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2023 IEEE International Conference on Omni-Layer Intelligent systems, COINS 2023

作者： Singh, Amandeep Margaria, Tiziana Demrozi, Florenc University of Limerick Dep. of Computer Science and Information Systems Ireland University of Stavanger Dep. of Electrical Engineering and Computer Science Norway

ISBN: (纸本)9798350346473

Human Activity Recognition (HAR) is a research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as healthcare, sports, industry, and entertainment. However, executing HAR algorithms on remote devices or the cloud can lead to issues such as latency, bandwidth requirements, and energy consumption. Transitioning towards Edge HAR can be a more effective and versatile solution, overcoming the challenges of traditional HAR techniques. We present a novel HAR model for computation on edge devices: we design a Convolutional Neural Network (CNN) Deep Learning approach and compare its performance with cloud-computing HAR models. The paper is accompanied by a self-collected dataset. The experiments on this dataset demonstrate that the proposed edge computing model achieves promising results (\geq 92 %) in terms of Precision, Recall, and Fl-score. Furthermore, the model exhibits significantly reduced latency, with only 117 ms, and utilizes minimal memory, with a peak of 18.8 Kb RAM and 956 Kb Flash memory. © 2023 IEEE.

关键词： Edge computing

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Assessment of Combined Cooling, Heating, and Power (CCHP) systems for Energy Efficiency in Administrative Buildings in Hot and Dry Climate 28

Assessment of Combined Cooling, Heating, and Power (CCHP) Sy...

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28th International Electrical Power Distribution Conference, EPDC 2024

作者： Zeynali, Shahrzad Nazififard, Mohammad Hashemi-Dezaki, Hamed University of Kashan Dep. of Energy Systems Engineering Kashan Iran University of Kashan Dep. of Electrical and Computer Engineering Kashan Iran

ISBN: (纸本)9798350385502

Combined cooling, heating, and power (CCHP) systems have garnered significant attention as a cost-effective solution for reducing energy consumption in buildings due to their ability to provide higher thermal efficiency compared to single power generation systems. This study focuses on modeling and evaluating the energy consumption of an administrative building within the University of Kashan, alongside the output values of a nearby CCHP power plant. The consumption values of the university's central administrative building have been obtained using available measuring devices. Also, the validity of the modeling results has been assessed using Design-Builder software. Additionally, the study investigates the role of the CCHP system in the building in terms of energy production and consumption by comparing the building's consumption with the production of CCHP electricity and heat. The simulation results indicate that the total annual energy consumption of the office building is 182.67 kWh/m2, while the values recorded by the engine room measuring devices show 199.75 kWh/msystems. This suggests a potential discrepancy between the simulated and measured values, with the simulated value being approximately ten percent lower than the measured value. Possible factors contributing to this variance include discrepancies in people's activity models used in the simulation software and the actual consumption behavior patterns. Furthermore, the study highlights that the 1 MW CCHP system can meet the thermal and electrical needs of a 3-story administrative building, such as the central administrative building of the University of Kashan, while also being able to supply excess electrical energy to the grid. This underscores the potential of CCHP systems in effectively addressing energy demands in buildings. © 2024 IEEE.

关键词： Office buildings

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On the classification of weather based on the production of photovoltaic installations 24

On the classification of weather based on the production of ...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Parczyk, Pawel Puslecki, Tobiasz Burduk, Robert Wroclaw University of Science and Technology Dep. of Systems and Computer Networks Wroclaw Poland

ISBN: (纸本)9798331530631

In recent years, there has been an energy transition in which fossil fuel-fired power plants are being replaced by renewable energy sources such as photovoltaics. While the influence of factors such as location and method of installation or the sun's position above the horizon is deterministic, the weather factor is rather random. Weather data can be easily obtained from nearby weather stations, but some weather phenomena are so dynamic that data obtained in this way can become useless. When considering the problem of classifying weather conditions based on production data of photovoltaic installations, we propose two new features extracted from the production data that describe the overall level of sunshine and the variability of sunlight. Three supervised learning methods were used to classify the dataset: CNN, Random Forest classifier and Decision Tree Classifier. Reference values were obtained by generating classification results from the raw output and compared with those generated from the two new features. The experiments showed a statistically significant advantage for the models fitted to the data extended with the new features. © 2024 IEEE.

关键词： Photovoltaic (PV) Renewable Energy Sources (RES) Weather Classification

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss dep.oyment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Traffic sign recognition based on CNN vs different transfer learning techniques 5

Traffic sign recognition based on CNN vs different transfer ...

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5th Symposium on Pattern Recognition and Applications, SPRA 2024

作者： Ghouse, Mohammed Farag, Saber Butt, Usman Engineering and Environment Dep. Northumbria University London United Kingdom Computer and Information Science Dep. British University in Dubai Dubai United Arab Emirates

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Traffic sign detection and recognition (TSDR) is a pivotal component of contemporary transportation systems, designed to enhance road safety and operational efficiency. In an era where automation is indispensable, TSDR stands out as a critical tool for optimizing driving experiences. This study presents the development of a TSDR model employing advanced deep learning techniques, with a particular emphasis on convolutional neural networks (CNNs). Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which comprises annotated images of various traffic signs captured under diverse environmental conditions, we achieved remarkable results. Our CNN models demonstrated a 99% detection rate, underscoring their efficacy in traffic sign recognition tasks. Furthermore, transfer learning models yielded impressive accuracies, with VGG19 achieving 99.64%, EfficientNetB7 reaching 99%, and ASNet attaining 99.72%. Despite these promising results, we identified several limitations, including dataset-specific dep.ndencies and the need for further enhancements in model design and training methodologies. Addressing these challenges is necessary for ensuring the robustness and reliability of intelligent transportation systems, thereby significantly improving road traffic management, safety, and efficiency. Copyright © 2025 SPIE.

关键词： Convolutional neural networks

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SANA at NADI 2023 shared task: Ensemble of Layer-Wise BERT-based models for Dialectal Arabic Identification 1

SANA at NADI 2023 shared task: Ensemble of Layer-Wise BERT-b...

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1st Arabic Natural Language Processing Conference, ArabicNLP 2023

作者： Almarwani, Nada Aloufi, Samah Dep. of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (纸本)9781959429272

Our system, submitted to the Nuanced Arabic Dialect Identification (NADI-23), tackles the first sub-task: Closed Country-level dialect identification. In this work, we propose a model that is based on an ensemble of layer-wise fine-tuned BERT-based models. The proposed model ranked fourth out of sixteen submissions, with an F1-macro score of 85.43. © 2023 Association for Computational Linguistics.

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Two-Rated-Power, Dual-Winding, Three-Phase Induction Motor

Two-Rated-Power, Dual-Winding, Three-Phase Induction Motor

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Ferreira, Fernando J. T. E. Institute of Systems and Robotics University of Coimbra Dep. of Electrical and Computer Engineering Coimbra3030-250 Portugal

ISBN: (纸本)9798350370607

This paper proposes using star-delta and star-series connection modes in three-phase dual-winding squirrelcage induction motors to have available 100% and 55% of the original rated power for a given supply line-to-line voltage. The connection mode can be changed on a nine-terminal block. The experimental results obtained with a commercial 4-kW, 400-V, 50-Hz, 4-pole induction motor whose stator windings were redesigned to provide 4-kW and 2.2-kW output powers are presented and validate the proposed solution. Two-rated-power motors with the proposed output power ratio could be used as spare motors to replace faulty/damaged motors with the same frame size. The proposed solution allows fine-tuning the motor power to the actual maximum power required by the driven application/load and can be considered cost-effective as it may lead to significant gains in the motor efficiency and power factor when the low-power mode is adopted. In many cases, it may avoid the underloaded operation of induction motors, while keeping their original rated power available. © 2024 IEEE.

关键词： Microelectronics

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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach 2

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Al-Na'amneh, Qais Aljaidi, Mohammad Gharaibeh, Hasan Nasayreh, Ahmad Emhamed Al Mamlook, Rabia Almatarneh, Sattam Alzu'bi, Dalia Suliman Husien, Abla Applied Science Private University Dep. of Cyber Security Amman Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Yarmouk University Dep. Computer Science Irbid Jordan Trine University Dep. of Business Administration Indiana United States University of Zawia Department of Industrial Engineering Tripoli Libya Zarqa University Dep. of Data Science and Artificial Intelligence Zarqa Jordan Concordia University Dep. Information and Systems Engineering Montreal Canada

ISBN: (纸本)9798350373363

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A 'spoofing attack' refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems. © 2023 IEEE.

关键词： Internet of things

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Comparative Analysis of ML Algorithms for Fraud Detection 4

Comparative Analysis of ML Algorithms for Fraud Detection

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4th IEEE International Conference on Smart Information systems and Technologies, SIST 2024

作者： Abitova, Gulnara Abalkanov, Miras Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University BinghamtonNY United States Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350374865

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss. © 2024 IEEE.

关键词： Logistic regression

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor 2

Logistic Regression Classification for Assessing the Risk of...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Alzubi, Dalia Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Aljaidi, Mohammad Alazab, Rami Gharaibeh, Hasan Al-Naamneh, Qais Concordia University Dep. Information and Systems Engineering Montreal Canada Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Jordan University of Science and Technology Dep. Surgery and Urology Irbid Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses © 2023 IEEE.

关键词： Tumors

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