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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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CNN-based Human Activity Recognition on Edge Computing Devices

CNN-based Human Activity Recognition on Edge Computing Devic...

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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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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Sensing voltage of MEMS Accelerometer using Polycrystalline Silicon (Si),Gallium Arsenide (GaAs) and Germanium (Ge) 6

Sensing voltage of MEMS Accelerometer using Polycrystalline ...

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6th International Youth Conference on Radio electronics, Electrical and Power engineering, REEPE 2024

作者： Elsayed, Amr N. Morsi, Iman G. Abdelrassoul, Roshdy A. Mahmoud, Ahmed K. Arab Academy for Science and Technology Dep. of Electronics and Communications Engineering Alexandria Egypt Air Defense College Dep. of Electrical and Electronic Engineering Alexandria Egypt

ISBN: (纸本)9798350382891

Accelerometers are inertial sensors that detect acceleration and forces along three orthogonal axes, then the readings are produced as a digital or analog signal and used in various applications. Microelectromechanical systems (MEMS) have recently been utilized to perform inertial sensing techniques on an integrated circuit (IC) at micro size. Due to the various characteristics of MEMS materials and how they affect the sensitivity of the MEM Accelerometer with these variations. In this study, a MEMS accelerometer model was created using the COMSOL program. The design was then applied to polycrystalline Silicon (Si), Gallium Arsenide (GaAs), and Germanium (Ge), and the sensing voltage was determined using each material to clarify which of these materials performed best. © 2024 IEEE.

关键词： Germanium

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Enhancing the accuracy of fitness competitions results validation via designing and implementing of Fitness Auto Referee (FAR) evaluation system based on Internet of Things (IOT)

Enhancing the accuracy of fitness competitions results valid...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Fayad, Youssef Mahmoud, Ahmed Khairy Miligy, Ahmed F. El Monem, Mahmoud Ahmed Abd El Shafei, Ahmed Osama Mabrouk, Kareem Ehab Air defense college Alexandria University Electronics and Communication Dep. Alexandria Egypt Alexandria University Computer and communications engineering Dep. Alexandria Egypt

ISBN: (纸本)9798350351408

Internet of Things (IOT) has its importance in the field of data exchange and control, makes great effects when applied in the fitness field. Applying IOT technology in fitness field could introduce an auto judge for fitness competitions by counting the repetitions of any exercise accurately, and exchange data with an accurate preprepared data base. In this paper a novel fitness auto referee (FAR) system has been presented. This system could be emerged as a specialized groundbreaking assistive technology to enhance the accuracy of fitness competitions results validation. The algorithm of the presented system relies on the distinctive physical properties, gender, and age of each player as inputs. The golden ratio or the golden number 1.618 has been employed in this system to locate sensors accurately, also to count the correct repetitions as function of the player tall. This system has the ability to deal with multiplayers simultaneously indoor or outdoor. Last but not least it provides a flexible means to carry out exercises with different groups (males/females - special forces/combat forces - adults/children - athletes/nonathletes. etc.) accurately within a short period of time. © 2024 IEEE.

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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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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Study on the Robotic Arm Trajectory Compensation System with Deviation Sensing 3

Study on the Robotic Arm Trajectory Compensation System with...

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3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Liu, Chao-Chung Liu, Jun-Chi Liu, Chao-Shu Dep. of Computer Science Zhaoqing University Zhaoqing526061 China Dep. of Industrial Design National United University Miaoli360301 Taiwan Dep. of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan

ISBN: (纸本)9781643685649

Robotic arms play an important role in the automation industry. Robotic arms plan processing paths based on the shape of the workpiece. However, the processing path often deviates due to inaccurate placement of the workpiece. To solve the issue of deviations, this paper develops a trajectory compensation system for a robotic arm with offset perception. This system uses the concept of workpiece boundary projection and designs an offset calculation algorithm by comparing the dimensions of the workpiece drawings, correcting errors caused by missing corners of the workpiece. In terms of operation, the system uses photoelectric sensors to quickly scan the workpiece boundary and immediately calculate the displacement and rotational offset of the workpiece. Subsequently, when deviations occur due to different placement positions of the workpiece, the robotic arm's motion path and action must be corrected and compensated to maintain precision. Tests on selected workpieces show that the corrected rotational angle error is less than 0.4 degrees, and the translational error is less than 0.7 millimeters. Since these correction error values are lower than the 1.0 mm error threshold for the robotic arm's trajectory tracking control. The offset-sensing robotic arm trajectory compensation system developed in this study can satisfy the requirements of smart automation production lines and arrive at the manufacturer's application standards. © 2024 The Authors.

关键词： Robotic arms

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Satellite Still Image Classification Using CNN

Satellite Still Image Classification Using CNN

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Den, B.M. Elbialy, Samar Delta University for Science and Technology Fauclty of engineering Electronics and Communication Dep. Gamasa city Egypt

ISBN: (纸本)9798350351408

Satellite still image plays a crucial role in various domains, such as law enforcement, disaster response, and environmental monitoring. The ability to manually identify objects and facilities within these images is often crucial for these applications. However, given the extensive geographic areas that require coverage and the limited number of analysts available, automation has become a necessity. Traditional object detection and classification algorithms have proven to be insufficiently accurate and reliable for solving this problem. Fortunately, deep learning, a branch of machine learning, offers promising solutions for automating such tasks. One particular approach that has achieved success in image understanding is the use of convolutional neural networks (CNNs). Deep learning has experienced substantial advancements in diverse domains, including computer vision and natural language processing, Despite the progress made in this area, there remains a dearth of comprehensive evaluations concerning datasets and techniques tailored specifically for scene classification using satellite still image. This research paper seeks to bridge this gap by conducting a comprehensive analysis of deep learning, its development over time, and its notable applications within the domain of satellite still image. © 2024 IEEE.

关键词： Convolutional neural networks

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