Machine Learning for Intelligent Decision Science
Machine Learning for Intelligent Decision Science
edited by Jitendra Kumar Rout, Minakhi Rout, Himansu Das.
- First edition
- 1 recurso en línea (XII, 209 páginas) 113 ilustraciones, 78 ilustraciones a color
- Algorithms for Intelligent Systems 2524-7565 Intelligent Technologies and Robotics (Springer-42732) .
Development of Different Machine Learning Ensemble Classifier for Gully Erosion Susceptibility in Gandheswari Watershed of West Bengal, India -- Classification of ECG Heartbeat using Deep Convolutional Neural Network -- Breast Cancer Identification and Diagnosis Techniques -- Energy Efficient Resource Allocation in Data Centers using a Hybrid Evolutionary Algorithm -- Root Cause Analysis using Ensemble Model for Intelligent Decision-Making -- Spider Monkey Optimization Algorithm in Data Science: A Quantifiable Objective Study -- Multi-Agent Based Systems In Machine Learning and Its Practical Case Studies -- Computer Vision and Machine Learning Approach for Malaria Diagnosis in Thin Blood Smears from Microscopic Blood Images. .
The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.
9789811536892
10.1007/978-981-15-3689-2 doi
Aprendizaje automático
Q325.5 / 2020 EB
Development of Different Machine Learning Ensemble Classifier for Gully Erosion Susceptibility in Gandheswari Watershed of West Bengal, India -- Classification of ECG Heartbeat using Deep Convolutional Neural Network -- Breast Cancer Identification and Diagnosis Techniques -- Energy Efficient Resource Allocation in Data Centers using a Hybrid Evolutionary Algorithm -- Root Cause Analysis using Ensemble Model for Intelligent Decision-Making -- Spider Monkey Optimization Algorithm in Data Science: A Quantifiable Objective Study -- Multi-Agent Based Systems In Machine Learning and Its Practical Case Studies -- Computer Vision and Machine Learning Approach for Malaria Diagnosis in Thin Blood Smears from Microscopic Blood Images. .
The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.
9789811536892
10.1007/978-981-15-3689-2 doi
Aprendizaje automático
Q325.5 / 2020 EB