Titlebook: Soft Computing for Problem Solving 2019; Proceedings of SocPr Atulya K. Nagar,Kusum Deep,Kedar Nath Das Conference proceedings 2020 Springe

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Conference proceedings 2020chers, engineers and practitioners to discuss thought-provoking developments and challenges in order to identify potential future directions. The book presents the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers in areas such as al

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,Comparison of PSO and Sequential Search Algorithms for Improvisation of Entropy-Based Ear Localization (PSO) and sequential search algorithms such as sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated on roughly localized ear images. The experimental results show a promising performance of PSO attaining . efficiency for ear localization on pointing head pose database images.

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水獭

Analysis of Rotation-Based Diffusion Functions,e analyse this aspect of diffusion functions. We consider a class of rotation-based linear diffusion functions and derive a necessary condition under which a diffusion function of this class is invertible. Further, we find a particular case where this necessary condition is sufficient also.

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Conference proceedings 2020tions (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It is a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that cannot easily be solved using traditional methods. 

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,Reinforcement Learning for Multiple HAPS/UAV Coordination: Impact of Exploration–Exploitation Dilem RL technique for this specific application scenario. The work goes on to suggest methods to minimise this impact, and some insights for applying RL techniques for multi-agent coordination for communications area coverage.

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Finger-Induced Motor Imagery Classification from Hemodynamic Response Using Type-2 Fuzzy Sets,ned confirm that the proposed hemodynamic response-based classification outperforms the reported results of electroencephalography (EEG)-based classification in terms of classification accuracy. Statistical tests included confirm the efficacy of the proposed technique over its competitors.

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