DRL-Based IRS-Assisted Secure Visible Light Communications حسب Danya A. Saifaldeen (19498705)

"…The DDPG-based algorithm provides an optimized solution that can adapt to the large size of design parameters and act fast to the channel variations due to users’ mobility. …"

A Comprehensive Review of Digital Twin Technology in Building Energy Consumption Forecasting حسب Maissa Boukaf (22282279)

"…The digitalization of building energy forecasting systems, enhanced by Energy Digital Twin technology alongside IoT devices and advanced data-driven algorithms, offers substantial improvements in energy management and optimization, servicing, maintenance, and energy-efficient design. …"

Analysis of Multi-User-Based UAV System With Outdated CSI حسب Parvez Shaik (21633041)

"…In this framework, altitude and location-dependent path loss modeling are considered for the air-to-ground links. Optimization of UAV location and altitude is performed through a limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm to attain minimum OP (MOP). …"

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. It achieves a near-optimal solution compared to the benchmark technique within the close gap and improves both achievable transmission rates and energy efficiency compared to related works by 50-70%.…"

A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model حسب Md. Nazmul Islam Shuzan (16888827)

"…The best ML model and the best feature selection algorithm combination were fine-tuned to optimize its performance using hyperparameter optimization. …"

A novel hybrid methodology for fault diagnosis of wind energy conversion systems حسب Khaled Dhibi (16891524)

"…In the second step, we applied a wrapper technique called equilibrium optimizer to get optimized features and better classification accuracy. …"

Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose حسب Atiq Ur Rehman (8843024)

"…These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. …"

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

Joint Beamforming Design and Power Minimization for Friendly Jamming Relaying Hybrid RF/VLC Systems حسب Jaber Al-Khori (16855470)

Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition حسب Dabeeruddin Syed (16864260)

"…The developed deep neural network consists of six layers and employs Adam optimization using the TensorFlow framework. The STLF is a day-ahead hourly horizon forecasting. …"

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting حسب Mohamed Massaoudi (16888710)

"…Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. …"

An Adaptive Sliding Mode Control for a Dual Active Bridge Converter With Extended Phase Shift Modulation حسب Farzaneh Bagheri (17947811)

"…The conventional single-phase shift (SPS) modulation-based DAB converter is known to be inefficient. Hence, an optimization algorithm based on the Lagrange multiplier method (LMM) is proposed to minimize both backflow power and inductor current stress simultaneously. …"

A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI حسب Oishi Jyoti (21593819)

"…After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. …"

Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas حسب Marwan Dhuheir (19170898)

"…We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. …"

Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems حسب Khaled Dhibi (16891524)

Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain حسب Farshad Rahimi Ghashghaei (20880995)

"…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …"

Secrecy Performance of Decode-and-Forward Based Hybrid RF/VLC Relaying Systems حسب Jaber Al-Khori (16855470)

Boosting the Training of Neural Networks Through Hybrid Metaheuristics حسب Abou Doush, Iyad

"…In this paper, the learning process of multilayer perceptron (MLP) neural network is boosted using hybrid metaheuristic optimization algorithms. Normally, the learning process in MLP requires suitable settings of its weight and bias parameters. …"
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A METHODOLOGY FOR NETWORK TOPOLOGY DESIGN USING FUZZY EVALUATIONS حسب Khan, Salman A.

"…The topology design of campus networks (CNs), a class of computer networks, is a NP-hard optimization problem. The design consists of three main steps and requires the optimization of several conflicting objectives such as minimization of cost, minimization of network delay, and minimization of maximum number of hops etc. …"
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Power system output feedback stabilizer design via geneticalgorithms حسب Abdel-Magid, Y.L.

"…A digital simulation of the power system is then used in conjunction with the genetic algorithm to determine the output feedback gains. In the second method, the problem of selecting the output feedback gains is converted to a simple optimization problem with an eigenvalue based objective function, which is solved by a genetic algorithm. …"
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