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Energy-Efficient Functional Safety Design Methodology Using ASIL Decomposition for Automotive Cyber-Physical Systems

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IEEE Transactions on Reliability 2024年 1-23页

作者： Xie, Guoqi Peng, Hao Huang, Jing Li, Renfa Li, Keqin Department of Computer Engineering College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China and also with the Key Laboratory for Embedded and Cyber-Physical Systems of Hunan Province Changsha 410082 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China and also with the Department of Computer Science State University of New York New Paltz NY 12561 USA

Automotive cyber-physical systems (ACPS) are typical cyber-physical systems because of the joint interaction between the cyber part and physical part. Functional safety requirement (including response time and reliability requirements) for an ACPS function must be assured for safe driving. Auto industry is cost-sensitive, power-sensitive, and environment-friendly. Energy consumption affects the development efficiency of the ACPS and the living environment of people. This paper solves the problem of optimizing the energy consumption for an ACPS function while assuring its functional safety requirement (i.e., energy-efficient functional safety for ACPS). However, implementing minimum response time, maximum reliability, and minimum energy consumption is a conflicting problem. Consequently, solving the problem is a challenge. In this paper, we propose a three-stage design process toward energy-efficient functional safety for ACPS. The topic problem is divided into three sub-problems, namely, response time requirement verification (first stage), functional safety requirement verification (second stage), and functional safety-critical energy consumption optimization (third stage). The proposed energy-efficient functional safety design methodology is implemented by using automotive safety integrity level decomposition, which is defined in the ACPS functional safety standard ISO 26262. Experiments with real-life and synthetic ACPS functions reveal the advantages of the proposed design methodology toward energy-efficient functional safety for ACPS compared with state-of-the-art algorithms. IEEE

关键词： Energy utilization

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Disaster Management Systems: Utilizing YOLOv9 for Precise Monitoring of River Flood Flow Levels Using Video Surveillance

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SN computer science 2025年第3期6卷 1-14页

作者： Shankar, G. Geetha, M. Kalaiselvi Ezhumalai, P. Department of Computer Science and Engineering FEAT Annamalai University Chidambaram 608002 India Department of Computer Science and Engineering R.M.D. Engineering College Chennai 601206 India

Flood disasters pose significant threats to human lives and infrastructure, necessitating advanced methods for the timely and accurate monitoring of water levels in rivers. This study introduces an innovative approach utilizing the recent YOLOv9 (You Only Look Once, version 9) deep learning model to detect and monitor river water flow levels effectively. By leveraging YOLOv9’s robust object detection capabilities, the proposed system achieves precise segmentation and real-time analysis of river water regions. The method not only identifies river water areas with high accuracy but also quantifies flow levels, providing critical data for flood prediction and management. Comprehensive testing was conducted using diverse datasets representing various river conditions and flow scenarios. The results demonstrate that YOLOv9 significantly outperforms traditional methods in terms of detection speed and accuracy, making it a valuable tool for enhancing flood disaster response and management strategies. This work contributes to the development of more resilient and proactive flood disaster management systems through the integration of state-of-the-art machine learning techniques, offering significant improvements in predictive capabilities and response effectiveness. The proposed system was trained and evaluated using real-time river flood datasets, achieving a notable accuracy rate of 98%, a mean average precision (mAP) of 97.5%, precision of 98%, and recall of 98% in predicting flood level characteristics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Flood disaster management Machine learning Object detection River water flow detection Water level segmentation YOLOv9

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Development of Fake Network for Detection of Deep Fake Images and Videos with Advanced Computational Intelligence 1

Development of Fake Network for Detection of Deep Fake Image...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Mahar, Alap Verma, Pushpneel Singh, Ajit Bhagwant University Department of Computer Science and Engineering Rajasthan Ajmer305004 India VMSB University Department of Computer Science and Engineering India

ISBN: (纸本)9798331518578

Increased digitization leads to huge number of threats increasing along with it at faster pace. Deep fake videos and images generation may not require any computational device memory hardware or particular knowledge. But the process of detecting the deep fake video and images is challenging. Many methods have been evolved in recent times for solving these challenges but the computational costis very high for the highly efficient model. Thus, the proposed deep fake network model architecture involves highly efficient technique for accurate detection of deep fake video and imageswhich is comprised of linear stack of Max pooling and convolution layers. Activation function and Lime Gradient Boosting Machine (Light GBM) are used for the detection of deep fake videos. The develop model has high level of accuracy when compared with other models such as Efficient net and Xception and the study has been validated for the performance based on deep fake detection challenge data set. The study achieved 94.56% accuracy and 92.34% precision using this data set. Additionally, the study analyses the facial manipulations in the real-world images and videos accurately using advanced computational intelligence. © 2025 IEEE.

关键词： computational intelligence Deep fake Deep Fake Network Images Light GBM Videos

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Comparative Analysis of Structural Characteristics of Social Networks and Their Relevance in Community Detection 1st

Comparative Analysis of Structural Characteristics of Social...

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1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Solanki, Shano Kumar, Mukesh Kumar, Rakesh Department of Computer Science and Engineering NITTTR Chandigarh India Department of Computer Science and Engineering UIET Panjab University Chandigarh India Department of Computer Science and Engineering Central University of Haryana Mahendergarh India

ISBN: (纸本)9783031487804

Social Network Analysis based on structural properties is relevant to many application domains including influence maximization, product recommendation, and viral marketing to name a few. The study of structural properties has a vital role in effective community detection and researchers have suggested several algorithms in this direction. The research paper not only gives details of various structural characteristics and their relevance for community detection but it also covers community visualization tools and their useful features. Along with the mapping of structural properties and community detection, efforts have also been made towards understanding the essential community evaluation metrics to know the goodness of communities resulting after applying various algorithms. Network characteristics vary for disjoint and overlapping community detection, so, social network datasets are analyzed to produce different network statistics for a better understanding of their suitability for disjoint and overlapping community detection. The research paper also covers applications of community detection to motivate the researchers for exploring the possibility of using characteristics of social networks for detecting communities for reducing computation time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Structural properties

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Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1063-1082页

作者： Sharif, Muhammad Tanvir, Uroosha Munir, Ehsan Ullah Khan, Muhammad Attique Yasmin, Mussarat Department of Computer Science COMSATS University Islamabad Wah Campus Islamabad Pakistan Department of Computer Science and Engineering HITEC University Museum Road Taxila Pakistan

A malignant tumor in brain is detected using images from Magnetic Resonance scanners. Malignancy detection in brain and separation of its tissues from normal brain cells allows to correctly localizing abnormal tissues in brain’s Magnetic Resonance Imaging (MRI). In this article, a new method is proposed for the segmentation and classification of brain tumor based on improved saliency segmentation and best features selection approach. The presented method works in four pipe line procedures such as tumor preprocessing, tumor segmentation, feature extraction and classification. In the first step, preprocessing is performed to extract the region of interest (ROI) using manual skull stripping and noise effects are removed by Gaussian filter. Then tumor is segmented in the second step by improved thresholding method which is implemented by binomial mean, variance and standard deviation. In the third step, geometric and four texture features are extracted. The extracted features are fused by a serial based method and best features are selected using Genetic Algorithm (GA). Finally, support vector machine (SVM) of linear kernel function is utilized for the classification of selected features. The proposed method is tested on two data sets including Harvard and Private. The Private data set is collected from Nishtar Hospital Multan, Pakistan. The proposed method achieved average classification accuracy of above 90% for both data sets which shows its authenticity. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Magnetic resonance imaging

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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

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computers, Materials & Continua 2023年第4期75卷 2331-2346页

作者： Hammad Rustam Muhammad Muneeb Suliman A.Alsuhibany Yazeed Yasin Ghadi Tamara Al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports *** have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to *** have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.

关键词： Genetic algorithm human locomotion activity recognition human–computer interaction human gestures recognition principal hand gestures recognition inertial sensors principal component analysis linear discriminant analysis stochastic neighbor embedding

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Unveiling the web of privacy: A comprehensive review of threats to information security in social networks 4th

Unveiling the web of privacy: A comprehensive review of thre...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Singh, Shalini Baloni, Dev Sarkar, Manash Department of Computer Science and Engineering Quantum University Roorkee India Department of Computer Science and Engineering Atria Institute of Technology Banglore India

ISBN: (纸本)9781032911571

This review paper explores emerging threats to information privacy and security within the dynamic landscape of Online Social Networks (OSNs), which serve as repositories of vast amounts of user data. The rise in social media use emphasizes how crucial it is to inform users about the dangers that come with it, like cyber-attacks and privacy violations. Even though various social media platforms have been made possible by technological advancements, consumers’ ignorance of these platforms creates serious security risks. Although academics have come up with creative security solutions for social networks, there are obstacles in the way of their actual application. It is essential to continuously assess security vulnerabilities in light of technology improvements. This study examines several dangers to online social networks and suggests countermeasures based on a variety of models, frameworks, and encryption methods. Despite advancements, privacy concerns remain unaddressed, requiring parents to actively supervise their children’s online activity. © 2025 the Author(s).

关键词： Differential privacy

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Detection of Quality Deep Fake Images and Videos Using Customised Convolutional Neural Networks 1

Detection of Quality Deep Fake Images and Videos Using Custo...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

ISBN: (纸本)9798331518578

Due to advancements in artificial intelligence there are numerous deep fake images and video collections are available on the Internet and social media. The primary aim of the study is to analyse the deep fake images and videos since the quality is constantly improving a novel method was developed for accurate detection of quality deep fakes. The suggested approach uses a customised convolutional neural network (CNN) technique that uses facial landmark detection to extract structured data from photos and video frames before feeding it into the methods of CNN. The customised CNN model is augment-based CNN for generation of fake images and fake data. Involves about 260 films from the data set in which 202 images are made up while others were real images. About 300 videos are used in which 250 videos are fake and 50 were real the proposed model achieved the accuracy of 95.58% and 0.97 AUC score that outperforms the existing models like MLP-CNN and CNN. Additionally, the method succeeds with greater accuracy then the conventional models like DST -Net, VGG 16 Efficient Net. This research study's primary goal is to create a new CNN learning method for identifying high-quality deep fake photos and videos. © 2025 IEEE.

关键词： Video analysis

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Automating skin cancer screening: a deep learning

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Journal of engineering and Applied science 2025年第1期72卷 1-18页

作者： Rashad, Nada M. Abdelnapi, Noha MM. Seddik, Ahmed F. Sayedelahl, M.A. Department of Computer Science Faculty of Computers and Information Suez University Suez43221 Egypt Biomedical Engineering Department Faculty of Engineering Helwan University Cairo11795 Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

Skin cancer presents in various forms, including squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. Established risk factors include ultraviolet (UV) radiation exposure from solar or artificial sources, lighter skin pigmentation, a history of sunburns, and a family history of the disease. Early detection and prompt intervention are crucial for achieving a favorable prognosis. Traditionally, treatment modalities include surgery, radiation therapy, and chemotherapy. Recent advancements in immunotherapy have revolutionized skin cancer diagnosis, but manual identification remains time-consuming. Artificial intelligence (AI) has shown potential in skin cancer classification, leading to automated screening methods. To support dermatologists, we improved the model for classifying images. This model is able to recognize seven different kinds of skin lesions. On the ISIC dataset, an analysis has been done. This study offers a novel approach to early skin cancer diagnosis based on image processing. Our approach leverages the high accuracy of a specific convolutional neural network architecture, utilizing transfer learning with pre-trained data to further enhance detection performance. Our findings demonstrate that the employed ResNet-50 transfer learning model achieves a remarkable accuracy of 97%, while ResNet50 without augmentation gives an accuracy of 81.57% and an F1-score of 75.75%. © The Author(s) 2025.

关键词： Convolutional neural networks

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