Global smart cities classification using a machine learning approach to evaluating livability, technology, and sustainability performance across key urban indices by Aya Hasan Alkhereibi (17151070)

“…This study aims to classify the highest 50 global smart cities based on key livability and technology indices, using advanced <u>machine learning</u> (ML) models to assess city performance comprehensively. …”

A comparative analysis to forecast carbon dioxide emissions by Md. Omer Faruque (17545671)

“…This leads to the second step, which involves formulating the multivariate time series CO₂ emissions forecasting challenges considering its influential factors. Based on multivariate time series prediction, four deep learning algorithms are analyzed in this work, those are convolution neural network (CNN), CNN long short-term memory (CNN–LSTM), long short-term memory (LSTM), and dense neural network (DNN). …”

Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data by Arfan Ahmed (17541309)

“…One of the key aspects of WDs with machine learning (ML) algorithms is to find specific data signatures, called Digital biomarkers, that can be used in classification or gaging the extent of the underlying condition. …”

Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks by Javed Butt , Usman

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Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators by Abu Zitar, Raed

“…Datasets with such characteristics pose a challenge to machine learning algorithms. This is because they impede the training and testing process and entail high resource computations that deteriorate the classification performance. …”
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Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort by Sergio Márquez-Sánchez (19437985)

“…<p dir="ltr">Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. …”

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks by Yassine Himeur (14158821)

“…Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. …”

Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition by Abboud, Ralph

“…In this paper, we introduce a new algorithmic framework for autonomous music sentiment-based expression and composition, titled MUSEC, that perceives an extensible set of six primary human emotions (e.g., anger, fear, joy, love, sadness, and surprise) expressed by a MIDI musical file and then composes (creates) new polyphonic (pseudo) thematic, and diversified musical pieces that express these emotions. …”
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Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV by Zulkifli, Muhammad

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Semi-supervised Clustering of Facial Expressions by Jalal, Ahsan

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A hybrid model to predict the pressure gradient for the liquid-liquid flow in both horizontal and inclined pipes for unknown flow patterns by Md Ferdous Wahid (13485799)

“…This study proposes a hybrid scheme where two machine-learning (ML) models are coupled in a series to predict the PG value without any conclusive FP information. …”

Face Flow: Constrained Optical Flow Framework for Faces by Alkaddour, Muhannad

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Modeling and thermoeconomic analysis of new polygeneration system based on geothermal energy with sea water desalination and hydrogen production by Wulaer Shaersaikai (21436652)

“…With strong R-squared values and high predictive accuracy, the Random Forest machine learning model predicts exergy efficiency, freshwater production, unit specific product cost (USPC), net present value (NPV), and environmental impact. …”

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques by SALLOUM, SAID

“…We study the key research areas in phishing email detection using NLP, machine learning algorithms used in phishing detection email, text features in phishing emails, datasets and resources that have been used in phishing emails, and the evaluation criteria. …”
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An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System by Alkhatib, Osama

“…Therefore, for this paper multiple machine learning algorithms are investigated to predict system failure based on vehicle trips information as well as maintenance management historical data including preventive maintenance and corrective maintenance. …”
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An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control by Mahmoud Dahmani (18810247)

Toward automatic motivator selection for autism behavior intervention therapy by Siyam, Nur

“…We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. …”
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A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT by Harun Surej Ilango (17545728)

“…The performance of FFCNN is compared to the machine learning algorithms-J48, Random Forest, Random Tree, REP Tree, SVM, and Multi-Layer Perceptron (MLP). …”

Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet by Vineet Dhanawat (22361395)

A heuristics for HTTP traffic identification in measuring user dissimilarity by Adeyemi R. Ikuesan (14157123)

“…The study utilizes HTTP request method of GET filtering, to develop a heuristic algorithm to identify user-initiated requests. The algorithm was experimentally tested on a group of users, to ascertain the certainty of identifying user-initiated requests. …”