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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Pandey, Surya Prabhu, Nagendra Kumar, Adithya Suresh New Horizon College of Engineering Computer Science and Engineering Bangalore India Srm Institute of Science Technology Kattankulathur School of Computing Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9781665400527

Vein identification, also known as vascular technology, it is a biometric identification approach that analyses the blood patterns visible on the skin's surface. Despite the fact that it is used by FBI and CIA for community security and criminal investigations, such as forensic investigation and law enforcement, this recognition method is still in development. Vein recognition technology offers great solutions to challenges. Some challenges are ID or password leakage and then reusing it, lost smart cards or keys. Finger Vein Recognition can become most promising for high security applications. Our paper explains about the different cutting-edge machine learning algorithms that are applied so far. At the end, a comparative analysis is provided. © 2022 IEEE.

关键词： Machine learning

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Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model 1

Measuring COVID-19 Opinion in the Online Debate using an Uns...

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1st IEEE International Conference on Automation, computing and Renewable Systems, ICACRS 2022

作者： Priyadarsini, K. Jeba Sonia, J. Vijayalakshmi, K. Anbazhagu, U.V. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9781665460842

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

关键词： COVID-19

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Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Thilagavathy, A. Naveen Raju, D. Priyanka, S. RamBalaji, G. Gopirajan, P.V. Sureshkumar, K. Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Mandatory Automated Safety Kernel (MASK): Face Mask Recognition Using Deep Learning Computer Vision 2

Mandatory Automated Safety Kernel (MASK): Face Mask Recognit...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Dinesh, G. Singh, Amarjeet Shinz, Nehar Sravanya, Mogalluru Venkata Agrawal, Gaurangi Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9781665400527

This paper involves utilizing modules to create an efficient and accurate machine learning face mask detection model that can give an accuracy close to 100 percent and a value loss of less than 5%. This paper sees up to the fact that it is the local clusters that need to take care of themselves and this is what will eventually help the society on the larger scale as a whole and aims to aid the concerning authorities in keeping their crowd wearing masks at their respective premises. This is done by programming a system that can recognize the visuals from a camera at the entrance of such premises flagging offenders who do not wear a face mask and notifying authorities as it happens. This can be implemented in private organizations like educational institutions or company offices making it possible to combine it with a facial recognition model thereby notifying the offenders themselves via their cell phones by fetching their information from database resources that would be already existing in the case of these private organizations. © 2022 IEEE.

关键词： Face recognition

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Fuzzy Filter Design for Affine Systems with Sensor Faults:A Dynamic Event-Triggered Approach

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Journal of Systems science & Complexity 2022年第5期35卷 1761-1784页

作者： CHENG Jun WU Yuyan WU Zhengguang LI Kezan School of Mathematics and Statistics Center for Applied Mathematics of GuangxiGuangxi Normal Univer-sityGuilin 541006China School of Mathematics and Statistics Guangxi Normal UniversityGuilin 541006China School of Information Science and Engineering Chengdu UniversityChengdu 610106China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China

This study addresses the issue of dynamic event-triggered-based filtering for fuzzy affine *** alleviate the utilization of constraint bandwidth resources and improve the efficiency of the signals exchange,a dynamic event-triggered protocol is forwarded to regulate the trigger instants with objective system ***,the nonhomogeneous Markov process is proposed to characterize the dynamic behaviors of sensor faults,where the time-varying transition probabilities belong to a convex polytope ***,the validity and applicability of devised filter design methodology for fuzzy affine systems are displayed via two practical models.

关键词： Dynamic event-triggered protocol fuzzy affine systems nonhomogeneous Markov process sensor fault

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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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Security Analysis and Improvement of Authenticated Key Agreement Protocol for Remote Patient Monitoring IoMT

Security Analysis and Improvement of Authenticated Key Agree...

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2024 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Gautam, Deepika Thakur, Garima Obaidat, Mohammad S. Hsiao, Kuei-Fang Kumar, Pankaj Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics HP Dharamshala176215 India School of Computer and Communication Engineering University of Science and Technology Beijing China School of Computing SRM University Department of Computational Intelligence TN Kattankulathur India School of Engineering The Amity University UP 201301 India

ISBN: (纸本)9798350349832

The Internet of Medical Things (IoMT) is a network of medical devices, hardware infrastructure, and software that accumulates real-Time data through remote monitoring and minimizes doctor-patient interaction, enhancing the quality of remedies. However, the facile availability and accessibility of data over the web have given rise to serious information security and credibility issues in the IoMT. Recently, Chen et al. [1] proposed a provably secure authenticated key agreement protocol for remote patient monitoring in IoMT. However, after revisiting their scheme, we found security flaws such as user impersonation, ephemeral secret leakage attacks, denial-of-service attacks, and session key disclosure attacks. Therefore, this paper presents cryptanalysis as well as an improvement of Chen et al. 's scheme that strives to furnish protocols with robust security in opposition to the mentioned attacks. The security and effectiveness are displayed through informal analysis and evaluation with other existing state-of-The-Art metrics like computational and commu-nication cost. At last, the findings project the superiority of the proposed improved scheme. © 2024 IEEE.

关键词： Authentication Protocol

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Generative Adversarial Networks (GANs) for Artistic Style Transfer in Images

Generative Adversarial Networks (GANs) for Artistic Style Tr...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jamalpur, Bhavana Kiruthiga, S. Ranjitha, B. Lefty Joyson, J. Jayaram, K. Soundar, K. Ruba S R University Department of Computer Science and Artificial Intelligence India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Mohan Babu University Department of Mathematics India Jp College of Engineering Department of Computer Science and Engineering Ayikudi India College of Engineering Adoor Department of Computer Science and Engineering Pathanamthitta India Department of Computer Science and Engineering Sivakasi India

ISBN: (纸本)9798350375442

Many in computer vision and computational imaging consider Generative Adversarial Networks (GANs) a big advance. Style transfer converts semantic visual content into creative styles. Recently, GANs for style transfer have gained popularity. Adversarial training of GANs to create realistic counterfeits has yielded encouraging results. High-quality style transfer results have been achieved using this method. Mode collapse is a common problem in classical GANs. This causes unsteady training and poor style transmission. Additionally, the GAN generator only supports one style. Therefore, numerous GANs must be trained to allow users to shift styles. This study examines artistic style transfer in photographs using depth extraction generative adversarial network (DE-GAN). This research aims to improve photo aesthetics by adding famous artworks' distinctive qualities. This study investigates DE-GANs' mechanics and how they modify text while keeping a style's creative qualities. By studying adversarial training, this work hopes to understand how DE-GAN achieves this dual goal. This research analyzes DE-GAN-based style transfer models to evaluate their effectiveness, identify their limits, and recommend areas for improvement. The experiments show that DE-GAN creates realistic and attractive stylized visuals. This study also discusses DE-GAN's practical applications, problems, and future possibilities in artistic style transfer. © 2024 IEEE.

关键词： Generative adversarial networks

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Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

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science China(Information sciences) 2023年第4期66卷 301-302页

作者： Hongbo GAO Juping ZHU Fei ZHANG Ruidong YAN Junjie ZHOU Lei JIANG Jianqiang WANG Keqiang LI Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Department of Computational Mathematics School of Mathematical Sciences Anhui University School of Traffic and Transportation Beijing Jiaotong University Autonomous Driving Business Unit Chery Automobile School of College of Computer Science and Technology Zhejiang University China North Vehicle Research Institute School of Automotive Engineering Tsinghua University

Dear editor, Vehicle control is one of the key steps of intelligent driving [1]. Algorithms based on receding horizon optimization(RHO) can predict future trajectories and handle multiobjective constraint conditions; therefore, RHO-based methods have attracted considerable attention in the field of vehicle control. Although existing methods use RHO to design controllers, they do not simultaneously meet the multiobjective optimal control performance requirements on tracking, fuel economy, and ride comfort.

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An Effective Two-Stage Column Generation Strategy for Solving Unit Commitment

An Effective Two-Stage Column Generation Strategy for Solvin...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Zhao-Gang Su Sheng-Jie Chen Yu-Fei Li Liang Chen Jia-Yi Deng Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China College of Sciences Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Unit commitment (UC) has been extensively studied in power systems literature and is typically formulated as a mixed-integer programming (MIP) problem. However, existing methods and commercial MIP solvers often struggle to efficiently solve large-scale UC instances. This paper proposes a novel MIP model for addressing UC by employing an innovative column generation-based two-stage approach to efficiently compute high-quality solutions. Our method leverages parallel computing to solve pricing problems and generate columns effectively. Following column generation, we solve the master problem as a MIP model to obtain high-quality feasible solutions for the UC problem. Comprehensive numerical experiments demonstrate that our proposed method can effectively tackle large-scale UC problems involving over 1,000 units, significantly outperforming state-of-the-art MIP solvers in both solution quality and computational efficiency. The results underscore the potential of our approach for practical application in large-scale power system scheduling.

关键词： Processor scheduling computational modeling Refining Pricing System integration Parallel processing Programming computational efficiency Power system reliability Reliability

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