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Record Nr. |
UNINA9910483472203321 |
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Titolo |
Machine Learning for Intelligent Decision Science / / edited by Jitendra Kumar Rout, Minakhi Rout, Himansu Das |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (219 pages) |
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Collana |
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Algorithms for Intelligent Systems, , 2524-7573 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Engineering - Data processing |
Data mining |
Computational Intelligence |
Machine Learning |
Data Engineering |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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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Sommario/riassunto |
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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, |
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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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