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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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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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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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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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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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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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Cross-Layer Distributed Attack Detection Model for the IoT

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International Journal of Ambient Computing and Intelligence 2022年第1期13卷 1-17页

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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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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A comparative analysis of chaos theory based medical image steganography to enhance data security 2

A comparative analysis of chaos theory based medical image s...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Ghosh, Sharmila Saha, Ashim Pal, Tannistha Jha, Anand Kumar Department of Computer Science and Engineering National Institute of Technology Agartala India Department of Information and Technology VelTech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

The security of patient data is a critical problem for health networks due to the rising popularity of telehealth services and the requirements for clinical data sharing between surgeons, consultants, and medical groups. In contrast to protecting medical data, which includes the cover (i.e., clinical picture), the requirements for protecting other pertinent data are more general. The paper advocates for data encryption before concealment as a robust strategy to uphold patient privacy during medical information exchange. The primary focus of this analysis revolves around a comprehensive exploration of different steganography methods. Through a meticulous examination of various steganographic techniques, including LSB, PVD, and transform domain approaches, this paper provides a detailed analysis of their strengths and limitations. Objective metrics like PSNR and SSIM are employed to dissect the trade-offs between data security and visual fidelity. The research leads to the conclusion that the diagonal queue-based steganog-raphy methodology, supported by chaotic methods, the Linear Feedback Shift Register (LFSR), and the durable Rabin encryption system, is the best course of action. This method enables autonomous data transmission among numerous contacts while ensuring the highest level of confidentiality and integrity of patient data inside e-health networks. In summary, this study provides a comprehensive solution that protects patient data and speeds data transmission inside the expanding framework of e-health networks in order to meet urgent data security concerns in telemedicine and healthcare data exchange. © 2024 Elsevier B.V.. All rights reserved.

关键词： Cryptography

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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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