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An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis
Published 2016“…In conclusion, the proposed algorithm is able to overcome the rare item issue by implementing covariance based support value normalization and high computational costs issue by implementing indexing enumeration tree structure.Future work of this study should focus on rule interpretation to generate more human understandable rule by novice in data mining. …”
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AAβ public key cryptosystem - a new practical asymmetric implementation based on the square root problem
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Wind power forecasting with metaheuristic-based feature selection and neural networks
Published 2024“…Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. …”
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White root disease auto-detection system for rubber trees based on dynamic electro-biochemical latex properties / Mohd Suhaimi Sulaiman
Published 2019“…These measurement input were then went through the process of classification in ANN to generate the most optimized models by using LM and SCG algorithm. …”
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SUDOKU HELPER
Published 2015“…In this paper research, author presents an algorithm to provide a tutorial for any Sudoku player who got stuck during the solving process. …”
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Final Year Project -
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Quaternion-based dynamic algorithm for random generation of solid 4D cylindrical curves in RVE modeling
Published 2025“…A quaternion‐based dynamic algorithm is developed to populate Representative Volume Elements (RVEs) with solid 4D cylindrical fibers, combining spatial centerline coordinates (x,y,z) and quaternion‐encoded orientation. …”
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Pemetaan Pm10 Dan Aot Menggunakan Teknik Penderiaan Jauh Di Semenanjung Malaysia
Published 2006“…The two-band model, terma linear and modified algorithms were selected based on the highe.st correlation coefficient (R) value {> 80%) and the lowest root-mean-square (RMS) error value. …”
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…The error measurements of the proposed method such as Mean Absolute Percentage Error, Mean Absolute Error, And Root Mean Square Error for islanding detection are less than 0.02% for ideal and noisy conditions which shows that the algorithm is not sensitive to noise. …”
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Performance of Levenberg-Marquardt neural network algorithm in power quality disturbances classification / Adibah I’zzah Mohamad Kasim
Published 2025“…A simulation-based methodology was adopted, leveraging MATLABO/Simulink to model a power grid and generate synthetic PQD waveforms. …”
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Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar
Published 2021“…This algorithms were selected based on previous literature review in price prediction. …”
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Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
Published 2024“…Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. …”
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Modem Recognition Algorithm for Software Defined Radio Base Station
Published 2004“…The proposed algorithm was developed based on 8th order statistical moment and the technique was chosen based on the research work done by Keith et al [15]. …”
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Collision avoidance mechanisms using artificial potential field for UAVs
Published 2025“…The APF algorithm, based on the combination of attractive and repulsive potential functions, is modeled and simulated in MATLAB to guide UAVs toward target destinations while avoiding obstacles. …”
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Non-fiducial based electrocardiogram biometrics with kernel methods
Published 2017“…The effectiveness of this algorithm is investigated by comparing with other AC based feature extraction algorithms involving AC/LDA (Linear Discriminant Analysis) and AC/PCA (Principal Component Analysis). …”
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