Number of training and test samples.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\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:"5480",leadTitle:null,fullTitle:"Towards 5G Wireless Networks - A Physical Layer Perspective",title:"Towards 5G Wireless Networks",subtitle:"A Physical Layer Perspective",reviewType:"peer-reviewed",abstract:"This book intends to provide highlights of the current research topics in the field of 5G and to offer a snapshot of the recent advances and major issues faced today by the researchers in the 5G physical layer perspective. Various aspects of 5G system is deeply discussed (in three parts and ten chapters) with emphasis on its physical layer. Each chapter provides a comprehensive survey of the subject area and ends with a rich list of references to provide an in-depth coverage of the application at hand.",isbn:"978-953-51-2834-2",printIsbn:"978-953-51-2833-5",pdfIsbn:"978-953-51-4139-6",doi:"10.5772/63098",price:119,priceEur:129,priceUsd:155,slug:"towards-5g-wireless-networks-a-physical-layer-perspective",numberOfPages:244,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:"da8d6b0a60a9c1ad989e111fde79785c",bookSignature:"Hossein Khaleghi Bizaki",publishedDate:"December 14th 2016",coverURL:"https://cdn.intechopen.com/books/images_new/5480.jpg",numberOfDownloads:38575,numberOfWosCitations:59,numberOfCrossrefCitations:49,numberOfCrossrefCitationsByBook:8,numberOfDimensionsCitations:67,numberOfDimensionsCitationsByBook:8,hasAltmetrics:1,numberOfTotalCitations:175,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 11th 2016",dateEndSecondStepPublish:"May 2nd 2016",dateEndThirdStepPublish:"August 6th 2016",dateEndFourthStepPublish:"November 4th 2016",dateEndFifthStepPublish:"January 18th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7,8",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"21746",title:"Dr.",name:"Hossein",middleName:null,surname:"Khaleghi Bizaki",slug:"hossein-khaleghi-bizaki",fullName:"Hossein Khaleghi Bizaki",profilePictureURL:"https://mts.intechopen.com/storage/users/21746/images/1616_n.jpg",biography:"H. Khaleghi Bizaki received the B.S. and M.S. degrees in Electrical Engineering, Tehran, Iran, in 1998 and 2001, respectively. He received his PhD degree in Electrical Engineering, Communication system, about “precoding and Blind/Semi-Blind Estimation in MIMO Fading Channels” from Iran University of Science & Technology (IUST), Tehran, Iran, in 2008. Dr. Bizaki is author or co-author of more than 20 publications. His research interests include Information Theory, Coding Theory, Wireless Communication, MIMO Systems, Space Time Processing, and other topics on Communication System and Signal Processing.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"761",title:"Wireless Communication Network",slug:"electrical-and-electronic-engineering-wireless-communication-network"}],chapters:[{id:"52817",title:"Analysis of Candidate Waveforms for 5G Cellular Systems",doi:"10.5772/66051",slug:"analysis-of-candidate-waveforms-for-5g-cellular-systems",totalDownloads:4554,totalCrossrefCites:8,totalDimensionsCites:9,hasAltmetrics:0,abstract:"Choice of a suitable waveform is a key factor in the design of 5G physical layer. New waveform/s must be capable of supporting a greater density of users, higher data throughput and should provide more efficient utilization of available spectrum to support 5G vision of “everything everywhere and always connected” with “perception of infinite capacity”. Although orthogonal frequency division multiplexing (OFDM) has been adopted as the transmission waveform in wired and wireless systems for years, it has several limitations that make it unsuitable for use in future 5G air interface. In this chapter, we investigate and analyse alternative waveforms that are promising candidate solutions to address the challenges of diverse applications and scenarios in 5G.",signatures:"Ayesha Ijaz, Lei Zhang, Pei Xiao and Rahim Tafazolli",downloadPdfUrl:"/chapter/pdf-download/52817",previewPdfUrl:"/chapter/pdf-preview/52817",authors:[{id:"189754",title:"Dr.",name:"Pei",surname:"Xiao",slug:"pei-xiao",fullName:"Pei Xiao"},{id:"189921",title:"Dr.",name:"Ayesha",surname:"Ijaz",slug:"ayesha-ijaz",fullName:"Ayesha Ijaz"},{id:"189928",title:"Dr.",name:"Lei",surname:"Zhang",slug:"lei-zhang",fullName:"Lei Zhang"},{id:"190297",title:"Prof.",name:"Rahim",surname:"Tafazolli",slug:"rahim-tafazolli",fullName:"Rahim Tafazolli"}],corrections:null},{id:"52919",title:"Waveform Design Considerations for 5G Wireless Networks",doi:"10.5772/66050",slug:"waveform-design-considerations-for-5g-wireless-networks",totalDownloads:3459,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"In this chapter, we first introduce new requirements of 5G wireless network and its differences from past generations. The question “Why do we need new waveforms?” is answered in these respects. In the following sections, time‐frequency (TF) lattice structure, pulse shaping, and multicarrier schemes are discussed in detail. TF lattice structures give information about TF localization of the pulse shape of employed filters. The structures are examined for multicarrier, single‐carrier, time‐division, and frequency‐division multiplexing schemes, comparatively. Dispersion on time and frequency response of these filters may cause interference among symbols and carriers. Thus, effects of different pulse shapes, their corresponding transceiver structures, and trade‐offs are given. Finally, performance evaluations of the selected waveform structures for 5G wireless communication systems are discussed.",signatures:"Evren Çatak and Lütfiye Durak‐Ata",downloadPdfUrl:"/chapter/pdf-download/52919",previewPdfUrl:"/chapter/pdf-preview/52919",authors:[{id:"19414",title:"Prof.",name:"Lutfiye",surname:"Durak-Ata",slug:"lutfiye-durak-ata",fullName:"Lutfiye Durak-Ata"},{id:"189749",title:"M.Sc.",name:"Evren",surname:"Çatak",slug:"evren-catak",fullName:"Evren Çatak"}],corrections:null},{id:"52893",title:"Spectral Efficiency Analysis of Filter Bank Multi‐Carrier (FBMC)‐ Based 5G Networks with Estimated Channel State Information (CSI)",doi:"10.5772/66057",slug:"spectral-efficiency-analysis-of-filter-bank-multi-carrier-fbmc-based-5g-networks-with-estimated-chan",totalDownloads:3178,totalCrossrefCites:1,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Filter bank multi‐carrier (FBMC) modulation, as a potential candidate for physical data communication in the fifth generation (5G) wireless networks, has been widely investigated. This chapter focuses on the spectral efficiency analysis of FBMC‐based cognitive radio (CR) systems, and spectral efficiency comparison is conducted with another three types of multi‐carrier modulations: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), and universal‐filtered multi‐carrier (UFMC). In order to well evaluate and compare the spectral efficiency, we propose two resource allocation (RA) algorithms for single‐cell and two‐cell CR systems, respectively. In the single‐cell system, the RA algorithm is divided into two sequential steps, which incorporate subcarrier assignment and power allocation. In the two‐cell system, a noncooperative game is formulated and the multiple access channel (MAC) technique assists to solve the RA problem. The channel state information (CSI) between CR users and licensed users cannot be precisely known in practice, and thus, an estimated CSI is considered by defining a prescribed outage probability of licensed systems. Numerical results show that FBMC can achieve the highest channel capacity compared with another three waveforms.",signatures:"Haijian Zhang, Hengwei Lv and Pandong Li",downloadPdfUrl:"/chapter/pdf-download/52893",previewPdfUrl:"/chapter/pdf-preview/52893",authors:[{id:"188778",title:"Prof.",name:"Haijian",surname:"Zhang",slug:"haijian-zhang",fullName:"Haijian Zhang"},{id:"195140",title:"MSc.",name:"Hengwei",surname:"Lv",slug:"hengwei-lv",fullName:"Hengwei Lv"},{id:"195141",title:"MSc.",name:"Pandong",surname:"Li",slug:"pandong-li",fullName:"Pandong Li"}],corrections:null},{id:"52822",title:"Non-Orthogonal Multiple Access (NOMA) for 5G Networks",doi:"10.5772/66048",slug:"non-orthogonal-multiple-access-noma-for-5g-networks",totalDownloads:14904,totalCrossrefCites:31,totalDimensionsCites:41,hasAltmetrics:0,abstract:"In this chapter, we explore the concept of non-orthogonal multiple access (NOMA) scheme for the future radio access for 5G. We first provide the fundamentals of the technique for both downlink and uplink channels and then discuss optimizing the network capacity under fairness constraints. We further discuss the impacts of imperfect receivers on the performance of NOMA networks. Finally, we discuss the spectral efficiency (SE) of the networks that employ NOMA with its relations with energy efficiency (EE). We demonstrate that the networks with NOMA outperform other multiple access schemes in terms of sum capacity, EE and SE.",signatures:"Refik Caglar Kizilirmak",downloadPdfUrl:"/chapter/pdf-download/52822",previewPdfUrl:"/chapter/pdf-preview/52822",authors:[{id:"188668",title:"Dr.",name:"Refik Caglar",surname:"Kizilirmak",slug:"refik-caglar-kizilirmak",fullName:"Refik Caglar Kizilirmak"}],corrections:null},{id:"53333",title:"Physical-Layer Transmission Cooperative Strategies for Heterogeneous Networks",doi:"10.5772/66053",slug:"physical-layer-transmission-cooperative-strategies-for-heterogeneous-networks",totalDownloads:1975,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The deployment of small cells within the boundaries of a macro-cell is considered to be an effective solution to cope with the current trend of higher data rates and improved system capacity. In the current heterogeneous configuration with the mass deployment of small cells, it is preferred that these two cell types coexist over the same spectrum, because acquiring additional spectrum licenses for small cells is difficult and expensive. However, the coexistence leads to cross-tier/inter-system interference. In this context, this contribution investigates interference alignment (IA) methods in order to mitigate the interference of macro-cell base station towards the small cell user terminals. More specifically, we design a diversity-oriented interference alignment scheme with space-frequency block codes (SFBC). The main motivation for joint interference alignment with SFBC is to allow the coexistence of two systems under minor inter-system information exchange. The small cells just need to know what space-frequency block code is used by the macro-cell system and no inter-system channels need to be exchanged, contrarily to other schemes recently proposed. Numerical results show that the proposed method achieves a performance close to the case where full-cooperation between the tiers is allowed.",signatures:"Syed Saqlain Ali, Daniel Castanheira, Adão Silva and Atílio Gameiro",downloadPdfUrl:"/chapter/pdf-download/53333",previewPdfUrl:"/chapter/pdf-preview/53333",authors:[{id:"5130",title:"Dr.",name:"Daniel",surname:"Castanheira",slug:"daniel-castanheira",fullName:"Daniel Castanheira"},{id:"30839",title:"Prof.",name:"Adão",surname:"Silva",slug:"adao-silva",fullName:"Adão Silva"},{id:"38908",title:"Prof.",name:"Atilio",surname:"Gameiro",slug:"atilio-gameiro",fullName:"Atilio Gameiro"},{id:"188913",title:"Dr.",name:"Syed Saqlain",surname:"Ali",slug:"syed-saqlain-ali",fullName:"Syed Saqlain Ali"}],corrections:null},{id:"52936",title:"Achievable Energy Efficiency and Spectral Efficiency of Large‐ Scale Distributed Antenna Systems",doi:"10.5772/66049",slug:"achievable-energy-efficiency-and-spectral-efficiency-of-large-scale-distributed-antenna-systems",totalDownloads:1987,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In the large‐scale distributed antenna system (LS‐DAS), a large number of antenna elements are densely deployed in a distributed way over the coverage area, and all the signals are gathered at the cloud processor (CP) via dedicated fiber links for globally joint processing. Intuitively, the LS‐DAS can inherit the advantage of both large‐scale multiple‐input‐multiple‐output (MIMO) and network densification; thus, it offers enormous gains in terms of both energy efficiency (EE) and spectral efficiency (SE). However, as the number of distributed antenna elements (DAEs) increases, the overhead for acquiring the channel state information (CSI) will increase accordingly. Without perfect CSI at the CP, which is the majority situation in practical applications due to limited overhead, the claimed gain of LS‐DAS cannot be achieved. To solve this problem, this chapter considers a more practical case with only the long‐term CSI including the path loss and shadowing known at the CP. As the long‐term channel fading usually varies much more slowly than the short‐term part, the system overhead can be easily controlled under this framework. Then, the EE‐oriented and SE‐oriented power allocation problems are formulated and solved by fractional programming (FP) and geometric programming (GP) theories, respectively. It is observed that the performance gain with only long‐term CSI is still noticeable and, more importantly, it can be achieved with a practical system cost.",signatures:"Wei Feng, Ning Ge and Jianhua Lu",downloadPdfUrl:"/chapter/pdf-download/52936",previewPdfUrl:"/chapter/pdf-preview/52936",authors:[{id:"11528",title:"Dr.",name:"Wei",surname:"Feng",slug:"wei-feng",fullName:"Wei Feng"},{id:"191379",title:"Prof.",name:"Ning",surname:"Ge",slug:"ning-ge",fullName:"Ning Ge"},{id:"195085",title:"Prof.",name:"Jianhua",surname:"Lu",slug:"jianhua-lu",fullName:"Jianhua Lu"}],corrections:null},{id:"52922",title:"Energy Efficiency for 5G Multi-Tier Cellular Networks",doi:"10.5772/66052",slug:"energy-efficiency-for-5g-multi-tier-cellular-networks",totalDownloads:2226,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The heterogeneous cellular network (HCN) is most significant as a key technology for future fifth-generation (5G) wireless networks. The heterogeneous network consists of randomly macrocell base stations (MBSs) overlaid with femtocell base stations (FBSs). Stochastic geometry has been shown to be a very powerful tool to model, analyze, and design networks with random topologies such as wireless ad hoc, sensor networks, and multi-tier cellular networks. HCNs can be energy-efficiently designed by deploying various BSs belonging to different networks, which has drawn significant attention to one of the technologies for future 5G wireless networks. In this chapter, we propose switching off/on systems enabling the BSs in the cellular networks to efficiently consume the power by introducing active/sleep modes, which is able to reduce the interference and power consumption in the MBSs and FBSs on an individual basis as well as improve the energy efficiency of the cellular networks. We formulate the minimization of the power consumption for the MBSs and FBSs as well as an optimization problem to maximize the energy efficiency subject to throughput outage constraints, which can be solved by the Karush-Kuhn-Tucker (KKT) conditions according to the femto tier BS density. We also formulate and compare the coverage probability and the energy efficiency in HCN scenarios with and without coordinated multi-point (CoMP) to avoid coverage holes.",signatures:"Md. Hashem Ali Khan and Moon Ho Lee",downloadPdfUrl:"/chapter/pdf-download/52922",previewPdfUrl:"/chapter/pdf-preview/52922",authors:[{id:"171897",title:"Prof.",name:"Moon Ho",surname:"Lee",slug:"moon-ho-lee",fullName:"Moon Ho Lee"},{id:"190233",title:"Dr.",name:"Md. Hashem Ali",surname:"Khan",slug:"md.-hashem-ali-khan",fullName:"Md. Hashem Ali Khan"}],corrections:null},{id:"53332",title:"Beamforming in Wireless Networks",doi:"10.5772/66399",slug:"beamforming-in-wireless-networks",totalDownloads:2206,totalCrossrefCites:6,totalDimensionsCites:6,hasAltmetrics:0,abstract:"This chapter is about the beamforming approach in wireless 5G networks, which involves communication between multiple source-destination pairs. The relays can be multiple-input multiple-output (MIMO) and/or distributed single-input single-output (SISO), and full channel state information of source-relays and relay-destinations are assumed to be available. Our design consists of a two-step amplify-and-forward (AF) protocol. The first step includes signal transmission from the sources to the relays, and the second step contains transmitting a version of the linear precoded signal to the destinations. Beamforming is investigated only in relay nodes to reduce end user’s hardware complexity. Accordingly, the optimization problem is defined to find the relay beamforming coefficients that minimize the total relay transmit power by keeping the signal-to-interference-plus-noise ratio (SINR) of all destinations above a certain threshold value. It is shown that this optimization problem is a non-convex, and can be solved efficiently.",signatures:"Mohammad-Hossein Golbon-Haghighi",downloadPdfUrl:"/chapter/pdf-download/53332",previewPdfUrl:"/chapter/pdf-preview/53332",authors:[{id:"190600",title:"Ph.D. Student",name:"Mohammad-Hossein",surname:"Golbon-Haghighi",slug:"mohammad-hossein-golbon-haghighi",fullName:"Mohammad-Hossein Golbon-Haghighi"}],corrections:null},{id:"52937",title:"Superallocation and Cluster‐Based Cooperative Spectrum Sensing in 5G Cognitive Radio Network",doi:"10.5772/66047",slug:"superallocation-and-cluster-based-cooperative-spectrum-sensing-in-5g-cognitive-radio-network",totalDownloads:2139,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Consequently, the research and development for the 5G systems have already been started. This chapter presents an overview of potential system network architecture and highlights a superallocation technique that could be employed in the 5G cognitive radio network (CRN). A superallocation scheme is proposed to enhance the sensing detection performance by rescheduling the sensing and reporting time slots in the 5G cognitive radio network with a cluster‐based cooperative spectrum sensing (CCSS). In the 4G CCSS scheme, first, all secondary users (SUs) detect the primary user (PU) signal during a rigid sensing time slot to check the availability of the spectrum band. Second, during the SU reporting time slot, the sensing results from the SUs are reported to the corresponding cluster heads (CHs). Finally, during CH reporting time slots, the CHs forward their hard decision to a fusion center (FC) through the common control channels for the global decision. However, the reporting time slots for the SUs and CHs do not contribute to the detection performance. In this chapter, a superallocation scheme that merges the reporting time slots of SUs and CHs by rescheduling the reporting time slots as a nonfixed sensing time slot for SUs to detect the PU signal promptly and more accurately is proposed. In this regard, SUs in each cluster can obtain a nonfixed sensing time slot depending on their reporting time slot order. The effectiveness of the proposed chapter that can achieve better detection performance under –28 to –10 dB environments and thus reduce reporting overhead is shown through simulations.",signatures:"Md Sipon Miah, Md Mahbubur Rahman and Heejung Yu",downloadPdfUrl:"/chapter/pdf-download/52937",previewPdfUrl:"/chapter/pdf-preview/52937",authors:[{id:"189976",title:"Dr.",name:"Md Sipon",surname:"Miah",slug:"md-sipon-miah",fullName:"Md Sipon Miah"},{id:"189978",title:"Dr.",name:"Heejung",surname:"Yu",slug:"heejung-yu",fullName:"Heejung Yu"},{id:"194759",title:"Prof.",name:"Dr. Md Mahbubur",surname:"Rahman",slug:"dr.-md-mahbubur-rahman",fullName:"Dr. Md Mahbubur Rahman"}],corrections:null},{id:"53156",title:"Selective Control Information Detection in 5G Frame Transmissions",doi:"10.5772/66256",slug:"selective-control-information-detection-in-5g-frame-transmissions",totalDownloads:1947,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Control signalling information within wireless communication systems facilitates efficient management of limited wireless resources, plays a key role in improving system performance of 5G systems. This chapter focuses detection of one particular form of control information, namely, selective control information (SCI). Maximum-likelihood (ML) is one of the conventional SCI detection techniques. Unfortunately, it requires channel estimation, which introduces some implementation constraints and practical challenges. This chapter uses generalized frequency division multiplexing (GFDM) to evaluate and demonstrate the detection performance of a new form of SCI detection that uses a time-domain correlation (TDC) technique. Unlike the ML scheme, the TDC technique is a form of blind detection that has the capability to improve detection performance with no need for channel estimation. In comparison with the ML based receiver, results show that the TDC technique achieves improved detection performance. In addition, the detection performance of the TDC technique is improved with GFDM receivers that use the minimum mean square error (MMSE) scheme compared with the zero-forcing (ZF) technique. It is also shown that the use of a raised cosine (RC) shaped GFDM transmit filter improves detection performance comparison with filters that employ root raised cosine (RRC) pulse shape.",signatures:"Saheed A. Adegbite and Brian G. 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\r\n\tLiver Transplant is the gold standard and only the cure for end-stage liver diseases. However, throughout history, liver transplant has been surrounded by lots of controversies. Despite the better outcomes, liver transplant still seems to face lots of challenges and controversies primarily concerned with the marginal supply of organ donors, liver transplantation criteria for different conditions, and new surgical innovations. Thus, these challenges and controversies need to be discussed among the transplant community.
\r\n\r\n\tThis book aims to present an important disparity and controversies that are specific to outcomes of liver transplantation. Overall, this book will serve as an invaluable "tool" for liver transplant surgeons and hepatologists in the decision-making process before liver transplantation.
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Further, he obtained his PhD and Postdoc in Hepatobiliary Surgery and Liver Transplantation from the School of Medicine, Zhejiang University, China. He was a winner of Postdoctoral Exchange Fellowship Program (Talent Introduction Program) in 2019 from Postdoctoral Foundation of China. He has published articles in top journals like GUT, npj Precision Oncology, oncotarget, frontiers in immunology, Canadian journal of gastroenterology and hepatology, annals of transplantation, etc. He is an experienced surgeon in open/laparoscopic hepatobiliary and pancreatic surgery, including: liver transplantation, Whipples surgery, hepatectomy, laparoscopic pancreaticoduodenectomy, pancreas Distal resection, etc. His research interest includes Liver Transplant, Intestine Transplant, Liver Cancer, Pancreatic Cancer, Hepatobiliary and Pancreatic Surgery, Transplant Oncology and Transplant Immunology.",institutionString:"First Affiliated Hospital Zhejiang University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"First Affiliated Hospital Zhejiang University",institutionURL:null,country:{name:"China"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"81869",title:"Economic Evaluations of Liver Transplantation as Tools for Decision-Making and Implications in Clinical Practice",slug:"economic-evaluations-of-liver-transplantation-as-tools-for-decision-making-and-implications-in-clini",totalDownloads:11,totalCrossrefCites:0,authors:[null]},{id:"82100",title:"Acute Decompensated Liver: When to Transplant?",slug:"acute-decompensated-liver-when-to-transplant",totalDownloads:9,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"441704",firstName:"Ana",lastName:"Javor",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/441704/images/20009_n.jpg",email:"ana.j@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Especially, the generous spectral information comprises of hyperspectral images (HSIs) establishes new application domains and poses new technological challenges in data analysis [1]. With the available high spectral resolution, subtle objects and materials can be extracted by hyperspectral imaging sensors with very narrow diagnostic spectral bands for the variety of purposes such as detection, urban planning [2], agriculture [3], identification, surveillance [4], and quantification [5, 6]. HSIs allow the characterization of objects of interest (e.g., land cover classes) with unprecedented accuracy, and keep inventories up to date. Improvements in spectral resolution have called for advances in signal processing and exploitation algorithms.
\nHyperspectral image is a 3D data cube, which contains two-dimensional spatial information (image feature) and one-dimensional spectral information (spectral-bands). Especially, the spectral bands occupy very fine wavelengths, while the image features such as Land cover features and shape features disclose the disparity and association among adjacent pixels from different directions at a confident wavelength.
\nIn the remote sensing community, the term classification is used to denote the process that assigns individual pixels to a set of classes. The output of the classification step is known as the classification map. With respect to the availability of training samples, classification approaches can be split into two categories, i.e., supervised and unsupervised classifiers. Supervised approaches classify input data for each class using a set of representative samples known as training samples. Hyperspectral (HS) image classification always suffers from varieties of artifacts, such as high dimensionality, limited or unbalanced training samples [7], spectral variability, and mixing pixels. The Hughes phenomenon is a common problem in the supervised classification process [8]. The power of classification increases with the increase of available training samples. The limited availability of training samples decreases the classification performance with the increase of feature dimension. This effect is famously termed as “Hughes phenomenon” [9]. It is well known that increasing data dimensionality and high redundancy between features might cause problems during data analysis. There are many significant challenges that need to be addressed when performing hyperspectral image classification. Primarily, supervised classification faces challenge about the imbalance between high dimensionality and incomplete accessibility of training samples or the presence of mixed pixels in the data [10]. Further, it is desirable to integrate the essential spatial as well as spectral information so as to combine the complementary features that stem from source images [11]. A considerable amount of literature has been published with regard to overcoming these challenges, and performing hyperspectral image classification effectively.
\nHyperspectral image classification could attract scientific community which aims at assigning a pixel (or a spectrum) to one of a certain set of predefined classes. Maximum likelihood (ML) methods, neural networks architectures [12], support vector machine (SVM) [13], Bayesian approach [14] as well as kernel methods [15] are the prominent methods which have been investigated in recent years for the identification or classification of hyperspectral data.
\nBased on the usage of training sample, image classification task is categorized as supervised, unsupervised and semi-supervised hyperspectral image classification.
\nThe paramount challenge for HSI classification is the curse of dimensionality which is also termed as Hughes phenomenon. To confront with this difficulty, feature extraction methods are used to reduce the dimensionality by selecting the prominent features. In unsupervised methods, the algorithm or method automatically groups pixels with similar spectral characteristics (means, standard deviations, etc.) into unique clusters according to some statistically determined criteria. Further, unsupervised classification methods do not require any prior knowledge to train the data. The familiar unsupervised methods are principal component analysis (PCA) [16] and independent component analysis (ICA) [17].
\nIt is the most widely used technique for dimensionality reduction. In comparative sense, appreciable reduction in the number of variables is possible while retaining most of the information contained by the original dataset. The substantial correlation between the hyperspectral bands is the basis for PCA. The analysis attempts to eliminate the correlation between the bands and further determines the optimum linear combination of the original bands accounting for the variation of pixel values in an image [18].
\nThe mathematical principle of PCA relies upon the eigen value decomposition of covariance matrix of HSI bands. The pixels of hyperspectral data are arranged as a vector having its size same as the number of bands. \n
where M = p \n
The covariance matrix can also be written as:
\nD is the diagonal matrix composed of eigen values \n
In hypespectral data, most of the elements are covered by the sensors with high spectral resolution which cannot be well described by the second order characteristics. Hence, PCA is not an effective tool for HS image classification since it deals with only second-order statistics.
\nIndependent component analysis successfully executes the independence of the components with higher-order statistics, and is relatively more suitable to encounter high dimensionality of HS images. ICA is an attractive tool for dimensionality reduction, feature extraction, blind source separation, etc., as well as to preserve the information which cannot be retrieved using second order statistics [19, 20].
\nLet us consider a mixture of random variables \n
where \n
If \n
where N is the number of pixels in each band, d represents the number of spectral bands and n gives the number of sources or materials present in the image. The estimation of the ICA model is conceivable, only if the following presumptions and limitations are fulfilled: (i) Sources should be statistically independent (ii) Independent components should possess non Gaussian distribution (iii) Matrix A should be a square and full rank matrix.
\nThe supervised classification takes the advantage of rich spectral information and has explored many applications including urban development [21], the monitoring of land changes [22], target detection [23], and resource management [24]. In supervised classification only labeled data is used to train the classifier. A large number of supervised classification methods have been discussed in the literature, some of the prominent methods are maximum likelihood (ML), nearest neighbor classifier, decision trees, random forest, support vector machines (SVMs), etc.
\n\n\nFigure 1\n shows the conventional steps of supervised classification of HSIs.
\nFlowchart of HSI supervised classification.
The ML classifier assumes that the statistics for each class in each band are normally distributed and estimates the probability that a given pixel belongs to a certain specific class [25]. Unless a probability threshold is selected, all pixels are classified. Each pixel is assigned to a particular class that manifests the maximum probability. If the estimated maximum probability is smaller than a threshold, the pixel remains unclassified. The following discriminant functions for each pixel in the image are implemented in ML classification.
\nwhere i = class; x = n-dimensional data (where n represents the number of bands); \n
Implementation of the ML classification involves the estimation of class mean vectors and covariance matrices using training pattern chosen from known examples of each particular class [26]. It usually acquires higher classification accuracy compared to other traditional classification approaches. It assumes that each band is normally distributed and the chosen training samples are comprised of exhaustively defined set of classes. For hyperspectral data with tens of hundreds of spectral bands, discrimination of land cover classes is not an easy task, whereas, the classification accuracy of ML classifier is based on the accurate selection of the training samples. Thus, for the hyperspectral imagery with poorly represented labeled training samples, it is preferable to adapt an alternative to the standard multiclass classifier.
\n\n
kNN method operates on majority voting rule, presumes that all the neighbors make equal contributions to the classification of the testing point. Another important feature of kNN classifier is Euclidian is used as distance metric, which assumes the data is homogeneous.
\nLet \n
The kNN classifier finds the k nearest neighbors of a testing point in the training data and assigns the testing point to the most frequently occurring class of its k neighbors. The classification of \n
where \n
A distance metric learned from the given training data is used to enhance the accuracy of \n
T denotes a linear transformation.
\nThe decision rule of \n
It is also referred as decision rule for weighted \n
SAM is a supervised classification technique for HSIC [29]. SAM classifier admits very quick classification using the spectral angle information of HSI data.
\nThe reference spectra are usually determined from the field measurements or from the image data, is used to measure the spectral angle. The spectral angle is a n-dimensional vector between image and reference spectra. Smaller the angles between two spectrums, higher the similarity and vice versa. The classification approach using SAM is described in \nFigure 2\n.
\nSAM classification approach.
This technique is comparatively insensitive to illumination and albedo effects when reflectance data is used for analysis. The spectral angle can be calculated as follows:
\nSVM is typically a linear classifier associative with kernel functions and optimization theory and is prominent for HSI classification [13, 30, 31]. SVM outperforms the conventional supervised classification methods particularly in prevailing conditions like increased number of spectral bands and the limited availability of training samples [32, 33, 34].
\nLet \n
where \n
The optimal hyperplane can be estimated by solving the following convex problem.
\nFor practical data classification problem, the linearly separable condition may not be true in different conditions. To solve the classification problem of nonseparable data, hyperplane separation has been generalized. A cost function is formulated comprising two conditions: margin maximization (as in the case of linearly separable data) and error minimization (to penalize the wrongly classified samples).
\nWhere, \n
The minimization of the cost function defined in Eq. (15) is subject to the following conditions:
\nFor nonseparable data, two types of support vectors coexist: (1) margin support vectors that lie on the hyperplane margin and (2) nonmargin support vectors that fall on the “wrong” side of this margin [13].
\nThe effective discriminant function to solve the nonlinear classification problem can be expressed as:
\nA common example of kernel type that fulfills Mercer’s condition is the Gaussian radial basis function:
\nwhere, \n
A random forest (RF) is a group of tree-based classifiers where each tree is trained with a bootstrapped set of training data. The data to be classified is applied as an input to each tree in the forest. The classification given by each tree is known as a “vote” for that class. In the classification, the forest chooses the class having the most votes (over all the trees in the forest). In RF classification a split is determined by searching across a random subset of variables at each node [36, 37].
\nThe Random forest classifier (RFC) features two main characteristics: relatively high accuracy and the speed of processing. However, the correlation/independence of trees can affect the accuracy of final land cover map. The primitive components of Random Forest are explored as:
\nClassification and regression tree (CART), a binary tree in which splits are resolved by the variables obtained from the strong change in impurity or minimum impurity (\n
where \n
In contrary to CART, the split on each node in BHC is based on classes. The optimal split at each node is based on class separability and further the splits are pure.
\nLet us consider a single meta-class case, which split into two into 2 meta-classes and so on, until the true classes are realized in the leaves, while simultaneously computing the Fisher discriminant and projection.
\nLet \n
The inverse of class covariance matrix W
\nP() is a prior probability. The discriminant \n
Where, B is the covariance matrix between classes.
\nLike the CART trees, the BHC trees can be combined as a forest (RF-BHC) to realize an ensemble of classifiers, where the best splits on classes are performed on a subset of the features in the data to diversify individual trees and/or to stabilize the W.
\nThe pixel-wise classification methods incur some difficulties: Discriminating the classes is very difficult due to less interclass spectral variability. If interclass variability is high, it is very hard to determine a given class. The pixel-wise classification capability can be enhanced by the exploration of additional information called spatial dependency. The classification performance can be improved by incorporating spatial information into HSIC. This rationale motivates the study of spatial-spectral classification methodologies [38]. The spatial dependency system for spectral-spatial-based classification is depicted in \nFigure 3\n. The spatial dependency (primary information for spatial-spectral classification techniques) is carried by two identities called pixel and associated label. The correlation among spatially related pixels is spatial dependency, hence spatially related pixels are termed as neighboring pixels. The spatial dependency is associated with (i) Pixel dependency indicates the correlation of neighboring pixels and (ii) Label dependency indicates the correlation of labels of neighboring pixels. Distinct approaches of spatial-spectral classification are as follows [39]:
Structural filtering: The spatial information from a region of the hyperspectral data is extracted by evaluating the metrics like mean and standard deviation of neighboring pixels over a window. The relevant methods include spectral-spatial wavelet features [40], Gabor features [41], Wiener filtering [42], etc.
Morphological profile (MP): mathematical morphology (MM) intent to investigate spatial relationships between pixels using a set of known shape and size which is called the structuring element (SE). Dilation and erosion are the two elemental MM operations used for nonlinear image processing. The concept of extracting the information regarding contrast and size of the structures present in an image is termed as granulometry. The morphological profile (MP) of size n has been defined as the composition of a granulometry of size n built with opening by reconstruction and a (anti)granulometry of size n built with closing by reconstruction [43].
Spatial dependency system in spectral-spatial classification.
From a single panchromatic image, the MP results in a (2n + 1)-band image. However, for hyperspectral images the direct construction of the MP is not straightforward, because of the lack of ordering relation between vector. In order to overcome this shortcoming, several approaches have been considered [44].
Random field: random field-based methods have been studied broadly for HSI classification. Markov random fields (MRFs) and conditional random fields (CRFs) are two major variants of RF-based classification methods. CRF methods adapt conditional probability for labeling the data and attain favorable performance by utilizing the optimal spatial information; whereas, MRF-based techniques achieve substantial reduction in computational complexity by estimating class parameters independently from field parameters. The basic formulation of random fields as follows:
Let \n
The normalizing facor \n
where, \n
\n\n
\n\n
The CRFs not only avoids label bias problem but also its conditional nature motivates the relaxation of independence assumptions. Recently, Distributed random Forest (DRF) have gained interest for HSIC [45] owing to its inherent merit.
\nThe salient features of DRF are (1) the relaxation of conditional independence of the observed data. (2) the exploitation of probabilistic discriminative models instead of the generative MRFs. and (3) the simultaneous estimation of all DRF parameters from the training data.
\nThe role of SR theory has become prevalent in almost all the image processing applications. The SR theory presumes that the training samples can be represented as a linear combination of smallest possible number of atoms (columns) of an over-complete dictionary.
\nThe test sample \n
The term \n
For HSIC, the Eq. (28) can be replaced as:
\nwhere, the parameter \n
In order to incorporate the spatial information a spatial weight is added and the modified SR model for HSIC is formulated as:
\nThe choice of a spatial weight matrix W, yields different classification strategies for HSIs namely neighboring pixels [47], neighboring filtering [38], histogram-based [47], spatial information based on super pixels [48], etc.
\nThe class labels can be implied on the basis of the following formulation:
\nA sparsity-based algorithm to improve the classification performance is proposed in [49]. The principle depends on the sparse representation of a hyperspectral pixel by a linear combination of a few training samples from a structured dictionary. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Zhang et al. [50] proposed a nonlocal weighted joint sparse representation (NLW-JSRC) to further improve the classification accuracy. The method enforced a weight matrix on the pixels of a patch in order to discard the invalid pixels whose class was different from that of the central pixel. A few of the recent investigations [51, 52, 53] approved that a compact and discriminative dictionary learned from the training samples can significantly reduce the computational complexity.
\nThe segmentation process is performed after spectral-based classification in some of HSIC techniques. The extraction and classification of homogeneous objects is presented in [54] is the first classifier that used spatial postprocessing. The comprehensive survey of other methodologies of this category is presented in [43].
\nDeep learning involves a class of models which try to hierarchically learn deep features of input data with very deep neural networks, typically deeper than three layers. The network is first layer-wise initialized via unsupervised training and subsequently, tuned in a supervised manner. In this scheme, high level features are learned from low level ones, whereas, the proper features can be formulated for pattern classification towards the end. Deep models can potentially lead to progressively more abstract and complex features at higher layers, and more abstract features are generally invariant to the most local changes experienced by the input data.
\nThe DL theory presents a dynamic way for unsupervised feature learning using very large raw image dataset. Unlike the traditional classification techniques, DL-based techniques can represent and organize multiple levels of information to express complex relationships between data.
\nDeep Learning (DL) is a sort of more complex architecture simulating human brains, based on neural networks begins to apply hyperspectral image classification [55]. The deep learning models for HSIC usually consists of three layers, to extract the more complex characteristics layer by layer. (i) Input data (ii) Deep layer construction (iii) Classification [56]. The notable methodologies include deep belief network (DBN) [57], stacked auto encoder (SAE) [58], and convolutional neural network (CNN) [59].
\nDeep belief networks (DBNs) [60] are an important development in DL research and train one layer at a time in an unsupervised manner by restricted Boltzmann machines (RBMs) [61]. The DBNs admit unsupervised pretraining over unlabeled samples at first and then a supervised fine-tuning over labeled samples. Since the pretrained DBN captures the useful information from the unlabeled samples, the fine-tuning with the pretrained DBN performes well over small number of labeled samples [57, 62]. The simple structure of DBN is presented in \nFigure 4\n.
\nThe simple structure of the standard DBN. (RBM- Restricted Boltzmann Machine).
The conventional training of DBN incur two problems; The first is coadaptation of latent factors [63, 64]. This activity is described as several latent factors tend to behave very similarly. This phenomenon implies that the model parameters corresponding to the latent factors might be very similar. These similar latent factors make most of the computations to be performed redundantly and also decrease DBN’s description ability. The second is the set of many “dead” (never responding) or “potential over-tolerant” (always responding) latent factors (neurons) in the DBN learned with the usual sparsity promoting priors [65]. The “dead” or “potential over-tolerant” latent factors directly correspond to the decrease of the model’s description sources. These problems reduce the DBN’s description ability as well as the classification performance. The first problem is solved by trying to perform the latent factors diversely. The “dead” and “potential over-tolerant” latent factors (neurons) are related to the sparsity and selectivity of activations of visual neurons and the selectivity and sparsity are just two epiphenomena of the diversity of receptive fields. Hence, both the problems can be solved together by diversifying the DBN models.
\nThe classification performance enhancement through the diversification of latent factors of a given model has became attractive topic in recent years [66, 67, 68]. The determinantal point process (DPP) is used as a prior for probabilistic latent variable models in [68]. Probabilistic latent variable models are one of the vital elements of machine learning. The determinantal point process enables a modeler to specify a notion of similarity on the space of interest, which in this case is a space of possible latent distributions, via a positive definite kernel. The DPP then assigns probabilities to particular configurations of these distributions according to the determinant of the Gram matrix. This construction naturally leads to a generative latent variable model in which diverse sets of latent parameters are preferred over redundant sets.
\nRestricted Boltzmann Machine (RBM)s has demonstrate immense effectiveness in clustering and classification. In [69], divesified RBM (DRDM) is proposed to enhance the diversity of the hidden units in RBM. To combat the phenomenon that many redundant hidden units are learned to characterize the dominant topics as best as possible with the price of ignoring long-tail topics by imposing a diversity regularizer over these hidden units to reduce their redundancy and improve their coverage of long-tail topics. First-order Hidden Markov Models (HMM) provides a fundamental approach for unsupervised sequential labeling. A diversity-encouraging prior over transition distributions is incorporated to extend HMM to diversified HMM (dHMM) [66]. The dHMM shows great effectiveness in both the unsupervised and supervised settings of sequential labeling problems. A successful attempt has been made to improve the HSI classification by diversifying a deep model in [70]. A new diversified DBN is developed through regularizing pretraining and fine-tuning procedures by a diversity promoting prior over latent factors. Moreover, the regularized pretraining and fine-tuning can be efficiently implemented through usual recursive greedy and back-propagation learning framework.
\nThe conventional applications of the diversified models include image classification [69], image restoration [67], and video summarization [71].
\nTwo hyperspectral data sets, Indian Pines and the University of Pavia scenes are selected for the evaluation of diversified DBN (D-DBN)-based classification method. The Indian Pines data set has 220 spectral channels in 0.4 to 2.45 μm region of the visible and infrared spectrum with a spatial resolution of 20 m × 20 m. The 20 spectral bands were removed due to noise and water absorption, and the data set contains 200 bands of size 145 × 145 pixels. A three-band false color image and the ground truth data are presented in \nFigure 5\n. The University of Pavia data set with a spectral coverage ranging from 0.43 to 0.86 μm is presented in \nFigure 6\n. The image contains 610 × 340 pixels and 115 bands. After removing 12 bands due to noise and water absorption, the image contains 103 bands with a spatial resolution as 1.3 m × 1.3 m.
\nIndian Pines data set. (a) Original image produced by the mixture of three bands. (b) Ground truth with eight classes. (c) Map color.
University of Pavia data set. (a) Original image produced by the mixture of three bands. (b) Ground truth with nine classes. (c) Map color.
The structure of the DBN for the Indian Pines data set is set as 200–50 - \n
ID | \nIndian pines | \nUniversity of Pavia | \n||||
---|---|---|---|---|---|---|
Class name | \nTraining | \nTest | \nClass name | \nTraining | \nTest | \n|
1 | \nCorn-notill | \n200 | \n1234 | \nAsphalt | \n200 | \n6431 | \n
2 | \nCorn-mintill | \n200 | \n634 | \nMeadows | \n200 | \n18,499 | \n
3 | \nGrass-pasture | \n200 | \n297 | \nGravel | \n200 | \n1899 | \n
4 | \nHay-windrowed | \n200 | \n289 | \nTrees | \n200 | \n2864 | \n
5 | \nSoybean-notill | \n200 | \n768 | \nSheets | \n200 | \n1145 | \n
6 | \nSoybean-mintill | \n200 | \n2268 | \nBare soil | \n200 | \n4829 | \n
7 | \nSoybean-clean | \n200 | \n414 | \nBitumen | \n200 | \n1130 | \n
8 | \nWoods | \n200 | \n1094 | \nBricks | \n200 | \n3482 | \n
9 | \n\n | \n | \n | Shadows | \n200 | \n747 | \n
Total | \n\n | 1600 | \n\n | \n | 1800 | \n40,976 | \n
Number of training and test samples.
Quite a few number of neural network-based classification methods have been proposed in the literature to deal with both supervised and unsupervised nonparametric approaches [72, 73, 74]. The feedforward neural network (FN)-based classifiers are extensively used with the variation of second-order optimization-based strategies, which are faster and need fewer input parameters [75, 76]. The extreme learning machine (ELM) learning algorithm has became popular that train single hidden-layer FNs (SLFN) [77, 78]. Then, the concept has been extended to multi-hidden-layer networks [79], radial basis function (RBF) networks [80], and kernel learning [81, 82]. ELM-based networks are remarkably efficient in terms of accuracy and computational complexity and have been successfully applied as nonlinear classifiers for hyperspectral data, providing results comparable with state-of-the-art methodologies.
\nIn recent years, convolutional neural network (CNN) has acquired auspicious achievements in remote sensing [58, 83, 84, 85]. The deep structure of CNNs allows the model to learn highly abstract feature detectors and to map the input features into representations that can clearly boost the performance of the subsequent classifiers. The advantage of such approaches over probabilistic methods result mainly from the fact that neural networks do not need prior knowledge about the statistical distribution of the classes. Their attractiveness increased because of the availability of feasible training techniques for nonlinearly separable data citepbenediktsson1990statistical, although their use has been traditionally affected by their algorithmic and training complexity [86] as well as by the number of parameters that need to be tuned.
\nThe CNN is a multi-layer architecture with multiple stages for effective feature-extraction. Generally, each stage of CNN is composed of three layers. (i) convolutional layer (ii) nonlinearity layer and and (iii) pooling layer. The classical CNN is composed of one, two, or three feature-extraction stages, followed by one or more fully connected layers and a final classifier layer.
\nConvolutional layer: The input to the convolutional layer is represented as \n
Where, b is the bias paprameter.
\nNonlinearity layer: The nonlinearity layer measures the output feature map \n
Pooling Layer: The pooling layer involves executing a max operation over the activations within a small spatial region G of each feature map: \n
where l indexes the layer number. Primary goal is to minimize \n
The three fundamental parts of a CNN are a convolutional layer, non linear function and a pooling layer. A deep CNN can be formulated by stacking several convolution layers with nonlinear operation and several pooling layers. A deep CNN can hierarchically extract the features of inputs, which tend to be invariant and robust [87]. The architecture of a deep CNN for spectral classification is shown in \nFigure 7\n.
\nA spectral classifier based on a deep CNN.
A systematic survey on deep networks for remote sensing data has been presented in [56]. In [83], CNN was investigated to exploit deep representation based on spectral signatures and the performance proved to be superior to that of SVM. The high level spatial features are extracted using CNN [88], deep CNN for pixel classification while learning unsupervised sparse features [59], deep CNN to learn pixel-pair features [89] and few more.
\nThe performance of the HSI classification method proposed in [83] termed as deep CNN (D-CNN) is compared with a traditional SVM classifier. Two hyperspectral data sets including Indian Pines and University Of Pavia are used for the evaluation. The Indian Pines data set consists of 220 spectral channels in the 0.4–2.45 μm region of the visible and infrared spectrum with a spatial resolution of 20 m. The University of Pavia data set with a spatial coverage of 610 × 340 pixels covering the city of Pavia and has 103 spectral bands prior to water band removal. It has a spectral coverage from 0.43 to 0.86 μm and a spatial resolution of 1.3 m. All the layer parameters of these two data sets for CNN classifier are set as specified in [83]. The comparison of classification performance between D-CNN and SVM is presented in \nTable 2\n. \nFigures 8\n and \n9\n interpret the corresponding classification maps obtained with D-CNN and SVM classifier. Furthermore, compared with traditional SVM the D-CNN classifier has higher classification accuracy for the overall data sets.
\nData set | \nD-CNN (%) | \nSVM (%) | \n
---|---|---|
Indian pines | \n90.18 | \n87.54 | \n
University of Pavia | \n92.64 | \n90.42 | \n
Comparison of results between the D-CNN and SVM using two data sets.
RGB composition maps resulting from classification for the Indian Pines data set. From left to right: ground truth, SVM, and D-CNN.
Thematic maps resulting from classification for University of Pavia data set. From left to right: ground truth, SVM, and D-CNN.
Furthermore, the application of Deep learning to hyperspectral image classification has some potential issues to be investigated.
Deep learning methods may lead to a serious problem called overfitting, which means that the results can be very good on the training data but poor on the test data. To deal with this issue, it is necessary to use powerful regularization methods.
In contrast to natural images, the high resolution remote sensing (RS) images are complex in nature. The complexity of RS images leads to some difficulty in descriminative representation and learning features from the objects with DL.
The deepaer layers in supervised networks like CNNs can learn more complex distributions. Research on appropriate depth for a DL model for a given data set is still an open research topic to be explored.
Deep learning methods can be combined with other methods, such as sparse coding and ensemble learning which is another research area in hyperspectral data classification.
The way of running businesses in the last decade has drastically changed in such a way that it is impossible to imagine doing any business without the application of information communications technology (ICT). This, naturally, is becoming even more important with the outbreak of the COVID-19 pandemic [1]. However, even before this pandemic, ICT applications became an integral part of the everyday business of a large portion of business systems [2]. Over time, ICT has been gaining its importance becoming more and more important in the daily functioning of all business organizations.
Higher educational institutions are regarded as organizations, which according to their business, are considered special companies (justifiably to some extent), and yet many good practices that have been applied in successful business systems, particularly corporations, are not applied in higher educational institutions, neither state nor privately owned ones. However, a pandemic does not make a difference between corporations and higher educational institutions but points out that educational institutions cannot be regarded as separate entities but have to adjust to changed circumstances and different ways of working [1].
Although higher educational institutions organize their business dealings differently when compared with classic companies, certain segments of their work function exactly the same way as in classical companies (they both require human resources management, financial policy, goods or services procurement, work promotion, etc.).
With the development of competition in the higher education market and the emergence of many new higher educational institutions and study programs, some elements that have never been considered before in higher education attract the limelight—approaching students as stakeholders [3], developing and maintaining the relationship with students [4], struggling to attract new students [5], taking care about relationships with students and employees, etc. The complex ecosystem of a higher educational institution is made up of many relations similar to those already mentioned, and which imply a whole series of business processes and activities. In order to satisfy those relations in a proper manner, it takes a thorough and comprehensive approach at the level of higher educational institutions, which should integrate and take care of many intertwined business activities.
How can proper use of technologies affect the quality rating improvement of a higher educational institution in the present competitive market? The Association of Colleges in North Carolina established that a corporate information system aimed at a student as a stakeholder can also provide better chances to attract enrollment and retain new students [6]. Let us take, for example, the situation which has changed the manner in which people behave—the coronavirus pandemic. If there had not existed some use of e-services on behalf of higher educational institutions until the onset of the pandemic, the relationship between universities and students would have been totally disrupted. Never before the application of information technologies in the everyday relationship between teachers and students have become so prominent. It is not just about the educational process itself but also about the comprehensive relationship that an educational institution has with its user—the need to digitalize as many points of contact as possible.
Although some find it difficult to accept the idea that students are the same as stakeholders in reality that is what they are. In today’s market, more competitive than ever before, institutions offer students study programs, inter-faculty exchanges, practices, and similar activities that enrich a student’s life. They are customers who enroll in study programs, aimed at getting their diplomas, and donating as alumni members. The longer the satisfaction gained from the processes on both sides, the more durable their relationship will be thus bringing benefits to both sides. One of the ways in which faculties can affect the duration of this positive relationship with students is something that many corporations have already incorporated—managing relationships with stakeholders by means of contemporary information communications technologies [7]. The objective of an institution is to manage those relationships in an efficient and effective way in order to create firm and long-lasting relations.
Managing relationships involves the development of strategies, processes, and technologies with the purpose of strengthening relations of the company with a customer during their lifetime—from marketing and sale all the way to post-sale support [8, 9]. The concept of relationship management (customer relationship management, CRM) is widely accepted in the world of corporations but it is still a relatively new phenomenon in the sector of higher education [9]. Similar to other business systems, by accepting the CRM concept relationship managers in higher educational institutions aspire to improve business performance, promote better management practices, and advance relationships with current and potential students.
From the point of view of a customer, CRM business strategy enables him the interaction with the higher education institution through just one entity which encompasses all the necessary data of the individual in question. In the case of a student, this can be seen through his/her interaction with the employees who take his/her data for enrollment and registration, through financial matters, student’s account, etc. From the educational institution’s point of view, CRM provides a clear and comprehensive picture of every individual and the activities connected with him/her [10].
In order to pay attention to students and realize contact with them, and in doing to gain their loyalty and make them satisfied, higher educational institutions can nowadays turn to e-service concepts in their functioning. The application of e-service for the management of relationships with the users has become a part of business strategy and is not considered merely a marketing tool. That strategy supports the management in decision-making by using the existing information within the organization and, more importantly, by using information technologies for marketing programs and activities of the organization. The primary objective of managing the relationships with users is to notice and deliver services adjusted to each user himself/herself.
Bejou [7] proposes the adoption of customer relationship management as a way to establish and maintain a relationship between a student and a higher education institution. Customer care is represented in a certain form in most educational institutions today, so there are departments for providing services to students. However, real customer service must contain more than a department or a few individuals. CRM must include a special, service-oriented environment, and everyone within the organization should be engaged in trying to achieve that goal [11]. The fact is that today a strong relationship with customers is the key to success in any business [12, 13, 14, 15].
If we make an analogy with the development of computer applications, it can be said that e-service technology connects the frontend of the departments, that is, the ones protruding toward the user (such as sales, marketing, and customer care), and the backend departments such as management, finance, logistics, and human resources services, with user’s touchpoints [16]. “Touchpoints” represent channels through which the company has contact with its customers, and they can be the Internet, e-mail, sales, call centers, advertising, physical points of sale, and the like. These “touchpoints” are often under the control of special information systems [17].
Tapp et al. [18] state that the use of direct and marketing databases generated through the CRM system can greatly enhance the potential of a higher education institution to attract new candidates. Adoption of the CRM strategy leads to gaining insight into various relevant data on students that may be important, that is, on the basis of which a basis can be formed later for providing a service that will correspond with their expectations, build loyalty on behalf of students, and all that taken together will affect their retention within the institution. Potential customers are likely to hear positive experiences about the services provided by other users. In his study, Hill [19] reveals that there is a correlation between students’ expectations regarding services and their satisfaction. The same author also claims that higher education institutions should detect the expectations of their future students from the entry point to the institution until the moment of successful completion of the study program [20].
Very often, the choice of one higher education institution over another one with a similar level of quality may depend on the way the institution communicates with its (potential) students. The modern age is such that most students want established, relevant, and complete communication with the staff of the educational institution. The easiest way for higher education institutions to establish harmonious relationships with their stakeholders is by using an e-service system based on the application of modern ICT. Such a system provides valuable insight into information about students, creating a possibility to build a stronger and more personalized connection with former (alumni), current, and future students. In other words, the mentioned system applied in higher educational institutions enables the institution to constantly update information about stakeholders, facilitating and monitoring every interaction that the institution has with them through various platforms, regardless of where they are located. The e-service system can be designed so that it helps employees by simplifying their work process, and ultimately, improves their relationships with students. The result of all this is an increased interest in enrolling in the institution, increased student retention rates, better communication with students, and a happier and more satisfied team [21].
e-Service system can enable sending automated messages that will remind students of the future exam terms. Simple things such as reminders can help students to better organize their time, prepare more efficiently for exam obligations, and can have a positive effect on their welfare and the results to be achieved.
Collecting means and donations through the creation of long-lasting relationships with the Alumni club members is very much present at many universities. Diversification of higher educational institution revenue apart from traditional students’ tuition and public financing has become very important for universities. Nowadays many institutions are faced with the reduction of state financing which leads to financial pressure and the request to reduce their expenses. For that very reason, many higher educational institutions in other countries pay great attention to the relationships with former students (alumni). A comprehensive e-service system centralizes information on donors and Alumni club members and uses them to send automated notifications at certain periods during the year in order to inform all interested parties about the success of the program and the students in the particular institution. The incorporated analytical system examines which events and types of communication that year contributed the most.
The focus of marketing today is not so much on attracting new customers as it is directed at the customer’s satisfaction concept by offering users what they want, taking care of the relationship with them—the relationship which is not only commercial, unidirectional, and impersonal. The consumerist concept in higher education should be in the context of relationship marketing and not transactional marketing. Although students can be considered users, the nature of educational services, especially the relationship duration, suggests the development of the relationship rather than the approach of “pay and buy” [22, 23]. Adoption of CRM on behalf of educational institutions implies in itself the use of appropriate instruments which will help in collecting information about students’ requirements regarding education, and adjust services to measure their characteristics and requirements, all with the aim to enhance the educational process and to obtain better results which will reduce the number of students who drop out of university or abandon further education [20, 23, 24, 25, 26].
One of the features that separates higher educational institutions from business systems in other branches, and which makes the application of contemporary technologies aimed at CRM ideal for higher education, is the dedication and long-term relationship to which students agree when they enroll in the study program [27]. Other industries do not have that “luxury” to affect the life cycle of a client, which lasts several years. On the other hand, it can be good practice for higher educational institutions to consider their students as users since such an approach enables competitive advantage and enhances the ability of an institution to attract, retain, and offer appropriate service to its students/users.
From the relationship management point of view, the concept of a client’s life cycle can be transported to a student’s life cycle which is made up of the phases through which a student passes while considering and using the services of an educational institution. The phases in the life cycle of a student are as follows:
Possible → potential → candidate → accepted → enrolled → former student (alumni)
Possible: any student who, according to the Law, can be a candidate for enrollment in the university.
Potential: a student who is currently in the phase of gathering information and forming an opinion about a higher education institution.
Candidate: a student who has made a decision to apply for enrollment in an institution.
Accepted: the student is accepted when the institution has made a decision to accept the student.
Enrolled: this is the stage when a student officially becomes a student, that is, part of a higher education institution.
Former student (alumni): a student who has successfully completed the study program, and if he is satisfied with his academic experience, he will develop a long-term loyalty relationship with his institution.
From the point of view of relationship management activities, student enrollment activities related to the conversion of a possible into an accepted student constitute a marketing component. The conversion of accepted into enrolled is a component of sales, while the continuous enrollment and the presence of alumni is a component of customer support and retention. However, the student life cycle involves several complex interactions, especially during the retention phase because the academic program, counseling, and student life play a key role in student retention.
According to the results of a study conducted by Ogunnaike et al. [28], it was found that a student’s desire to recommend the institution of which he was a part, or still is, increases if the student life cycle is well managed by the institution. It was also noticed that a strong connection of the educational institution with parents leads to the situation that the student recommends his/her institution to others. Managing a good relationship with the parents of students also increases the desire of students to continue their postgraduate studies at the same institution.
The research conducted by Hilbert et al. [5] expresses a slightly different approach. It states that the student life cycle consists of three phases: entry, presence, and departure. In the entry phase, students are considered as potential clients, the present phase is made up of existing clients, and in the departure phase, they are already lost. However, the departure of students from the university/college does not mean that their communication with the institution is interrupted. At that point, universities strive to keep relationships with their students using some strategic methods [15].
The comprehensive model of the e-service system in the complex business ecosystem of higher education is shown in Figure 1. The model takes care of managing relations with a large portion of stakeholders of the higher education institution, among which students and employees are the most demanding in terms of the number and scope of electronic services. In addition to relations with stakeholders, the model represents support for the proper and efficient conduct of business functions: marketing, education, science, human resource management, finance and accounting, and procurement.
Infrastructure of the comprehensive e-service system.
The model consists of numerous elements and is based on cloud computing. The central part of the model is the infrastructure as a service (IaaS). A public cloud consisting of several virtual private servers (VPS) was chosen as the appropriate form of cloud computing. Each of the servers has a dedicated purpose and they have been separated with the intention, primarily because of the better optimization, higher security offered by this approach, and other practical reasons.
The comprehensive e-service system is based on open-source technologies. The system was developed by using Python programming language in combination with Django as a web framework. MySQL was chosen as a relational database management system.
As shown in Figure 1, the system consists of several modules. Some of the modules are actually specific Django applications, which have been integrated together into the main project. This has been done respecting one of the principles of Django design—DRY (Do not Repeat Yourself). The important part of the e-service system’s architecture is web services which are used to improve the performance of the system by utilizing Django’s cache framework.
Which are the benefits offered by implementing a comprehensive e-service system in higher education in the field of student relationship management? Using adequate communication channels, potential students can obtain appropriate and timely information that is the focus of their interest. When using the online learning platform, candidates are in a position to attend classes without physical presence and prepare for taking the entrance exam for enrollment in the institution. The process of applying for admission to the faculty can be completed much faster and easier with the help of online tools on the portal of the institution. Processing the results of the entrance exam with ICT support drastically shortens the time required for the formation of preliminary and final ranking lists, which shortens the period of stress exposure for future students. By using the already existing data on candidates in the system, and for the purpose of realizing the enrollment of candidates, there is no data redundancy, and the whole process of enrollment of candidates is accelerated.
Potential students, who decide to enroll in the educational institution, extend their life cycles and become current students of a higher education institution, where their previously existing data are taken over. In this way, a path is established for nurturing the relationship with the student during his entire life cycle from the moment he becomes a potential candidate until the act of graduating. The whole process of studying is then further facilitated for current students in several aspects—by using students’ modules they are able to apply for and cancel exams, follow all notifications, monitor all their teaching and pre-examination obligations, and have access to all information from their student file and use the platform for online learning. These students also have at their disposal timely notifications and all the necessary information relevant to their education through web portals, student services, social networks, email marketing platforms, and mobile services. More importantly, they are given the opportunity to leave feedback on various issues, fill out questionnaires regarding the pedagogical work of teachers and associates, the work of professional services and management, and in doing so greatly influence the work of the institution. There are several channels through which one can contact the institution and get answers to their questions in a short time. This way of working increases the retention rate, raises the reputation of the institution to a higher level, and at the same time reduces the costs of attracting new students, which, on the other hand, protects the income of the institution coming from tuition fees.
Upon graduating from the university, the students become members of the Alumni community thus completing their life cycle at the university. It is important for the institution to stay in touch with its former students and to work on maintaining good relations. It is in this field that the Alumni Portal is important, which helps in informing and organizing various types of events, gatherings, and professional training that are necessary for graduates, sends reminders about events, monitors events, and sends accompanying information.
The comprehensive e-service system can help an institution in one of the following ways:
nurtures the relationship with the student throughout his/her entire life cycle—from the moment he/she becomes a potential candidate to the act of graduation;
maintains complete student profiles—centralizes all contact information and helps to understand and analyze potential candidates, at every point of contact;
realizes contact with each member of the community: potential candidates, current students, alumni, employees, parents, and other contacts;
delivers relevant and appropriate offers, recommendations, advice, and actions at the moment optimal for potential candidates;
automates communication, freeing up time for other activities;
increases the retention rate by improving reputation, reduces attraction costs, and protects the income of the institution coming from tuition fees;
achieves a more efficient workflow, enabling employees to complete their tasks faster and easier. Reduces paper usage and the need to use a variety of storage solutions;
enables access, addition, and data modification 24/7, either in the office or on the road, with the use of mobile devices;
provides students with the fastest possible form of information, helps them to manage their responsibilities in a simple and clear manner, improves communication with the staff of the higher education institution, and provides an easy way to update data;
invites prospective students, current students, and alumni students to events, facilitates the event management process, sends event reminders, monitors events, and sends accompanying notifications;
measures the return on investment, provides insight into marketing activities, promotion channels, and determines what exactly brings revenue, and how.
The services covered in the work process by applying the comprehensive e-service model are as follows:
providing service information (notices, news, current events, and other important information on relevant activities);
management of student files (monitoring and recording of complete student files);
administration of the study program (support for organizing and conducting classes, organizing and monitoring exam terms);
monitoring, planning, and keeping records of teaching staff, their engagement, and workload;
generating the necessary documents, certificates, and other reports for students;
administration of employees (their personal identification data, contracts, salaries, records of attendance at work);
online learning environment;
accounting activities;
monitoring and recording scientific research work;
financial statements;
preparation of various statistical reports on enrollment, student achievement, and other reports necessary for state bodies, the University, or the governing bodies of the college;
ALUMNI portal for relationships with former graduates;
different forms and reports necessary for self-evaluation and accreditation of institutions and study programs;
monitoring, procurement, and records of inventory, materials, and fixed assets.
This section presents the functionalities of the comprehensive e-services system through the user interface and explanations of the logic behind it. As an integrated system, it can be logically divided into three parts: e-teacher module, e-student module, and supporting departments module.
This module enables teaching staff to manage a wide spread of activities:
informs teaching staff about ongoing exams and details (Figure 2);
gives an overview of students who registered for an exam within the given term (Figure 3);
gives an overview of subjects for which grades were not supplied (Figure 4);
allows teaching staff to add students’ grades for the appropriate exam period and the appropriate subject (Figure 5);
archives previously held exams for the appropriate subjects (Figure 6);
shows the records of those students who have registered for the relevant exam within the appropriate term (Figure 7);
shows an overview of teaching staff engagement across subjects in the study program (Figure 8);
provides an overview of published scientific results for each employee within the teaching staff (Figure 9).
Overview of ongoing exams.
Overview of students who registered for the exam within the given term.
Overview of subjects for which grades were not provided.
Adding students’ grades for the appropriate subject and exam period.
Archive of the previously held exams for the appropriate subjects.
Students details for those who have registered for the exam within the appropriate term.
Shows an overview of employee engagement in the relevant study program.
An overview of the employee’s scientific results published so far.
In the Ongoing exams section, teachers and associates have the opportunity to review registered students, within the given term, for the subjects they are engaged in (Figure 2).
Students registered for the selected subject can be displayed by selecting one of the subjects from the table (Figure 3).
In the Grades entry for previous exams section, grades are added by selecting the appropriate subject from the table (Figure 4). The list shows all subjects for which grades in previous exam terms have not been added.
If the teaching staff wants to leave the entry for later, the teacher can temporarily save grades that have been added and later continue with the entry and confirmation of those grades, or they can do so immediately by clicking the
The
Clicking on a certain record in the table gives a more detailed presentation of the respective exam term, which includes a list of students who have applied for the exam within the specified term, grades, and the date of the exam (Figure 7).
In the
The
This module deals with relevant student data, viewed from two perspectives:
Students’ data managed by higher education institution.
Students’ data accessed through students user accounts.
Students’ data are accessed through the
Search form for students by several different parameters.
By selecting one of the students from the table, the Student file and more detailed data are accessed (Figure 11).
Student search results.
The section assigned to Students contains personal identification data of students, data on schooling or enrollment in the following years of study in the study program, an overview of all details related to exams taken by students and not passed so far, as well as data on the final paper, diploma supplement, and diploma export opportunities (Figure 12).
Page with basic student data and the possibility of updating them.
In the section with Exams, all the data on passed and failed exams are shown (Figure 13). These data are added on the other page, that is, in the module for teachers, who are obliged to add grades for each of the students after the exam period. By clicking on the appropriate row in the table, a new page opens in which it is possible to change the data on the held exam.
Report with the list of passed exams.
The second Tab with failed exams shows data on the subjects left for the student to pass in order to complete his/her education (Figure 14).
Report on the second Tab with a list of failed exams.
The section
Overview of the student’s education so far - managed by higher education institution.
In the section on the Final examination, after the student has successfully defended it, data on the topic of the final paper, the date of the defense, the grade as well as the members of the commission are added (Figure 16).
Page with student’s final examination data.
The
Archive of previously held exam terms.
By clicking on one of the entries in the table, it is possible to change the existing or add a new exam term.
Students can access their files using their user accounts, which offer an overview of all relevant data concerning the students. Among the information offered to the students are notices intended for students (Figure 18), data on their previous education, a list of passed exams, the ability to apply for and cancel exams, and an overview of their personal identification data.
Notices for students created in the system.
In the section Study year, students have an insight into their previous student education, that is, enrollment by school years, and the number of ECTS credits earned (Figure 19).
Overview of the student’s education so far - accessed by student.
In the section
Overview of the exams passed so far.
At the same time, the student has an insight into his current average grade and the current number of ECTS credits earned in the given study program.
In the
Exam application section.
Section for canceling previously applied exams.
The
Overview of general student data.
This module represents the core of the e-services system.
There are several functions provided by this module:
Human resources data management (employees’ contracts, specializations, sick leaves, and similar data management);
Teaching staff engagement overview;
Financial data and analytics;
Scientific research results management;
Institution’s publishing activity overview;
Institution’s assets management.
This section shows the records on master data of employees (Figures 24–26) and the records of inactive or former employees (Figure 27). Records contain data for full-time employees as well as for employees with other types of contracts. The functions available here are:
search by any of the parameters or employees data,
data sort for each of the columns,
exporting data in .xls format,
showing/hiding certain columns for easier and more clear viewing if necessary.
Overview of employee master data.
Overview of master data on employees, filtered data.
Overview of master data on employees, hiding certain data columns.
Records of employees who are no longer employed at the institution (former employees).
Clicking on one of the employees from the table displays a page from which employees’ data can be edited.
The section dedicated to editing employee data enables the revision of master data as well as the entry of various types of contracts and details related to them (these are treated as data objects). Administrators can add data on employment contracts, absences (due to sick leave, maternity leave, paid or unpaid leave), and academic advancement. Also, it is possible to record the termination of employment and to add a new employee with relevant data. In that way, the basis for the orderly and accurate keeping of records on teaching and non-teaching staff has been established, which will be further used for planning human resources policy and other services that use the given data.
A
The
Teaching staff engagement overview.
In addition to engagement by subjects and employees, it is possible to summarize engagement in the entire study program, with additional data such as academic promotion dates, period of retirement approaches, and similar (Figure 29).
Summary of the engagement of teachers and associates at the study program level.
The
Financial data for employees overview.
Change of financial data for employees.
The
Overview of the scientific research results published by teaching staff.
Prior to adding the scientific publication record to the system, it is necessary to ensure that a certain scientific journal or conference against which this record can be added is already in the system. To make it easier for users to add scientific publications results, asynchronous JavaScript calls have been implemented (Figure 33). By starting data entry, results are obtained in real-time, so users can have an insight into previous entries that may be related to a given record. In this way, feedback is received immediately on whether it is necessary to add publication data only or whether a journal or a conference record must be added previously.
Adding a new scientific research publication.
If a given journal does not already exist in the database for a given publication, it must be added first by selecting the
The next important feature of this section is the ability to export publication records in .docx format, based on several different parameters (Figures 34 and 35). The scientific research activity is an important aspect of higher education institutions’ work, and as such serves as a basis for the accreditation of scientific research activity. Given the volume of work on collecting documentation for application in the mentioned process, if the system is kept up to date, this section can be of significant help. It is possible to export all publications for a certain period amd filter publications that originate from doctoral dissertations or those from scientific research projects. Data can be exported in accordance with different needs, so publications can be sorted by authors, by relevant categorization, or by publishing date.
Exporting scientific publications.
Data exported in .docx format.
The appearance of the obtained data exported in .docx format is shown in Figure 35.
In the
The textbook overview contains basic information about each textbook issued by the institution—its name, code, and value generated by its distribution (Figure 36). Selecting one of the entries from the table proceeds to the status display for the corresponding textbook.
Summary overview of the textbooks published by the institution.
The
Service-level overview;
Department-level overview;
Institution-level overview, grouped by departments;
Comprehensive review at the institution-level.
The system records all services provided by the institution (Figure 37). By selecting a specific service the system displays detailed information: the number of services performed by months, service revenue, and comparison with the specific period (in terms of frequency and revenue) (Figure 38).
Reports for the selected service.
Reports on services provided for the selected department.
Given that each service belongs to one of the institution’s department, all of them can thus be grouped and presented in the report on
The last type of report is a summary of the services provided at the level of the institution (Figure 39). All services provided since the beginning of monitoring are recorded here, so the overview is very practical for comparing different periods. Also, the numbers of performed services and clients are shown tabularly and graphically, across the time.
Summary reports on services provided at the level of the institution.
Keeping records of the institution’s assets and inventory is possible through the provided section in the system that offers data review, entry of new data, and export of lists of assets and inventory (Figure 40).
Overview of the institution’s assets and inventory.
Data on the equipment of the institution can be sorted by columns and quickly filtered through the search. Also, there is an option to export lists of assets and inventory, that is important for classifying assets in an appropriate manner, thus making it easier for those responsible to keep records.
By integrating a comprehensive e-service model with the REST program interface, one of the instant messaging applications, a system for direct communication with students has been established, which immediately provides the necessary transactional notifications, service information, and reports to the most important stakeholder group of the higher educational institution.
One of the most commonly used instant messaging applications on a global scale today (Rakuten Viber) allows programmers to develop their own bots whose purpose is direct communication 1-1 with users who are subscribed for this type of service.
The following elements are required for the implementation of the Viber REST API [29]:
Active account on the platform that supports bots.
Active bot that has to be created beforehand.
Token for account authentication used for account validation. This token must be included in each individual API request sent to the service.
One-time Webhook request during initial setup that defines the types of responses that the bot wants to receive from users.
Viber Chatbots enable free communication with users, but only with those who first contacted the business account, that is, who subscribed to the service. Also, API does not enable downloading information about the user’s phone number, but each of the users has its own identification code (User ID).
The flow of message exchange between a Viber business account and regular Viber users is shown in Figure 41.
Diagram of the flow of messages sent between the user and the public account.
Once all the prerequisites have been met, a one-time Webhook registration for the public account is required. A webhook is a type of application programming interface which is event-based. It can be defined as a service that enables one application to send data to another when a certain event occurs. Webhook works practically one way, unlike classic APIs whose essence is to a get response based on a previously sent request.
Webhook can be configured programmatically or by sending a POST request with the appropriate parameters in the Header and Body, to the URL https://chatapi.viber.com/pa/set-webhook. The parameters required for sending are shown in Table 1.
Name | Description | Validation |
---|---|---|
url | Required. Account webhook URL to receive callbacks and messages from users | Webhook URL must use SSL Note: Viber does not support self signed certificates |
event_types | Optional. Indicates the types of Viber events that the account owner would like to be notified about. Do not include this parameter in your request to get all events | Possible values: delivered, seen, failed, subscribed, unsubscribed and conversation_started |
send_name | Optional. Indicates whether or not the bot should receive the user name. Default false | Possible values: true, false |
send_photo | Optional. Indicates whether or not the bot should receive the user photo. Default false | Possible values: true, false |
Parameters required for sending in order to arrange Webhook requests.
Depending on the type of event that can be registered through the callback function, different types of responses to the user can be defined. The following is an example of the Python code which is in charge of sending a response to a user according to whether the user started a conversation or signed up for the service:
from viberbot import Api
from viberbot.api.bot_configuration import BotConfiguration
from viberbot.api.messages.text_message import TextMessage
from viberbot.api.messages.data_types.contact import Contact
from viberbot.api.viber_requests import ViberConversationStartedRequest
from viberbot.api.viber_requests import ViberFailedRequest
from viberbot.api.viber_requests import ViberMessageRequest
from viberbot.api.viber_requests import ViberSubscribedRequest
from viberbot.api.viber_requests import ViberUnsubscribedRequest
from django.views.decorators.csrf import csrf_exempt
viber = Api(BotConfiguration(
name = ‘UniversityChatBot’,
avatar = ‘https://example.com/avatar.jpg’,
auth_token = ‘universitytoken’
))
def hook(request):
logger.debug(“received request. post data: {0}”.format(request.get_data()))
viber_request = viber.parse_request(request.get_data())
if isinstance(viber_request, ViberConversationStartedRequest):
viber.send_messages(viber_request.get_user().get_id(), [
TextMessage(text = “Welcome!”)
])
elif isinstance(viber_request, ViberSubscribedRequest):
viber.send_messages(viber_request.get_user.id, [
TextMessage(text = “Thank you!”)
])
return Response(status = 200)
This type of communication is used for sending information about the grade students received after successfully passing an exam. The teachers add the grades that the students receive at the exam and the e-service simultaneously sends a notification to the students to their instant messaging accounts through which they previously establish communication with the University bot. In that way, and at the same time, they voluntarily applied for the service. It is possible to establish this type of communication for any needs so that personalized communication 1-1 with each of the university students can be achieved at any time. In addition to information on the grades obtained, it is possible to establish communication for all other types of information and needs, both with students and employees.
In the uncertain forthcoming days, institutions must be prepared for new technological challenges that may appear. It is of the utmost importance for an institution to investigate what can be done about the creation of high-quality relationships with interested parties in order to not only maintain but also enhance the relationship with them. In such circumstances, proper ICT application can be of inestimable value for the leaders of higher educational institutions, deans, decision-makers, administrators, and others who are to make important strategic decisions.
In addition, there is another very important fact in the future that should not be ignored—as the years go by, new generations of students come with increasing knowledge, higher previous formal education, and, most importantly, the expectation of ICT presence in their “student life.” Prospective students will need to be provided with an easier way of learning (including various types of distance learning systems), but also the opportunity to quickly and easily complete all their obligations outside the classroom. They should be provided with all relevant information at their fingertips. From a technological point of view, only the adequate application of modern technologies can provide a basis for meeting these conditions.
Students’ expectations regarding the presence of various forms of technology within student activities are today at a much higher level than they were until recently [30]. The availability and proper use of digital technologies in the portfolio of a higher educational institution were especially evident during the coronavirus pandemic, which imposed a completely new way of interaction and communication between students and higher educational institutions [31].
Adoption of the correct use of digital technologies could lead not only to greater student satisfaction but also to the improvement of the technological reputation of the institution. Today, digital technology has become an essential part of students’ daily lives and plays a key role in their studying and extracurricular activities. We are living in a new digital reality [32] and educational institutions will have to adapt to it quickly. The proper use of digital technologies could play an important role in the sustainable development of educational institutions in the future [31]. Future generations of higher education students will have more and more prominent previous experience with the significant use of digital technologies in their primary and secondary education [33].
The author declares no conflict of interest.
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\\n\\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\\n\\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
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\\n\\nFeel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
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\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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\n\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\n\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\n\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\n\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\n\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\n\nDownload Waiver Request Form
\n\nFeel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
\n\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
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Experimental results collected with an IPv6-based simulator comprising several distributed computational devices and heterogeneous communication networks show the relevance and inherent benefits of this approach.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Fábio Emanuel Pais Januário, Joaquim Leitão, Alberto Cardoso and\nPaulo Gil",authors:[{id:"202364",title:"Ph.D. Student",name:"Fábio",middleName:null,surname:"Januário",slug:"fabio-januario",fullName:"Fábio Januário"},{id:"203414",title:"Prof.",name:"Alberto",middleName:null,surname:"Cardoso",slug:"alberto-cardoso",fullName:"Alberto Cardoso"},{id:"203415",title:"Prof.",name:"Paulo",middleName:null,surname:"Gil",slug:"paulo-gil",fullName:"Paulo Gil"},{id:"206481",title:"MSc.",name:"Joaquim",middleName:null,surname:"Leitão",slug:"joaquim-leitao",fullName:"Joaquim Leitão"}]},{id:"56014",doi:"10.5772/intechopen.69352",title:"Robust Adaptive Cooperative Control for Formation-Tracking Problem in a Network of Non-Affine Nonlinear Agents",slug:"robust-adaptive-cooperative-control-for-formation-tracking-problem-in-a-network-of-non-affine-nonlin",totalDownloads:1517,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"In this chapter, a decentralized cooperative control protocol is proposed with application to any network of agents with non-affine nonlinear multi-input-multi-output (MIMO) dynamics. Here, the main purpose of cooperative control protocol is to track a time-variant reference trajectory while maintaining a desired formation. The reference trajectory is defined to a leader, which has at least one information connection with one of the agents in the network. The design procedure includes a robust adaptive law for estimating the unknown nonlinear terms of each agent’s dynamics in a model-free format, that is, without the use of any regressors. Moreover, an observer is designed to have an approximation on the values of control parameters for the leader at the agents without connection to the leader. The entire design procedure is analysed successfully for the stability using Lyapunov stability theorem. Finally, the simulation results for the application of the proposed method on a network of nonholonomic wheeled mobile robots (WMR) are presented. Desirable leader-following tracking and geometric formation control performance have been successfully demonstrated through simulated group of wheeled mobile robots.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Muhammad Nasiruddin bin Mahyuddin and Ali Safaei",authors:[{id:"204129",title:"Dr.",name:"Muhammad Nasiruddin",middleName:null,surname:"Mahyuddin",slug:"muhammad-nasiruddin-mahyuddin",fullName:"Muhammad Nasiruddin Mahyuddin"},{id:"206489",title:"Mr.",name:"Ali",middleName:null,surname:"Safaei",slug:"ali-safaei",fullName:"Ali Safaei"}]},{id:"56255",doi:"10.5772/intechopen.69844",title:"Time Critical Mass Evacuation Simulation Combining A Multi- Agent System and High-Performance Computing",slug:"time-critical-mass-evacuation-simulation-combining-a-multi-agent-system-and-high-performance-computi",totalDownloads:1413,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter presents an application of multi-agent systems to simulate tsunami-triggered mass evacuations of large urban areas. The main objective is to quantitatively evaluate various strategies to accelerate evacuation in case of a tsunami with a short arrival time, taking most influential factors into account. Considering the large number of lives in fatal danger, instead of widely used simple agents in 1D networks, we use a high-resolution model of environment and complex agents so that wide range of influencing factors can be taken into account. A brief description of the multi-agent system is provided using a mathematical framework as means to easily and unambiguously refer to the main components of the system. The environment of the multi-agent system, which mimics the physical world of evacuees, is modelled as a hybrid of a high-resolution grid and a graph connecting traversable spaces. This hybrid of raster and vector data structures enables modelling large domain in a scalable manner. The agents, which mimic the heterogeneous crowd of evacuees, are composed of different combinations of basic constituent functions for modelling interaction with each other and environment, decision-making, etc. The results of tuning and validating of constituent functions for pedestrian-pedestrian, car-car and car-pedestrian interactions are presented. A scalable high-performance computing (HPC) extension to address the high-computational demand of complex agents and high-resolution model of environment is briefly explained. Finally, demonstrative applications that highlight the need for including sub-meter details in the environment, different modes of evacuation and behavioural differences are presented.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Leonel Aguilar, Maddegedara Lalith and Muneo Hori",authors:[{id:"203530",title:"Prof.",name:"Maddgedara",middleName:null,surname:"Lalith",slug:"maddgedara-lalith",fullName:"Maddgedara Lalith"},{id:"204168",title:"Dr.",name:"Leonel",middleName:null,surname:"Aguilar",slug:"leonel-aguilar",fullName:"Leonel Aguilar"},{id:"204169",title:"Prof.",name:"Muneo",middleName:null,surname:"Hori",slug:"muneo-hori",fullName:"Muneo Hori"}]},{id:"56327",doi:"10.5772/intechopen.69845",title:"Motion Coordination Problems with Collision Avoidance for Multi-Agent Systems",slug:"motion-coordination-problems-with-collision-avoidance-for-multi-agent-systems",totalDownloads:1406,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter studies the collision avoidance problem in the motion coordination control strategies for multi-agent systems. The proposed control strategies are decentralised, since agents have no global knowledge of the goal to achieve, knowing only the position and velocity of some agents. These control strategies allow a set of mobile agents achieve formations, formation tracking and containment. For the collision avoidance, we add a repulsive vector field of the unstable focus type to the motion coordination control strategies. We use formation graphs to represent interactions between agents. The results are presented for the front points of differential-drive mobile robots. The theoretical results are verified by numerical simulation.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Jesús Santiaguillo-Salinas and Eduardo Aranda-Bricaire",authors:[{id:"16943",title:"Prof.",name:"Eduardo",middleName:null,surname:"Aranda-Bricaire",slug:"eduardo-aranda-bricaire",fullName:"Eduardo Aranda-Bricaire"},{id:"204125",title:"Ph.D.",name:"Jesús",middleName:null,surname:"Santiaguillo-Salinas",slug:"jesus-santiaguillo-salinas",fullName:"Jesús Santiaguillo-Salinas"}]}],mostDownloadedChaptersLast30Days:[{id:"56573",title:"Introductory Chapter: Multi-Agent Systems",slug:"introductory-chapter-multi-agent-systems",totalDownloads:2429,totalCrossrefCites:5,totalDimensionsCites:10,abstract:null,book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Jorge Rocha, Inês Boavida-Portugal and Eduardo Gomes",authors:[{id:"145918",title:"Ph.D.",name:"Jorge",middleName:null,surname:"Rocha",slug:"jorge-rocha",fullName:"Jorge Rocha"},{id:"214969",title:"Prof.",name:"Inês",middleName:null,surname:"Boavida-Portugal",slug:"ines-boavida-portugal",fullName:"Inês Boavida-Portugal"},{id:"214971",title:"MSc.",name:"Eduardo",middleName:null,surname:"Gomes",slug:"eduardo-gomes",fullName:"Eduardo Gomes"}]},{id:"55920",title:"Hybrid Architecture to Support Context‐Aware Systems",slug:"hybrid-architecture-to-support-context-aware-systems",totalDownloads:1644,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Any system that is said to be context‐aware is capable of monitoring continuously the surrounding environment, that is, capable of prompt reaction to events and changing conditions of the environment. The main objective of a context‐aware system is to be continuously recognizing the state of the environment and the users present, in order to adjust the environment to an ideal state and to provide personalized information and services to users considering the user profile. In this chapter, we describe an architecture that relies on the incorporation of intelligent multi‐agent systems (MAS), sensor networks, mobile sensors, actuators, Web services and ontologies. We describe the interaction of these technologies into the architecture aiming at facilitating the construction of context‐aware systems.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Maricela Bravo, José A. Reyes‐Ortiz, Leonardo Sánchez‐Martínez\nand Roberto A. Alcántara‐Ramírez",authors:[{id:"204084",title:"Dr.",name:"Maricela",middleName:null,surname:"Bravo",slug:"maricela-bravo",fullName:"Maricela Bravo"},{id:"204085",title:"Dr.",name:"Alejandro",middleName:null,surname:"Reyes",slug:"alejandro-reyes",fullName:"Alejandro Reyes"},{id:"204086",title:"Dr.",name:"Leonardo",middleName:null,surname:"Sánchez",slug:"leonardo-sanchez",fullName:"Leonardo Sánchez"},{id:"204087",title:"MSc.",name:"Roberto",middleName:null,surname:"Alcántara-Ramírez",slug:"roberto-alcantara-ramirez",fullName:"Roberto Alcántara-Ramírez"}]},{id:"56184",title:"Multiagent Systems in Automotive Applications",slug:"multiagent-systems-in-automotive-applications",totalDownloads:1773,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The multiagent systems have proved to be a useful tool in the design of solutions to problems of distributed nature. In a distributed system, it is possible that the data, the control actions or even both, be distributed. The concept of agent is a suitable notion for capturing situations where the global knowledge about the status of a system is complex or even impossible to acquire in a single entity. In automotive applications, there exist a great number of scenarios of distributed nature, such as the traffic coordination, routes load balancing problems, traffic negotiation among the infrastructure and cars, to mention a few. Even more, the autonomous driving features of the new generation of cars will require the new methods of car to car communication, car to infrastructure negotiation, and even infrastructure to infrastructure communication. This chapter proposes the application of multiagent system techniques to some problems in the automotive field.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Raul Campos‐Rodriguez, Luis Gonzalez‐Jimenez, Francisco\nCervantes‐Alvarez, Francisco Amezcua‐Garcia and Miguel\nFernandez‐Garcia",authors:[{id:"178524",title:"Dr.",name:"Raul",middleName:null,surname:"Campos-Rodriguez",slug:"raul-campos-rodriguez",fullName:"Raul Campos-Rodriguez"},{id:"204584",title:"Dr.",name:"Francisco",middleName:null,surname:"Cervantes Alvarez",slug:"francisco-cervantes-alvarez",fullName:"Francisco Cervantes Alvarez"},{id:"204586",title:"Mr.",name:"Francisco",middleName:null,surname:"Amezcua Garcia",slug:"francisco-amezcua-garcia",fullName:"Francisco Amezcua Garcia"},{id:"204587",title:"BSc.",name:"Miguel",middleName:null,surname:"Fernandez Garcia",slug:"miguel-fernandez-garcia",fullName:"Miguel Fernandez Garcia"},{id:"207383",title:"Dr.",name:"Luis",middleName:null,surname:"Gonzalez-Jimenez",slug:"luis-gonzalez-jimenez",fullName:"Luis Gonzalez-Jimenez"}]},{id:"56297",title:"Multiagent System for Image Mining",slug:"multiagent-system-for-image-mining",totalDownloads:1381,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The overdone growth, wide availability, and demands for remote sensing databases combined with human limits to analyze such huge datasets lead to a need to investigate tools, techniques, methodologies, and theories capable of assisting humans at extracting knowledge. Image mining arises as a solution to extract implicit knowledge intelligently and semiautomatically or other patterns not explicitly stored in the huge image databases. However, spatial databases are among the ones with the fastest growth due to the volume of spatial information produced many times a day, demanding the investigation of other means for knowledge extraction. Multiagent systems are composed of multiple computing elements known as agents that interact to pursuit their goals. Agents have been used to explore information in the distributed, open, large, and heterogeneous platforms. Agent mining is a potential technology that studies ways of interaction and integration between data mining and agents. This area brought advances to the technologies involved such as theories, methodologies, and solutions to solve relevant issues more precisely, accurately and faster. AgentGeo is evidence of this, a multiagent system of satellite image mining that, promotes advances in the state of the art of agent mining, since it relevant functions to extract knowledge from spatial databases.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Nicksson Ckayo Arrais de Freitas and Marcelino Pereira dos Santos\nSilva",authors:[{id:"203201",title:"B.Sc.",name:"Nicksson",middleName:"Ckayo Arrais",surname:"De Freitas",slug:"nicksson-de-freitas",fullName:"Nicksson De Freitas"},{id:"203204",title:"Dr.",name:"Marcelino",middleName:null,surname:"Silva",slug:"marcelino-silva",fullName:"Marcelino Silva"}]},{id:"56255",title:"Time Critical Mass Evacuation Simulation Combining A Multi- Agent System and High-Performance Computing",slug:"time-critical-mass-evacuation-simulation-combining-a-multi-agent-system-and-high-performance-computi",totalDownloads:1413,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter presents an application of multi-agent systems to simulate tsunami-triggered mass evacuations of large urban areas. The main objective is to quantitatively evaluate various strategies to accelerate evacuation in case of a tsunami with a short arrival time, taking most influential factors into account. Considering the large number of lives in fatal danger, instead of widely used simple agents in 1D networks, we use a high-resolution model of environment and complex agents so that wide range of influencing factors can be taken into account. A brief description of the multi-agent system is provided using a mathematical framework as means to easily and unambiguously refer to the main components of the system. The environment of the multi-agent system, which mimics the physical world of evacuees, is modelled as a hybrid of a high-resolution grid and a graph connecting traversable spaces. This hybrid of raster and vector data structures enables modelling large domain in a scalable manner. The agents, which mimic the heterogeneous crowd of evacuees, are composed of different combinations of basic constituent functions for modelling interaction with each other and environment, decision-making, etc. The results of tuning and validating of constituent functions for pedestrian-pedestrian, car-car and car-pedestrian interactions are presented. A scalable high-performance computing (HPC) extension to address the high-computational demand of complex agents and high-resolution model of environment is briefly explained. Finally, demonstrative applications that highlight the need for including sub-meter details in the environment, different modes of evacuation and behavioural differences are presented.",book:{id:"5996",slug:"multi-agent-systems",title:"Multi-agent Systems",fullTitle:"Multi-agent Systems"},signatures:"Leonel Aguilar, Maddegedara Lalith and Muneo Hori",authors:[{id:"203530",title:"Prof.",name:"Maddgedara",middleName:null,surname:"Lalith",slug:"maddgedara-lalith",fullName:"Maddgedara Lalith"},{id:"204168",title:"Dr.",name:"Leonel",middleName:null,surname:"Aguilar",slug:"leonel-aguilar",fullName:"Leonel Aguilar"},{id:"204169",title:"Prof.",name:"Muneo",middleName:null,surname:"Hori",slug:"muneo-hori",fullName:"Muneo Hori"}]}],onlineFirstChaptersFilter:{topicId:"526",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,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:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"25",title:"Environmental Sciences",doi:"10.5772/intechopen.100362",issn:"2754-6713",scope:"\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems.
\r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.