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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 deployment 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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Using Machine Learning Algorithms in Edge Intelligence for Ultra-Reliable and Low-Latency Communications in the Industrial Metaverse 29

Using Machine Learning Algorithms in Edge Intelligence for U...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Mohammadvand, Reza Mozayani, Naser Dept. Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331523114

Given the rapid advancements in technologies such as the metaverse and digital twins, the need for effective integration of communication, computation, and storage in complex systems and edge computing environments is becoming increasingly apparent. This paper proposes an architecture to support an industrial metaverse based on digital twins that optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communication. This architecture reduces latency by employing task offloading and storage methods on nearby edge servers, meeting the precise requirements of future metaverses in terms of reliability and latency minimization. The proposed model, utilizing reinforcement learning algorithms such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Dueling Double Deep Q-Network (Dueling DDQN), is capable of achieving adaptability in dynamic conditions and making intelligent decisions. The simulation results demonstrate that the proposed method reduces by more than 10% on average compared to the best method available in the literature and improves resource utilization efficiency. © 2025 IEEE.

关键词： Reinforcement learning

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Advances in Remote Sensing Technologies for Ecological Monitoring and Applications in Carbon Stock Estimation 3

Advances in Remote Sensing Technologies for Ecological Monit...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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Breathing+: A Game-based System for Enhancing Respiratory Training and Motivation 6

Breathing+: A Game-based System for Enhancing Respiratory Tr...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Lin, Chang Yu Chu, Edward T.-H. National Yunlin University of Science and Technology Dept. of Computer Science and Information Engineering Yunlin Taiwan

ISBN: (纸本)9798350323535

Breathing difficulties, such as shortness of breath or rapid breathing, can significantly harm health and diminish the quality of life. Breathing exercises are widely recognized for their effectiveness in resolving such difficulties. They increase lung function, ease discomfort, and alleviate stress. However, current breathing-assisted methods, such as the incentive spirometer, do not accurately track the duration and frequency of training despite their advantages. Furthermore, the process of performing breathing exercises is tedious over time, making users feel bored and reducing their motivation. Thus, we presented Breathing+, a breathing video game designed to address these limitations by providing a series of game challenges. Breathing+ incorporated sensors to detect breathing signals and provided users with real-time feedback on a screen. According to the experimental results, Breathing+ achieved a breathing recognition accuracy of 95%. In addition, the response time to the user's breathing was around 0.23 s. © 2023 IEEE.

关键词： Motivation

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A Case Study of Detecting HULK-based DDoS Traffic with Random Forest Classifier

A Case Study of Detecting HULK-based DDoS Traffic with Rando...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chang, Tsu-Chen Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Modern network administration desires to have early detection of DDoS traffic before damages occur. Nevertheless, as Internet traffic grows over years, it becomes more challenging to detect DDoS traffic in an efficient and effective manner. The survey of existing literature shows that random forest classifier, when applied to DDoS detection, yields great performance at low learning costs. This work is devoted to a case study of implementing and using random forest classifier to detect DDoS traffic generated by a well-known DDoS tool named HULK. Our result indicates that when used with a good feature selection mechanism, random forest classifier can achieve a high detection accuracy with fast training time. © 2023 IEEE.

关键词： HTTP

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Self-supervised Federated Learning for Anomaly Detection

Self-supervised Federated Learning for Anomaly Detection

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Eng, Tze-Qian Pao, Hsing-Kuo Liao, Chi-Chen National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Federated learning-based strategy can confirm data privacy, as well as other related ones like split learning and differential privacy. When enjoying secured data sharing, we also worry that the data privacy preserving may harm the model's overall performance. Given the most recent progress in machine learning and AI methodology, we incorporate self-supervised learning to plug in the federated learning framework and the integrated system can guarantee the model performance and data privacy preservation simultaneously. In the integrated framework, we have different clients to keep their own data, and the data are well separated into the attribute half and the label half, for enhanced privacy, not to mention the additional privacy-preserving skill like differential privacy. Given all the aforementioned components, we can still have the privacy-preserving components equipped model performed superior to or is compatible with the performance without the privacy-preserving components. That is, one client can perform better when adding more information from other clients, without the data sharing in between, and no clients own both the attributes and the corresponding labels. The focused topic is anomaly detection and we pay attention to the imbalanced nature of the data which shows additional challenges to the problem. Given the problem, self-supervised learning is especially useful when obtaining the label information is considered non-trivial. After all, we demonstrate the overall model effectiveness when compared to methods without any federated learning components. © 2023 IEEE.

关键词： Anomaly detection differential privacy federated learning self-supervised learning split learning

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Automail AI: Intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for Automated Communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 IEEE.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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Optimizing Kidney Stone Detection with Vision Transformers and CNNs: A Transfer Learning Strategy

Optimizing Kidney Stone Detection with Vision Transformers a...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Suhaas Rao, S. Chopra, Tushar Kumar Yeshwanth, S.P. Bhanushree, K.J. Suneetha, K.R. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Kidney stones are a common and impactful urological condition, with timely and accurate diagnosis being critical to prevent complications such as renal damage and avoid invasive treatments. This study explores the use of advanced deep learning models, including Vision Transformers (ViT), Swin Transformer and Convolutional Neural Networks (CNNs) - specifically XResNet-50, VGG-16, MobileNetV2 and AlexNet - to enhance kidney stone detection from CT scan images. Leveraging a robust dataset of annotated CT scans in JPG format, the approach employs transfer learning which leverages features learnt from the ImageNet dataset to optimize model performance, focusing on classifying cases as either "Kidney Stone Present"or "Kidney Stone Absent". To further improve diagnostic reliability, the Grad-CAM (Gradient-weighted Class Activation Mapping) identifies and localizes kidney stones within CT images, enhancing understanding of the model's decision-making and supporting interpretability. Addressing challenges related to dataset imbalance, model interpretability, and generalizability across healthcare settings. By refining these models for kidney stone detection, the main objective of this work is to build a reliable diagnostic tool that aids in clinical judgement and treatment planning, ultimately enhancing patient outcomes and encouraging broader clinical adoption. © 2025 IEEE.

关键词： Convolutional neural networks

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Real-Time Helmet Detection System with Vehicle Number Extraction for Two-Wheeler Vehicles using YOLOv8 2

Real-Time Helmet Detection System with Vehicle Number Extrac...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Kumar, Abhash Guru Prasad, M.S. Soni, Dheeraj Rani, Vibha Arora, Kanika Ganesh, D.R. Dept. of Computer Science & Engineering Dehradun India REVA University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350371314

In the face of increasing two-wheeler usage and the persistent issue of riders neglecting helmet compliance, this paper proposes a groundbreaking Real-Time Helmet Detection System with Vehicle Number Extraction. The existing literature on helmet detection systems is reviewed, revealing a range of methodologies, accuracies, and limitations in current approaches. Building upon this foundation, the three-stage detector combines the efficiency of YOLO (You Only Look Once) for object detection with a specialized number plate extraction system. The system aims to provide real-time detection of two-wheeler riders, classification of helmet usage, and extraction of vehicle numbers. The proposed methodology, utilizing a pre-trained YOLO model for object detection and EasyOCR library, demonstrates an accuracy rate of 64% for vehicle detection, 78% for helmet detection, and 92% for number plate extraction. The practical implementation of this system showcases its potential for enhancing road safety, automating traffic management, and promoting regulatory enforcement. While showing potential, the ongoing research and development face challenges such as limitation of computational resources and privacy considerations. The Real-Time Helmet Detection System stands as a promising solution for improving road safety, with the potential for broader use in traffic management and safety enforcement. Continuous improvement and adaptation will guarantee its effectiveness in contributing to a safer and more controlled traffic environment. © 2024 IEEE.

关键词： Accident prevention

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Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity 9

Early Discovery of Emerging Entities in Persian Twitter with...

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9th International Conference on Web Research, ICWR 2023

作者： Yousefi, Shahin Hooshmand, Mohsen Afsharchi, Mohsen Dept. of Computer Science and Information Technology Zanjan Iran University of Zanjan Dept. of Electrical & Computer Engineering Zanjan Iran

ISBN: (纸本)9798350399691

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academy and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT that is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment. © 2023 IEEE.

关键词： Semantics

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