Reinforced steering Evolutionary Markov Chain for high-dimensional feature selection by Atiq ur Rehman (3044409)

“…Although Evolutionary Algorithms (EAs) have shown promise in the literature for feature selection, creating EAs for high dimensions is still challenging. …”

Data Redundancy Management in Connected Environments by Mansour, Elio

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Optimal scheduling algorithm for residential building distributed energy source systems using Levy flight and chaos-assisted artificial rabbits optimizer by D., Sathish Kumar

“…The execution time of the proposed LFCARO is less than 1 sec. for 10 numerical problems and less than 1.5 sec. for the resource scheduling of residential distribution systems. Based on the average Friedman’s ranking test values, the proposed algorithm stands first with 1.82 for numerical and real-world scheduling problems.…”
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Optimal scheduling algorithm for residential building distributed energy source systems using Levy flight and chaos-assisted artificial rabbits optimizer by M. Premkumar (12757280)

“…The execution time of the proposed LFCARO is less than 1 sec. for 10 numerical problems and less than 1.5 sec. for the resource scheduling of residential distribution systems. Based on the average Friedman’s ranking test values, the proposed algorithm stands first with 1.82 for numerical and real-world scheduling problems. …”

A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method by Amit Kumar Balyan (18288964)

“…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. …”

Privacy-Preserving Framework for Blockchain-Based Stock Exchange Platform by Hamed Al-Shaibani (19497412)

“…Moreover, to ensure long-term unlinkability, the process of anonymization is repeated at regular time intervals (every trading session). …”

Mobile Robot Trajectory Tracking Using Neural Networks by Mohareri, Omid

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Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle by Reza Jafari (3494018)

“…In addition to the deployment without requiring an explicit model of the plant, the simulation results demonstrate that the proposed solution modifies vehicle dynamics and maneuverability in most cases compared to the model-based conventional controller. Furthermore, the reduction in sideslip angle, excellent traction through minimizing tire slip ratio, avoiding oversteering and understeering, and maintaining an acceptable range of energy optimization are demonstrated for DRL controllers, especially for the TD3 and CL TD3 algorithms.…”

Design of adaptive arrays based on element position perturbations by Dawoud, M.M.

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A parallel ant colony optimization to globally optimize area in high-level synthesis. (c2011) by Kozhaya, David Elias

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Establishing Autonomous AUS-Quadrotor by Al Younes, Younes

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Reinforcement Learning-Based School Energy Management System by Yassine Chemingui (18891757)

Large-scale annotation dataset for fetal head biometry in ultrasound images by Mahmood Alzubaidi (15740693)

“…Its detailed annotations, broad compatibility, and ethical compliance make it a highly reusable and adaptable tool for the development of algorithms aimed at improving maternal and Fetal health.…”

A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm by Amirsajjad Rahmani (17541453)

“…The t-test and the binary whale optimization algorithm (BWOA) were applied to choose the best features and train the support vector machine (SVM) model with validation and training data. …”

Variable Selection in Data Analysis: A Synthetic Data Toolkit by Mitra, Rohan

“…This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. …”
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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System UsingA Time-Multiplied Cost Function by Boukas, EK

“…The performance is calculated first and an LMI-based algorithm is developed to design a state feedback control law with constant gain matrices which robustly stabilizes the system in the mean-square quadratically stable sense.…”
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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System Using A Time-Multiplied Cost Function by Boukas, EK

“…The performance is calculated first and an LMI-based algorithm is developed to design a state feedback control law with constant gain matrices which robustly stabilizes the system in the mean-square quadratically stable sense.…”
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Guaranteed Cost Control Of A Markov Jump Linear Uncertain System Using A Time-Multiplied Cost Function by Boukas, EK

“…The performance is calculated first and an LMI-based algorithm is developed to design a state feedback control law with constant gain matrices which robustly stabilizes the system in the mean-square quadratically stable sense.…”
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A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions by Chakarajamula Hussaian Basha (21633167)

“…<p dir="ltr">At present, fossil fuel-based power generation systems are reducing drastically because of their less availability in nature. …”

Adaptive PPO With Multi-Armed Bandit Clipping and Meta-Control for Robust Power Grid Operation Under Adversarial Attacks by Mohamed Massaoudi (16888710)

“…Specifically, our approach introduces three key innovations: 1) multi-armed bandit (MAB) mechanism for dynamic epsilon-clipping that adaptively adjusts exploration-exploitation trade-offs; 2) meta-controller framework that automatically tunes hyperparameters including the activation learning rate (ALR) penalties and exploration factors; and 3) integrated gradient-based optimization approach that combines policy gradients with environmental feedback. …”