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245 1 0 _aComputational Intelligence in Machine Learning
_h[electronic resource] :
_bProceedings of the 2nd International Conference ICCIML 2022 /
_cedited by Vinit Kumar Gunjan, Amit Kumar, Jacek M. Zurada, Sri Niwas Singh.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXII, 707 p. 385 illus., 318 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
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_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aLecture Notes in Electrical Engineering,
_x1876-1119 ;
_v1106
505 0 _aMachine Learning-based Project Resource Allocation Fitment Analysis System – (ML-PRAFS) -- Electric Theft Detection using Un-supervised Machine Learning Based Matrix Profile and K Means Clustering Technique -- Placement Analysis – A New Approach to Ease the Recruitment Process -- Continuous Assessment Analyzer using Django -- Fuzzy Logic in Battery Energy Storage System (Bess) -- Fault Classification of Cooling Fans using a CNN-based Approach -- Violence Recognition using Convolutional Neural Networks -- Automated Grading of Citrus Suhuiensis Fruit using Deep Learning Method -- The Future of Car Automation Field with Smart Driverless Technologies -- Diagnosis and Medicine Prediction for Covid-19 using Machine Learning Approach -- Automated Guided Vehicle Robot Localization with Sensor Fusion -- Implementation of Industrial Automation Water Distribution System Utilizing PLC: A Laboratory Set-up -- Control of Thin McKibben Muscles in an Antagonistic Pair Configuration -- Defect Severity Classification of Complex Composites using CWT and CNN -- Detection of Mobile Phone Usage While Driving using Computer Vision and Deep Learning -- Industry Revolution 4.0 Knowledge Assessment in Malaysia.
520 _aThis volumes comprises select proceedings of the International Conference on Computational Intelligence in Machine Learning (ICCIML 2022). The contents cover latest research trends and developments in the areas of machine learning, smart cities, IoT, Artificial Intelligence, cyber physical systems, cybernetics, data science, neural network, cognition, among others. It also addresses the comprehensive nature of computational intelligence, AI, ML and DL to emphasize its character in modelling, identification, optimization, prediction, forecasting, and control of future intelligent systems. This volume will be a useful guide to those working as researchers in academia and industry by presenting in-depth fundamental research contributions from a methodological/application perspective in understanding Artificial intelligence and machine learning approaches and their capabilities in solving diverse range of problems in industries and its real-world applications.
650 0 _aComputational intelligence.
_964052
650 0 _aMachine learning.
_955059
650 0 _aArtificial intelligence
_xData processing.
650 0 _aArtificial intelligence.
_958441
650 1 4 _aComputational Intelligence.
_964052
650 2 4 _aMachine Learning.
_955059
650 2 4 _aData Science.
650 2 4 _aArtificial Intelligence.
_958441
700 1 _aGunjan, Vinit Kumar.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
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700 1 _aKumar, Amit.
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700 1 _aZurada, Jacek M.
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700 1 _aSingh, Sri Niwas.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aLecture Notes in Electrical Engineering,
_x1876-1119 ;
_v1106
856 4 0 _uhttps://doi.org/10.1007/978-981-99-7954-7
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