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1. |
Record Nr. |
UNISA996550550803316 |
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Autore |
Kumar Amit |
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Titolo |
Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing [[electronic resource] ] : ICCIC 2022, 27–28 December, Hyderabad, India; Volume 1 / / edited by Amit Kumar, Gheorghita Ghinea, Suresh Merugu |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (755 pages) |
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Collana |
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Cognitive Science and Technology, , 2195-3996 |
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Altri autori (Persone) |
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GhineaGheorghita |
MeruguSuresh |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Artificial intelligence |
Data mining |
Internet of things |
Computational Intelligence |
Machine Learning |
Artificial Intelligence |
Data Mining and Knowledge Discovery |
Internet of Things |
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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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Making Cell- Free Massive MIMO using MRC technique -- VIP Development of SPI Controller for Open-Power Processor Based Fabless SOC -- Cell-Free Massive MIMO versus Small Cells -- High Precision Navigation using Particle Swarm Optimization based KF -- Recent Advancements for Detection and Prediction of Breast Cancer using Deep Learning A Review. |
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Sommario/riassunto |
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This book includes original, peer-reviewed articles from the 2nd International Conference on Cognitive & Intelligent Computing (ICCIC-2022), held at Vasavi College of Engineering Hyderabad, India. It covers the latest trends and developments in areas of cognitive computing, |
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intelligent computing, machine learning, smart cities, IoT, artificial intelligence, cyber-physical systems, cybernetics, data science, neural network, and cognition. This book addresses the comprehensive nature of computational intelligence, cognitive computing, AI, ML, and DL to emphasize its character in modeling, identification, optimization, prediction, forecasting, and control of future intelligent systems. Submissions are original, unpublished, and present in-depth fundamental research contributions either 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. |
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2. |
Record Nr. |
UNINA9910820990503321 |
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Autore |
Sonnenschein Bernard |
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Titolo |
Collective dynamics in complex networks of noisy phase oscillators : towards models of neuronal network dynamics / / von M.Sc. Bernard Sonnenschein |
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Pubbl/distr/stampa |
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Berlin : , : Logos Verlag Berlin, , [2016] |
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©2016 |
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ISBN |
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Descrizione fisica |
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1 online resource (vi, 118 pages) |
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Disciplina |
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Soggetti |
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Oscillations - Mathematical models |
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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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Note generali |
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PublicationDate: 20161121 |
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Nota di bibliografia |
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Includes bibliographical references. |
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Sommario/riassunto |
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Long description: This work aims to contribute to our understanding of the effects of noise and non-uniform interactions in populations of oscillatory units. In particular, we explore the collective dynamics in various extensions of the Kuramoto model. We develop a theoretical framework to study such noisy systems and we show through many examples that indeed new insights can be gained with our method. The first step is to coarse-grain the complex networks. The oscillatory units |
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are then characterized solely by their individual quantities, so that identical units can be grouped together. The second step consists of the ansatz that in all these groups the distributions of the oscillators' phases follow time-dependent Gaussians. We apply this analytical two-step method to oscillator networks with correlations between coupling strengths and natural frequencies, to populations with mixed positive and negative coupling strengths, and to noise-driven active rotators, which can perform excitable dynamics. We calculate the rich phase diagrams that delineate the emergent rhythms. Extensive numerical simulations are performed to show both the validity and the limitations of our theoretical results. |
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