Search Results - (( java application scheduling algorithm ) OR ( data visualisation clustering algorithm ))
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A COMPARISON STUDY OF DATA CLUSTERING AND VISUALISATION TECHNIQUES WITH VARIOUS DATA TYPES
Published 2020“…Clustering is used to identify the intrinsic grouping of a set of unlabelled data. …”
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Final Year Project Report / IMRAD -
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Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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Conference or Workshop Item -
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Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
Published 2017“…This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. …”
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A Toolkit for Simulation of Desktop Grid Environment
Published 2014“…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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Final Year Project -
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Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT
Published 2006“…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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Thesis -
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Improving Class Timetabling using Genetic Algorithm
Published 2006“…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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Thesis -
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Examination timetabling using genetic algorithm case study: KUiTTHO
Published 2005“…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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Thesis -
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Dynamic modeling by usage data for personalization systems
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Conference or Workshop Item -
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Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO
Published 2005“…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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Thesis -
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Batch mode heuristic approaches for efficient task scheduling in grid computing system
Published 2016“…Many algorithms have been implemented to solve the grid scheduling problem. …”
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Thesis -
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Optimizing E-commerce inventory management using a machine-learning approach
Published 2025“…The results were visualised to generate actionable insights, enabling data-driven decisions. …”
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Article -
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Classroom finder system with student availability, space and time constraint
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Final Year Project / Dissertation / Thesis -
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Smart student timetable planner
Published 2025“…Course data is managed in CSV format, parsed into JSON for fast processing, while sessionStorage and localStorage handle user data within active sessions. A Genetic Algorithm forms the core scheduling engine, generating optimized timetables that respect both hard constraints, such as avoiding clashes, and soft constraints, such as personal preferences.The final output of this project is a functional web-based timetable planner that successfully enhances scheduling efficiency, reduces the likelihood of errors, and improves the overall academic planning experience. …”
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Final Year Project / Dissertation / Thesis -
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Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization
Published 2021“…Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. …”
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