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Machine learning for intelligent dec...
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Das, Himansu.
Machine learning for intelligent decision science
Record Type:
Electronic resources : Monograph/item
Title/Author:
Machine learning for intelligent decision scienceedited by Jitendra Kumar Rout, Minakhi Rout, Himansu Das.
other author:
Rout, Jitendra Kumar.
Published:
Singapore :Springer Singapore :2020.
Description:
xii, 209 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Machine learning.
Online resource:
https://doi.org/10.1007/978-981-15-3689-2
ISBN:
9789811536892$q(electronic bk.)
Machine learning for intelligent decision science
Machine learning for intelligent decision science
[electronic resource] /edited by Jitendra Kumar Rout, Minakhi Rout, Himansu Das. - Singapore :Springer Singapore :2020. - xii, 209 p. :ill., digital ;24 cm. - Algorithms for intelligent systems,2524-7565. - Algorithms for intelligent systems..
Development of Different Machine Learning Ensemble Classifier for Gully Erosion Susceptibility in Gandheswari Watershed of West Bengal, India -- Classification of ECG Heartbeat using Deep Convolutional Neural Network -- Breast Cancer Identification and Diagnosis Techniques -- Energy Efficient Resource Allocation in Data Centers using a Hybrid Evolutionary Algorithm -- Root Cause Analysis using Ensemble Model for Intelligent Decision-Making -- Spider Monkey Optimization Algorithm in Data Science: A Quantifiable Objective Study -- Multi-Agent Based Systems In Machine Learning and Its Practical Case Studies -- Computer Vision and Machine Learning Approach for Malaria Diagnosis in Thin Blood Smears from Microscopic Blood Images.
The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.
ISBN: 9789811536892$q(electronic bk.)
Standard No.: 10.1007/978-981-15-3689-2doiSubjects--Topical Terms:
188639
Machine learning.
LC Class. No.: Q325.5 / .M334 2020
Dewey Class. No.: 006.31
Machine learning for intelligent decision science
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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.
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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.
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Intelligent Technologies and Robotics (Springer-42732)
based on 0 review(s)
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000000179311
電子館藏
1圖書
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EB Q325.5 .M149 2020 2020
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https://doi.org/10.1007/978-981-15-3689-2
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