Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management by Alhusin, Mohammed Omer Alamin

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Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models by Theng, Lau Wei

“…Deep learning is the most promising algorithm compared to another Machine Learning (ML) algorithm. …”
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Novel hybrid informational model for predicting the creep and shrinkage deflection of reinforced concrete beams containing GGBFS by Iman Faridmehr (14150616)

“…After 150 days, the average creep deflection of RC beams containing 20, 40, and 60% GGBFS was 30, 70, and 100% higher than the ones for conventional concrete beams, respectively. A hybrid artificial neural network coupled with a metaheuristic Whale optimization algorithm has been developed to estimate the overall deflection of concrete beams due to creep and shrinkage. …”

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

“…The k-fold cross-validation technique is used for model training, and the developed neural network-based model is used to investigate the effects of operating parameters on the premixed turbulent flames. …”

An efficient claim management assurance system using EPC contract based on improved monarch butterfly optimization models by K. Mukilan (22224793)

“…To address these challenges, a claim management system is developed based on the Improved Monarch Butterfly Optimization Algorithm (IMBOA) and the principles of EPC. Conventional construction models typically optimize only a single objective, such as time, cost, or delay, which may not effectively enhance overall performance. …”

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore (17727684)

“…The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. …”

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review by Zainab Jan (17306614)

“…Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). …”

The Assessment and Allocation of Public Private Partnership Risks in the UAE by Shaban, Mazhd

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Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier by Syed Ali Jafar Zaidi (19563178)

“…Specifically, this study exhibits people’s predilection toward the COVID-19 vaccine and its results based on the reviews. Five models, e.g., random forest (RF), a support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and an artificial neural network (ANN), were used for forecasting the overall predilection toward the COVID-19 vaccine. …”

A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass by Uzma Nawaz (21980708)

“…By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.…”

LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications by Aldamani, Raghad

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Forward simulation of three-dimensional advection-diffusion using a fundamental solution for a linear flow by Wehbe, Rawan

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An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control by Mahmoud Dahmani (18810247)

“…The first three techniques were automatic, employ correlation, and were variants of template matching, whereas the last one uses convolutional neural networks (CNNs). Different metrics to quantitatively evaluate the performance of each algorithm in terms of accuracy and latency were computed and overall comparison is presented. …”

Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach by Haitham Osman (11737057)

“…A comprehensive techno-economic analysis is conducted, supported by a machine learning-assisted optimization framework that combines artificial neural networks with genetic algorithms. Considering optimum conditions, the system attains an exergetic efficiency of 30.13 % and a power generation of 7.24 MW, with a cost rate of 232.06 $/h and a payback period of 4.09 years. …”

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information by M. Ghoniem, Rania

“…For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. …”
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Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods by Sivakavi Naga Venkata Bramareswara Rao (15944992)

“…From the results, it is found that the Levenberg–Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.…”

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces by Uzair Sajjad (19646296)

“…In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. …”

ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification by Md. Iqbal Hossain (723299)

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network by Sakib Mahmud (15302404)

“…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. …”

Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks by Mohamed Massaoudi (16888710)