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learning algorithm » learning algorithms (Expand Search)
coding algorithm » cosine algorithm (Expand Search), colony algorithm (Expand Search), scheduling algorithm (Expand Search)
element » elements (Expand Search)
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STEM: spatial speech separation using twin-delayed DDPG reinforcement learning and expectation maximization
Published 2025“…In this paper, a novel speech separation algorithm is proposed that integrates the twin-delayed deep deterministic (TD3) policy gradient reinforcement learning (RL) agent with the expectation maximization (EM) algorithm for clustering the spatial cues of individual sources separated on azimuth. …”
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CNN and HEVC Video Coding Features for Static Video Summarization
Published 2022Get full text
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Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction
Published 2025“…The application of machine learning (ML) and deep learning (DL) models in the field of toxicity has gained burgeoning interest. …”
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Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification
Published 2025“…<p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. …”
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Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study
Published 2021“…This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). …”
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MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network
Published 2022“…Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. …”
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Nonlinear analysis of shell structures using image processing and machine learning
Published 2023“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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Automatic Video Summarization Using HEVC and CNN Features
Published 2022Get full text
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Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices
Published 2016Get full text
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Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach
Published 2025“…The convolutional neural network is optimized using genetic algorithms, which dynamically tune hyper-parameters such as learning rate, batch size, and momentum to improve performance and generalizability across diverse environmental conditions. …”
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