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1
Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm
Published 2008“…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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2
An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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4
A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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5
Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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6
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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7
Loss reduction in distribution networks using new network reconfiguration algorithm
Published 1996“…For this purpose a simple-to-use formula, called loop loss reduction formula, has been developed. …”
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Comparative analysis of sine cosine and social network search-based algorithm for software test redundancy reduction optimization
Published 2024“…Addressing this issue as an optimization problem, many existing works adopts metaheuristic algorithms to eliminate redundancy. Acknowledging the fact that no single metaheuristic algorithm is superior than its counterparts as well as taking the opportunity to adopt recent algorithm, our work present a comparative study (i.e., solving the TRR problem) using two recently developed metaheuristic algorithms namely the Social Network Search Algorithm (SNS) and the Sine Cosine Algorithm (SCA). …”
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9
Parallelization of noise reduction algorithm for seismic data on a beowulf cluster
Published 2010“…The parallel algorithm was developed using C language with the utilization of the Message Passing Interface (MPI) library. …”
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10
Development of CT Metal Artifact Reduction Algorithm for use in Radiotherapy Treatment Planning System
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Loss reduction in distribution networks by network reconfiguration: a two stage solution approach
Published 2004“…For this purpose a simple-to-use formula, called loop loss reduction formula has been developed. …”
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12
Active intelligent control of vibration of flexible plate structures
Published 2011“…Tests with various disturbance signals show that good vibration reduction is achieved with the developed model-based SISO-AVC algorithms. …”
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13
Performance of various forecasting algorithms to reduce the number of transmitted packets by sensor node in wireless sensor networks
Published 2018“…The selected algorithms for the reduction algorithm in the transmissions include Move Average algorithm (MA), Autoregressive all-pole model parameters — Burg’s algorithm. …”
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14
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2023“…The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. …”
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Numerical Integration And Data Reduction For Application In Photometry
Published 2003“…The first derivatives at the interior knots are estimated from the average of the angles subtended to the horizontal by two adjacent slopes. In a further development, the algorithm is extended to create another algorithm using quintic polynomial interpolation that has C2 continuity, providing smoother interpolation curves but retaining the local nature of interpolation.…”
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20
Real time nonlinear filtered-x lms algorithm for active noise control
Published 2012“…Simulation and experimental results have shown that the developed THF-NLFXLMS achieves additional noise reduction of 19% from that being achieved by the linear FXLMS algorithm.…”
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