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learning algorithm » learning algorithms (Expand Search)
method algorithm » mould algorithm (Expand Search)
based methods » based method (Expand Search), mixed methods (Expand Search)
element » elements (Expand Search)
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Teaching–learning-based optimization algorithm: analysis study and its application
Published 2024Subjects: “…Teaching–learning-based optimization algorithm…”
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A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems
Published 2025“…Given that Android applications are frequent targets for malware, ensuring the integrity of FL-based malware detection systems is critical. We introduce an attack framework that integrates Genetic Algorithms (GA) with two prominent adversarial techniques, namely, the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), specifically designed for FL environments. …”
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Published 2023“…In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. …”
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Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
Published 2021“…We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. …”
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AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques
Published 2023“…This paper proposes a novel automated algorithm for the health monitoring of concrete column base cover degradation based on IoT and the state-of-the-art deep learning framework, Convolutional Neural Network (CNN). …”
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A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition
Published 2025“…The rapid development of intelligent technologies has led researchers to strive to improve facial emotion recognition techniques. A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. …”
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Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models
Published 2022“…There are many techniques that can be used for fruit classification using computer vision technology. Deep learning is the most promising algorithm compared to another Machine Learning (ML) algorithm. …”
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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Distributed DRL-Based Downlink Power Allocation for Hybrid RF/VLC Networks
Published 2021“…We implement a simulation environment to benchmark the proposed distributed DRL-based method against other methods such as Q-Learning (QL) and Deep Q-Networks (DQN), and centralized heuristic power allocation algorithms. …”
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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
Published 2022“…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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Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice
Published 2020“…Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). …”
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A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network
Published 2019“…In order to address this issue, in this paper, we propose a novel hybrid approach with both convolutional and recurrent neural networks combined, which is based on the long short-term memory module. Featuring the capability of learning the correlations of time-series data, the proposed deep learning method is well-suited for extracting the valuable transient feature contained in the very beginning of the response curve. …”