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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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Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system
Published 2011“…Replication in the heterogeneous system is a very promising and challenging platform which is a compound of multi environment. …”
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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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A cluster-based hybrid replica control protocol for high availability in data grid
Published 2019“…A clustering algorithm has been proposed to group the large nodes into clusters and organize these clusters into a tree structure. …”
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Optimized placement, sizing, and selection of distributed generation using the salp swarm algorithm
Published 2025“…SSA is a probabilistic algorithm that simulates the behavior of a population of agents, specifically by replicating the foraging behavior of salps in water. …”
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An Enhanced Simulated Annealing Routing Algorithm for Semi-Diagonal Torus Network
Published 2017“…Each of these nodes is connected to each other through interconnection networks and passes message using a standard message passing mechanism. In this paper, we present a routing algorithm based on enhanced simulated annealing technique to provide the connection between nodes in a semi-diagonal torus (SD-Torus) network. …”
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Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis
Published 2025“…Furthermore, classification using the Random Forest algorithm depicted that a 95.3% accuracy (k=0.768), confirming robust predictive capability in identifying course approval status and demand trends. …”
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