MARC Record from marc_columbia

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035 $a(OCoLC)on1229166120
035 $a(NNC)15275073
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024 7 $a10.1201/9781003007265$2doi
035 $a(OCoLC)1229166120$z(OCoLC)1228031599
037 $a9781003007265$bTaylor & Francis
050 00 $aTK7872.D48
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049 $aZCUA
245 00 $aArtificial intelligence techniques in IoT-sensor networks /$cedited by Mohamed Elhoseny, K. Shankar, Mohamed Abdel-Basset.
250 $aFirst edition.
264 1 $aBoca Raton :$bCRC Press/Taylor and Francis Group,$c2021.
300 $a1 online resource (x, 221 pages).
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 0 $aChapman & Hall/CRC distributed sensing and intelligent systems series
520 $a"Artificial Intelligence Techniques in IoT-Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in Artificial Intelligence (AI), Sensor Networks and Internet of Things (IoT). This book intends to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications, and their implications on the design of solutions for sensor networks"--$cProvided by publisher.
504 $aIncludes bibliographical references and index.
588 $aDescription based on print version record and CIP data provided by publisher; resource not viewed.
505 0 $aPrefaceChapter 1Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images -- An Artificial Intelligence Based IoT Implementation for Teleradiology Network 1.1 Introduction1.2 Proposed Methodology 1.2.1 Fuzzy C Means Clustering1.3 Results and Discussion1.4 ConclusionReferencesChapter 2Artificial Intelligence Based Fuzzy Logic with Modified Particle Swarm Optimization Algorithm for Internet of Things Enabled Logistic Transportation Planning 2.1. Introduction2.2. Related works2.3. Proposed Method 2.3.1. Package Partitioning 2.3.2. Planning of delivery path using HFMPSO algorithm 2.3.3. Inserting Pickup Packages2.4. Experimental Validation 2.4.1. Performance analysis under varying package count 2.4.2. Performance analysis under varying vehicle capacities 2.4.3. Computation Time (CT) analysis2.5. ConclusionReferences Chapter 3Butterfly Optimization based Feature Selection with Gradient Boosting Tree for Big Data Analytics in Social Internet of Things 3.1. Introduction3.2. Related works3.3. The Proposed Method 3.3.1. Hadoop Ecosystem 3.3.2. BOA based FS process 3.3.3. GBT based Classification3.4. Experimental Analysis 3.4.1. FS Results analysis 3.4.2. Classification Results Analysis 3.4.3. Energy Consumption Analysis 3.4.4. Throughput Analysis3.5. ConclusionReferencesChapter 4An Energy Efficient Fuzzy Logic based Clustering with Data Aggregation Protocol for WSN assisted IoT system 4. 1. Introduction4. 2. Background Information 4. 2.1. Clustering objective 4. 2. 2. Clustering characteristics4. 3. Proposed Fuzzy based Clustering and Data Aggregation (FC-DR) protocol 4. 3. 1. Fuzzy based Clustering process 4. 3. 2. Data aggregation process 4. 4. Performance Validation4. 5. ConclusionReferencesChapter 5Analysis of Smart Home Recommendation system from Natural Language Processing Services with Clustering Technique 5. 1. Introduction5. 2. Review of Literatures5. 3. Smart Home- Cloud Backend Services 5. 3.1 Internet of Things (IoT)5. 4. Our Proposed Approach 5. 4.1 Natural Language Processing Services (NLPS) 5. 4. 2 Pipeline Structure for NLPS 5. 4. 3 Clustering Model5. 5. Results and analysis5. 6. ConclusionReferencesChapter 6Metaheuristic based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks 6. 1. Introduction6. 2. Proposed Methodology 6. 2. 1. Deflate based Compression Model 6. 2. 2. SMO-KELM based Diagnosis Model6. 3. Experimental results and validation6. 4. ConclusionReferencesChapter 7Fuzzy Support Vector Machine with SMOTE for Handling Class Imbalanced Data in IoT Based Cloud Environment 7. 1. Introduction7. 2. The Proposed Model 7. 2.1. SMOTE Model 7. 2.2. FSVM based Classification Model7. 3. Simulation Results and Discussion7. 4. ConclusionReferencesChapter 8Energy Efficient Unequal Clustering Algorithm using Hybridization of Social Spider with Krill Herd in IoT Assisted Wireless Sensor Networks 8. 1. Introduction8. 2. Research Background8. 3. Literature survey8. 4. The proposed SS-KH algorithm 8. 4. 1. SS based TCH selection 8. 4. 2. KH based FCH algorithm8. 5. Experimental validation 8. 5. 1 Implementation setup 8. 5. 2. Performance analysis8. 6. ConclusionReferencesChapter 9IoT Sensor Networks with 5G Enabled Faster RCNN Based Generative Adversarial Network Model for Face Sketch Synthesis 9. 1. Introduction9. 2. The Proposed FRCNN-GAN Model 9. 2.1. Data Collection 9. 2.2. Faster R-CNN based Face Recognition 9. 2.3. GAN based Synthesis Process9. 3. Performance Validation9. 4. ConclusionReferencesChapter 10Artificial Intelligence based Textual Cyberbullying Detection for Twitter Data Analysis in Cloud-based Internet of Things 10. 1. Introduction10. 2. Literature review10. 3. Proposed Methodology 10. 3.1. Preprocessing 10. 3.2. Feature extraction 10. 3.3. Feature selection using ranking method 10. 3.4. Cyberbully detection 10. 3.5. Dataset Description10. 4. Result and discussion 10. 4.1. Evaluation Metrics 10. 4.2. Comparative analysis10. 5. ConclusionReferencesChapter 11An Energy Efficient Quasi Oppositional Krill Herd Algorithm based Clustering Protocol for Internet of Things Sensor Networks 11. 1. Introduction11. 2. The Proposed Clustering algorithm11. 3. Performance Validation11. 4. ConclusionReferencesChapter 12An effective Social Internet of Things (SIoT) Model for Malicious node detection in wireless sensor networks 12. 1. Introduction12. 2. Review of Recent Kinds of literature12. 3. Network Model: SIoT12. 3.1 Malicious Attacker Model in SIoT12. 4. Proposed MN in SIoT System12. 4.1 Trust based Grouping in SIoT network12. 4.2 Exponential Kernel Model for MN detection12. 4.3.1 Example of Proposed Detection System12. 4.4 Detection Model12. 5. Results and analysis12. 6. ConclusionReferencesChapter 13IoT Based Automated Skin Lesion Detection and Classification using Grey Wolf Optimization with Deep Neural Network 13. 1. Introduction13. 2. The Proposed GWO-DNN Model 13. 2.1. Feature Extraction 13. 2.2. DNN based classification13. 3. Experimental Validation13. 4. ConclusionReferencesIndex
650 0 $aSensor networks.
650 0 $aInternet of things.
650 6 $aRéseaux de capteurs.
650 6 $aInternet des objets.
650 7 $aCOMPUTERS$xComputer Engineering.$2bisacsh
650 7 $aCOMPUTERS$xMachine Theory.$2bisacsh
650 7 $aInternet of things.$2fast$0(OCoLC)fst01894151
650 7 $aSensor networks.$2fast$0(OCoLC)fst01112600
655 4 $aElectronic books.
700 1 $aElhoseny, Mohamed,$eeditor.
700 1 $aShankar, K.,$eeditor.
700 1 $aAbdel-Basset, Mohamed,$d1985-$eeditor.
776 08 $iPrint version:$tArtificial intelligence techniques in IoT-sensor networks$dBoca Raton : CRC Press/Taylor & Francis Group, 2021.$z9780367439255$w(DLC) 2020037221
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15275073$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS