Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

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Titel
Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
verantwortlich
Devi, K. Gayathri (VerfasserIn); Rath, Mamata (MitwirkendeR); Linh, Nguyen Thi Dieu (MitwirkendeR)
veröffentlicht
Milton: Taylor & Francis Group, 2020
©2021
Erscheinungsjahr
2020
Teil von
Artificial Intelligence (AI): Elementary to Advanced Practices Ser.
Erscheint auch als
Artificial intelligence trends for data analytics using machine learning and deep learning approaches, Boca Raton : CRC Press, Taylor & Francis Group, 2021, xvi, 250 Seiten
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520 |a Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- Editors -- Contributors -- Chapter 1: An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits -- Contents -- 1.1. Introduction -- 1.1.1. Previous Work Using BPNN and ANFIS -- 1.2. Training and Testing Data -- 1.3. Power Estimation Using a Neural Network -- 1.3.1. Construction of a Neural Network -- 1.3.2. BPNN Training Phase -- 1.3.3. BPNN Testing Phase -- 1.3.4. Network Parameters -- 1.4. Proposed Power Estimation Using ANFIS Technique -- 1.4.1. Overview of the Proposed Work -- 1.4.2. Training and Checking Used in ANFIS -- 1.4.3. Designing the ANFIS -- 1.5. Results and Discussions -- 1.5.1. BPNN-Based Method -- 1.5.2. Calculating Prediction Error -- 1.5.3. ANFIS-Based Method -- 1.5.4. Performance Evaluation -- 1.6. Conclusion -- References -- Chapter 2: Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach -- Contents -- 2.1. Introduction -- 2.1.1. Literature Survey -- 2.2. Usage Analysis and Negotiable (UAN) Approach -- 2.3. Age Categorization Usage Analysis -- 2.3.1. Rules Defined for 18-25 Age Categorization -- 2.3.2. Rules Defined for 26-30 Age Categorization -- 2.3.3. Rules Defined for 31-35 Age Categorization -- 2.4. User View Analysis -- 2.4.1. Likes and Dislikes Views -- 2.4.2. Comments Posting Category -- 2.4.3. Content Sharing -- 2.5. User Interested Test Rate (UITR) Analysis Algorithm -- 2.5.1. Rating Statement Analysis -- 2.5.1.1. Like and Dislikes Rating Algorithm -- 2.5.1.2. Comments Posting -- 2.5.1.3. Content Sharing -- 2.6. Feedback Analysis -- 2.7. Result and Discussions -- 2.8. Conclusion -- References -- Chapter 3: Object Detection and Tracking in Video Using Deep Learning Techniques: A Review -- Contents -- 3.1. Introduction. 
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author Devi, K. Gayathri
author2 Rath, Mamata, Linh, Nguyen Thi Dieu
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contents Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- Editors -- Contributors -- Chapter 1: An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits -- Contents -- 1.1. Introduction -- 1.1.1. Previous Work Using BPNN and ANFIS -- 1.2. Training and Testing Data -- 1.3. Power Estimation Using a Neural Network -- 1.3.1. Construction of a Neural Network -- 1.3.2. BPNN Training Phase -- 1.3.3. BPNN Testing Phase -- 1.3.4. Network Parameters -- 1.4. Proposed Power Estimation Using ANFIS Technique -- 1.4.1. Overview of the Proposed Work -- 1.4.2. Training and Checking Used in ANFIS -- 1.4.3. Designing the ANFIS -- 1.5. Results and Discussions -- 1.5.1. BPNN-Based Method -- 1.5.2. Calculating Prediction Error -- 1.5.3. ANFIS-Based Method -- 1.5.4. Performance Evaluation -- 1.6. Conclusion -- References -- Chapter 2: Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach -- Contents -- 2.1. Introduction -- 2.1.1. Literature Survey -- 2.2. Usage Analysis and Negotiable (UAN) Approach -- 2.3. Age Categorization Usage Analysis -- 2.3.1. Rules Defined for 18-25 Age Categorization -- 2.3.2. Rules Defined for 26-30 Age Categorization -- 2.3.3. Rules Defined for 31-35 Age Categorization -- 2.4. User View Analysis -- 2.4.1. Likes and Dislikes Views -- 2.4.2. Comments Posting Category -- 2.4.3. Content Sharing -- 2.5. User Interested Test Rate (UITR) Analysis Algorithm -- 2.5.1. Rating Statement Analysis -- 2.5.1.1. Like and Dislikes Rating Algorithm -- 2.5.1.2. Comments Posting -- 2.5.1.3. Content Sharing -- 2.6. Feedback Analysis -- 2.7. Result and Discussions -- 2.8. Conclusion -- References -- Chapter 3: Object Detection and Tracking in Video Using Deep Learning Techniques: A Review -- Contents -- 3.1. Introduction.
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spelling Devi, K. Gayathri VerfasserIn aut, Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches, Milton Taylor & Francis Group 2020, ©2021, 1 online resource (267 pages), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Artificial Intelligence (AI): Elementary to Advanced Practices Ser., Description based on publisher supplied metadata and other sources, Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- Editors -- Contributors -- Chapter 1: An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits -- Contents -- 1.1. Introduction -- 1.1.1. Previous Work Using BPNN and ANFIS -- 1.2. Training and Testing Data -- 1.3. Power Estimation Using a Neural Network -- 1.3.1. Construction of a Neural Network -- 1.3.2. BPNN Training Phase -- 1.3.3. BPNN Testing Phase -- 1.3.4. Network Parameters -- 1.4. Proposed Power Estimation Using ANFIS Technique -- 1.4.1. Overview of the Proposed Work -- 1.4.2. Training and Checking Used in ANFIS -- 1.4.3. Designing the ANFIS -- 1.5. Results and Discussions -- 1.5.1. BPNN-Based Method -- 1.5.2. Calculating Prediction Error -- 1.5.3. ANFIS-Based Method -- 1.5.4. Performance Evaluation -- 1.6. Conclusion -- References -- Chapter 2: Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach -- Contents -- 2.1. Introduction -- 2.1.1. Literature Survey -- 2.2. Usage Analysis and Negotiable (UAN) Approach -- 2.3. Age Categorization Usage Analysis -- 2.3.1. Rules Defined for 18-25 Age Categorization -- 2.3.2. Rules Defined for 26-30 Age Categorization -- 2.3.3. Rules Defined for 31-35 Age Categorization -- 2.4. User View Analysis -- 2.4.1. Likes and Dislikes Views -- 2.4.2. Comments Posting Category -- 2.4.3. Content Sharing -- 2.5. User Interested Test Rate (UITR) Analysis Algorithm -- 2.5.1. Rating Statement Analysis -- 2.5.1.1. Like and Dislikes Rating Algorithm -- 2.5.1.2. Comments Posting -- 2.5.1.3. Content Sharing -- 2.6. Feedback Analysis -- 2.7. Result and Discussions -- 2.8. Conclusion -- References -- Chapter 3: Object Detection and Tracking in Video Using Deep Learning Techniques: A Review -- Contents -- 3.1. Introduction., Artificial intelligence-Industrial applications, Electronic books, s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd, s (DE-588)1135597375 (DE-627)890512922 (DE-576)489847412 Deep learning gnd, s (DE-588)4123037-1 (DE-627)105758051 (DE-576)209556331 Datenanalyse gnd, (DE-627), Rath, Mamata MitwirkendeR ctb, Linh, Nguyen Thi Dieu MitwirkendeR ctb, 9780367417277, Erscheint auch als Druck-Ausgabe$ Artificial intelligence trends for data analytics using machine learning and deep learning approaches Boca Raton : CRC Press, Taylor & Francis Group, 2021 xvi, 250 Seiten (DE-627)1700700170 9780367417277, https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6305306 X:EBC Aggregator lizenzpflichtig
spellingShingle Devi, K. Gayathri, Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches, Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- Editors -- Contributors -- Chapter 1: An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits -- Contents -- 1.1. Introduction -- 1.1.1. Previous Work Using BPNN and ANFIS -- 1.2. Training and Testing Data -- 1.3. Power Estimation Using a Neural Network -- 1.3.1. Construction of a Neural Network -- 1.3.2. BPNN Training Phase -- 1.3.3. BPNN Testing Phase -- 1.3.4. Network Parameters -- 1.4. Proposed Power Estimation Using ANFIS Technique -- 1.4.1. Overview of the Proposed Work -- 1.4.2. Training and Checking Used in ANFIS -- 1.4.3. Designing the ANFIS -- 1.5. Results and Discussions -- 1.5.1. BPNN-Based Method -- 1.5.2. Calculating Prediction Error -- 1.5.3. ANFIS-Based Method -- 1.5.4. Performance Evaluation -- 1.6. Conclusion -- References -- Chapter 2: Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach -- Contents -- 2.1. Introduction -- 2.1.1. Literature Survey -- 2.2. Usage Analysis and Negotiable (UAN) Approach -- 2.3. Age Categorization Usage Analysis -- 2.3.1. Rules Defined for 18-25 Age Categorization -- 2.3.2. Rules Defined for 26-30 Age Categorization -- 2.3.3. Rules Defined for 31-35 Age Categorization -- 2.4. User View Analysis -- 2.4.1. Likes and Dislikes Views -- 2.4.2. Comments Posting Category -- 2.4.3. Content Sharing -- 2.5. User Interested Test Rate (UITR) Analysis Algorithm -- 2.5.1. Rating Statement Analysis -- 2.5.1.1. Like and Dislikes Rating Algorithm -- 2.5.1.2. Comments Posting -- 2.5.1.3. Content Sharing -- 2.6. Feedback Analysis -- 2.7. Result and Discussions -- 2.8. Conclusion -- References -- Chapter 3: Object Detection and Tracking in Video Using Deep Learning Techniques: A Review -- Contents -- 3.1. Introduction., Artificial intelligence-Industrial applications, Electronic books, Maschinelles Lernen, Deep learning, Datenanalyse
title Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
title_auth Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
title_full Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
title_fullStr Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
title_full_unstemmed Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
title_short Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
title_sort artificial intelligence trends for data analytics using machine learning and deep learning approaches
title_unstemmed Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
topic Artificial intelligence-Industrial applications, Electronic books, Maschinelles Lernen, Deep learning, Datenanalyse
topic_facet Artificial intelligence-Industrial applications, Electronic books, Maschinelles Lernen, Deep learning, Datenanalyse
url https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6305306
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