A systematic review of recent advances in the application of machine learning in membrane-based gas separation technologies by Farideh Abdollahi (22303153)

“…The outcomes of a comprehensive systematic literature review further underscore the diverse and extensive applications of ML in the domain of membrane-based gas separation. These applications encompass the prediction of gas separation performance in various types of membranes, including pure membranes, <u>thin film nanocomposite membranes</u> (TFN), and <u>mixed matrix membranes</u> (MMMs). …”

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

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Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework by Tayyabah Hasan (18427887)

“…<div><p>Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. …”

Data Generation for Path Testing by Mansour, Nashat

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Blue collar laborers’ travel pattern recognition: Machine learning classifier approach by Aya Hasan Alkhereibi (17151070)

“…Raw data preprocessing and outliers detection and filtering algorithms were applied at the first stage of the analysis, and consequently, an activity-based travel matrix was developed for each household. …”

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques by Amith Khandakar (14151981)

“…Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. …”

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

“…We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). …”

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

Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk by Mohamed Chaouch (17983846)

“…Additionally, an online classifier is developed for streaming data, combining online PCA with a kernel-based recursive classifier using a stochastic approximation algorithm. …”

A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna by Nupur Chhaule (21224987)

DRL-Based IRS-Assisted Secure Hybrid Visible Light and mmWave Communications by Danya A. Saifaldeen (19498705)

“…To address this complexity optimally, we propose a deep reinforcement learning (DRL) approach based on the deep deterministic policy gradient (DDPG) technique. …”

Predicting Dropouts among a Homogeneous Population using a Data Mining Approach by BILQUISE, GHAZALA

“…Our study also reveals that ensemble machine learning algorithms are more reliable and outperform standard algorithms.…”
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Predict Student Success and Performance factors by analyzing educational data using data mining techniques by ATIF, MUHAMMAD

“…The model is then applied to data collected from a reputable university that included 126,698 records with twenty-six (26) initial data attributes. …”
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Boosting the visibility of services in microservice architecture by Ahmet Vedat Tokmak (17773479)

“…These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. …”

Correlation Clustering with Overlaps by Fakhereldine, Amin

“…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network by Tawfik Beghriche (19563184)

“…A performance comparison between the DNN algorithm and some well‐known machine learning techniques as well as the state‐of‐the‐art methods is presented. …”

A machine learning model for early detection of diabetic foot using thermogram images by Amith Khandakar (14151981)

“…We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. …”

Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment by Zakaria Tolba (16904718)

“…This paper provides a deep learning-based decryptor for investigating the permutation primitives used in multimedia block cipher encryption algorithms.We aim to investigate how deep learning can be used to improve on previous classical works by employing ciphertext pair aspects to maximize information extraction with low-data constraints by using convolution neural network features to discover the correlation among permutable atoms to extract the plaintext from the ciphered text without any P-box expertise. …”

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

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.…”