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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Alsultan, Aqeel Mezher Thajeel Bani, Nurul Aini Muhtazaruddin, Mohd Nabil Muhammad-Sukki, Firdaus Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur Malaysia Edinburgh Napier University School of Computing Engineering and Built Environment Edinburgh United Kingdom

ISBN: (纸本)9798350340204

Hybrid Renewable Energy Sources (HRES) in microgrids present a cost-effective option for supplying power to remote areas. This research focuses on optimizing HRES systems for Base Transceiver stations in Basra, Iraq, by using the Multiobjective Improve Salp Swarm algorithm (MOISSA) to enhance reliability, reduce energy costs, and improve energy distribution through effective design variables such as photovoltaic power, wind turbines, and battery autonomy days. Through MATLAB simulations with the MOISSA algorithm, and comparative analysis to other algorithms, the study shows a significant reduction in energy costs and a decrease in power supply probability, offering valuable design solutions for Hybrid Microgrid Systems. © 2024 IEEE.

关键词： Hybrid power

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Application of Machine Learning Techniques to Predict Teenage Obesity Using Earlier Childhood Measurements from Millennium Cohort Study 23

Application of Machine Learning Techniques to Predict Teenag...

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2023 7th International Conference on Cloud and Big Data computing, ICCBDC 2023

作者： Singh, Balbir Gorbenko, Anatoliy Palczewska, Anna Tawfik, Hissam School of Built Environment Engineering and Computing Leeds Beckett University United Kingdom Department of Electrical Engineering University of Sharjah United Arab Emirates

ISBN: (纸本)9798400707339

Obesity is a major global concern with more than 2.1 billion people overweight or obese worldwide, which amounts to almost 30% of the global population. If the current trend continues, the overweight and obese population is likely to increase to 41% by 2030. Individuals developing signs of weight gain or obesity are also at the risk of developing serious illnesses such as type 2 diabetes, respiratory problems, heart disease, stroke, and even death. It is essential to detect childhood obesity as early as possible since children who are either overweight or obese in their younger age tend to stay obese in their adult lives. This research utilises the vast amount of data available via UK's millennium cohort study to construct machine learning driven framework to predict young people at the risk of becoming overweight or obese. The focus of this paper is to develop a framework to predict childhood obesity using earlier childhood data and other relevant features. The use of novel data balancing technique and inclusion of additional relevant features resulted in sensitivity, specificity, and F1-score of 77.32%, 76.81%, and 77.02% respectively. The proposed technique utilises easily obtainable features making it suitable to be used in a clinical and non-clinical environment. © 2023 Owner/Author.

关键词： Machine learning

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Machine Learning for the Classification of Obesity Levels Based on Lifestyle Factors 23

Machine Learning for the Classification of Obesity Levels Ba...

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2023 7th International Conference on Cloud and Big Data computing, ICCBDC 2023

作者： Khater, Tarek Tawfik, Hissam Singh, Balbir Department of Electrical Engineering University of Sharjah United Arab Emirates School of Built Environment Engineering and Computing Leeds Beckett University United Kingdom

ISBN: (纸本)9798400707339

In recent years, the prevalence of obesity and its related co-morbidities have been increasing significantly. Therefore, it is an important challenge to pursue an early prediction of obesity risk that could help in reducing the pace of obesity rise when appropriate interventions are placed, accordingly. The prediction and classification of obesity depend on different factors such as body mass index (BMI) and lifestyle aspects, including eating habits. By focusing on these lifestyles and eating habit factors, we can develop a more holistic approach to weight management and prevention of obesity. The aim of this paper is to propose a machine-learning model that can classify weight levels using lifestyle variables without relying on BMI which enables us to investigate how lifestyle factors affect different levels of weight categorization. Although BMI is the most widely used estimation of obesity, there are other factors that can contribute to gaining weight such as lifestyle factors. The accuracy of our lifestyle-based model reached 75% excluding weight, height, and family history. Our model could serve as a starting point for using an interpretable machine learning model to better understand the effect of lifestyle factors on obesity levels. © 2023 ACM.

关键词： Machine learning

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Interpretable Models For ML-Based Classification of Obesity 23

Interpretable Models For ML-Based Classification of Obesity

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2023 7th International Conference on Cloud and Big Data computing, ICCBDC 2023

作者： Khater, Tarek Tawfik, Hissam Sowdagar, Sahar Singh, Balbir Department of Electrical Engineering University of Sharjah United Arab Emirates School of Built Environment Engineering and Computing Leeds Beckett University United Kingdom

ISBN: (纸本)9798400707339

Artificial Intelligence is revolutionizing the healthcare business, thanks to the rising availability of structured and unstructured data and the rapid advancement of analytical methodologies. With artificial intelligence's expanding importance in healthcare, there are growing issues about a lack of transparency and explainability, as well as potential bias in model predictions. The goal of this paper is to use interpretable ML to provide a better understanding of the lifestyle factors that influence the model's predictions of the weight levels, as well as to identify the most critical features for the classification task. We aim to create more accurate and effective predictive models for weight management, which could eventually help individuals make more informed decisions about their diet and lifestyle choices. Our machine learning model got an accuracy of 76% using XG-boost and Random Forest and we tried to interpret the result using a model-agnostic method including, the permutation importance method and partial dependence plot. The results showed that the number of main meals, frequent consumption of vegetables, and time using technology are the most important features. Further explanation was performed using a partial dependence plot which interprets the relationship between these important features and the model behavior. © 2023 ACM.

关键词： Machine learning

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An interpretable machine learning model to predict hospitalizations

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Clinical eHealth 2025年 8卷 53-65页

作者： Elbatanouny, Hagar Tawfik, Hissam Khater, Tarek Gorbenko, Anatoliy Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom Department of Biomedical Engineering Khalifa University Abu Dhabi United Arab Emirates

Hospital management plays a pivotal role in ensuring the efficient delivery of medical services, especially in the face of challenges posed by pandemics such as COVID-19. This paper explores the application of machine learning techniques in addressing the challenge of hospitalization during pandemics. Leveraging a comprehensive dataset sourced from the Mexican government, various supervised learning algorithms including Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron are trained and evaluated to discern factors contributing to hospitalizations. Feature importance analysis and dimensionality reduction techniques are employed to enhance models predictive performance. The best model was Gradient Boosting algorithm with an accuracy of 85.63% and AUC score of 0.8696. The interpretability plots showed that pneumonia had a positive impact on the hospitalization prediction of the model. Our analysis indicates that women aged over 45 with pneumonia and concurrent COVID-19 exhibit the highest likelihood of hospitalization. This study underscores the potential of interpretable machine learning in aiding hospital managers to optimize resource allocation, hospitalization cases, and make data-driven decisions during pandemics. © 2025

关键词： COVID-19 Explainable AI Hospital management Interpretable models Machine learning Pandemics

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Compact Microstrip-Fed Monopole Antenna with Dual Notch Bands and Ultra-Wideband Characteristics for Wireless Communications

Compact Microstrip-Fed Monopole Antenna with Dual Notch Band...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Parchin L, Naser Ojaroudi Basherlou, Haleh Jahanbakhsh Ismail Amar, Ahmed Sayed Eldin, Saied Alaa School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Air Defense College Alexandria University Department of Electronics and Communication Alexandria Egypt

ISBN: (纸本)9798350351408

This study represents a new antenna design optimized for ultra-wideband (UWB) wireless communication systems, boasting a dual band-notched feature. The design is characterized by a trapezoidal radiating patch, a reimagined rectangular feedline with a lateral E-shaped slot, and a trapezoidal ground plane highlighted by two rectangular slits. Performance testing showcases the antenna's broad operational range from 3 to 13 GHz, covering the entire UWB spectrum of 3.1 to 10.6 GHz comprehensively. Remarkably, the antenna features dual band-notched functions at 3.8 GHz (covering 3.3 to 4.2 GHz) and 5.5 GHz (spanning 5 to 6 GHz), strategically implemented to avoid interference from WLAN, C-band, and WiMAX frequencies. Detailed evaluations affirm the antenna's ability to provide desired frequency responses, alongside advantageous radiation patterns and metrics of efficiency and gain, in accordance with its design objectives. The suggested compact antenna configuration successfully adheres to the rigorous standards required for band-notched UWB communication systems, showcasing its readiness for integration into contemporary wireless networks. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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EV-IRP Manager: An Electric Vehicle Incident Response Playbook Manager and Visualizer Toolkit 17

EV-IRP Manager: An Electric Vehicle Incident Response Playbo...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Alpdag, Kerem Moradpoor, Naghmeh Andriambelo, Ny Hasina Wooderson, Paul Maglaras, Leandros School of Computing and Mathematical Sciences University of Greenwich London United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Engineering Sciences and Techniques and Innovation University of Antananarivo Madagascar Horiba Mira Ltd Nuneaton United Kingdom

ISBN: (纸本)9798331509736

In the rapidly evolving realm of electric vehicle technology, safeguarding the cybersecurity of both electric vehicles and their charging infrastructure has become fundamental. The integration of electric vehicles and their charging stations into the broader grid introduces complex cybersecurity challenges, necessitating robust incident response strategies. Traditional cybersecurity playbooks often fall short in addressing the unique vulnerabilities associated with electric vehicles and their charging systems. The lack of publicly available community playbooks tailored to these needs leaves the electric vehicle ecosystem vulnerable to cyber threats that could compromise user privacy, vehicle functionality, and grid stability. In response to this, the project undertakes the creation of a foundational playbook for electric vehicle and electric vehicle charging station incident response, addressing a significant void in current cybersecurity practices. This paper introduces a Playbook Manager and Visualizer application, called EV-IRP, designed to enable users to upload, manage, and visualize electric vehicle and electric vehicle charging station incident response playbooks efficiently. Utilizing Python, Tkinter for GUI development, SQLite for database management, and Graphviz for visualization, the application facilitates a dynamic and responsive approach to maintaining up- to-date incident response strategies. The application is expected to streamline the management of incident response playbooks through procedure visualization, enhancing the cybersecurity posture of electric vehicle infrastructure. By converting textual playbook procedures into easily understandable diagrams, it facilitates a clearer understanding of response steps among users, thereby enhancing the overall efficiency and efficacy of incident response practices. Additionally, the research and development process, informed by a comprehensive literature review, contributes to the academic and practical

关键词： Electric accidents

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An Ultra-Wideband Trapezoid Monopole Antenna Exhibiting Dual Notch Bands Using an Inverted Ω-Shaped Slot and Hook-Shaped Slits

An Ultra-Wideband Trapezoid Monopole Antenna Exhibiting Dual...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Parchin, Naser Ojaroudi Amar, Ahmed Sayed Ismail Soliman, Mohamed A. School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Air Defense College Alexandria University Department of Electronics and Communication Alexandria Egypt

ISBN: (纸本)9798350351408

This study unveils an ultra-wideband (UWB) antenna with a dual band-notched feature, designed for wireless communication applications. The structure of the antenna includes a trapezoidal radiation patch adorned with hook-shaped slits, a reconfigured rectangular feedline equipped with a rotated Ω-shaped strip, and a trapezoidal ground plane. Test results demonstrate the antenna's ability to operate across a broad frequency range of 3 to 13 GHz, adequately spanning the entire UWB spectrum from 3.1 to 10.6 GHz. Particularly significant are the antenna's dual band-notched functions at 3.8 GHz (covering the range of 3.5 to 4.2 GHz) and 5.5 GHz (spanning 5 to 6 GHz), designed to effectively reduce interference from WLAN, C-band, and WiMAX frequencies. Detailed evaluation of the antenna confirms its proficiency in providing desired frequency responses, radiation patterns, and satisfactory efficiency and gain metrics, aligning with the design goals. Overall, the presented antenna design fulfills the critical criteria for band-notched UWB communications, highlighting its suitability for incorporation into contemporary wireless communication infrastructures. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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3.6 GHz 5G MIMO Smartphone Antenna Design with Z-Shaped Monopole Resonators

3.6 GHz 5G MIMO Smartphone Antenna Design with Z-Shaped Mono...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Parchin, Naser Ojaroudi Basherlou, Haleh Jahanbakhsh Amar, Ahmed Sayed Ismail Salama, Ahmed Gamal School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Air Defense College Alexandria University Department of Electronics and Communication Alexandria Egypt

ISBN: (纸本)9798350351408

The imperative for cutting-edge multi-port MIMO antennas has surged, driven by the evolving demands of advanced technologies, notably in the context of 5G networks and beyond. This paper introduces a new MIMO antenna utilizing discrete-fed Z-shaped monopole resonators, exhibiting desirable characteristics for sub-6 GHz applications. The MIMO array comprises eight stepped-ring slits etched in the same layer as the ground plane, operating within 3.3-3.6 GHz, aligning with the sub-6 GHz cellular bands. Its compact footprint allows seamless integration into smartphones, offering a practical solution for space-constrained devices. Despite its size, the suggested MIMO antenna demonstrates satisfactory performance across critical parameters. © 2024 IEEE.

关键词： 5G mobile communication systems

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Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis 17

Enhancing AI-Generated Image Detection with a Novel Approach...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Weir, Stuart Khan, Muhammad Shahbaz Moradpoor, Naghmeh Ahmad, Jawad School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia

ISBN: (纸本)9798331509736

This study explores advancements in AI-generated image detection, emphasizing the increasing realism of images, including deepfakes, and the need for effective detection meth-ods. Traditional Convolutional Neural Networks (CNNs) have shown success but face limitations in generalization and accu-racy, particularly with newer technologies like Diffusion Models. With the evolution of AI image generation models, from CNNs to Generative Adversarial Networks (GANs) and Diffusion Models, detecting synthetic images has become more challenging. Issues include dataset diversity, adversarial attacks, and inconsistencies in pre-processing methods. While state-of-the-art models like CNNs, Vision Transformers (ViTs), and hybrid approaches exist, their accuracy in detecting increasingly sophisticated fake images remains suboptimal. This research proposes a novel hybrid detection model combining CNNs and ViTs with an additional attention mechanism layer. This structure aims to improve the interaction between local and global features, enhancing detection accuracy. The model was trained using the CIFAKE dataset, which contains 120,000 real and AI -generated images. The added attention mechanism enhances feature extraction, addressing limitations in existing models when faced with next-generation synthetic images. The hybrid CNNNiT +Attention model demonstrated improved detection accuracy, achieving 99.77%, surpassing previous methods. This research lays a foundation for stronger AI -generated image detection, helping to mitigate the risks of synthetic image fraud. © 2024 IEEE.

关键词： Convolutional neural networks

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