-being plays an important role in the development of children and adolescents. The literature reports that high levels of well-being facilitate positive functioning in children and adolescents. They contribute to the overall development of the individual and are a key factor in helping children and adolescents to integrate into society. ADHD, the most prevalent neurodevelopmental disorder, affects more than 5% of children and adolescents, and the distress caused by its symptom can seriously undermine the well-being of children and adolescents. Therefore, this chapter discusses this noticeable issue focusing on the following key parts: An understanding of the well-being in children and adolescents, the factors that affect the well-being of children and adolescents with ADHD, and how to improve the well-being of children and adolescents with ADHD.",book:{id:"11444",title:"Happiness - Biopsychosocial and Anthropological Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11444.jpg"},signatures:"Jenson Yin and Jie Luo"},{id:"82949",title:"Corruption and Deterioration of Democracy: The Brazilian Lesson",slug:"corruption-and-deterioration-of-democracy-the-brazilian-lesson",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.106194",abstract:"Although it has emerged, nationally and internationally, as one of the largest investigations against political corruption, Operation Car Wash—at its peak of popular prestige—cleared the path for the political rise of Jair Bolsonaro to the Presidency of the Republic of Brazil. And by doing so, to a certain extent, it paved the way for a set of arbitrary practices that today threaten and weaken the main Brazilian democratic institutions. Brazilian democracy today pays a high price for the Judiciary’s lethargic and condescending response to the unorthodox and illegal practices of Federal Judge Sérgio Moro during the golden years of Operation Car Wash (2014–2018). The lesson that the Brazilian episode brings to the international legal community is that the constant disrespect for the rules of due criminal procedure in large cases of corruption erodes the institutional bases that support the proper confrontation of this type of crime. The pertinent fight against corruption in a democracy can only take place in strict obedience to the law.",book:{id:"11772",title:"Corruption - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11772.jpg"},signatures:"Fabio Roberto D’Avila and Theodoro Balducci de Oliveira"},{id:"82867",title:"Indigenous Cultural Expressions and Methodological Frameworks: Some Thoughts",slug:"indigenous-cultural-expressions-and-methodological-frameworks-some-thoughts",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.106236",abstract:"Within the contemporary global world, there appears to be an inevitable lag between the changing factual reality and the concepts and categories scholars use to analyze it, i.e., “indigenous peoples,” “traditional oral expressions,” “ethnicity,” “cultural identity,” and “cultural heritage.” But are these discrepancies insurmountable? This article delves into such mismatches, examining the relentless search for heuristic instruments to deal with the diverse indigenous artistic expressions in their socio-historical and political contexts. It presents some thoughts about the methodological frameworks used to ponder indigenous cultural expressions. The main argument is based on ethnographic research among Zoque and Mayan peoples in the states of Oaxaca and Chiapas in Southern Mexico, while establishing a dialog with ethnographies by other authors on different indigenous regions.",book:{id:"11434",title:"Indigenous Populations - Perspectives From Scholars and Practitioners in Contemporary Times",coverURL:"https://cdn.intechopen.com/books/images_new/11434.jpg"},signatures:"Marina Alonso-Bolaños"},{id:"82930",title:"Psychosocial Factors Linked to Severe Mental Disorders in a Convenience Sample of Teenage Students",slug:"psychosocial-factors-linked-to-severe-mental-disorders-in-a-convenience-sample-of-teenage-students",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.104936",abstract:"Students with severe mental disorders (SMDs) are a vulnerable population with higher risks of early school dropout than the general population. Our aim has been to define psychosocial factors of students aged 12–18 years who have been diagnosed with severe mental disorders. So, we have defined the psychosocial factors of a group of students aged 12 to 18 years who have been diagnosed with a SMD. We have made the selection of the sample through an intentional nonprobability sampling. One hundred and nine cases of students were analyzed. We have analyzed the evolution of the student throughout their academic history until the moment in which they are hospitalized in serious condition by means of an exploratory factor analysis, with the application of the KMO sample adequacy of 0.776 and the significance of Bartlett’s test of sphericity p < .001, we have obtained a high correlation between the variables. The factors obtained are study limitations, symptomatology representation, study facilitators, other limitations. The results show that it is necessary to take into account the conditions that prevent them from permanence, inclusion, coexistence, and educational achievement. Likewise, symptomatic expression and family support are key elements in improving the educational process of pupils with SMD. These factors allow us to infer pedagogical practices that are more appropriate to their needs.",book:{id:"10671",title:"Adolescences",coverURL:"https://cdn.intechopen.com/books/images_new/10671.jpg"},signatures:"Cristina Sánchez Romero and Francisco Crespo Molero"},{id:"82928",title:"Utilizing Environmental Analytical Chemistry to Establish Culturally Appropriate Community Partnerships",slug:"utilizing-environmental-analytical-chemistry-to-establish-culturally-appropriate-community-partnersh",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.106237",abstract:"In the United States, minority communities are disproportionately exposed to environmental contaminants due to a combination of historically discriminatory based racial policies and a lack of social political capital. American Indian/Alaska Native (AI/AN) communities have additional factors that increase the likelihood of contaminant exposure. Some of these factors include the disparity of social, cultural, and political representation, differences in cultural understandings between AI/AN communities and western populations, and the unique history of tribal sovereignty in the US. Since the 1990s, research from both private and federal organizations have sought to increase research with AI/AN communities. However, although rooted in beneficence, the rift in cultural upbringing can lead to negative outcomes as well as further isolation and misrepresentation of AI/AN communities. Environmental analytical chemistry (EAC) is one approach that provides a means to establish productive and culturally appropriate collaborations with AI/AN populations. EAC is a more holistic approach that incorporates numerous elements and disciplines to understand underlying environmental questions, while allowing direct input from AI/AN communities. Additionally, EAC allows for a myriad of experimental approaches that can be designed for each unique tribal community, to maintain cultural respect and probe individual nuanced questions.",book:{id:"11434",title:"Indigenous Populations - Perspectives From Scholars and Practitioners in Contemporary Times",coverURL:"https://cdn.intechopen.com/books/images_new/11434.jpg"},signatures:"Jonathan Credo, Jani C. Ingram, Margaret Briehl and Francine C. Gachupin"},{id:"82845",title:"Revisiting Crisis Governance: Toward Collaborative Crisis Management",slug:"revisiting-crisis-governance-toward-collaborative-crisis-management",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.106129",abstract:"This chapter attends to three main modes of crisis governance: centralization, decentralization, and collaborative crisis management (CCM). While the first two modes focus almost exclusively on government actors, CCM goes beyond them by involving private sectors and civil society. CCM is a more robust form of crisis governance since it combines knowledge and resources from multiple actors, which is a key to managing the more complex nature of modern crises. This chapter uses the case of Indonesia in dealing with the COVID-19 pandemic to show the dynamics of crisis governance. Indonesia moved from a centralized mode of crisis governance toward a more decentralized one. Simultaneously, there were several collaborative initiatives involving multiple stakeholders to deal with the crisis, such as in the case of SONJO. The case illustrates that while CCM provides a more effective response, it has some limitations as it has a smaller scale, may create internal conflict, lacks sustainability, and has a nonbinding character. The experience of Indonesia lends the lesson that for CCM to be robust crisis governance, and there needs to be a clear arrangement to boost its scale, manage internal conflict, improve sustainability, and induce a more permanent and binding framework.",book:{id:"11439",title:"Crisis Management - Principles, Roles and Application",coverURL:"https://cdn.intechopen.com/books/images_new/11439.jpg"},signatures:"Gabriel Lele"}],onlineFirstChaptersTotal:276},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:18,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"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"}}}},{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"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. 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He 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. 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In 1982 he became a Doctor of the Natural Sciences from General Biology, Natural Faculty, Šafarik’s University in Košice. In 1995 he received a PhD. – Physiology and Patophysiology, Natural Faculty Šafarik’s University in Košice. In 2005 he became an Associate Professor from Normal and Patological Physiology, Medical Faculty, Šafarik’s University in Košice. From 1982 to 1983 Dr.Švorc worked as an independent specialist in the local museum in Poprad, Slovakia. In 1983 he started working as a lecturer at the Department of Physiology, Šafarik’s University in Kosice, Slovakia. From\r\n2011 until 2014 he was a Head of the Institute of Physiology and Pathophysiology, Medical Faculty, University of Ostrava, Czech Republic. His research interest includes:\r\nChronobiology of cardiovascular system, respiratory system and autonomic nervous system.",institutionString:"Pavol Josef Safarik University",institution:{name:"University of Pavol Jozef Šafárik",country:{name:"Slovakia"}}},{id:"187859",title:"Prof.",name:"Kusal",middleName:"K.",surname:"Das",slug:"kusal-das",fullName:"Kusal Das",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBDeQAO/Profile_Picture_1623411145568",biography:"Kusal K. Das is a Distinguished Chair Professor of Physiology, Shri B. M. Patil Medical College and Director, Centre for Advanced Medical Research (CAMR), BLDE (Deemed to be University), Vijayapur, Karnataka, India. Dr. Das did his M.S. and Ph.D. in Human Physiology from the University of Calcutta, Kolkata. His area of research is focused on understanding of molecular mechanisms of heavy metal activated low oxygen sensing pathways in vascular pathophysiology. He has invented a new method of estimation of serum vitamin E. His expertise in critical experimental protocols on vascular functions in experimental animals was well documented by his quality of publications. He was a Visiting Professor of Medicine at University of Leeds, United Kingdom (2014-2016) and Tulane University, New Orleans, USA (2017). For his immense contribution in medical research Ministry of Science and Technology, Government of India conferred him 'G.P. Chatterjee Memorial Research Prize-2019” and he is also the recipient of 'Dr.Raja Ramanna State Scientist Award 2015” by Government of Karnataka. He is a Fellow of the Royal Society of Biology (FRSB), London and Honorary Fellow of Karnataka Science and Technology Academy, Department of Science and Technology, Government of Karnataka.",institutionString:"BLDE (Deemed to be University), India",institution:null},{id:"243660",title:"Dr.",name:"Mallanagouda Shivanagouda",middleName:null,surname:"Biradar",slug:"mallanagouda-shivanagouda-biradar",fullName:"Mallanagouda Shivanagouda Biradar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243660/images/system/243660.jpeg",biography:"M. S. Biradar is Vice Chancellor and Professor of Medicine of\nBLDE (Deemed to be University), Vijayapura, Karnataka, India.\nHe obtained his MD with a gold medal in General Medicine and\nhas devoted himself to medical teaching, research, and administrations. He has also immensely contributed to medical research\non vascular medicine, which is reflected by his numerous publications including books and book chapters. Professor Biradar was\nalso Visiting Professor at Tulane University School of Medicine, New Orleans, USA.",institutionString:"BLDE (Deemed to be University)",institution:{name:"BLDE University",country:{name:"India"}}},{id:"289796",title:"Dr.",name:"Swastika",middleName:null,surname:"Das",slug:"swastika-das",fullName:"Swastika Das",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/289796/images/system/289796.jpeg",biography:"Swastika N. Das is Professor of Chemistry at the V. P. Dr. P. G.\nHalakatti College of Engineering and Technology, BLDE (Deemed\nto be University), Vijayapura, Karnataka, India. She obtained an\nMSc, MPhil, and PhD in Chemistry from Sambalpur University,\nOdisha, India. Her areas of research interest are medicinal chemistry, chemical kinetics, and free radical chemistry. She is a member\nof the investigators who invented a new modified method of estimation of serum vitamin E. She has authored numerous publications including book\nchapters and is a mentor of doctoral curriculum at her university.",institutionString:"BLDEA’s V.P.Dr.P.G.Halakatti College of Engineering & Technology",institution:{name:"BLDE University",country:{name:"India"}}},{id:"248459",title:"Dr.",name:"Akikazu",middleName:null,surname:"Takada",slug:"akikazu-takada",fullName:"Akikazu Takada",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248459/images/system/248459.png",biography:"Akikazu Takada was born in Japan, 1935. After graduation from\nKeio University School of Medicine and finishing his post-graduate studies, he worked at Roswell Park Memorial Institute NY,\nUSA. He then took a professorship at Hamamatsu University\nSchool of Medicine. In thrombosis studies, he found the SK\npotentiator that enhances plasminogen activation by streptokinase. He is very much interested in simultaneous measurements\nof fatty acids, amino acids, and tryptophan degradation products. By using fatty\nacid analyses, he indicated that plasma levels of trans-fatty acids of old men were\nfar higher in the US than Japanese men. . He also showed that eicosapentaenoic acid\n(EPA) and docosahexaenoic acid (DHA) levels are higher, and arachidonic acid\nlevels are lower in Japanese than US people. By using simultaneous LC/MS analyses\nof plasma levels of tryptophan metabolites, he recently found that plasma levels of\nserotonin, kynurenine, or 5-HIAA were higher in patients of mono- and bipolar\ndepression, which are significantly different from observations reported before. In\nview of recent reports that plasma tryptophan metabolites are mainly produced by\nmicrobiota. He is now working on the relationships between microbiota and depression or autism.",institutionString:"Hamamatsu University School of Medicine",institution:{name:"Hamamatsu University School of Medicine",country:{name:"Japan"}}},{id:"137240",title:"Prof.",name:"Mohammed",middleName:null,surname:"Khalid",slug:"mohammed-khalid",fullName:"Mohammed Khalid",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/137240/images/system/137240.png",biography:"Mohammed Khalid received his B.S. in Chemistry in July 2000, and his Ph.D. in Physical Chemistry in 2007 from the University of Khartoum, Sudan. In 2009 he joined the Dr. Ron Clarke research group at the School of Chemistry, Faculty of Science, University of Sydney, Australia as a postdoctoral fellow where he worked on the Interaction of ATP with the phosphoenzyme of the Na+, K+-ATPase, and Dual mechanisms of allosteric acceleration of the Na+, K+-ATPase by ATP. He then worked as Assistant Professor at the Department of Chemistry, University of Khartoum, and in 2014 was promoted to Associate Professor ranking. In 2011 he joined the staff of the Chemistry Department at Taif University, Saudi Arabia, where he is currently active as an Assistant Professor. His research interests include:\r\n(1) P-type ATPase Enzyme Kinetics and Mechanisms; (2) Kinetics and Mechanism of Redox Reactions; (3) Autocatalytic reactions; (4) Computational enzyme kinetics; (5) Allosteric acceleration of P-type ATPases by ATP; (6) Exploring of allosteric sites of ATPases and interaction of ATP with ATPases located in the cell membranes.",institutionString:"Taif University",institution:{name:"Taif University",country:{name:"Saudi Arabia"}}},{id:"63810",title:"Prof.",name:"Jorge",middleName:null,surname:"Morales-Montor",slug:"jorge-morales-montor",fullName:"Jorge Morales-Montor",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/63810/images/system/63810.png",biography:"Dr. Jorge Morales-Montor was recognized with the Lola and Igo Flisser PUIS Award for best graduate thesis at the national level in the field of parasitology. He received a fellowship from the Fogarty Foundation to perform postdoctoral research stay at the University of Georgia. He has 153 journal articles to his credit. He has also edited several books and published more than fifty-five book chapters. He is a member of the Mexican Academy of Sciences, Latin American Academy of Sciences, and the National Academy of Medicine. He has received more than thirty-five awards and has supervised numerous bachelor’s, master’s, and Ph.D. students. Dr. Morales-Montor is the past president of the Mexican Society of Parasitology.",institutionString:"National Autonomous University of Mexico",institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"217215",title:"Dr.",name:"Palash",middleName:null,surname:"Mandal",slug:"palash-mandal",fullName:"Palash Mandal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217215/images/system/217215.jpeg",biography:null,institutionString:"Charusat University",institution:null},{id:"49739",title:"Dr.",name:"Leszek",middleName:null,surname:"Szablewski",slug:"leszek-szablewski",fullName:"Leszek Szablewski",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49739/images/system/49739.jpg",biography:"Leszek Szablewski is a professor of medical sciences. He received his M.S. in the Faculty of Biology from the University of Warsaw and his PhD degree from the Institute of Experimental Biology Polish Academy of Sciences. He habilitated in the Medical University of Warsaw, and he obtained his degree of Professor from the President of Poland. Professor Szablewski is the Head of Chair and Department of General Biology and Parasitology, Medical University of Warsaw. Professor Szablewski has published over 80 peer-reviewed papers in journals such as Journal of Alzheimer’s Disease, Biochim. Biophys. Acta Reviews of Cancer, Biol. Chem., J. Biomed. Sci., and Diabetes/Metabol. Res. Rev, Endocrine. He is the author of two books and four book chapters. He has edited four books, written 15 scripts for students, is the ad hoc reviewer of over 30 peer-reviewed journals, and editorial member of peer-reviewed journals. Prof. Szablewski’s research focuses on cell physiology, genetics, and pathophysiology. He works on the damage caused by lack of glucose homeostasis and changes in the expression and/or function of glucose transporters due to various diseases. He has given lectures, seminars, and exercises for students at the Medical University.",institutionString:"Medical University of Warsaw",institution:{name:"Medical University of Warsaw",country:{name:"Poland"}}},{id:"173123",title:"Dr.",name:"Maitham",middleName:null,surname:"Khajah",slug:"maitham-khajah",fullName:"Maitham Khajah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/173123/images/system/173123.jpeg",biography:"Dr. Maitham A. Khajah received his degree in Pharmacy from Faculty of Pharmacy, Kuwait University, in 2003 and obtained his PhD degree in December 2009 from the University of Calgary, Canada (Gastrointestinal Science and Immunology). Since January 2010 he has been assistant professor in Kuwait University, Faculty of Pharmacy, Department of Pharmacology and Therapeutics. His research interest are molecular targets for the treatment of inflammatory bowel disease (IBD) and the mechanisms responsible for immune cell chemotaxis. He cosupervised many students for the MSc Molecular Biology Program, College of Graduate Studies, Kuwait University. Ever since joining Kuwait University in 2010, he got various grants as PI and Co-I. He was awarded the Best Young Researcher Award by Kuwait University, Research Sector, for the Year 2013–2014. He was a member in the organizing committee for three conferences organized by Kuwait University, Faculty of Pharmacy, as cochair and a member in the scientific committee (the 3rd, 4th, and 5th Kuwait International Pharmacy Conference).",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"195136",title:"Dr.",name:"Aya",middleName:null,surname:"Adel",slug:"aya-adel",fullName:"Aya Adel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/195136/images/system/195136.jpg",biography:"Dr. Adel works as an Assistant Lecturer in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. Dr. Adel is especially interested in joint attention and its impairment in autism spectrum disorder",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"94911",title:"Dr.",name:"Boulenouar",middleName:null,surname:"Mesraoua",slug:"boulenouar-mesraoua",fullName:"Boulenouar Mesraoua",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94911/images/system/94911.png",biography:"Dr Boulenouar Mesraoua is the Associate Professor of Clinical Neurology at Weill Cornell Medical College-Qatar and a Consultant Neurologist at Hamad Medical Corporation at the Neuroscience Department; He graduated as a Medical Doctor from the University of Oran, Algeria; he then moved to Belgium, the City of Liege, for a Residency in Internal Medicine and Neurology at Liege University; after getting the Belgian Board of Neurology (with high marks), he went to the National Hospital for Nervous Diseases, Queen Square, London, United Kingdom for a fellowship in Clinical Neurophysiology, under Pr Willison ; Dr Mesraoua had also further training in Epilepsy and Continuous EEG Monitoring for two years (from 2001-2003) in the Neurophysiology department of Zurich University, Switzerland, under late Pr Hans Gregor Wieser ,an internationally known epileptologist expert. \n\nDr B. Mesraoua is the Director of the Neurology Fellowship Program at the Neurology Section and an active member of the newly created Comprehensive Epilepsy Program at Hamad General Hospital, Doha, Qatar; he is also Assistant Director of the Residency Program at the Qatar Medical School. \nDr B. Mesraoua's main interests are Epilepsy, Multiple Sclerosis, and Clinical Neurology; He is the Chairman and the Organizer of the well known Qatar Epilepsy Symposium, he is running yearly for the past 14 years and which is considered a landmark in the Gulf region; He has also started last year , together with other epileptologists from Qatar, the region and elsewhere, a yearly International Epilepsy School Course, which was attended by many neurologists from the Area.\n\nInternationally, Dr Mesraoua is an active and elected member of the Commission on Eastern Mediterranean Region (EMR ) , a regional branch of the International League Against Epilepsy (ILAE), where he represents the Middle East and North Africa(MENA ) and where he holds the position of chief of the Epilepsy Epidemiology Section; Dr Mesraoua is a member of the American Academy of Neurology, the Europeen Academy of Neurology and the American Epilepsy Society.\n\nDr Mesraoua's main objectives are to encourage frequent gathering of the epileptologists/neurologists from the MENA region and the rest of the world, promote Epilepsy Teaching in the MENA Region, and encourage multicenter studies involving neurologists and epileptologists in the MENA region, particularly epilepsy epidemiological studies. \n\nDr. Mesraoua is the recipient of two research Grants, as the Lead Principal Investigator (750.000 USD and 250.000 USD) from the Qatar National Research Fund (QNRF) and the Hamad Hospital Internal Research Grant (IRGC), on the following topics : “Continuous EEG Monitoring in the ICU “ and on “Alpha-lactoalbumin , proof of concept in the treatment of epilepsy” .Dr Mesraoua is a reviewer for the journal \"seizures\" (Europeen Epilepsy Journal ) as well as dove journals ; Dr Mesraoua is the author and co-author of many peer reviewed publications and four book chapters in the field of Epilepsy and Clinical Neurology",institutionString:"Weill Cornell Medical College in Qatar",institution:{name:"Weill Cornell Medical College in Qatar",country:{name:"Qatar"}}},{id:"282429",title:"Prof.",name:"Covanis",middleName:null,surname:"Athanasios",slug:"covanis-athanasios",fullName:"Covanis Athanasios",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/282429/images/system/282429.jpg",biography:null,institutionString:"Neurology-Neurophysiology Department of the Children Hospital Agia Sophia",institution:null},{id:"190980",title:"Prof.",name:"Marwa",middleName:null,surname:"Mahmoud Saleh",slug:"marwa-mahmoud-saleh",fullName:"Marwa Mahmoud Saleh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/190980/images/system/190980.jpg",biography:"Professor Marwa Mahmoud Saleh is a doctor of medicine and currently works in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. She got her doctoral degree in 1991 and her doctoral thesis was accomplished in the University of Iowa, United States. Her publications covered a multitude of topics as videokymography, cochlear implants, stuttering, and dysphagia. She has lectured Egyptian phonology for many years. Her recent research interest is joint attention in autism.",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259190/images/system/259190.png",biography:"Dr. Naqvi is a radioanalytical chemist and is working as an associate professor of analytical chemistry in the Department of Chemistry, Government College University, Faisalabad, Pakistan. Advance separation techniques, nuclear analytical techniques and radiopharmaceutical analysis are the main courses that he is teaching to graduate and post-graduate students. In the research area, he is focusing on the development of organic- and biomolecule-based radiopharmaceuticals for diagnosis and therapy of infectious and cancerous diseases. Under the supervision of Dr. Naqvi, three students have completed their Ph.D. degrees and 41 students have completed their MS degrees. He has completed three research projects and is currently working on 2 projects entitled “Radiolabeling of fluoroquinolone derivatives for the diagnosis of deep-seated bacterial infections” and “Radiolabeled minigastrin peptides for diagnosis and therapy of NETs”. He has published about 100 research articles in international reputed journals and 7 book chapters. Pakistan Institute of Nuclear Science & Technology (PINSTECH) Islamabad, Punjab Institute of Nuclear Medicine (PINM), Faisalabad and Institute of Nuclear Medicine and Radiology (INOR) Abbottabad are the main collaborating institutes.",institutionString:"Government College University",institution:{name:"Government College University, Faisalabad",country:{name:"Pakistan"}}},{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.png",biography:"Gyula Mózsik MD, Ph.D., ScD (med), is an emeritus professor of Medicine at the First Department of Medicine, Univesity of Pécs, Hungary. He was head of this department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition, and dietetics. His research fields are biochemical pharmacological examinations in the human gastrointestinal (GI) mucosa, mechanisms of retinoids, drugs, capsaicin-sensitive afferent nerves, and innovative pharmacological, pharmaceutical, and nutritional (dietary) research in humans. He has published about 360 peer-reviewed papers, 197 book chapters, 692 abstracts, 19 monographs, and has edited 37 books. He has given about 1120 regular and review lectures. He has organized thirty-eight national and international congresses and symposia. He is the founder of the International Conference on Ulcer Research (ICUR); International Union of Pharmacology, Gastrointestinal Section (IUPHAR-GI); Brain-Gut Society symposiums, and gastrointestinal cytoprotective symposiums. He received the Andre Robert Award from IUPHAR-GI in 2014. Fifteen of his students have been appointed as full professors in Egypt, Cuba, and Hungary.",institutionString:"University of Pécs",institution:{name:"University of Pecs",country:{name:"Hungary"}}},{id:"277367",title:"M.Sc.",name:"Daniel",middleName:"Martin",surname:"Márquez López",slug:"daniel-marquez-lopez",fullName:"Daniel Márquez López",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/277367/images/7909_n.jpg",biography:"Msc Daniel Martin Márquez López has a bachelor degree in Industrial Chemical Engineering, a Master of science degree in the same área and he is a PhD candidate for the Instituto Politécnico Nacional. His Works are realted to the Green chemistry field, biolubricants, biodiesel, transesterification reactions for biodiesel production and the manipulation of oils for therapeutic purposes.",institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a Ph.D. in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes, and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. He is the co-editor of The Open Biology Journal and associate editor for Oxidative Medicine and Cellular Longevity.",institutionString:"Universidad Nacional de La Plata",institution:{name:"National University of La Plata",country:{name:"Argentina"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",slug:"francisco-javier-martin-romero",fullName:"Francisco Javier Martin-Romero",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",biography:"Francisco Javier Martín-Romero (Javier) is a Professor of Biochemistry and Molecular Biology at the University of Extremadura, Spain. He is also a group leader at the Biomarkers Institute of Molecular Pathology. Javier received his Ph.D. in 1998 in Biochemistry and Biophysics. At the National Cancer Institute (National Institute of Health, Bethesda, MD) he worked as a research associate on the molecular biology of selenium and its role in health and disease. After postdoctoral collaborations with Carlos Gutierrez-Merino (University of Extremadura, Spain) and Dario Alessi (University of Dundee, UK), he established his own laboratory in 2008. The interest of Javier's lab is the study of cell signaling with a special focus on Ca2+ signaling, and how Ca2+ transport modulates the cytoskeleton, migration, differentiation, cell death, etc. He is especially interested in the study of Ca2+ channels, and the role of STIM1 in the initiation of pathological events.",institutionString:null,institution:{name:"University of Extremadura",country:{name:"Spain"}}},{id:"198499",title:"Dr.",name:"Daniel",middleName:null,surname:"Glossman-Mitnik",slug:"daniel-glossman-mitnik",fullName:"Daniel Glossman-Mitnik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/198499/images/system/198499.jpeg",biography:"Dr. Daniel Glossman-Mitnik is currently a Titular Researcher at the Centro de Investigación en Materiales Avanzados (CIMAV), Chihuahua, Mexico, as well as a National Researcher of Level III at the Consejo Nacional de Ciencia y Tecnología, México. His research interest focuses on computational chemistry and molecular modeling of diverse systems of pharmacological, food, and alternative energy interests by resorting to DFT and Conceptual DFT. He has authored a coauthored more than 270 peer-reviewed papers, 32 book chapters, and 4 edited books. He has delivered speeches at many international and domestic conferences. He serves as a reviewer for more than eighty international journals, books, and research proposals as well as an editor for special issues of renowned scientific journals.",institutionString:null,institution:null},{id:"318757",title:"Associate Prof.",name:"Irina Alexandrovna",middleName:null,surname:"Savvina",slug:"irina-alexandrovna-savvina",fullName:"Irina Alexandrovna Savvina",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/318757/images/18742_n.jpg",biography:null,institutionString:null,institution:null}]}},subseries:{item:{id:"9",type:"subseries",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11405,editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",slug:"luis-villarreal-gomez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",biography:"Dr. Luis Villarreal is a research professor from the Facultad de Ciencias de la Ingeniería y Tecnología, Universidad Autónoma de Baja California, Tijuana, Baja California, México. Dr. Villarreal is the editor in chief and founder of the Revista de Ciencias Tecnológicas (RECIT) (https://recit.uabc.mx/) and is a member of several editorial and reviewer boards for numerous international journals. He has published more than thirty international papers and reviewed more than ninety-two manuscripts. His research interests include biomaterials, nanomaterials, bioengineering, biosensors, drug delivery systems, and tissue engineering.",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,series:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343"},editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",slug:"cecilia-cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",slug:"gil-goncalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",slug:"johann-f.-osma",fullName:"Johann F. 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