Search Results - (( generating application testing algorithm ) OR ( java visualization learning algorithm ))
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Development Of Machine Learning User Interface For Pump Diagnostics
Published 2022“…Build up a user interface by using Visual Studio Code (VSC) to run the coding of Cascading Style Sheet (CSS), Hyper Text Markup Language (HTML) and JavaScript (JS) as a webpage and connect to Azure Machine Learning Model and this will allow the user from using the model from a webpage when they have active internet with any devices.…”
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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform
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An improved algorithm in test case generation from UML activity diagram using activity path
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An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion
Published 2018“…The elaborated result of the study will help test engineers to choose the algorithm they need to generate test cases efficiently and optimally.…”
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Test suite generation based on hybrid flower pollination algorithm and hill climbing
Published 2021“…The application of meta-heuristic algorithms in t-way tests generation, as an example of SBST, has as of late gotten to be predominant. …”
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Combinatorial test suites generation strategy utilizing the whale optimization algorithm
Published 2020“…In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. …”
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Mobile application for real time baby sign language recognition using YOLOv8 / Siti Aishah Idris and Ahmad Firdaus Ahmad Fadzil
Published 2024“…The model will be designed and developed using a deep learning algorithm, which is YOLOv8, the latest version of YOLO. …”
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Latin hypercube sampling Jaya algorithm based strategy for T-way test suite generation
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Faculty timetabling using genetic algorithm
Published 2011“…Faculty Timetabling using Genetic Algorithm (FTGA) is an application that generate optimum timetable for faculty.The target user of this application is faculty staff who responsible in generate timetable.The problem statement of the project is many clashing exist in the timetable.Faculty staff needs to solve the clashing manually.This will waste time and it is a problem for staff to solve the clashing.By implement GA,clashing will be reduced.The objective of the project is to develop aprototype in scheduling application for generates an optimum timetable for a faculty.Genetic algorithm will be implemented.The scope of FTGA is Faculty of Computer Systems & Software Engineering (FCSSE).The methodology use in this project is prototype model.The testing result show 95 out of 100 test cases achieved the maximum fitness value which means there is no clashing in the timetable.The maximum generation is set to 15 generation.Population for each generation is 3 populations.Percentage of FTGA solve the problem is 95%.…”
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Undergraduates Project Papers -
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Test case minimization applying firefly algorithm
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Sequence t-way test generation using the barnacles mating optimizer algorithm
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