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Advances in Emerging Technologies and Computing Innovations : Proceedings of First International Conference on Emerging Technologies and Computing Innovations (ICETCI-2025) / / edited by Mangesh M. Ghonge, Haipeng Liu, Mudassir Khan, Tien Anh Tran
| Advances in Emerging Technologies and Computing Innovations : Proceedings of First International Conference on Emerging Technologies and Computing Innovations (ICETCI-2025) / / edited by Mangesh M. Ghonge, Haipeng Liu, Mudassir Khan, Tien Anh Tran |
| Autore | Ghonge Mangesh M |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (967 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
LiuHaipeng
KhanMudassir TranTien Anh |
| Collana | Sustainable Artificial Intelligence-Powered Applications, IEREK Interdisciplinary Series for Sustainable Development |
| Soggetto topico |
Artificial intelligence
Machine learning Artificial intelligence - Data processing Artificial Intelligence Machine Learning Data Science |
| ISBN | 3-031-92854-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Bayesian Network to Model the Influence of Energy Consumption on Greenhouse Gases in Italy -- ISIS: IoT Enabled Smart Irrigation System.-Advancing Educational Insights: A Review of Machine Learning and Deep Learning Approaches for Analyzing Students' Study Habits -- Exploring Deepfake Generation and Detection Techniques: Challenges, Datasets, and Emerging Solutions -- Advances in Retrieval-Augmented Generation Frameworks: A Comprehensive Review of NLP Applications for Women Empowerment and Social Justice -- Optimizing Indoor Positioning Systems with Machine Learning and RSS-based Algorithms -- Towards Precision in Skin Cancer Diagnosis: A Deep Learning Framework for Segmentation and Severity Analysis -- Biomarker Discovery in Alzheimer’s Disease Using Machine Learning -- Role of Machine Learning Approaches to Enhance Security Mechanisms in Wireless Sensor Networks -- Evolution of Malicious URL Detection: A Review of Techniques for Malicious URL Detection and Classification. |
| Record Nr. | UNINA-9911034940403321 |
Ghonge Mangesh M
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence and Machine Learning for Industry 4. 0
| Artificial Intelligence and Machine Learning for Industry 4. 0 |
| Autore | Thirunavukkarasan M |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (276 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
MaryS. A. Sahaaya Arul
RSathiyaraj GhantasalaG S Pradeep KhanMudassir |
| Soggetto topico |
Artificial intelligence
Machine learning |
| ISBN |
1-394-27505-6
1-394-27506-4 1-394-27507-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Industry 4.0 and the AI/ML Era: Revolutionizing Manufacturing -- 1.1 Introduction -- 1.1.1 Key Traits of Industry 4.0 -- 1.2 Literature Survey -- 1.2.1 Foundations of Industry 4.0 -- 1.2.2 Integration of AI and ML -- 1.2.3 Smart Automation and Human-Robotic Collaboration -- 1.2.4 Cognitive Manufacturing -- 1.2.5 Disturbing Situations and Opportunities -- 1.3 The AI/ML Era Within the Industrial Revolution -- 1.3.1 The Role of AI and ML -- 1.3.2 Opportunities -- 1.4 The Nexus of Industry 4.0 and the AI/ML Era: A Symbiotic Evolution -- 1.5 Challenges and Opportunities in the Integration of Industry 4.0 and the AI/ML Era -- 1.6 Implementation Techniques -- 1.6.1 Future Suggestions -- 1.7 Conclusion -- References -- Chapter 2 Business Intelligence and Big Data Analytics for Industry 4.0 -- 2.1 Introduction -- 2.1.1 The Biggest Challenge of Industry 4.0 -- 2.2 Literature Review -- 2.3 Business Intelligence -- 2.3.1 Challenges of Business Intelligence in Industry 4.0 -- 2.4 Big Data Analytics -- 2.4.1 Five Pillars of Big Data -- 2.4.2 Big Data to the Rescue -- 2.4.3 Challenges in Big Data Analytics for Industry 4.0 -- 2.4.4 Advantage in Big Data Analysis for Industry 4.0 -- 2.5 Result and Discussion -- 2.6 Conclusion -- References -- Chapter 3 "AI-Powered Mental Health Innovations": Handling the Effects of Industry 4.0 on Health -- 3.1 Introduction -- 3.1.1 An Overview of Industry 4.0's Development in Healthcare Over Time -- 3.1.2 The Advancement of AI in Mental Health -- 3.2 Related Work -- 3.2.1 Recognizing AI's Place in Healthcare -- 3.2.2 Comprehending AI's Impact on Mental Health -- 3.3 Machine Learning in Healthcare -- 3.3.1 SML-Supervised Machine Learning -- 3.3.2 Unsupervised Machine Learning (UML) -- 3.3.3 Deep Learning (DL).
3.3.4 NLP - Natural Language Processing -- 3.4 Genetics and Machine Learning for Understanding and Prediction of Complicated Illnesses -- 3.5 AI-Driven Virtual Healthcare Support for Patient Care -- 3.6 AI's Advantages for Mental Health Treatment -- 3.7 AI's Predictive Capabilities: Revolutionizing Mental Health Treatment -- 3.8 AI's Limitations and Research on Mental Health -- 3.9 Ethical Issues and Difficulties with AI-Powered Mental Health -- 3.10 Healthcare AI Governance -- 3.11 Artificial Intelligence in Augmented and Virtual Reality (AR & -- VR) -- 3.12 Methodology -- 3.13 Results and Discussions -- 3.13.1 Synopsis of AI Research in Mental Health -- 3.13.2 AI-Driven Intervention as the Future of Mental Healthcare -- 3.14 Conclusion -- References -- Chapter 4 AI ML Empowered Smart Buildings and Factories -- 4.1 Introduction -- 4.1.1 An Account of How Machine Learning Contributes to Task Automation -- 4.1.2 A Description of How Mobile Phones and Computers Facilitate the Completion of Tasks in Intelligent Buildings -- 4.1.3 Intelligent Buildings as well as IoT -- 4.1.4 Utilizing the Ubiquitous Internet of Things Plus the Global Web to Link Buildings -- 4.2 The Advancement of Computational Intelligence within Smart Building Technology and Its Worldwide Consequences -- 4.2.1 Industrial 4.0 Along with IoT -- 4.2.2 An Exploration of the Web of Things and Its Role in Making 4.0 -- 4.3 An Examination on ML, DL and AI Algorithms Used for Engineering and Construction -- 4.3.1 Utilization in Intelligent Structures -- 4.3.2 A Few Examples of the Numerous Uses in Smart Buildings are Automation, Material Efficiency, Off-Site Production, Designing Buildings, and the Combination of Big Data -- 4.3.3 Detectors, and Computational AI Enabling Intelligent Management and Energy Efficiency -- 4.4 Conclusion. 4.5 Future Advances in Urban Energy Efficiency and Smart Building Technologies -- References -- Chapter 5 Applications of Artificial Intelligence and Machine Learning in Industry 4.0 -- 5.1 Introduction -- 5.1.1 Overview of Industry 4.0 -- 5.1.2 Key Components and Technologies -- 5.2 Smart Manufacturing and Predictive Maintenance -- 5.2.1 Integration of AI/ML in Manufacturing Process -- 5.2.2 Predictive Maintenance Strategies -- 5.3 Supply Chain Optimization -- 5.3.1 AI/ML for Supply Chain Management -- 5.3.2 Optimizing Logistics and Inventory -- 5.4 Quality Control and Defect Detection -- 5.4.1 AI/ML for Quality Assurance -- 5.4.2 Automated Defect Detection System -- 5.5 Robotics and Automation -- 5.5.1 Robotics in Smart Factories -- 5.5.2 AI-Driven Automation Process -- 5.6 Data Analytics and Decision Support -- 5.6.1 Big Data Analytics in Industry 4.0 -- 5.6.2 Decision Support System with AI/ML -- 5.7 Cybersecurity in Industry 4.0 -- 5.7.1 Challenges and Threats -- 5.7.2 AI-Enhanced Cybersecurity Solutions -- 5.8 Human-Machine Collaboration -- 5.8.1 Human-Centric AI Applications -- 5.8.2 Collaboration Interfaces in Smart Manufacturing -- 5.9 Energy Efficiency and Sustainability -- 5.9.1 Role of AI ML in Energy Management -- 5.9.2 Sustainable Practices in Industry 4.0 -- 5.10 Emerging Trends and Future Prospects -- Conclusion -- References -- Chapter 6 Application of Machine Learning in Moisture Content Prediction of Coffee Drying Process -- 6.1 Introduction -- 6.2 Literature Reviews -- 6.2.1 Related Works -- 6.2.2 Background of Machine Learning and Credit Risk Prediction Techniques -- 6.2.2.1 Non-Linear Regression -- 6.2.2.2 Artificial Neural Networks (ANN) -- 6.2.2.3 Adaptive Network-Based Fuzzy Inference System (ANFIS) -- 6.3 Methodology -- 6.3.1 Data Collection -- 6.3.2 Data Preprocessing. 6.3.2.1 Missing Value Detection and Attribute Visualization -- 6.3.2.2 Normalization -- 6.3.2.3 Standardization -- 6.3.2.4 Cross-Validation -- 6.3.3 Research Methodology -- 6.3.3.1 Multi-Layer Perceptron (MLP) Regression -- 6.3.3.2 Adaptive Neuro-Fuzzy Inference System - ANFIS -- 6.3.3.3 Feature Selection Techniques -- 6.4 Results and Analysis -- 6.4.1 Model Evaluation -- 6.4.2 Analysis Results -- 6.4.3 Analysis Results with Feature Selection -- 6.4.3.1 Feature Selection with ANN -- 6.4.3.2 Feature Selection with ANFIS -- 6.5 Conclusion -- References -- Chapter 7 Survivable AI for Defense Strategies in Industry 4.0 -- 7.1 Introduction -- 7.2 Purpose -- 7.3 Scope -- 7.4 History of AI for Defense Strategies in Industry 4.0 -- 7.4.1 AI in Defense -- 7.4.2 AI in Defense Strategies in Industry 4.0 -- 7.5 AI Applications in Defense Strategies in Industry 4.0 -- 7.6 Era of AI in Industry -- 7.6.1 Era of AI Applications in Industry 4.0 -- 7.7 Importance of AI in the Defense Industry -- 7.8 Future of AI in the Defense Industry -- 7.8.1 Cyberattacks in Defense Industry -- 7.8.2 Trade-Offs of AI in Industry 4.0 -- 7.8.3 Cyberattacks in Defense Industry 4.0 -- 7.9 Conclusion -- References -- Chapter 8 Industry 4.0 Based Turbofan Performance Prediction -- 8.1 Introduction -- 8.2 Search Methodology -- 8.2.1 Sensor-Based Technique -- 8.2.2 Data-Driven Approaches -- 8.2.3 Benefits and Challenges of Machine Learning for PdM -- 8.2.4 Challenges -- 8.3 Literature Review -- 8.3.1 Identification of Problem -- 8.3.2 Objectives -- 8.4 Methodology -- 8.5 Experimental Results -- 8.5.1 Data Preprocessing -- 8.5.2 Developing Models -- 8.5.3 Training and Validation -- 8.5.4 Evaluation -- 8.5.5 Comparison with Baseline -- 8.5.6 Sensitivity Analysis -- 8.6 Conclusion and Future Work -- 8.7 Additional Considerations -- References. Chapter 9 Industrial Predictive Maintenance for Sustainable Manufacturing -- 9.1 Introduction -- 9.1.1 IoT Internet of Things -- 9.1.2 Industry 4.0 -- 9.2 Search Methodology -- 9.3 Methodology -- 9.3.1 Types of Maintenance -- 9.3.2 IoT Technologies for Predictive Maintenance -- 9.3.3 Predictive Maintenance Workflow -- 9.3.4 Predictive Maintenance Model -- 9.3.5 Data Collection Techniques -- 9.3.6 Data Analysis Techniques -- 9.3.7 Predictive Analytics Algorithms -- 9.3.8 Machine Learning Techniques in PdM -- 9.3.9 Comparative Analysis -- 9.3.10 Limitations and Considerations -- 9.4 Conclusion -- References -- Chapter 10 Enhanced Security Framework with Blockchain for Industry 4.0 Cyber-Physical Systems, Exploring IoT Integration Challenges and Applications -- 10.1 Introduction -- 10.2 Related Works -- 10.3 Industry 4.0 Elements -- 10.3.1 CPS in Critical Industry 4.0 -- 10.3.2 Challenges in IoT Integration -- 10.3.3 Security Provided Through Blockchain -- 10.3.4 Blockchain Replaces the Certificate Authority -- 10.4 Results and Discussions -- 10.5 Conclusions -- References -- Chapter 11 Integrating Artificial Intelligence and Machine Learning for Enhanced Cyber Security in Industry 4.0: Designing a Smart Factory with IoT and CPS -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Proposed Model -- 11.3.1 Smart Factory -- 11.3.2 The Mechanical Design -- 11.3.3 Proposed IDS Architecture -- 11.3.4 IDS in CCPS -- 11.4 Results and Discussions -- 11.5 Conclusions -- References -- Chapter 12 Application of AI and ML in Industry 4.0 -- 12.1 Introduction -- 12.2 Application of AI and ML in Industry 4.0 -- 12.3 Benefits of AI and ML in Industry 4.0 -- 12.4 Challenges and Considerations in Adopting AI and ML in Industry 4.0 -- 12.5 Case Studies and Examples of AI and ML in Industry 4.0 -- 12.6 Emerging AI and ML Technologies in Industry 4.0 -- 12.7 Conclusion. R eferences. |
| Record Nr. | UNINA-9911020074303321 |
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Thirunavukkarasan M
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital Twins and ESG
| Digital Twins and ESG |
| Autore | Mondal Surajit |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (349 pages) |
| Disciplina | 003/.3 |
| Altri autori (Persone) |
KumarAdesh
KhanMudassir |
| Soggetto topico | Digital twins (Computer simulation) - Environmental aspects |
| ISBN |
1-394-30324-6
1-394-30323-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911019194803321 |
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Mondal Surajit
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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