Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Computational Intelligence in Internet of Agricultural Things / / edited by M. G. Sumithra, Malathy Sathyamoorthy, M. Manikandan, Rajesh Kumar Dhanaraj, Mariya Ouaissa
| Computational Intelligence in Internet of Agricultural Things / / edited by M. G. Sumithra, Malathy Sathyamoorthy, M. Manikandan, Rajesh Kumar Dhanaraj, Mariya Ouaissa |
| Autore | Sumithra M. G |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (464 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
SathyamoorthyMalathy
ManikandanM DhanarajRajesh Kumar OuaissaMariya |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Agriculture Computational Intelligence Artificial Intelligence |
| ISBN |
9783031674501
3031674502 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | -- 1: Computational Intelligence and Internet of Things in the Agriculture Sector: An Introduction. -- 2: Role of Big Data Analytics in Intelligent Agriculture. -- 3: Machine learning-based remote monitoring and predictive analytics system for apple harvest storage: A statistical model based approach. -- 4: Revolutionizing Agriculture: Integrating IoT Cloud, And Machine Learning for Smart Farm Monitoring and Precision Agriculture. -- 5: Impact of Advanced Sensing Technologies in Agriculture with Soil, Crop, Climate and Farmland-based approaches using Internet of Things. -- 6: An analytical approach and concept mapping of agricultural issues using deep learning techniques. -- 7: Explainable AI for next generation Agriculture - Current Scenario and Future Prospects. -- 8: Barriers to implementing computational intelligence-based agriculture system. -- 9: Agri-Chain: A Blockchain-Empowered Smart Solution for Agricultural Industry. -- 10: Exploiting Internet of Things and AI-Enabled for Real-Time Decision Support in Precision Farming Practices. -- 11: Advancing Plant Disease Detection with Hybrid Models: Vision Transformer and CNN-Based Approaches. -- 12: Optimizing Agricultural Risk Management with Hybrid Block-chain and Fog Computing Architectures for Secure and Efficient Data Handling. -- 13: Innovating with Quantum Computing Approaches in Block-chain for Enhanced Security and Data Privacy in Agricultural IoT Systems. -- 14: Implementing Fog Computing in Precision Agriculture for Real-time Soil Health Monitoring and Data Management. -- 15: Empowering Farmers: An AI-Based Solution for Agricultural Challenges. -- 16: Artificial Intelligence in Agriculture: Potential Applications and Future Aspects. -- 17: Case study on Smart irrigation using Internet of Things and XAI Techniques. |
| Record Nr. | UNINA-9910882897703321 |
Sumithra M. G
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computer Vision in Smart Agriculture and Crop Management
| Computer Vision in Smart Agriculture and Crop Management |
| Autore | Dhanaraj Rajesh Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (402 pages) |
| Disciplina | 338.10285 |
| Altri autori (Persone) |
BalusamyBalamurugan
SamuelPrithi SathyamoorthyMalathy BashirAli Kashif |
| Soggetto topico |
Precision farming
Sustainable agriculture |
| ISBN |
9781394186662
1394186665 9781394186686 1394186681 9781394186679 1394186673 |
| 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 Computer Vision-Based Innovations for Smart Agriculture and Crop Surveillance: Evolution, Trends, and Future Challenges -- 1.1 Introduction -- 1.2 Artificial Intelligence in Agriculture -- 1.3 Evolution of Smart Agriculture -- 1.4 AI Technology Trends in Computer Vision -- 1.5 Benefits of Artificial Intelligence in Agriculture -- 1.5.1 Improving the Whole Supply Chain -- 1.5.2 Agricultural Robotics -- 1.5.3 Policy, Governance and Market Access -- 1.5.4 Early Warning System -- 1.5.5 Food Safety and Traceability -- 1.5.6 Financial Inclusion and Risk Management -- 1.5.7 Capacity Building and Empowerment -- 1.5.8 Growth Driven by IoT -- 1.5.9 Image-Dependent Insight Generation -- 1.5.10 Identification of Optimal Mix for Agronomic Products -- 1.5.11 Monitoring of Crops and Soil Health -- 1.5.12 Automation Techniques in Irrigation and Enabling Farmers -- 1.5.13 Drones: The New Buzz in AI-Driven Agriculture -- 1.6 Precision Farming -- 1.7 Future Challenges -- 1.8 Conclusion -- References -- Chapter 2 Cyber Biosecurity Solutions for Protecting Smart Agriculture and Precision Farming |
| Record Nr. | UNINA-9911019976603321 |
|
Dhanaraj Rajesh Kumar
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Ethical Decision-Making Using Artificial Intelligence
| Ethical Decision-Making Using Artificial Intelligence |
| Autore | Juneja Sapna |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (425 pages) |
| Disciplina | 174/.90063 |
| Altri autori (Persone) |
DhanarajRajesh Kumar
JunejaAbhinav SathyamoorthyMalathy ShaikhAsadullah |
| Soggetto topico | Artificial intelligence - Moral and ethical aspects |
| ISBN |
1-394-27531-5
1-394-27530-7 |
| 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 Standards, Policies, Ethical Guidelines and Governance in Artificial Intelligence: Insights on the Financial Sector -- 1.1 Introduction -- 1.2 Chatbots in the Financial Industry -- 1.3 Background of the Study -- 1.4 Literature Review -- 1.5 Understanding Bias in Customer Service Chatbots -- 1.5.1 Categorizing Biases in Financial Chatbots -- 1.5.2 Sources and Origins of Bias in Financial Chatbots -- 1.5.3 User Feedback and Bias Detection -- 1.5.4 The Role of Explainability in Unveiling Bias -- 1.6 Impact of Bias in Financial Chatbot Interactions -- 1.6.1 Customer Trust and Satisfaction -- 1.6.2 Perpetuation of Inequalities -- 1.6.3 Reputational Risks for Financial Institutions -- 1.6.4 Regulatory Compliance Challenges -- 1.6.5 Implications for Brand Image -- 1.7 Strategies for Mitigating Bias in Financial Customer Service Chatbots -- 1.7.1 Diverse and Representative Training Data -- 1.7.2 Continuous Monitoring and Iterative Improvement -- 1.7.3 Explainability Features for User Trust -- 1.7.4 Inclusive User Testing -- 1.7.5 Ethical Guidelines and Governance -- 1.7.6 Collaborative Partnerships with Ethical AI Experts -- 1.8 Ethical Considerations and Transparency in Financial Chatbot Firms -- 1.9 Future Directions and Recommendations -- 1.10 Conclusion -- References -- Chapter 2 Domain-Specific AI Algorithms and Models in Decision-Making: An Overview -- 2.1 Introduction -- 2.1.1 Overview of the Role of AI in Decision Making -- 2.1.1.1 The Emergence of Artificial Intelligence: How it is Changing Decision-Making in Several Domains of Economics -- 2.1.1.2 Putting the Power of Artificial Intelligence to Work in a Particular Field -- 2.1.1.3 The AI-Assisted Decision-Making Process -- 2.1.1.4 Benefits and Future of AI-Powered Decision-Making.
2.1.2 Importance of Domain-Specific Approaches -- 2.1.2.1 Advantages of Domain-Specific AI -- 2.1.2.2 Instances of Domain-Specific AI in Action -- 2.1.2.3 General AI versus Domain-Specific AI: Powering Intelligent Decisions -- 2.2 Understanding Domain-Specific Decision Making -- 2.2.1 Bridging the Gap: Explainable AI for Effective Collaboration between Machine Learning and Domain Expertise -- 2.3 Building Blocks of AI for Decision-Making -- 2.3.1 Overview of AI Approaches -- 2.3.2 Machine Learning for Data-Driven Decision Generating -- 2.3.3 Knowledge-Based Systems for Rule-Based Decision-Making -- 2.3.4 Reinforcement Learning in Dynamic Environments -- 2.4 Domain-Specific AI: Revolutionizing Industries -- 2.4.1 Healthcare -- 2.4.1.1 The Importance of Patient-Centered Design in Regulating Large Language Models or Generative AI -- 2.4.1.2 XAI in Biomedicine: A Post-Pandemic Surge for Trustworthy AI in Healthcare Delivery -- 2.4.2 Finance -- 2.4.2.1 Explainable AI: A Path Toward Trustworthy and Ethical Applications of Machine Learning in Finance -- 2.4.2.2 Learning Machines, Evolving Markets: The Need for Adaptable Generative AI in Finance -- 2.4.3 Manufacturing -- 2.4.3.1 The Rise of Generative AI: A Call for Responsible AI Frameworks in MSME Manufacturing -- 2.4.3.2 Guiding the Future of Manufacturing: Responsible AI as a Cornerstone for Sustainable and Ethical Production -- 2.4.4 Transportation -- 2.4.4.1 Revolutionizing Urban Mobility: The Power of Machine Learning and AI in Smart City Transportation -- 2.4.4.2 AI Revolutionizes Transportation: Boosting Efficiency, Safety, and New Business Opportunities -- 2.4.5 Agriculture -- 2.4.5.1 Cultivating a Sustainable Future: How AI and Big Data are Revolutionizing Precision Agriculture. 2.4.5.2 AI in the Fields: From Precision Irrigation to Smart Robots, How Artificial Intelligence Is Revolutionizing Agribusiness -- 2.4.6 Retail -- 2.4.6.1 The Generative Retail Revolution: How AI is Personalizing Customer Experience, Optimizing Inventory, and Driving Sales -- 2.4.6.2 The Future of Retail: Leveraging AI for Efficiency and Personalization while Navigating Data Privacy and Ethical Challenges -- 2.4.7 Domain-Specific AI: A Comparative Analysis -- 2.5 Ethical and Societal Implications -- 2.6 Future Directions and Emerging Trends -- 2.7 Conclusion -- References -- Chapter 3 Role of AI in Decision-Making . A Comprehensive Study -- 3.1 Introduction -- 3.2 Need of AI-Based Decision-Making System -- 3.3 Major Obstacle for AI-Based Decision-Making System -- 3.4 Applications of AI-Based Decision-Making System -- 3.5 Case Study: AIDMS for Age-Related Macular Degeneration (AMD) -- 3.6 Conclusion and Future Directions -- References -- Chapter 4 Ethical Challenges in AI Decision-Making: From the User's Perspective -- 4.1 Introduction -- 4.1.1 Ethical Principles in AI -- 4.1.2 The Role of Data in AI Decision-Making -- 4.2 Public Perception towards AI -- 4.3 Ethical Dilemmas of AI -- 4.4 Emerging Issues that are Prevailing in the Current World -- 4.4.1 Case Studies -- 4.4.2 Collaboration and Stakeholder Involvement -- 4.5 Future Considerations -- 4.5.1 Conclusion -- References -- Chapter 5 Ethical Decision-Making in Yoga Posture Detection through AI: Fostering Responsible Technology Integration -- 5.1 Introduction -- 5.1.1 About Yoga -- 5.1.1.1 Advantages and Disadvantages of Yoga -- 5.1.2 Posture Detection System -- 5.1.2.1 Components of Posture Detection System -- 5.1.2.2 Process of Posture Detection System -- 5.1.2.3 Applications of Posture Detection System -- 5.1.2.4 Advantages and Disadvantages of Posture Detection System. 5.1.3 Ethical Decision-Making in Yoga Posture Detection through AI -- 5.2 Literature Review -- 5.3 Technologies Used -- 5.3.1 MediaPipe -- 5.3.2 OpenCV (Open-Source Computer Vision Library) -- 5.4 Dataset Used -- 5.5 Methodology -- 5.5.1 How Does It Work? -- 5.6 Conclusion -- References -- Chapter 6 Ethical AI: A Design of an Integrated Framework towards Intelligent Decision-Making in Stock Control -- 6.1 Introduction -- 6.1.1 The Effect of Artificial Intelligence on Controlling Inventory -- 6.1.2 Process of Evolution and Development in Stock Control -- 6.2 Benefits and Impact of AI on Inventory Control -- 6.2.1 Moral Considerations in AI-Primarily Based Selection Making -- 6.3 Best Practices for Implementing AI for Stock Management in E-Commerce -- 6.3.1 Consideration in Statistics and Statistics Safety -- 6.3.2 How AI Enables Stock Administration for Important Corporations -- 6.3.3 Synthetic Intelligence in Inventory Administration: Destiny Styles and Extension -- 6.3.4 Inventory Control with Predictive Renovation -- 6.4 Formulation of Proposed Model -- 6.4.1 Framework Discussion -- 6.4.2 Assumptions and Notations -- 6.4.3 Proposed Mathematical Model -- 6.4.4 Example -- 6.4.5 Sensitivity Analysis -- 6.5 Conclusion -- References -- Chapter 7 Integrating Machine Learning and Data Ethics: Frameworks for Intelligent Ethical Decision-Making -- 7.1 Introduction -- 7.2 Concept of Machine Learning and Data Ethics -- 7.3 Importance of ML and AI in Design Making -- 7.4 Defining an Intelligent Decision-Making Support System -- 7.5 Transformation of the Decision-Making System to Intelligent Decision-Making Support -- 7.6 Architecture Framework -- 7.6.1 Components of the IDSS Architecture -- 7.7 Conceptual Framework -- 7.7.1 Core Concepts -- 7.7.2 Components of the Conceptual Framework -- 7.7.3 Block Diagram of the Conceptual Framework. 7.7.4 Principles of Framework -- 7.7.4.1 Tools Used in IDMSS -- 7.7.4.2 Data Processing Tools -- 7.7.4.3 Machine Learning Frameworks -- 7.7.4.4 Cloud Computing Platforms -- 7.7.5 Analyzing Different Tools -- 7.7.6 Data Processing Tools -- 7.7.7 Machine Learning Frameworks -- 7.7.8 Convolutional Neural Networks (CNNs) -- 7.7.9 Recurrent Neural Networks (RNNs) -- 7.7.10 Cloud Computing Platforms -- 7.8 Cloud-Based Scalability with Auto Scaling -- 7.9 Case Study of Complex Problem Using Framework -- 7.10 Algorithm and Coding Analysis -- 7.11 Results and Impact Analysis -- 7.12 Conclusion -- References -- Chapter 8 Importance of Human Loop in AI-Based Decision-Making: Strengthening the Ethical Perspective -- 8.1 Introduction -- 8.1.1 Human-in-the-Loop -- 8.2 Human Interaction with AI Platform -- 8.3 Human and Machine Ethical Annotation -- 8.4 Exploring AI with Human-in-the-Loop Technique -- 8.4.1 AI-Ethical Module -- 8.4.2 Role of HITL in Ethical Decision-Making -- 8.5 Creating Ethical AI Using HTIL Technique -- 8.5.1 Distributed Ethical Decision System -- 8.5.2 Viability and Advantages of Decision-Making Using Ethical AI -- 8.5.3 Problem Statement -- 8.6 Conclusion -- References -- Chapter 9 AI in Finance and Business: Novel Method for Human Resource Recommendation Using Improved Gradient Boosting Tree Model -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Deep Learning Approach -- 9.2.2 Gradient Boosting Tree -- 9.2.3 Convolutional Neural Network -- 9.2.3.1 Layer of Convolution -- 9.2.3.2 Pool Layer -- 9.2.3.3 Active Layer -- 9.2.3.4 Full Connection Layer -- 9.2.4 Deeper Learning Organizational Techniques -- 9.3 The Proposed Model -- 9.4 Evaluation of the Impact of the Technology -- 9.4.1 Data Set -- 9.4.1.1 Evaluation Criteria -- 9.5 Conclusion -- References -- Chapter 10 Comprehensive View from Ethics to AI Ethics: With Multifaceted Dimensions. 10.1 Introduction. |
| Record Nr. | UNINA-9911020433703321 |
|
Juneja Sapna
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Networked Sensing Systems
| Networked Sensing Systems |
| Autore | Dhanaraj Rajesh Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (408 pages) |
| Disciplina | 681/.2 |
| Altri autori (Persone) |
SathyamoorthyMalathy
SBalasubramaniam KadrySeifedine |
| Soggetto topico | Sensor networks |
| ISBN |
9781394310890
1394310897 9781394310876 1394310870 9781394310883 1394310889 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911019111403321 |
|
Dhanaraj Rajesh Kumar
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||