STEM: spatial speech separation using twin-delayed DDPG reinforcement learning and expectation maximization حسب Muhammad Salman Khan (7202543)

"…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. …"

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features حسب Muhammad Arif (769250)

"…After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. …"

Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations حسب Md Mosarrof Hossen (21399056)

"…To address this challenge, this study aims to optimize household energy consumption while preserving data privacy by proposing an innovative two-stage Federated Learning (FL) framework that delivers real-time micro-moment-based recommendations. …"

Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization حسب Yassine Himeur (14158821)

"…While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. …"

Reinforcement Learning for Resilient Aerial-IRS Assisted Wireless Communications Networks in the Presence of Multiple Jammers حسب Zain Ul Abideen Tariq (17984107)

"…Experimental results demonstrate the effectiveness of our proposed DDPG-based approach in outperforming other RL algorithms. …"

Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters حسب Sakib Mahmud (15302404)

"…In this study, we introduce the multimodal feature fusion for non-intrusive occupancy monitoring (MMF-NIOM) framework, which leverages both classical and deep machine learning algorithms to achieve state-of-the-art occupancy detection performance using smart meter data. …"

Predicting Compression Modes and Split Decisions for HEVC Video Coding Using Machine Learning Techniques حسب Hassan, Mahitab Alaaeldin

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Hybridizing rule-based power system stabilizers with geneticalgorithms حسب Abido, M.A.

"…The proposed approach uses genetic algorithms (GA) to search for optimal settings of rule-based power system stabilizer (RBPSS) parameters. …"
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Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval حسب Mohammed Tahar Habib Kaib (21633176)

Development of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends حسب Abdellatif M. Sadeq (16931841)

"…<p dir="ltr">Studying the spatial and temporal evolution in turbulent flames represents one of the most challenging problems in the combustion community. Based on previous 3D numerical analyses, this study aims to develop data-driven machine learning (ML) models for predicting the flame radius evolution and turbulent flame speeds for diesel, gas-to-liquids (GTL), and their 50/50 blend (by volumetric composition) under different thermodynamic and turbulence operating conditions. …"

Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features حسب Mariam Bahameish (19255789)

"…This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. …"

Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting حسب Abdelkader Baggag (16864140)

"…The learned factors, with a graph-based temporal dependency, are then used in an autoregressive algorithm to predict the future state of the road network with a large horizon. …"

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions حسب Abdulmalik Alwarafy (17984104)

"…Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. …"

Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks حسب Mohammed Almehdhar (22046597)

"…This survey paper offers an in-depth examination of advanced machine learning (ML) and deep learning (DL) approaches employed in developing sophisticated IDS for safeguarding IVNs against potential cyber-attacks. …"

Optimal Trajectory and Positioning of UAVs for Small Cell HetNets: Geometrical Analysis and Reinforcement Learning Approach حسب Mohammad Taghi Dabiri (16904658)

"…Then, using geometrical analysis and deep reinforcement learning (RL) method, we propose several algorithms to find the optimal trajectory and select an optimal pattern during the trajectory. …"

Random vector functional link network: Recent developments, applications, and future directions حسب A.K. Malik (16003193)

"…Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. …"

Edge intelligence for network intrusion prevention in IoT ecosystem حسب Mansura, Habiba

"…This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. …"
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Acoustic Based Localization of Partial Discharge Inside Oil-Filled Transformers حسب Hamidreza Besharatifard (16904823)

"…<p dir="ltr">This paper addresses the localization of Partial Discharge through a 3D Finite Element Method analysis of acoustic wave propagation inside a 3-phase 35kV transformer with the help of COMSOL Multiphysics software. …"

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review حسب Asma Alamgir (18288895)

"…Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). …"

Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images حسب Rehan Raza (17019105)

"…Considering these shortcomings, computational methods especially machine learning and deep learning algorithms are leveraged as an alternative to accelerate the accurate detection of CT scans as cancerous, and non-cancerous. …"