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codon optimization » dog optimization (Expand Search), motor optimization (Expand Search), igdt optimization (Expand Search)
model optimization » level optimization (Expand Search), motor optimization (Expand Search), monkey optimizations (Expand Search)
codon optimization » dog optimization (Expand Search), motor optimization (Expand Search), igdt optimization (Expand Search)
model optimization » level optimization (Expand Search), motor optimization (Expand Search), monkey optimizations (Expand Search)
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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
Published 2022“…Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”
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Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems
Published 2022“…The proposed improved ABO method consists in reducing the number of samples in the training data set using the Euclidean distance and extracting the most significant features from the reduced data using ABO algorithm. …”
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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. …”
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Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control
Published 2021“…The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. …”
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Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks
Published 2024“…In a postfault scenario, a copula of processing blocks is implemented to ensure the reliability of the proposed method where high-importance features are incorporated into the TCN-GWO model. The proposed algorithm unlocks scalability and system adaptability to operational variability by adopting numeric imputation and missing-data-tolerant techniques. …”
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Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems
Published 2023“…First, a feature selection tool using PSO Algorithm is developed. Then, in order to maximize the diversity between data samples and improve the effectiveness of using PSO algorithm for feature selection, the Euclidean distance metric is used in order to reduce the data and maximize the diversity between data samples. …”
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Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes
Published 2025“…We evaluated thirteen machine learning models at each stage, selecting the top-performing classifiers to optimize results. …”
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Framework for rapid design and optimisation of immersive battery cooling system
Published 2025“…Two key parameters are optimised, namely: battery gap spacing (3–10 mm) and inlet/outlet width (5–15 mm), via Optimal Latin Hypercube Sampling, Support Vector Regression, and GDE3 algorithm. …”
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LDSVM: Leukemia Cancer Classification Using Machine Learning
Published 2022“…Currently, it is difficult to classify cancers using microarray data. Nearly many data mining techniques have failed because of the small sample size, which has become more critical for organizations. …”
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Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management
Published 2019Get full text
doctoralThesis -
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Just-in-time defect prediction for mobile applications: using shallow or deep learning?
Published 2023“…In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. …”
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Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging
Published 2023Get full text
doctoralThesis