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Artificial Intelligence and Sustainable Computing : Proceedings of ICSISCET 2023 / edited by Manjaree Pandit, M. K. Gaur, Sandeep Kumar.
Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2024 Available online
View online- Format:
- Book
- Author/Creator:
- Pandit, Manjaree.
- Series:
- Algorithms for Intelligent Systems, 2524-7573
- Language:
- English
- Subjects (All):
- Computational intelligence.
- Electronic circuits.
- Cooperating objects (Computer systems).
- Internet of things.
- Machine learning.
- Computational Intelligence.
- Electronic Circuits and Systems.
- Cyber-Physical Systems.
- Internet of Things.
- Machine Learning.
- Local Subjects:
- Computational Intelligence.
- Electronic Circuits and Systems.
- Cyber-Physical Systems.
- Internet of Things.
- Machine Learning.
- Physical Description:
- 1 online resource (714 pages)
- Edition:
- 1st ed. 2024.
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
- Summary:
- This book presents high-quality research papers presented at the 5th International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering and Technology (ICSISCET 2023) held at Madhav Institute of Technology & Science (MITS), Gwalior, India, during October 21–22, 2023. The book extensively covers recent research in artificial intelligence (AI) that knit together nature-inspired algorithms, evolutionary computing, fuzzy systems, computational intelligence, machine learning, deep learning, etc., which is very useful while dealing with real problems due to their model-free structure, learning ability, and flexible approach. These techniques mimic human thinking and decision-making abilities to produce systems that are intelligent, efficient, cost-effective, and fast. The book provides a friendly and informative treatment of the topics which makes this book an ideal reference for both beginners and experienced researchers.
- Contents:
- Preface
- Contents
- About the Editors
- 1 A Novel Intelligence System for Hybrid Crop Suitable Landform Prediction Using Machine Learning Techniques and IoT
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 4 Dataset Description
- 5 Feature Engineering
- 6 Experiments
- 6.1 Logistic Regression
- 6.2 K-Nearest Neighbours (KNN)
- 6.3 Extreme Gradient Boosting (XGBoost)
- 6.4 Implementation in Cloud
- 7 Results and Discussion
- 8 Conclusion
- 9 Future Work
- References
- 2 Indian Annual Report Assessment Using Large Language Models
- 1.1 Problem Statement
- 1.2 Objective
- 1.3 Contribution
- 3.1 Dataset Preparation
- 3.2 Class Labels
- 4 Results
- 4.1 Fine-Tuning Language Model
- 4.2 Sentence Transformers Generated by AI.
- Notes:
- Part of the metadata in this record was created by AI, based on the text of the resource.
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 9789819703272
- 9819703271
- OCLC:
- 1432592044
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