Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization حسب Yassine Himeur (14158821)

"…DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. …"

Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations حسب Md Mosarrof Hossen (21399056)

"…To address this challenge, this study aims to optimize household energy consumption while preserving data privacy by proposing an innovative two-stage Federated Learning (FL) framework that delivers real-time micro-moment-based recommendations. …"

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

Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper حسب Mutaz I. Othman (21186827)

"…<h3>Background</h3><p dir="ltr">Machine learning (ML) models can enhance patient–nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. …"

Automatic Recognition of Poets for Arabic Poetry using Deep Learning Techniques (LSTM and Bi-LSTM) حسب AL SHOUBAKI, HAMZA YOUNIS

"…We also explore a range of algorithms, including traditional classifiers and deep learning models, to determine and select the most suitable and accurate models of identifying poets' names from the verses. …"
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Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique حسب Iqbal Hassan (22155274)

"…The primary objective of this study is to estimate the CL index through an innovative approach that employs a hybrid, cluster-based, unsupervised learning technique seamlessly integrated with a 1D Convolutional Neural Network (CNN) architecture tailored for automated feature extraction, rather than conventional supervised algorithms, which facilitated in the acquisition of latent complex patterns without the need for manual categorization. …"

Arabic Hotel Reviews Sentiment Analysis Using Deep Learning حسب ALMANSOORI, MOHAMMAD

"…Our models utilized advanced text preprocessing, feature extraction, and classification algorithms to accurately predict sentiment polarity in Arabic hotel reviews. …"
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Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework حسب Tayyabah Hasan (18427887)

"…After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. …"

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

"…Based on previous 3D numerical analyses, this study aims to develop data-driven machine learning (ML) models for predicting the flame radius evolution and turbulent flame speeds for diesel, gas-to-liquids (GTL), and their 50/50 blend (by volumetric composition) under different thermodynamic and turbulence operating conditions. …"

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems حسب Agushaka, Jeffrey O.

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Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features حسب Mariam Bahameish (19255789)

"…<p dir="ltr">Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. …"

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

"…Study selection, quality assessment, and data extraction were performed independently by four authors. …"

Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks حسب Mohammed Almehdhar (22046597)

"…This survey paper offers an in-depth examination of advanced machine learning (ML) and deep learning (DL) approaches employed in developing sophisticated IDS for safeguarding IVNs against potential cyber-attacks. …"

Edge intelligence for network intrusion prevention in IoT ecosystem حسب Mansura, Habiba

"…This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. …"
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Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review حسب Asma Alamgir (18288895)

"…Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). …"

A novel few shot learning derived architecture for long-term HbA1c prediction حسب Marwa Qaraqe (10135172)

"…For the first time in the literature, this work proposes a novel FSL-derived algorithm for the long-term prediction of clinical HbA1c measures. …"

Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images حسب Rehan Raza (17019105)

"…Considering these shortcomings, computational methods especially machine learning and deep learning algorithms are leveraged as an alternative to accelerate the accurate detection of CT scans as cancerous, and non-cancerous. …"

CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children حسب Jayakanth, Kunhoth

"…Three machine learning algorithms support vector machine (SVM), AdaBoost, and Random forest are employed to assess the performance of the CNN features and fused CNN features. …"
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From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors حسب Michael R. Giordano (9976173)

"…The methods for correcting and calibrating these biases and dependencies that have been used in the literature likewise range from simple linear and quadratic models to complex machine learning algorithms. Here we review the needs and challenges when trying to get high-quality data from low-cost sensors. …"

Various Faults Classification of Industrial Application of Induction Motors Using Supervised Machine Learning: A Comprehensive Review حسب Rehaan Hussain (22302742)

"…In current literature, there are a number of papers that address all these faults using different methods, and this paper compiles the information from the written works for ease of access. Machine learning algorithms are a set of data-driven rules that are able to classify specific faults in induction motors, which will be explained further in this review paper. …"