Titlebook: Big Data, Machine Learning, and Applications; Proceedings of the 2 Malaya Dutta Borah,Dolendro Singh Laiphrakpam,Vale Conference proceeding

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Network Inference for Drug Discovery good performance. Researchers and developers frequently update their architecture to meet the requirements, but this process can be quite time-consuming. A crucial aspect of DNN architecture is the loss function, which plays a crucial role in calculating gradients. Most research and applications in

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https://doi.org/10.1007/978-3-031-05202-6ons and accidents. Official reports from various governments and private entities bolster the fact that, indeed, the current methods for traffic monitoring are inept to deal with the huge traffic density [., .]. These methods, which traditionally included the deployment of traffic police personnel a

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https://doi.org/10.1007/978-3-031-05202-6roblem. It can have applications in a number of domains including weather data analysis, price estimation, bioinformatics, etc. Various computational approaches have been devised for finding the best model parameter. In this work, we explore and establish the possibility of applying the Conjugate Gr

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Exponential and Logarithmic Functions II,t few decades and therefore, needs to be addressed immediately. This research aims to propose a weapon detection system that combines image processing techniques with the most suitable machine learning classifier. The experimental study has been executed in twofold: by providing a comparative analys

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Ordinary Differential Equations,lor thresholding, and then classified on the basis of local positional parameters like distance of pots from the robot and relative position of pots with the robot. This is a computationally efficient solution for simple regular objects like pots. The Kalman filter specifically works on providing be

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Conference proceedings 2024olume focuses on topics such as computing methodology; machine learning; artificial intelligence; information systems; security and privacy. This volume will benefit research scholars, academicians, and industrial people who work on data storage and machine learning..

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