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Applications and Techniques in Information Security : 10th International Conference, ATIS 2019, Thanjavur, India, November 22–24, 2019, Proceedings / / edited by V. S. Shankar Sriram, V. Subramaniyaswamy, N. Sasikaladevi, Leo Zhang, Lynn Batten, Gang Li
| Applications and Techniques in Information Security : 10th International Conference, ATIS 2019, Thanjavur, India, November 22–24, 2019, Proceedings / / edited by V. S. Shankar Sriram, V. Subramaniyaswamy, N. Sasikaladevi, Leo Zhang, Lynn Batten, Gang Li |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 313 p. 157 illus., 101 illus. in color.) |
| Disciplina | 005.8 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Data protection
Computers—Law and legislation Information technology—Law and legislation Artificial intelligence Application software Computer networks Data and Information Security Legal Aspects of Computing Artificial Intelligence Computer and Information Systems Applications Computer Communication Networks |
| ISBN | 981-15-0871-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Information Security -- Network Security -- Intrusion Detection System -- Authentication and Key Management System -- Security Centric Applications. . |
| Record Nr. | UNINA-9910357850203321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Cognitive analytics and reinforcement learning : theories, techniques and applications / / edited by Elakkiya R. and Subramaniyaswamy V
| Cognitive analytics and reinforcement learning : theories, techniques and applications / / edited by Elakkiya R. and Subramaniyaswamy V |
| Autore | R Elakkiya |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc. |
| Descrizione fisica | 1 online resource |
| Disciplina | 006.3 |
| Soggetto topico |
Soft computing
Big data |
| ISBN |
1-394-21406-5
1-394-21405-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Cognitive Analytics in Continual Learning -- Chapter 1 Cognitive Analytics in Continual Learning: A New Frontier in Machine Learning Research -- 1.1 Introduction -- 1.2 Evolution of Data Analytics -- 1.3 Conceptual View of Cognitive Systems -- 1.4 Elements of Cognitive Systems -- 1.5 Features, Scope, and Characteristics of Cognitive System -- 1.6 Cognitive System Design Principles -- 1.7 Backbone of Cognitive System Learning/Building Process -- 1.8 Cognitive Systems vs. AI -- 1.9 Use Cases -- 1.10 Conclusion -- References -- Chapter 2 Cognitive Computing System-Based Dynamic Decision Control for Smart City Using Reinforcement Learning Model -- 2.1 Introduction -- 2.2 Smart City Applications -- 2.3 Related Work -- 2.4 Proposed Cognitive Computing RL Model -- 2.5 Simulation Results -- 2.6 Conclusion -- References -- Chapter 3 Deep Recommender System for Optimizing Debt Collection Using Reinforcement Learning -- 3.1 Introduction -- 3.2 Terminologies in RL -- 3.3 Different Forms of RL -- 3.4 Related Works -- 3.5 Proposed Methodology -- 3.6 Result Analysis -- 3.7 Conclusion -- References -- Part II: Computational Intelligence of Reinforcement Learning -- Chapter 4 Predicting Optimal Moves in Chess Board Using Artificial Intelligence -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Proposed System -- 4.3.1 Human vs. Human -- 4.3.2 Human vs. Alpha-Beta Pruning -- 4.3.3 Human vs. Hybrid Algorithm -- 4.4 Results and Discussion -- 4.4.1 ELO Rating -- 4.4.2 Comparative Analysis -- 4.5 Conclusion -- References -- Chapter 5 Virtual Makeup Try-On System Using Cognitive Learning -- 5.1 Introduction -- 5.2 Related Works -- 5.3 Proposed Method -- 5.4 Experimental Results and Analysis -- 5.5 Conclusion -- References.
Chapter 6 Reinforcement Learning for Demand Forecasting and Customized Services -- 6.1 Introduction -- 6.2 RL Fundamentals -- 6.3 Demand Forecasting and Customized Services -- 6.4 eMart: Forecasting of a Real-World Scenario -- 6.5 Conclusion and Future Works -- References -- Chapter 7 COVID-19 Detection through CT Scan Image Analysis: A Transfer Learning Approach with Ensemble Technique -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Methodology -- 7.4 Results and Discussion -- 7.5 Conclusion -- References -- Chapter 8 Paddy Leaf Classification Using Computational Intelligence -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Methodology -- 8.4 Results and Discussion -- 8.5 Conclusion -- References -- Chapter 9 An Artificial Intelligent Methodology to Classify Knee Joint Disorder Using Machine Learning and Image Processing Techniques -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Proposed Methodology -- 9.4 Experimental Results -- 9.5 Conclusion -- References -- Part III: Advancements in Cognitive Computing: Practical Implementations -- Chapter 10 Fuzzy-Based Efficient Resource Allocation and Scheduling in a Computational Distributed Environment -- 10.1 Introduction -- 10.2 Proposed System -- 10.3 Experimental Results -- 10.4 Conclusion -- References -- Chapter 11 A Lightweight CNN Architecture for Prediction of Plant Diseases -- 11.1 Introduction -- 11.2 Precision Agriculture -- 11.3 Related Work -- 11.4 Proposed Architecture for Prediction of Plant Diseases -- 11.5 Experimental Results and Discussion -- 11.6 Conclusion -- References -- Chapter 12 Investigation of Feature Fusioned Dictionary Learning Model for Accurate Brain Tumor Classification -- 12.1 Introduction -- 12.1.1 Importance of Accurate and Early Diagnosis and Treatment -- 12.1.2 Role of Machine Learning in Brain Tumor Classification. 12.1.3 Sparsity Issues in Brain Image Analysis -- 12.2 Literature Review -- 12.3 Proposed Feature Fusioned Dictionary Learning Model -- 12.4 Experimental Results and Discussion -- 12.5 Conclusion and Future Work -- References -- Chapter 13 Cognitive Analytics-Based Diagnostic Solutions in Healthcare Infrastructure -- 13.1 Introduction -- 13.2 Cognitive Computing in Action -- 13.2.1 Natural Language Processing (NLP) -- 13.2.2 Application of Cognitive Computing in Everyday Life -- 13.2.3 The Importance of Cognitive Computing in the Development of Smart Cities -- 13.2.4 The Importance of Cognitive Computing in the Healthcare Industry -- 13.3 Increasing the Capabilities of Smart Cities Using Cognitive Computing -- 13.3.1 Cognitive Data Analytics for Smarter Cities -- 13.3.2 Predictive Maintenance and Proactive Services -- 13.3.3 Personalized Urban Services -- 13.3.4 Cognitive Computing and the Role It Plays in Obtaining Energy Optimization -- 13.3.5 Data-Driven Decisions for City Development and Governance -- 13.4 Cognitive Solutions Revolutionizing the Healthcare Industry -- 13.4.1 Artificial Intelligence-Driven Diagnostics and the Detection of Disease -- 13.4.2 Individualized and Tailored Treatment Programs -- 13.4.3 Real-Time Monitoring of Patients and Predictive Analytical Tools -- 13.4.3.1 Cognitively Assisted Robotic Surgery -- 13.4.4 Patient Empowerment with Health AI -- 13.5 Application of Cognitive Computing to Smart Healthcare in Seoul, South Korea (Case Study) -- 13.6 Conclusion and Future Work -- References -- Chapter 14 Automating ESG Score Rating with Reinforcement Learning for Responsible Investment -- 14.1 Introduction -- 14.2 Comparative Study -- 14.3 Literature Survey -- 14.4 Methods -- 14.5 Experimental Results -- 14.6 Discussion -- 14.7 Conclusion -- References. Chapter 15 Reinforcement Learning in Healthcare: Applications and Challenges -- 15.1 Introduction -- 15.2 Structure of Reinforcement Learning -- 15.3 Applications -- 15.3.1 Treatment of Sepsis with Deep Reinforcement -- 15.3.2 Chemotherapy and Clinical Trial Dosing Regimen Selection -- 15.3.3 Dynamic Treatment Recommendation -- 15.3.4 Dynamic Therapy Regimes Using Data from the Medical Registry -- 15.3.5 Encouraging Physical Activity in Diabetes Patients -- 15.3.6 Diagnosis Utilizing Medical Images -- 15.3.7 Clinical Research for Non-Small Cell Lung Cancer -- 15.3.8 Segmentation of Transrectal Ultrasound Images -- 15.3.9 Personalized Control of Glycemia in Septic Patients -- 15.3.10 An AI Structure for Simulating Clinical Decision-Making -- 15.4 Challenges -- 15.5 Conclusion -- References -- Chapter 16 Cognitive Computing in Smart Cities and Healthcare -- 16.1 Introduction -- 16.2 Machine Learning Inventions and Its Applications -- 16.3 What is Reinforcement Learning and Cognitive Computing? -- 16.4 Cognitive Computing -- 16.5 Data Expressed by the Healthcare and Smart Cities -- 16.6 Use of Computers to Analyze the Data and Predict the Outcome -- 16.7 Machine Learning Algorithm -- 16.8 How to Perform Machine Learning? -- 16.9 Machine Learning Algorithm -- 16.10 Common Libraries for Machine Learning Projects -- 16.11 Supervised Learning Algorithm -- 16.12 Future of the Healthcare -- 16.13 Development of Model and Its Workflow -- 16.13.1 Types of Evaluation -- 16.14 Future of Smart Cities -- 16.15 Case Study I -- 16.16 Case Study II -- 16.17 Case Study III -- 16.18 Case Study IV -- 16.19 Conclusion -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910877292003321 |
R Elakkiya
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| Hoboken, NJ : , : John Wiley & Sons, Inc. | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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