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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...

引用

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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Comparison of Faster RCNN and YOLO V3 for Video Anomaly Localization

Comparison of Faster RCNN and YOLO V3 for Video Anomaly Loca...

引用

2023 International Conference on Power, Instrumentation, Control and Computing, PICC 2023

作者： Anoopa, S. Salim, A. Nadera Beevi, S. College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala India Government Engineering College Thrissur Dept. of Computer Science and Engineering Kerala India Tkm College of Engineering and Technology Dept. of Computer Applications Kerala India

ISBN: (纸本)9798350334463

Video Anomaly Localization techniques are interesting and emerging tasks in computer vision that is used to locate the position of an anomalous object with bounding boxes. Convolutional neural network-based object localization methods have been developed which detect and classify objects with high precision and accuracy. Many algorithms have been developed for object detection and localization with excellent outcomes. These algorithms are fast RCNN, faster RCNN, and YOLO. In this paper, a comprehensive analysis of faster RCNN and YOLO V3 algorithms for anomaly localization is performed based on the parameters FPS, mAP, Precision, Recall and F1 Score. © 2023 IEEE.

关键词： Convolutional neural networks

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Cardiac Disease Diagnosis from Echocardiogram using Random Forest Classifier 5

Cardiac Disease Diagnosis from Echocardiogram using Random F...

引用

5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shruthi, T. Srimathi, P. Janani, S. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Cardiovascular diseases (CVDs) represent a leading global health issue, necessitating early detection and accurate diagnosis for effective treatment. Electrocardiography (ECG) serves as an essential tool that provides valuable insights into the heart's electrical activity, aiding in the identification of potential abnormalities. This project leverages machine learning techniques to enhance the diagnostic potential of ECG data, paving the way for improved cardiovascular care through precision medicine. The methodology focuses on training machine learning models to analyze ECG signals and classify heartbeats into categories that reflect various cardiac conditions. A com- prehensive dataset of approximately 20,000 ECG recordings, encompassing a wide range of heart rhythms and anomalies, establishes a robust foundation for model training. The Decision Tree classifier achieves an outstanding accuracy of 97%, demonstrating the ability to detect subtle patterns in ECG data. Furthermore, the Random Forest classifier showcases strong performance in distinguishing between different cardiac rhythms. Key metrics, such as precision, recall, and F1-score, provide a deeper understanding of the classifier's diagnostic effectiveness. The confusion matrix serves as a visualization tool to pinpoint areas where model adjustments are necessary, further enhancing accuracy. These promising results highlight the potential of machine learning in transforming cardiac health monitoring and improving diagnostic processes, ultimately contributing to better patient care and outcomes. By harnessing advanced algorithms, this project illustrates the immense possibilities for integrating machine learning into modern healthcare systems. © 2024 IEEE.

关键词： Electrocardiography

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A Survey on Blockchain-Based Certificate Authentication System: From Traditional to Digital 4

A Survey on Blockchain-Based Certificate Authentication Syst...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Saraswathi, R Vijaya Vasavi Swathi, Meda Venkata Punwatkar, Sakshi Manoj Konduru, Shreya Sami, Mohammed Abdul Vnr Vignana Jyothi Institute of Engineering and Technology Dept. Of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529635

This research study pertains to the essential problems of certificate authentication within academic organizations. It concentrates on the major issues of forgery and inefficiencies as well as administrative bottlenecks that arise in conventional systems. The usual ways, which are based on one authority organization, are likely to be compromised, move slowly on verification, and have increased overhead costs. Blockchain technology is a distributed way of doing things without a middleman, and thus it is able to improve security, transparency, and the immutability of educational records. The article identifies the shortcomings of the present blockchain-based implementations with the focus on scalability, interoperability with legacy infrastructure, and high transaction costs. In the paper, an experiment on Ethereum-based smart contracts as well as the Secure Hash Algorithm (SHA-256) and InterPlanetary File System (IPFS) technologies shows the potential of these technologies to a certificate validation system by providing cryptographic integrity and, therefore, data storage that cannot be altered. The proposed work introduces a decentralized architecture that uses smart contracts for certificate issuing and verification to some extent. By integrating cryptographic hashing mechanisms, the system provides real-time, verifiable, and immutable credential records, addressing both fraud prevention and operational inefficiencies. © 2024 IEEE.

关键词： Ethereum

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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Efficiency Improvement Mechanism Analysis of Sidewall Passivation GaN based Micro-LEDs by Atomic Layer Deposition

Efficiency Improvement Mechanism Analysis of Sidewall Passiv...

引用

International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Zhanghu, Mengyuan Liu, Yibo Liu, Zhaojun Dept. of Electric and Electronic Engineering Southern University of Science and Technology Shenzhen China Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Sidewall passivation using atomic layer deposition (ALD) can efficiently improve external quantum efficiency (EQE). Such treatment increases EQE, a combination of internal quantum efficiency (IQE) and light extraction efficiency (LEE), primarily by curtailing surface recombination according to the ABC model. Determined by the ratio of radiative recombination rate to total recombination rate, the IQE of an LED holds a significant correlation with the diode's ideality factor in the Shockley model. In this paper, we analysis the mechanism of ALD sidewall passivation enhancing device performance in detail from the aspects of electrical performance improvement and optical performance improvement. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Gallium nitride

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Expert Patient Interaction Language Model (EPILM)

Expert Patient Interaction Language Model (EPILM)

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratham Gala Pooja Vartak Jay Vithlani Naman Chheda Dept. of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering (Data Science) Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Today’s youth encounter numerous mental health challenges, many of which can be effectively addressed through professional intervention. However, there is a notable reluctance among young individuals to seek professional mental health services. The integration of language models in providing mental health support offers a non-intrusive and accessible approach for young people to engage with professional assistance for their mental health concerns. Although existing language models like GPT 3.5, Falcon-7B model have been effective, they also present certain challenges like high dependability of data from sources like Reddit, computational obstacles hindering access, and a preference for certain models over superior alternatives have impeded advancements in this area. that require thorough examination. Issues related to biases and the model’s performance across diverse scenarios underscore the necessity for more intelligent strategies. The proposed method involves enhancing the Falcon-7B model through the application of quantization low rank adaptation (Q-LoRA). This technique reduces the computational requirements and simplifies the accessibility for individuals equipped with the necessary technology while providing the average bertSCORE 85%. By addressing these issues, paper’s objective is to refine mental health support systems to benefit a broader audience.

关键词： Training Adaptation models Quantization (signal) Social networking (online) Computational modeling Graphics processing units Mental health Chatbots data models Tuning

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Parametric Cyber Defense: A Sophisticated Machine Learning Architecture for Advanced Intrusion Detection and Threat Classification 5

Parametric Cyber Defense: A Sophisticated Machine Learning A...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Lakshmi, S. Maalan, M.R. Kishore Kumar, R. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

In contemporary network environments, effective countermeasures against cybersecurity threats require highly effective Intrusion Detection Systems (IDS). This paper proposes an advanced methodology to enhance IDS by employing parametric analysis on network traffic characteristics. Through careful data selection and preprocessing from a comprehensive dataset, data integrity is maintained, with features such as packet size and transfer duration being leveraged. Machine learning algorithms, including Decision Tree, Extra Tree, and xgBoost, are applied to detect and classify various cyberattack categories. In addition, advanced feature selection and dimensionality reduction techniques are utilized to improve model efficiency. Extensive experimentation demonstrates superior accuracy in identifying and classifying cyber threats while reducing the occurrence of false positives. The proposed framework strengthens cybersecurity infrastructures through the integration of advanced machine learning techniques to address evolving threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancements in Left Ventricular Myocardium Analysis: A Comprehensive Review of Myocardium Segmentation, Fibrosis Detection and Wall Motion Estimation Using Cardiac Magnetic Resonance Imaging

Advancements in Left Ventricular Myocardium Analysis: A Comp...

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Sunanda, H.G. Kavitha, K.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru560004 India Visvesvaraya Technological University Belagavi590018 India Dayananda Sagar College of Engineering Dept. of Computer Science and Engineering Bengaluru560078 India

ISBN: (纸本)9798350372502

Cardiac Magnetic Resonance Imaging (CMRI) has established itself as a cornerstone in cardiovascular diagnostics, enabling precise assessment of left ventricular (LV) myocardium. This review meticulously examines the multifaceted aspects of LV myocardium analysis, focusing on three pivotal components: myocardium segmentation, fibrosis detection, and wall motion estimation. By exploring cutting-edge techniques and methodologies in these domains, this article sheds light on the profound impact CMR has had on the accurate diagnosis, prognosis and structuring treatment plans for diseases of the heart. In the realm of cardiac diagnostics, Left Ventricular (LV) myocardium analysis utilizing CMRI has undergone a transformation advancement. This review encapsulates the comprehensive exploration of LV myocardium analysis, Myocardium segmentation techniques, ranging from traditional methods to contemporary machine learning-driven approaches, are scrutinized for their accuracy and efficiency. The detection and characterization of myocardial fibrosis, crucial in diagnosing various cardiac conditions, are dissected through cutting-edge CMR techniques such as Late Gadolinium Enhancement (LGE) imaging and quantitative mapping methods. Additionally, advanced methods of wall motion estimation, including feature tracking and strain imaging, highlighting their applications in assessing myocardial function. Real-world clinical scenarios underscore the transformation impact of these techniques on diagnosing heart diseases, guiding treatment decisions, and improving patient outcomes. The current landscape of LV myocardium analysis, emphasizing the pivotal role of CMR in revolutionizing cardiac healthcare. © 2024 IEEE.

关键词： Motion estimation

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Secured Release through an Agile Security Growth Framework 29

Secured Release through an Agile Security Growth Framework

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29th ISSAT International Conference on Reliability and Quality in Design, RQD 2024

作者： Bhattacharya, Saikath Williams, Laurie Singh, Munindar P. Electrical Engineering and Computer Science Milwaukee School of Engineering Milwaukee United States Dept. of Computer Science North Carolina State University Raleigh United States

ISBN: (纸本)9798986576138

Software development teams need to produce and assess secure software products so that they can perform critical functionality. Because no software product can ever be "perfectly secure, " development teams need to decide when a software product is "secure enough" to be released, that is, to make a decision on the product’s release readiness. The goal of this paper is to aid software practitioners in predicting whether a software product is "secure enough" to be released through the evolution and evaluation of software security growth models (SSGM). In this paper, we adapt and evolve software reliability growth models (SSGMs) for the purpose of SSGM using empirical software vulnerability analysis. We quantitatively compare the quality of agile software versions by comparing the security correction and detection process over testing time and software testing cost. The framework is generic to incorporate software vulnerability data into different time frames and multiple agile software versions. We evaluated our SSGM using data from Google Chromium, which uses an agile process with releases every 50 days. © Conference Proceedings of the 29th ISSAT International Conference on Reliability and Quality in Design, RQD 2024.

关键词： Software quality

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