Applied learning algorithms for intelligent IoT:
Applied Learning Algorithms for Intelligent IoT vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behav...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2022
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| Ausgabe: | First edition |
| Schlagworte: | |
| Zusammenfassung: | Applied Learning Algorithms for Intelligent IoT vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devicesCyber physical systems (CPS)The Internet of Things (IoT) and industrial use casesThe industry 4.0 for smarter manufacturingPredictive and prescriptive insights for smarter systemsMachine vision and intelligenceNatural interfacesK-means clustering algorithmSupport vector machine (SVM) algorithmA priori algorithmsLinear and logistic regression The book clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now with the emergence of machine learning algorithms, the field of data analytics is bound to reach newer heights.This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research |
| Beschreibung: | 1. Convolutional Neural Network in Computer VisionAishwarya D and R.I.Minu; 2. Trends and Transition in the Machine Learning (ML) SpaceSruthi Anand, N Susila, and S Usha; 3. Deep Learning: Algorithms, Platforms, Applications, and Research Trends in IoTR. Ranjana, T. Sheela, B. Narendra Kumar Rao, and T. Subha; 4. The Next-Generation IoT Use Cases across Industry Verticals using Machine and Deep Learning AlgorithmsKalaiarasan T R, Sruthi Anand, V Anandkuma, and Ratheeshkumar A.M.; 5. A Panoramic View of Cyber Attack Detection and Prevention Using Machine Learning and Deep Learning ApproachesEsther Daniel, N. Susila, and S. Durga; 6. Regression Algorithms in Machine LearningS. Usha, Neha Singhal, Pethuru Raj, and Ashwini R Malipatil; 7. Machine Learning Based Industrial Internet of Things (IIoT) and Its ApplicationsM. Sureshkumar, Rachel S, and Kaviselvan M V; 8. Employee Turnover Prediction Using Single Voting ModelR. Valarmathi, M. Umadevi, and T. Sheela; 9. A Novel Implementation of Sentiment Analysis towards Data ScienceVijayalakshmi Saravanan, Ishpreet Singh, Emanuel Szarek, Jereon Hak, and Anju S Pillai; 10. Conspectus of K-Means Clustering AlgorithmS.Usha, Jyothi A P, N Susila, and T. Sheela; 11. Systematic Approach to Deal with Internal Fragmentation and Enhancing Memory Space during COVID-19Aparna Mohan, Maheswari R, and Thomas Abraham J. V.; 12. IoT Automated Spy Drone to Detect and Alert Illegal Drug Plants for Law EnforcementGotluru Arun Kumar, PuluguYamini, and Maheswari R; 13. Expounding K-Means-inspired Network Partitioning Algorithm for SDN Controller Placement Pushpa J. and Pethuru Raj; 14. An Intelligent Deep Learning Based Wireless Underground Sensor System for IoT Based Agricultural ApplicationPriscilla Rajadurai and G. Jaspher W. Kathrine; 15. Predicting Effectiveness of Solar Pond Heat Exchanger with LTES Containing CUO Nanoparticle Using Machine LearningK Karunamurthy, G Suganya, M Ananthi, and T Subha |
| Beschreibung: | xii, 356 Seiten Illustrationen 235 mm |
| ISBN: | 9780367635947 9781032113210 |
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| 520 | |a Applied Learning Algorithms for Intelligent IoT vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devicesCyber physical systems (CPS)The Internet of Things (IoT) and industrial use casesThe industry 4.0 for smarter manufacturingPredictive and prescriptive insights for smarter systemsMachine vision and intelligenceNatural interfacesK-means clustering algorithmSupport vector machine (SVM) algorithmA priori algorithmsLinear and logistic regression The book clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now with the emergence of machine learning algorithms, the field of data analytics is bound to reach newer heights.This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research | ||
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| 700 | 1 | |a Sakthivel, Usha |4 edt | |
| 700 | 1 | |a Nagarajan, Susila |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-003-11983-8 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-032968467 | |
Datensatz im Suchindex
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| id | DE-604.BV047583120 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:33:46Z |
| indexdate | 2024-12-12T13:03:12Z |
| institution | BVB |
| isbn | 9780367635947 9781032113210 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032968467 |
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| physical | xii, 356 Seiten Illustrationen 235 mm |
| publishDate | 2022 |
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| publisher | CRC Press, Taylor & Francis Group |
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| spelling | Applied learning algorithms for intelligent IoT edited by Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2022 xii, 356 Seiten Illustrationen 235 mm txt rdacontent n rdamedia nc rdacarrier 1. Convolutional Neural Network in Computer VisionAishwarya D and R.I.Minu; 2. Trends and Transition in the Machine Learning (ML) SpaceSruthi Anand, N Susila, and S Usha; 3. Deep Learning: Algorithms, Platforms, Applications, and Research Trends in IoTR. Ranjana, T. Sheela, B. Narendra Kumar Rao, and T. Subha; 4. The Next-Generation IoT Use Cases across Industry Verticals using Machine and Deep Learning AlgorithmsKalaiarasan T R, Sruthi Anand, V Anandkuma, and Ratheeshkumar A.M.; 5. A Panoramic View of Cyber Attack Detection and Prevention Using Machine Learning and Deep Learning ApproachesEsther Daniel, N. Susila, and S. Durga; 6. Regression Algorithms in Machine LearningS. Usha, Neha Singhal, Pethuru Raj, and Ashwini R Malipatil; 7. Machine Learning Based Industrial Internet of Things (IIoT) and Its ApplicationsM. Sureshkumar, Rachel S, and Kaviselvan M V; 8. Employee Turnover Prediction Using Single Voting ModelR. Valarmathi, M. Umadevi, and T. Sheela; 9. A Novel Implementation of Sentiment Analysis towards Data ScienceVijayalakshmi Saravanan, Ishpreet Singh, Emanuel Szarek, Jereon Hak, and Anju S Pillai; 10. Conspectus of K-Means Clustering AlgorithmS.Usha, Jyothi A P, N Susila, and T. Sheela; 11. Systematic Approach to Deal with Internal Fragmentation and Enhancing Memory Space during COVID-19Aparna Mohan, Maheswari R, and Thomas Abraham J. V.; 12. IoT Automated Spy Drone to Detect and Alert Illegal Drug Plants for Law EnforcementGotluru Arun Kumar, PuluguYamini, and Maheswari R; 13. Expounding K-Means-inspired Network Partitioning Algorithm for SDN Controller Placement Pushpa J. and Pethuru Raj; 14. An Intelligent Deep Learning Based Wireless Underground Sensor System for IoT Based Agricultural ApplicationPriscilla Rajadurai and G. Jaspher W. Kathrine; 15. Predicting Effectiveness of Solar Pond Heat Exchanger with LTES Containing CUO Nanoparticle Using Machine LearningK Karunamurthy, G Suganya, M Ananthi, and T Subha Applied Learning Algorithms for Intelligent IoT vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devicesCyber physical systems (CPS)The Internet of Things (IoT) and industrial use casesThe industry 4.0 for smarter manufacturingPredictive and prescriptive insights for smarter systemsMachine vision and intelligenceNatural interfacesK-means clustering algorithmSupport vector machine (SVM) algorithmA priori algorithmsLinear and logistic regression The book clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now with the emergence of machine learning algorithms, the field of data analytics is bound to reach newer heights.This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research bisacsh / COMPUTERS / Neural Networks Chelliah, Pethuru Raj ca. 20./21. Jahrhundert (DE-588)1088794912 edt Sakthivel, Usha edt Nagarajan, Susila edt Erscheint auch als Online-Ausgabe 978-1-003-11983-8 |
| spellingShingle | Applied learning algorithms for intelligent IoT bisacsh / COMPUTERS / Neural Networks |
| title | Applied learning algorithms for intelligent IoT |
| title_auth | Applied learning algorithms for intelligent IoT |
| title_exact_search | Applied learning algorithms for intelligent IoT |
| title_exact_search_txtP | Applied learning algorithms for intelligent IoT |
| title_full | Applied learning algorithms for intelligent IoT edited by Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan |
| title_fullStr | Applied learning algorithms for intelligent IoT edited by Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan |
| title_full_unstemmed | Applied learning algorithms for intelligent IoT edited by Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan |
| title_short | Applied learning algorithms for intelligent IoT |
| title_sort | applied learning algorithms for intelligent iot |
| topic | bisacsh / COMPUTERS / Neural Networks |
| topic_facet | bisacsh / COMPUTERS / Neural Networks |
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