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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Govind Shay Harron, Syed Singh, Akhilendra Pratap Vivekananda Global University Department of Mathematics Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

this paper gives a singular method for analyzing actual time-implemented applications in silicon-on-chip (SOC) architectures. The proposed technique uses a mixture of analytical answers and hardware degree simulations to benefit insights on system-level performance. An in-depth dialogue of the implementation and testing of the proposed approach is furnished, along with a detailed description of the simulation outcomes. The paper highlights the ability of a combination of analytical and hardware simulators to understand the machine's behavior better and attain a more accurate estimation of machine performance. Numerous examples reveal the effectiveness of the proposed technique and the insights obtained from the simulation consequences. In the end, the paper identifies several capability areas for destiny research. © 2024 IEEE.

关键词： Description Estimation Identifies Implemented Numerous Simulation

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Preserve the Medical Data using Secure Partially Homomorphic Encryption with Blockchain technology in Smart Healthcare 2

Preserve the Medical Data using Secure Partially Homomorphic...

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2nd International Conference on Data science and information system, ICDSIS 2024

作者： Kavitha, S. Pavithra, B.S. Shashikala, A.B. Jagadeesh, B.N. Mageswari, P. Uma Marichi, Dileep Nitte Meenakshi Institute Of Technology Department Of Mca Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Rns Institute Of Technology Department Of Information Science And Engineering Bengaluru India Presidency College Department Of Computer Applications Bangalore India

ISBN: (纸本)9798350381665

Healthcare information is vital for both individuals and service providers, necessitating secure sharing and maintenance of electronic healthcare records (EHR). Traditional EHR systems have relied on centralized architectures, such as the cloud, which may expose sensitive information to undisclosed intelligence. However, these systems have struggled to address the diverse needs of stakeholders while ensuring separation, security, and compliance with legal requirements. Blockchain technology offers a decentralized and distributed ledger system, providing secure, verifiable, and immutable data transfer capabilities. By employing cryptographic techniques like hashes, blockchain eliminates the need for a central authority, making data exploitation challenging and evident. This study proposes a blockchainbased architecture utilizing the Proof of Stake (POS) consensus mechanism to authenticate user identities and facilitate secure EHR exchange across multiple healthcare systems. Additionally, to enhance data security, Secure Partially Homomorphic Encryption (SPHE) is recommended for outsourced data, enabling users to perform operations on encrypted data. The proposed technique surpasses alternatives in terms of energy efficiency, authenticity, and protection of medical data. © 2024 IEEE.

关键词： Blockchain

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Automated Static Analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks

Automated Static Analysis with Bayesian Inference for Interf...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shankar, Shiv Shankar Mehta, Deepak Singh, Vaishali Vivekananda Global University Department of Mathematics Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper researches modern automated Static evaluation software with Bayesian Inference for interference mitigation in 5G cloud networks. The automatic static analysis approach affords a novel technique to discover the interference between the community nodes. The proposed studies a new modern Bayesian Inference to confirm the interference degree between nodes after the automated static evaluation method is widely recognized. The Bayesian Inference method is particularly beneficial, as it may provide sturdy statistical records approximately the level of contemporary interference among nodes. The proposed research evaluated the use of a couple of experiments, demonstrating the state-of-the-art method's accuracy and effectiveness. The outcome of today's research affords an efficient approach for interference mitigation in 5G cloud networks. It presents an automatic Static analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks, primarily based on system-gaining knowledge state modern and superior evaluation of modern 5G networks. The principle objective present day this technique is to reduce the signal-to-interference ratio (SIR) so that you can improve the overall performance of ultra-modern 5G cloud networks. Especially the proposed method of brand new Bayesian Inference to successfully examine the signal propagation sample state-of-the-art 5G network. Then, the learned values from the Bayesian Inference are used to optimize the inter-cell interference cancellation (IICC), inter-cell interference coordination (ICIC), and interference mitigation algorithms. ultimately, experimental effects reveal the effectiveness of today's the proposed method in terms of modern-day advanced community performance and decreased SIR. The outcomes recommend that the proposed automated static analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks can offer an efficient and dependable answer for optimizing overall performance in 5G

关键词： automatic Bayesian contemporary evaluation mitigation

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Covertvasion: Depicting threats through covert channels based novel evasive attacks in android

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International Journal of Intelligent Networks 2023年第1期4卷 337-348页

作者： Gautam, Sunil Pattani, Ketaki Zuhair, Mohd Rashid, Mamoon Ahmad, Nazir Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad Gujarat India Department of Computer Sciences and Engineering Institute of Advanced Research Gujarat Gandhinagar India School of Information Communication and Technology Bahrain Polytechnic Isa Town Bahrain Department of Information System College of Applied Sciences King Khalid University P.O. Box 61913 Muhayel63317 Saudi Arabia

Privacy and security issues concerning mobile devices have substantial consequences for individuals, groups, governments, and businesses. The Android operating system bolsters smartphone data protection by imposing restrictions on app behavior. Nevertheless, attackers conduct systematic resource analyses and divert privacy-sensitive information from plain view. They employ evasive mechanisms to evade system monitoring and create an illusion of benign and non-sensitive communication. Furthermore, covert channels amplify the impact of these malicious activities by facilitating information transfer through non-standard methods. The purpose of this research is to shed light on these novel threats targeting Android systems. The study delves into security and privacy attacks that compromise sensitive user information. The methodology leverages evasion concepts and employs sound-specific covert channel communication, particularly ultrasonic channels. This research work introduces novel evasive attacks, namely Prime-Composite Evasive information Invasion (PCEII) and File-lock-based Evasive information Invasion (FEII), both relying on covert channel communication. These unique variants of attacks successfully evade user data within a few milliseconds for both noisy as well as non-noisy environments and do not show any signs of detection by antivirus mechanisms like Anti-Virus Guard (AVG), 360 security, etc. and state-of-the-art tools such as TaintDroid, MockDroid and others. The paper not only assesses their impact on the privacy and security of information but also introduces avenues for their detection and mitigation. © 2023 The Authors

关键词： Android (operating system)

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Fuzzy Logic Based Brake Failure Detection & Collision Avoidance Model for Automated Driving system 6

Fuzzy Logic Based Brake Failure Detection & Collision Avoida...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Dar, Amil Roohani Riaz, Faisal Department of CSandIT AJandK Kotli Pakistan HEC Islamabad Pakistan Department of Computer Science and Information Technology Mirpur Pakistan

ISBN: (纸本)9798350355703

The manual and automated braking system is intended to prevent rear-end collisions. However, unforeseen malfunctions within the vehicle's system during braking could potentially cause accidents. Collision Warning (CW) systems are safety technologies in vehicles that aid drivers in preventing collisions. This paper introduces a fuzzy-assisted pre-warning system for brake testing that considers multiple factors contributing to brake failure, along with a post-failure CW algorithm tailored for heavy commercial vehicles. Simulations conducted using NetLogo demonstrate that accidents can be prevented during brake failure through appropriate adjustments in vehicle speed. The proposed collision avoidance system has been validated for its effectiveness, and the results are encouraging and enhance road user safety. © 2024 IEEE.

关键词： Brakes

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Wav2Vn: Scalable Unsupervised Pre-training and Adaptation for Multi-Accent Vietnamese ASR 9

Wav2Vn: Scalable Unsupervised Pre-training and Adaptation fo...

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9th International Conference on Applying New technology in Green Buildings, ATiGB 2024

作者： Kim, Viet Nguyen Ngoc, Huyen Nguyen Thi Minh, Hieu Dam Khoi, Nguyen Do Hoang Do, Tien-Dung Dung, Cao Truong Posts and Telecommunication Institute of Technology Department of Information Technology Ha Noi Viet Nam Swinburne University Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Faculty of Information Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Electronic Engineering Ha Noi Viet Nam

ISBN: (纸本)9798331505042

Automatic speech recognition (ASR) systems have seen significant advancements in recent years, driven by deep learning and large-scale pre-training methods. However, achieving high accuracy across multiple accents remains a challenging task, especially for languages with rich phonetic diversity like Vietnamese. The Vietnamese language encompasses a wide range of regional accents, each with unique phonetic characteristics that can hinder the performance of conventional ASR. To address this issue, we present a Vietnamese language processing model called Wav2Vn as a novel approach to scalable unsupervised pretraining and adaptation for ASR targeting multiple Vietnamese accents. Our method leverages the power of the Mixture of Experts (MoE) to effectively handle the diverse phonetic variations across different Vietnamese accents. Our results demonstrate a significant performance improvement in terms of word error rate (WER) and robustness to accent variability compared to the current state-of-the-art PhoWhisper. © 2024 IEEE.

关键词： Deep learning

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Modeling and Verification of a Sentiment Analysis system Using Aspect-Oriented Petri Nets

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Journal of Electronic science and technology 2022年第2期20卷 209-223页

作者： Shu-Hung Yang Yi-Nan Lin Cheng-Ying Yang Ming-Kuen Chen Victor R.L.Shen Yu-Wei Lin the Department of Business Management National Taipei University of TechnologyTaipei 10608 the Department of Computer Science University of TaipeiTaipei 100 the Department of Information Management Chaoyang University of TechnologyTaichung 413 the Department of Computer Science and Information Engineering National Taipei UniversityNew Taipei 23741

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets(AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.

关键词： own correctness companies

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Road Traffic Accident Prediction using Deep Learning 1

Road Traffic Accident Prediction using Deep Learning

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Girija, M. Divya, V. School of Computing Science Vistas Department of Computer Science Chennai India School of Computing Science Vistas Department of Information Technology Chennai India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Maintaining public safety and reducing the effects of unplanned incidents need real-time accident prevention. This research work presents a comprehensive plan for developing an advanced computer vision-based accident-avoidance system. The proposed system's primary data source is live video recordings, and it employs a number of procedures to detect anomalies in the environment and issue alerts in a timely manner. The proposed approach begins with the extraction of frames from the continuous video stream in order to enhance the relevance and quality of the returned frames. Effective image preparation techniques follow next. Data augmentation is then used to diversify the dataset and increase the model's generalization ability. In order to enable prompt reaction and intervention, the proposed system instantly notifies users when it detects irregularities, such as potential accidents or dangerous situations. The proposed method is then implemented using Python software. Utilizing a rule-based system, it delivers dynamic recommendations to drivers with enhanced road safety through adaptive speed limits, route suggestions, and real-time hazard alerts through car dashboard. The proposed modified YOLOv8 method shows better accuracy with 98.4%, which is 11.85% higher when compared with Faster RCNN, FAYOLO and FESSD. YOLO-v8 results in a powerful toolkit for accurate object tracking and recognition, guaranteeing a proactive and responsive system. The proposed model's evaluations show the way the method may be utilized to raise public safety by anticipating anomalies and preventing accidents. © 2024 IEEE.

关键词： Video recording

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer systems science & Engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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A Novel IoT-Enabled system for Real-Time Face Mask Recognition Based on Petri Nets

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IEEE Internet of Things Journal 2024年第4期11卷 6992-7001页

作者： Yang, Cheng-Ying Lin, Yi-Nan Shen, Victor R. L. Shen, Frank H. C. Wang, Chien-Chi University of Taipei Department of Computer Science Taipei100 Taiwan Ming Chi University of Technology Department of Electronic Engineering New Taipei City243 Taiwan Chaoyang University of Technology Department of Information Management Taichung City413 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan Fu Jen Catholic University Department of Electronic Engineering New Taipei City242 Taiwan

Due to coronavirus disease 2019 (COVID-19), many countries have formulated pandemic prevention regulations, requiring the masses to wear a face mask before entering public places and taking public transportation. However, if the entrances of some places are manually controlled to check whether people are wearing a face mask or not, it becomes not only labor intensive but also time consuming. Therefore, this article aims to develop a face mask recognition system based on an edge computing platform. The traditional manual inspection control method is replaced by artificial intelligence (AI) technology to achieve automatic recognition and control. As an edge computing platform, Jetson Nano is an embedded system equipped with an AI platform, which can be used for object detection and image classification. Developed by Ultralytics LLC, the YOLOv5 model with PyTorch framework runs on the edge computing platform, featuring high speed, high precision, and small size. According to the model training results, the average precision (AP) reaches 95.41%, while the mean average precision (mAP) reaches 94.42%. The average single-class running time is 0.016 s, and the file size of the training model is 3.8 MB. The recognition distance is up to 8 m, and the maximum face rotation angle is 90°. In addition, a Petri net (PN) software tool, workflow Petri net designer (WoPeD), with graphical features based on mathematical theories, is used to verify the mask recognition system and ensures that the system has acceptable precision and recall values. © 2014 IEEE.

关键词： Petri nets

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