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Image Processing Based Automatic Traffic Control System

Image Processing Based Automatic Traffic Control System

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ponnuru, Sudhakiran Sharma, Ambrish Kumar Khan, Patan Feridoz Poovannan, E. Lakshmi, B. Venkata Gopinath, S. New Horizon College of Engineering Department of Ece Karanataka Bangalore India Noida Institute of Engineering and Technology Department of Mca Greater Noida India School of Computing Mohan Babu University Department of Cse Andhra Pradesh Tirupati India Saveetha Engineering College Department of Ece Tamil Nadu Chennai602105 India Department of humanities YSR Kadapa India Gnanamani College Of Technology Tamilnadu Namakkal637018 India

ISBN: (纸本)9798331543617

As the number of people living in cities increases and more people drive, one of the most important problems is traffic congestion. An intelligent system that could effectively manage traffic congestion based on traffic density was required due to the rise in the number of cars. While current traffic management systems operate using fixed time-based methodologies, conventional traffic control systems are unable to manage the complicated traffic flow at junctions. There are numerous methods for establishing effective traffic control systems in urban areas. However, no method exists that is effective in real-time, and no system is prepared to accept changes on a constant basis. Using a digital image processing tool in MATLAB and the image processing technique known as morphological operations, the real-time traffic management system determines the percentage match to control the flow of traffic. By use MATLAB code that modifies the green, yellow, and red-light times in relation to traffic volume and density. To enhance its robustness and reliability the proposed traffic control system which is based on image processing also integrates advanced features such as scenarios involving emergency vehicles. © 2024 IEEE.

关键词： Traffic congestion

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A Note on the Security of ITS: Car Crash Analysis in Cruise Control Scenarios

arXiv

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arXiv 2023年

作者： Haghighi, Mohammad Sayad The School of Science Computing and Engineering Technologies Swinburne University of Technology VIC 3122 Australia

Security of Intelligent Transportation Systems (ITS) heavily depends on the security of the underlying components that create such a smart ecosystem. Adaptive Cruise Control (ACC) is embedded into most modern vehicles. In this report, we study the situations that the two vehicles involved in a cruise control scenario create. More precisely, after breaking down the phases the two vehicle go through (especially the ego one), we show how a simple formula can be used to predict collisions in hard brake cruise control scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Adaptive cruise control

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Effect of using silica fume as a partial replacement of cement in concrete

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Materials Today: Proceedings 2023年

作者： Tak, Sudhansu Gupta, Priyanshu Kumar, Alok Sofi, A. Yun, Chin Mei B.Tech Civil Engineering School of Civil Engineering Vellore Institute of Technology Vellore India Professor Department of Structural and Geotechnical Engineering School of Civil Engineering Vellore Institute of Technology Vellore India Engineering Computing and Science Swinburne University of Technology Sarawak Malaysia

Concrete, which acts as the basic structural material, is frequently used in building projects. Concrete uses Portland cement as a binder and the huge production of cement results in carbon dioxide emissions. To reduce CO2 emissions, a transition to alternate products is required. Our research examines the viability of substituting a portion of the cement in concrete with silica fume. On the specimen, tests were conducted utilizing 0%, 5%, 8%, 11%, and 15% silica fume to replace cement in the composition. Concrete specimens were casted and assessed after 7 days, 14 days, and 28 days of cure. The test samples were compared to a control sample of concrete, and statistical analysis was used to establish the ideal percentage that provides the maximum strength. The mechanical properties of concrete were enhanced when silica fume was being used as a binder;the resultant concrete was also cost-effective. The ideal percentage of silica fume for concrete was determined to be 11%. © 2024 Elsevier Ltd. All rights reserved.

关键词： Compressive strength

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining 23

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798350323481

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Neural Network-Based Automated Soil Salinity Mapping and Remediation Using Wireless Sensor and Cloud computing 10

Neural Network-Based Automated Soil Salinity Mapping and Rem...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Shadaksharappa, B. Sriram, S. Swathy, R. Mishra, Nilamadhab Meenakshi, R. Suganya, J. Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chenagalpattu Chennai Tamil Nadu Kattankulathur India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This paper uses neural networks, wireless sensor networks, and cloud computing to automate soil salinity mapping and treatment. Soil salinity affects agricultural output, but conventional techniques of monitoring and managing it are laborious. This system collects real-time soil salinity data from a specific agricultural region using a wireless sensor network. These sensor nodes provide data to a cloud computing platform for automatic soil salinity mapping using a neural network model. The neural network is trained on a large soil property and salinity dataset to accurately predict field salinity distribution. Farmers and land managers may make quick decisions using the cloud-based data storage, processing, and analysis solution. The suggested system incorporates remediation options to inform sustainable soil management. Through field testing, the methodology proves its accuracy and efficiency above older approaches. Precision agriculture producers may monitor soil salinity in real time and take targeted repair actions using the described method. Due to rising agricultural needs and environmental concerns, neural networks, wireless sensor networks, and cloud computing provide a solid foundation for sustainable and technology-driven soil management. © 2024 IEEE.

关键词： Data integration

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Breast Cancer Classification Using DDPM and Holographic Guidance Diffusion Framework 22

Breast Cancer Classification Using DDPM and Holographic Guid...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： You, Yuxin Gan, Hong-Seng Shimizu, Akinobu Ramlee, Muhammad Hanif Basarudin, Hafiz Vu, Viet-Vu Xi'an Jiaotong - Liverpool University School of Ai and Advanced Computing Suzhou China Tokyo University of Agriculture and Technology Institute of Engineering Tokyo Japan Universiti Teknologi Malaysia Department of Biomedical Engineering and Health Sciences Johor Bahru Malaysia Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Kajang Malaysia Cmc University Faculty of Information Technology and Communications Hanoi Viet Nam

ISBN: (纸本)9798331509712

Breast cancer is a serious health threat to women. The early detection of breast cancer is crucial to improving survival rates and reducing the difficulty of treatment. In recent years, denoising diffusion probability models (DDPM) have performed well in image generation, but their application to breast cancer classification remains unknown. Additionally, conditional priors are often the most important source of information for diffusion models to solve problems, and fuzzy lesions or high-noise images pose a significant challenge for the model to extract accurate prior information. To address these issues, we propose a new classification model based on DDPM and combine it with the holographic guided diffusion framework (HGDF) to remove noise from images through specific guidance and make full use of HPC resources to improve the classification performance of breast cancer images. Results from the experiment indicate that the model has excellent accuracy in ultrasound images (BUS dataset) and mammography images (MAMMO dataset), with accuracy of 86.17 % ± 1.74% and 94.32% ± 2.31%, respectively. © 2024 IEEE.

关键词： Lung cancer

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Residual VGGNet for Unveiling Optimal Process Parameters of Oil Rig Welding Through Mathematical Modeling and Optimization 15

Residual VGGNet for Unveiling Optimal Process Parameters of ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhas, John T. Mesia Saikia, Utpal Arun, K.K. Josphine, C.Vimala Kingslin, M.Theodore Padmavathi, N. School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Silapathar College Department of Mathematics Dhemaji Assam Silapathar India Kumaraguru College of Technology Department of Mechanical Engineering Tamilnadu Coimbatore India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai India R.M.K College of Engineering & Technology Department of Electronics & Communication Engineering Tamilnadu Puduvoyal India R.M.D. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India

ISBN: (纸本)9798350370249

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data. © 2024 IEEE.

关键词： mathematical modeling oil rig welding optimization Residual VGGNet welding parameters

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Advanced Persistent Threats (APT): evolution, anatomy, attribution and countermeasures

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Journal of Ambient Intelligence and Humanized computing 2023年第7期14卷 9355-9381页

作者： Sharma, Amit Gupta, Brij B. Singh, Awadhesh Kumar Saraswat, V.K. Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India International Center for AI and Cyber Security Research and Innovations Department of Computer Science and Information Engineering Asia University Taichung Taiwan NITI Aayog New Delhi110001 India Lebanese American University Beirut Lebanon School of Computing Skyline University College Sharjah United Arab Emirates Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India Birkbeck University of London London United Kingdom

In today’s cyber warfare realm, every stakeholder in cyberspace is becoming more potent by developing advanced cyber weapons. They have equipped with the most advanced malware and maintain a hidden attribution. The precocious cyber weapons, targeted and motivated with some specific intention are called as Advanced Persistent Threats (APT). Developing defense mechanisms and performing attribution analysis of such advanced attacks are extremely difficult due to the intricate design of attack vector and sophisticated malware employed with high stealth and evasive techniques. These attacks also include advanced zero-day and negative-day exploits and payloads. This paper provides a comprehensive survey on the evolution of advanced malware design paradigms, APT attack vector and its anatomy, APT attack Tactics, Techniques, and Procedures (TTP) and specific case studies on open-ended APT attacks. The survey covers a detailed discussion on APT attack phases and comparative study on threat life-cycle specification by various organizations. This work also addresses the APT attack attribution and countermeasures against these attacks from classical signature and heuristic based detection to modern machine learning and genetics based detection mechanisms along with sophisticated zero-day and negative day malware countermeasure by various techniques like monitoring of network traffic and DNS logs, moving target based defense, and attack graph based defenses. Furthermore, the survey addresses various research scopes in the domain of APT cyber-attacks. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Life cycle

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A Hybrid Bayesian-Genetic Approach for Micro Gear Tolerance Optimization

A Hybrid Bayesian-Genetic Approach for Micro Gear Tolerance ...

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International Conference on Automation and computing (ICAC)

作者： Jin Jin Yuanping Xu Jia He Chaolong Zhang Zhijie Xu Chao Kong Benjun Guo Qiuyan Gai School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Huddersfield UK School of Management Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

In precision engineering, particularly in microgear production, stringent tolerance levels are essential for ensuring optimal functionality. Traditional tolerance design models in this field focus primarily on product value, often overlooking aspects such as product robustness, which presents challenges in maintaining a balance between manufacturing precision and cost efficiency. This study proposes a hybrid optimization method that combines the predictive strengths of Bayesian techniques with the comprehensive search capabilities of genetic algorithms. The proposed methodology employed integrates statistical analysis with algorithmic modeling. Bayesian methods are utilized for forecasting and adjusting tolerance levels, using historical data and probabilistic models to enhance manufacturing accuracy. Concurrently, genetic algorithms are being applied to explore a range of design parameters, aiming to identify optimal tolerance settings. Combining these methods allows for an extensive exploration of potential solutions, seeking to achieve an equilibrium between precision and cost. Experimental results on a set of micro gear design cases demonstrate that the proposed BayesianGenetic Approach approach significantly outperforms the standalone Bayesian Optimization method and achieves competitive results compared to the standalone Genetic Algorithm method.

关键词： Costs Gears Statistical analysis Heuristic algorithms Optimization methods Robustness Bayes methods Manufacturing Genetic algorithms Convergence

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