MARC Record from marc_columbia

LEADER: 16878cam a2200853 i 4500
001 15437838
005 20220403002818.0
006 m o d
007 cr cnu---unuuu
008 200827t20212021flua ob 001 0 eng
010 $a 2020038793
035 $a(OCoLC)on1196820668
035 $a(NNC)15437838
040 $aDLC$beng$erda$cDLC$dOCLCF$dOCLCO$dYDX$dTYFRS$dN$T$dUKAHL$dSOE$dOCLCO
019 $a1245590956
020 $a9781003121619$qelectronic book
020 $a1003121616$qelectronic book
020 $a9781000337921$q(electronic bk. : EPUB)
020 $a1000337928$q(electronic bk. : EPUB)
020 $a9781000337884$q(electronic bk. : PDF)
020 $a100033788X$q(electronic bk. : PDF)
020 $a9781000337907$q(electronic bk. : Mobipocket)
020 $a1000337901$q(electronic bk. : Mobipocket)
020 $z9780367487966$qhardcover
035 $a(OCoLC)1196820668$z(OCoLC)1245590956
037 $a9781003121619$bTaylor & Francis
042 $apcc
050 04 $aQA76.9.S63$bC55 2021
072 7 $aCOM$x037000$2bisacsh
072 7 $aCOM$x014000$2bisacsh
072 7 $aTEC$x017000$2bisacsh
072 7 $aUYQ$2bicssc
082 00 $a006.3$223
049 $aZCUA
245 00 $aCognitive computing using green technologies :$bmodeling techniques and applications /$cedited by Asis Kumar Tripathy, Chiranji Lal Chowdhary, Mahasweta Sarkar, and Sanjaya Kumar Panda.
250 $aFirst edition.
264 1 $aBoca Raton, FL :$bCRC Press,$c2021.
264 4 $c©2021
300 $a1 online resource (xvi, 279 pages) :$billustrations.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 0 $aGreen energy and technology : concepts and applications
504 $aIncludes bibliographical references and index.
520 $a"Cognitive Computing is a new topic which aims to simulate human thought processes using computers that self-learn through data mining, pattern recognition, and natural language processing. This book focuses on the applications of Cognitive Computing in areas like Robotics, Blockchain, Deep Learning, and Wireless Technologies. This book covers the basics of Green Computing, discusses Cognitive Science methodologies in Robotics, Computer Science, Wireless Networks, and Deep Learning. It goes on to present empirical data and research techniques, modelling techniques and offers a data-driven approach to decision making and problem solving. This book is written for researchers, academicians, undergraduate and graduate students, and industry persons who are working on current applications of Cognitive Computing"--$cProvided by publisher.
588 $aDescription based on online resource; title from digital title page (viewed on April 12, 2021).
545 0 $aDr. Asis Kumar Tripathy received his Ph.D. in Computer Science and Engineering from National Institute of Technology, Rourkela, India and his M.Tech in Computer Science and Engineering from International Institute of Information Technology, Bhubaneswar, India. Currently, he is working as an Associate Professor in the School of Information Technology and Engineering, VIT Vellore, India. He has authored more than 10 national and international research papers to his credit. He is acting as a reviewer for some prestigious journals like Computers and Electrical Engineering (Elsevier), IET Networks, Wireless Personal Communications (Springer), Health and Technology (Springer), and International Journal of Biometrics (Inderscience). He is associated with many professional bodies OITS, IACSIT, CSTA, and IAENG. His current research interests include Wireless Sensor Networks, IoT and Computational Intelligence. Dr. Chiranji Lal Chowdhary. He have published quality papers/chapters on Deep Learning, Block Chain and Cognitive Computing, and has published books with IGI, AAP and CRC Press. He is also the editor of special issue Journals on these topics. Dr. Mahasweta Sarkar is an Associate Professor in the Department of Electrical and Computer Engineering at San Diego State University (SDSU), California, USA. Dr. Sarkar received her Ph.D in Computer Engineering from University of California at San Diego (UCSD) in 2005. Her research interest lies in the area of wireless data networks. Her work addresses issues like scheduling, routing, optimal resource allocation, power management in wireless networks like WLANs, WMANs, Sensor Networks, Ad Hoc Networks and wireless health. She has over 70 published research articles in technical journals, conference proceedings and book chapters. She is the Director of the Wireless Networks Research Group at SDSU where she leads a team of Ph.D and Masters students along with Post-doctoral Fellows and visiting faculty and PhD students from India, China, Denmark, Iran nad Korea. She is the recipient of the President’s Leadership award at SDSU in 2010 for her excellence in research and the Outstanding Faculty Award in 2014 for her excellence in teaching. Dr. Sanjaya Kumar Panda is working as an Assistant Professor and Head of the Department, CSE at IIITDM Kurnool, Andhra Pradesh, India. He worked as an Assistant Professor in the Department of IT at VSSUT, Burla, Odisha, India. He received Ph. D. degree from IIT (ISM) Dhanbad, Jharkhand, India, M. Tech. degree from NIT, Rourkela, Odisha, India and B. Tech. degree from VSSUT, Burla, Odisha, India in CSE. He received two silver medal awards for best graduate and best post-graduate in CSE. He also received Institution award, IEEE brand ambassador, SGSITS national award for the best research work by young teachers of engineering college for the year 2017, faculty with maximum publishing in CSI publications award, young IT professional award (2017 and 2016), young scientist award, CSI paper presenter award at international conference and CSI distinguished speaker award. He has published more than 60 papers in reputed journals and conferences. He is a member of IEEE, an associate member of IEI, Life member of ISTE, Life member of CSI, IAENG, IACSIT, UACEE, ACEEE and SDIWC. His current research interests include recommender system, cloud computing, big data analytics, grid computing, fault tolerance and load balancing.
505 0 $aIntro -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Contributors -- About the Book -- Green Engineering and Technology: Concepts and Applications -- Part I: Introduction -- 1. Green Communication Technology, IOT, VR, AR in Smart Environment -- 1. Introduction -- 2. Associated Work -- 3. Technical Technology -- 3.1. Strategies Based on Program Acquisition -- 3.2. Irregularity Grounded Interruption Recognition -- 3.3. IDSs: Presentation Assessment -- 4. Keen Metropolitan Tenders -- 4.1. Smart Transportation -- 4.2. Ambient Abetted Existing -- 4.3. Corruption Deterrence and Public Security Documentation of Convicts -- 4.4. Ascendency -- 4.5. Disruption of Nursing and Maintenance of Replacement -- 4.6. Disaster and Backup Organization -- 4.7. Ecological Monitoring -- 4.8. Waste and Equipment Administration -- 4.9. Canny Is Taken Home -- 4.10. Smart Energy -- 5. Conclusion -- References -- 2. Green Computing -- Uses and Design -- 1. Introduction -- 2. Origin of Green Computing -- 3. Related Works -- 4. Need for Green Computing -- 5. Challenges in Green Computing -- 5.1. Return of Investment -- 5.2. Disposal of Electronic Wastes -- 5.3. Perspective with Respect to Indian Scenario -- 5.4. Energy Efficiency and Miniaturization Board Level Witnessing the R&amp -- D Capabilities -- 6. Paradigms of Green Computing in IT -- 7. Application of Green Computing -- 7.1. In Distributed and Cloud Environments -- Server rooms -- 7.2. In Smart Portable Devices -- 7.3. In IoT -- 7.4. In Parallel Computing of Big Data Systems -- 8. Pros and Cons of Green Computing -- 9. Measures to be Taken in Developing IT Products as a Move Towards Green Computing -- 10. Conclusion -- References -- Part II: Analysis -- 3. Statistical Methods for Reproducible Data Analysis -- 1. Introduction -- 2. Overview Hierarchy.
505 8 $a3. Introduction to Descriptive Statistics -- 4. Introduction to Inferential Statistics -- 5. Introduction to Predictive Modelling -- 6. Conclusion -- References -- 4. An Approach for Energy-Efficient Task Scheduling in Cloud Environment -- 1. Introduction -- 2. Literature Survey -- 3. Proposed Model -- 4. Problem Formulation -- 5. Proposed Algorithm -- 6. Simulation and Experimental Result -- 7. Conclusion and Future Work -- References -- 5. Solar-Powered Cloud Data Center for Sustainable Green Computing -- 1. Introduction -- 2. Background -- 3. Energy Consumption Analysis -- 4 . Methodology -- 4.1. Cloud Data Centers Solar PV Model -- 4.2. Small Scale Routing Algorithm -- Detection of Route -- 4.3. MRR Analysis -- 5 . Results and Discussions -- 6. Conclusions -- References -- 6. State-of-the-Art Energy Grid with Cognitive Behavior and Blockchain Techniques -- 1. Introduction -- 1.1. Renewable Energy's Unreliable Nature -- 1.2. Distributed Architecture Benefits -- 1.3. Renewable Energy Resources -- 1.4. Need for Effective Utilization -- 2. Related Works -- 2.1. Existing Systems -- 2.2. Problems with Existing System -- 3. Proposed Grid Design -- 3.1. Architecture -- 3.2. Smart Energy Distribution -- 3.3. Storage Automation -- 3.4. Data Analysis -- 4. Grid Modules -- 4.1. Energy Storage Pool -- 4.2. Energy Flow Controller -- 4.3. Data Storage -- 4.4. Communication -- 5. Experimental Setup -- 6. Techniques Incorporated -- 6.1. BC Network -- 6.2. IoT Data Transmission -- 6.3. Smart Contracts -- 6.4. Distributed Storage Structure -- 6.5. Cognitive Character -- 7. Challenges and Issues -- 8. Results -- 8.1. SARIMAX Load Forecasting -- 8.2. Spectral Clustering -- 8.3. Distributed Storage -- 8.4. P2P Energy Trade with Reservations -- 9. Conclusion -- References.
505 8 $a7. Optimized Channel Selection Scheme Using Cognitive Radio Controller for Health Monitoring and Post-Disaster Management Applications -- 1. Introduction -- 1.1. Cognitive Radio Network -- 1.2. Dynamic Spectrum Access -- 2. Problem Identification -- 3. System Model -- 3.1. Channel Identification -- 3.2. Channel Assignment -- 3.3. Channel Mobility -- 4. Scheduling and Routing Process -- 4.1. Transmission Power -- 4.2. Minimizing Overhead Problem (MOP) -- 4.3. Overhead -- 5. Experimental Setup and Results -- 6. Application of DSA in Health Monitoring System -- 6.1. Data Extraction Using CRC (Tier 1) -- 6.2. Channel Selection Layer (Tier 2) -- 6.2.1 WBAN Controller -- 6.2.2 Cognitive Radio Controller -- 6.3. Application Layer (Tier 3) -- 7. Application of DSA in Post Disaster Management Applications -- 8. Conclusion -- References -- 8. TB-PAD: A Novel Trust-Based Platooning Attack Detection in Cognitive Software-Defined Vehicular Network (CSDVN) -- 1. Introduction -- 2. TB-PAD Proposed Methodology -- 3. Related Works -- 3.1. Network Model -- 3.2. Misbehavior Model -- 3.3. Proposed Methodology for TB-PAD -- 4. Simulation and Results -- 5. Conclusion -- References -- 9. Analysis of Security Issues in IoT System -- 1. Introduction -- 2. IoT Architecture -- 2.1. Perception Layer -- 2.2. Network Layer -- 2.3. Processing Layer -- 2.4. Application Layer -- 3. Some Important Technologies -- 3.1. Radio Frequency Identification -- 3.2. Wireless Sensor Network -- 3.3. Green IoT -- 4. Applications of IoT -- 4.1. IoT in Healthcare -- 4.2. IoT in Transport -- 4.3. IoT in Smart Houses -- 4.4. IoT in Agriculture -- 4.5. IoT in Industries -- 4.6. IoT in Education -- 4.7. IoT in Smart Cities -- 5. Security and Authenticity of Data in IoT -- 5.1. Data Confidentiality -- 5.2. Date Integrity -- 5.3. Data Access Control -- 5.4. Data Availability -- 5.5. Data Encryption.
505 8 $a6. Layered Analysis of Attacks and Countermeasures -- 6.1. Perception Layer Attacks and Countermeasures -- 6.1.1 Some Attacks on Perception Layer -- a Fake Node Insertion -- b Malicious Code Insertion -- c Side Channel Attack -- d Sinkhole Attack -- e Device Tampering -- f Social Engineering Attack -- g Node Capture Attacks -- h Sleep Deprivation Attack (SDA) -- 6.1.2 Some Countermeasures to Protect Perception Layer -- a Authentication -- b Data Integrity Schemes -- c IPSec (Internet Protocol Security) -- d Secure Physical Designing -- e Safe Booting -- 6.2. Network Layer Attacks and Countermeasures -- 6.2.1 Some Attacks on Network Layer -- a Traffic Analysis Attack -- b Man in the Middle Attack (MIMA) -- c Denial of Service Attack (DoS) -- d Sybil Attack -- e False Data Injection Attack (FDIA) -- f Black Hole Attack (BHA) -- g Worm Hole Attack -- h Routing Table Overflow Attack -- i Distributed Denial-of-Service Attack -- 6.2.2 Some Countermeasures to Protect Network Layer -- a Routing Security -- b Protection against Denial of Service Attacks -- c Sybil Attack Countermeasure -- d False Data Injection Attack Countermeasures -- 6.3. Processing Layer Attacks and Countermeasures -- 6.3.1 Processing Layer Attacks -- a Session Hijacking -- b XML Signature Wrapping Attack -- c SaaS Security Threats -- d SQL Injection -- e Flooding Attack -- 6.3.2 Processing Layer Countermeasures -- a Homomorphic Encryption -- b End-to-End Encryption -- 6.4. Application Layer Attacks and Countermeasures -- 6.4.1 Some Attacks on Application Layer -- a Data Modification Attack -- b Reprogramming Attack -- c Phishing Attack -- 6.4.2 Some Countermeasures to Protect Application Layer -- a Firewall -- b User Authentication -- c Sniffing Attack Countermeasure -- 7. Conclusion -- References -- 10. Resource Optimization of Cloud Services with Bi-layered Blockchain.
505 8 $a1. Introduction -- 2. Literature Review -- 3. Problem Statement -- 4. Bi-layering of Blockchain -- 5. Smart Contracts -- 6. Solidity -- 7. System Design -- 8. Implementation -- 9. Consensus Algorithm -- 9.1. Mathematical Analysis on Modified PoW -- 10. Application of Proposed Solution on AWS S3 -- 11. Signing and Authenticating Rest Request -- 12. Results -- 13. Conclusion -- References -- 11. Trust-Based GPS Faking Attack Detection in Cognitive Software-Defined Vehicular Network (CSDVN) -- 1. Introduction -- 2. Related Works -- 3. Trust-Based GPS Faking Attack Detection Methodology for CSDVN -- 3.1. Network Model -- 3.2. Misbehavior Model -- 3.3. Proposed Methodology for GPS Faking Attack Detection -- 4. Simulation and Results -- 5. Conclusion -- References -- Part III: Applications -- 12. Cognitive Intelligence-Based Framework for Financial Forecasting -- 1. Introduction -- 2. Artificial Neural Network -- 3. ANN Training Methods -- 3.1. Gradient Descent-Based Method -- 3.2. Evolutionary Optimization-Based Method -- 3.2.1 FWA -- 3.2.5 MBO -- 3.2.6 MVO -- 4. Financial Time Series Data -- 5. ANN-Based Financial Prediction -- 6. Simulation Studies and Results Analysis -- 7. Conclusions -- References -- 13. Benefits of IoT in Monitoring and Regulation of Power Sector -- 1. Introduction -- 1.1. Indian Power Distribution Reforms Scenario -- 1.2. Emergence of Internet of Things (IoT) -- 1.3. IoT in Renewable Energy Sources -- 2. IoT in Power Sector -- 3. IoT Architecture and Basic Blocks -- 3.1. Device Management -- 3.2. User Management -- 3.3. Security Monitoring -- 4. IoT Based Grid -- 4.1. Applications in Real-Time Systems -- 5. Comparison of Conventional Power Grid and Smart Grid -- 6. Application of IoT in the Electrical Power Industry -- 6.1. IoT SCADA -- 6.2. Smart Metering -- 6.3. Building Automation -- 6.4. Connected Public Lighting.
650 0 $aSoft computing.
650 0 $aMachine learning.
650 0 $aEnergy conservation$xData processing.
650 0 $aElectronic digital computers$xEnergy conservation.
650 6 $aInformatique douce.
650 6 $aApprentissage automatique.
650 6 $aÉconomies d'énergie$xInformatique.
650 6 $aOrdinateurs$xÉconomies d'énergie.
650 7 $aCOMPUTERS / Machine Theory$2bisacsh
650 7 $aCOMPUTERS / Computer Science$2bisacsh
650 7 $aTECHNOLOGY / Industrial Health & Safety$2bisacsh
650 7 $aEnergy conservation$xData processing$2fast$0(OCoLC)fst00909923
650 7 $aMachine learning$2fast$0(OCoLC)fst01004795
650 7 $aSoft computing$2fast$0(OCoLC)fst01124115
655 4 $aElectronic books.
700 1 $aTripathy, Asis Kumar,$eeditor.
700 1 $aChowdhary, Chiranji Lal,$d1975-$eeditor.
700 1 $aSarkar, Mahasweta,$eeditor.
700 1 $aPanda, Sanjaya Kumar,$eeditor.
776 08 $iPrint version:$tCognitive computing using green technologies$bFirst edition.$dBoca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021.$z9780367487966$w(DLC) 2020038792
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15437838$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS