Search Results - (( java implication based algorithm ) OR ( heat generation learning algorithm ))

Multi-stage thermal-economical optimization of compact heat exchangers: A new evolutionary-based design approach for real-world problems by Yousefi M., Darus A.N., Yousefi M., Hooshyar D.

A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System by Wong, Ze-Hao

“…Results show that the proposed algorithm required a learning dataset size as small as 5 samples and was resistant to learning labelling error up to 50%.…”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization by Kamal Z., Zamli

“…Many meta-heuristic algorithms have been developed to date (e.g. Simulated Annealing (SA), Particle Swarm Optimization (PSO), Teaching Learning based Optimization (TLBO), Grey Wolf Optimizer(GWO) to name a few). …”
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Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis by Kumar K P., Alruqi M., Hanafi H.A., Sharma P., Wanatasanappan V.V.

“…Prognostic models were developed using two sophisticated machine learning algorithms, XGBoost and Gradient Boosting Regression (GBR). …”

Impact of air pollutants on climate change and prediction of air quality index using machine learning models by Ravindiran G., Rajamanickam S., Kanagarathinam K., Hayder G., Janardhan G., Arunkumar P., Arunachalam S., AlObaid A.A., Warad I., Muniasamy S.K.

“…Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms. � 2023 Elsevier Inc.…”

Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques by Mohammed Alsumaidaee Y.A., Yaw C.T., Koh S.P., Tiong S.K., Chen C.P., Tan C.H., Ali K., Balasubramaniam Y.A.L.

“…The hybrid model 1D-CNN-LSTM was the preferred model for fault detection in switchgear because of its superior performance in both time and frequency domains, allowing for analysis of the generated sound wave during an arcing event. To investigate the effectiveness of the algorithms, experiments were conducted to locate arcing faults in switchgear, and the time and frequency domain analyses of performance were conducted. …”