TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection by Zina Chkirbene (16869987)

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

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Discrimination between genuine and acted expressions using EEG signals and machine learning by Alex, Meera

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Artificial intelligence models for predicting the mode of delivery in maternal care by Rawan AlSaad (14159019)

“…</p><h3>Objectives</h3><p dir="ltr">This study aims to evaluate and compare the predictive accuracy of AI algorithms in predicting the mode of delivery (vaginal or cesarean) using routinely collected antepartum data from electronic health records (EHRs). …”

Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques by Kais Abdulmawjood (17947784)

Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm by Khadija Attouri (18024307)

“…<p>This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. …”

Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring by Zina Chkirbene (16869987)

“…By formulating the video resource allocation challenge as a multi-objective optimization problem, the framework aims to minimize network delays while respecting node capacity limitations. The core of DRLLVT is its novel algorithm that leverages Deep Reinforcement Learning (DRL) to dynamically adapt to changing environmental conditions, facilitating real-time decisions that consider node capacities, latency, and the overall network dynamics. …”

Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation by Maymouna Ez Eddin (21633650)

A State-of-the-Art Comprehensive Review on Maximum Power Tracking Algorithms for Photovoltaic Systems and New Technology of the Photovoltaic Applications by Ahmed Badawi (19757055)

Recent advances on artificial intelligence and learning techniques in cognitive radio networks by Abbas, Nadine

“…The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. …”
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Reinforcement R-learning model for time scheduling of on-demand fog placement by Farhat, Peter

“…On the fly deployment of fog nodes near users provides the flexibility of pushing services anywhere and whenever needed. …”
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Sentiment Analysis for Arabic Social media Movie Reviews Using Deep Learning by MEZAHEM, FATEMA HAMAD

“…Prior to performing sentiment analysis, it is necessary to prepare the data so that it may be used to train machine learning (ML) algorithms. In order to label the data that was gathered from a corpus collection for ML use, manual annotation was made. …”
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Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark by Ameema Zainab (16864263)

“…<p>Machine learning algorithms have been intensively applied to perform load forecasting to obtain better accuracies as compared to traditional statistical methods. …”

Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches by Mohammad, Areej Osama

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Online dynamic ensemble deep random vector functional link neural network for forecasting by Ruobin Gao (16003195)

“…First, an online decomposition is utilized as a feature engineering block for the edRVFL. Then, an online learning algorithm is designed to learn the edRVFL. …”

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects by Majdi Mansouri (16869885)

“…The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. …”

Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters by 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. …”

Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting by 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. …”

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

“…The learning environment is based on a nonlinear double-track vehicle model, incorporating tire-road interactions. …”

H.264/AVC to HEVC Video Transcoder Based on Dynamic Thresholding and Content Modeling by Peixoto, Eduardo

“…Then, two types of mode mapping algorithms are proposed. In the first solution, a single H.264/AVC coding parameter is used to determine the outgoing HEVC partitions using dynamic thresholding. …”
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