Search Results - (( solution segmentation matching algorithm ) OR ( java application optimisation algorithm ))
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1
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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MSS-TCP: a congestion control algorithm for boosting TCP performance in mmwave cellular networks
Published 2025“…This paper proposes MSS-TCP, a novel congestion control algorithm designed for mmWave networks. MSS-TCP dynamically adjusts the congestion window (cwnd) based on the maximum segment size (MSS) and round-trip time (RTT), improving bandwidth utilization and congestion adaptability. …”
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Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
Published 2022“…The tri-agent reinforcement learning (TA-RL) algorithm has been used by the segment spectrum agent (SSA) in this suggested CSS to make the global decision. …”
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4
Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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5
Model of Bayesian tangent eye shape for eye capture
Published 2014“…To apply the maximum a posteriori procedure, tangent Eye shape vector was considered the state of the model which is hidden and expectation maximization depending on searching algorithm was adopted. Hence, after being tested and matched to future studies, the acquisitioned eye image has been proved to be adequate for Iris recognition system.…”
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APPLICATION OF LINK GRAMMAR IN SEMI-SUPERVISED NAMED ENTITY RECOGNITION FOR ACCIDENT DOMAIN
Published 2011“…The algorithm is utilized for predicting a huge set of unlabeled data, given a small number of labelled data. …”
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