Titlebook: Computational Intelligence and Data Analytics; Proceedings of ICCID Rajkumar Buyya,Susanna Munoz Hernandez,T. Hitendra Conference proceedin

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书目名称Computational Intelligence and Data Analytics影响因子(影响力)




书目名称Computational Intelligence and Data Analytics影响因子(影响力)学科排名




书目名称Computational Intelligence and Data Analytics网络公开度




书目名称Computational Intelligence and Data Analytics网络公开度学科排名




书目名称Computational Intelligence and Data Analytics被引频次




书目名称Computational Intelligence and Data Analytics被引频次学科排名




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书目名称Computational Intelligence and Data Analytics年度引用学科排名




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Conference proceedings 2023t platform for exchanging knowledge with the global community of scientists, engineers, and educators. This volume covers cutting-edge research in two prominent areas – computational intelligence and data analytics, and allied research areas.

Cultivate

Tom Sander,Biruta Sloka,Henrijs Kalkis tiny machine learning (TinyML) and introduce tiny deep learning (TinyDL) for the design, development, and deployment of machine and deep learning solutions for (an ecosystem of) tiny devices, hence supporting intelligent and pervasive applications following the computing everywhere paradigm.

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2367-4512 d educators. This volume covers cutting-edge research in two prominent areas – computational intelligence and data analytics, and allied research areas.978-981-19-3390-5978-981-19-3391-2Series ISSN 2367-4512 Series E-ISSN 2367-4520

Limpid

Raman V. Nair,B. A. Jerard,Regi J. Thomas the Bayesian networks: a score-based algorithm, a constraint-based algorithm, and a hybrid algorithm. Using tenfold cross-validation, it was found that among these three algorithms, the score-based algorithm performed best in terms of expected loss.

Limpid

https://doi.org/10.1007/978-3-319-22521-0cognition problem. We, also analysis the performance accuracy of both classifiers. The outcome demonstrates that the proposed fused descriptor based on the state of colour, texture, shape is more efficient.

Granular

Induced Mutations in Plant Breedingr Machines, Decision Tree, and Multilayer Perceptron Classifier models. The accuracy of the proposed Random Forest Classifier model on the given dataset was 91.06%. Our prediction model can go about as a specialist for the early finding of disease to guarantee the treatment can happen on schedule and lives can be saved.

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