Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems by Amal Hichri (21383672)

“…This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. …”

Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems by Khaled Dhibi (16891524)

“…In the past decades, neural networks (NN) has provided an effective performance in fault diagnosis. …”

Energy-Efficient Cell Association and Load Balancing for Low Battery Users in Heterogeneous Cellular Networks by Abdul Qadeer Khan (22502168)

“…The proposed battery based cell association models perform well in favor of LBUs and clearly show the significance of the proposed approach.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3568343" target="_blank">https://dx.doi.org/10.1109/access.2025.3568343</a></p>…”

GDLL: A Scalable and Share Nothing Architecture Based Distributed Graph Neural Networks Framework by Duong Thi Thu Van (19499206)

“…To address such issues, we propose a scalable, layered, fault-tolerance, and in-memory distributed computing-based graph neural network framework called Graph Distributed Learning Library (GDLL). …”

Willingness to pay for digital wellbeing features on social network sites: a study with Arab and European samples by Sameha Alshakhsi (17032311)

“…<p dir="ltr">In recent years, concerns over the potential negative impacts of social network sites (SNS) on users’ digital wellbeing are on the rise. …”

MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model by Md Abrar Jahin (20108252)

“…MCDFN utilizes Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) to extract spatial and temporal features from time series data. …”

21st Century Warfare: How Information Technology Has Fundamentally Changed Global Warfare - An Analysis of Insurgencies’ Use of IT by FIKRI, MOHAMMAD NADER MOHAMMAD ABDULLAH

“…Whereas the 20th century was characterized by decolonization and states warring against one another, the 21st century has witnessed a shift in global warfare where non-state actors such as radical groups are gaining momentum and causing significant mayhem. The accessibility of radical groups to wage war on a global level has been exacerbated in recent years by the widespread usage of information technology on an individual level and the resulting compression of time and space. …”
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Accessibility of green spaces in a metropolitan network using space syntax to objectively evaluate the spatial locations of parks and promenades in Doha, State of Qatar by Heba O. Tannous (16810707)

“…<p dir="ltr">Accessibility indicates the ease of reaching many destinations from many origins in the urban network at various scales of space. It plays an important role in determining the location of public facilities to maximize usability for a large population or limit use to a smaller community. …”

Exploring social media adoption in small and medium enterprises in Iraq: pivotal role of social media network capability and customer involvement by Hussam Al Halbusi (14492861)

“…However, trust was not significantly related to the use of social media. Notably, the positive effect of social media usage and SMEs’ performance was positively moderated by the capability of the social media networks and customer involvement. …”

Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach by Arshad Ali Khan (23152516)

“…<p dir="ltr">Air pollution poses significant threats to human health and the environment, making effective monitoring increasingly essential. …”

Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios by Hasan Abbas Al-Mohammed (16810674)

“…<p>Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices. …”

InCR: Inception and concatenation residual block-based deep learning network for damaged building detection using remote sensing images by Burak Tasci (17032302)

“…<p dir="ltr">In February 2023, Turkey experienced a series of earthquakes that caused significant damage to buildings and affected many people. …”

A dual-stage deep learning approach for robust detection and identification of hardware trojans using monte-carlo dropout by Arash Golabi (21841484)

Distribution network line protection in the presence of distributed generation by Margossian, Harag

“…To get the information needed from the network, a modified state estimation is proposed. …”
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Modeling of photovoltaic soiling loss as a function of environmental variables by Wasim Javed (6105866)

“…<p>In this study, an artificial neural network (ANN) approach was applied for modeling the relation between environmental variables and daily change in the Cleanness Index (ΔCI, a measure of performance loss due to soiling) of photovoltaic modules in the field in Doha, Qatar. …”

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

“…Specifically, we focus on the Controller Area Network (CAN) protocol, which is prevalent in in-vehicle communication systems, yet exhibits inherent security vulnerabilities. …”

Design of composite rectangular tubes for optimum crashworthiness performance via experimental and ANN techniques by Monzure-Khoda Kazi (17191207)

“…Experimental findings show that axially and laterally loaded rectangular tubes were significantly affected by their aspect ratio. Furthermore, the predictions obtained from the ANN models showed consistency with the experimental data. …”

Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach by Shaikh Afnan Birahim (22303750)

“…The system addresses key challenges such as the imbalanced nature of datasets and the evolving complexity of network attacks. By incorporating Synthetic Minority Oversampling Technique Tomek (SMOTE-Tomek) techniques to balance the dataset and employing explainable AI methods such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), the proposed model achieves significant improvements in detection accuracy, precision, recall, and F1 score while providing clear, interpretable results. …”

Navigating Nexus: Cross-Mapping Sustainable Development Goals and Competitiveness for a Resilient Future by Qazi, Abroon

“…This study investigates two established sustainability metrics—the Sustainable Development Goal Index (SDGI) and the Global Sustainable Competitiveness Index (GSCI)—to explore dependencies between their associated drivers using Bayesian Belief Networks (BBNs). The analysis considers sustainability performance data for 163 countries in 2023, using data from SolAbility’s Sustainable Competitiveness Report and the Sustainable Development Report. …”
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Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment by Aryan Bhambu (18767731)

“…The neuron pruning strategy is utilized to eliminate noisy information from random features, thereby improving the network’s performance. …”