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|a com.springer.onix.9783030285531
|b Springer Nature
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|a HCDD
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0 |
0 |
|a Nature-inspired computation in data mining and machine learning /
|c Xin-She Yang, Xing-Shi He, editors.
|
| 264 |
|
1 |
|a Cham, Switzerland :
|b Springer,
|c [2020]
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| 300 |
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|a 1 online resource (xi, 273 pages) :
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1 |
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|a Studies in computational intelligence,
|x 1860-9503 ;
|v volume 855
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| 504 |
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|a Includes bibliographical references.
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| 520 |
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|a This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
|
| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed September 9, 2019).
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| 505 |
0 |
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|a Adaptive Improved Flower Pollination Algorithm for Global Optimization -- Algorithms for Optimization and Machine Learning over Cloud -- Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks -- Comparative analysis of different classifiers on crisis-related tweets: An elaborate study -- An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm -- Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services -- A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks -- 3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures -- Performance-Based Prediction of Chronic Kidney Disease Using Machine Learning for High-Risk Cardiovascular Disease Patients -- Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management -- Classification and Clustering Algorithms of Machine Learning with their Applications -- Hybrid Binary Particle Swarm Optimization and Flower Pollination Algorithm Based on Rough Set Approach for Feature Selection Problem.
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| 650 |
|
0 |
|a Natural computation.
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| 650 |
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0 |
|a Data mining.
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| 650 |
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0 |
|a Machine learning.
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| 650 |
|
7 |
|a Data mining
|2 fast
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| 650 |
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7 |
|a Machine learning
|2 fast
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| 650 |
|
7 |
|a Natural computation
|2 fast
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| 700 |
1 |
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|a Yang, Xin-She,
|e editor.
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| 700 |
1 |
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|a He, Xing-Shi,
|e editor.
|
| 776 |
0 |
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|z 3030285529
|
| 830 |
|
0 |
|a Studies in computational intelligence ;
|v v. 855.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-28553-1
|y Click for online access
|
| 903 |
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|a SPRING-ROBOTICS2020
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| 994 |
|
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|a 92
|b HCD
|