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E-raamat: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

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Advances in Natural Computation, Fuzzy Systems and Knowledge DiscoverySuurem pilt
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This book consists of papers on the recent progresses in the state of the art in natural computation, fuzzy systems and knowledge discovery. The book is useful for researchers, including professors, graduate students, as well as R & D staff in the industry, with a general interest in natural computation, fuzzy systems and knowledge discovery. The work printed in this book was presented at the 2020 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020), held in Xi'an, China, from 19 to 21 December 2020. All papers were rigorously peer-reviewed by experts in the areas.
A medical image segmentation method based on pixel-to-pixel threshold.-
Radar Tasks Scheduling Based on Adaptive Neuro Fuzzy Inference and
Time-Interval Planning.- Ranking  Spatiotemporal RDF Properties Using
Machine-Learning.- Controllability For Nonlocal Evolution Inclusions With
Clarke's Subdifferential On Semi-Infinite Intervals.- Improved Parallel RSA
Cryptosystem with GPU for Network Information Security.- The Recommendation
Model of Personalized English Learning Resources Based on Intelligent
Education.- Binaural Blind Source Separation Based on Deep Clustering.- The
Approximation Of Fuzzy Number Value, Ambiguity And Expected Interval.-
Machine learning based approach for Weed Detection in Chilli field using RGB
images.- Disparity Estimation From Holoscopic Elemental Images.- Multi-agents
Based Architecture of Remote Monitoring and Intelligent Diagnosis of Faults
in Large-scale Petrochemical Systems.- An improved tracking learning
detection method based on AKAZE feature points.- The Influence of Intelligent
Capital on the Difference of Enterprise's Net Value and Value from the
Perspective of Value Chain.- Research on hybrid algorithm based on particle
swarm optimization and cuckoo search algorithm.- Application of Two-stage
Sampling in Sampling Inspection.
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