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Event-Triggered Fixed-Time Consensus of Second-Order Nonlinear Multi-Agent Systems with Delay and Switching Topologies

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Journal of Shanghai Jiaotong university(science) 2024年第4期29卷 625-639页

作者： XING Youjing GAO Jinfeng LIU Xiaoping WU Ping School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China Department of Systems and Computer Engineering Carleton UniversityOttawaK1S 5B6Canada

To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is ***,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system ***,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each *** is available to save communication resources,and the Zeno behaviors are ***,the delay and switching topologies of the system are also ***,the system stabilization is analyzed by Lyapunov stability ***,simulation results demonstrate the validity of the presented method.

关键词： event-triggered mechanism fixed-time consensus multi-agent systems switching topologies

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Text recognition by air drawing 4

Text recognition by air drawing

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4th International Conference on Computational Intelligence and Communication technologies, CCICT 2021

作者： Patel, Jay Mehta, Umang Panchal, Kevin Tailor, Dev Zanzmera, Devam Pandit Deendayal Energy University B.Tech in Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9781665423922

In this paper, the text drawn by the user in the air is captured by the computer's camera, followed by the identification of that text. So, the video camera will be turned on at the time of capturing the written text. Now the particular object is defined based on its colour to detect a movement done by the user. The colour is captured by the lower and upper bound of HSV (Hue Saturation Value), which finally leads to object detection at every instant. Lastly, the text will be recognized by the trained model. The model is trained by CNN (Convolution Neural Network) with an accuracy of 98.64% (training) and 98.24% (testing). For completion of the project, OpenCV, python programming language, and its libraries are used. This project requires only a camera and a defined object. © 2021 IEEE.

关键词： Python

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Development of Secure IoT Ecosystems for Healthcare 3

Development of Secure IoT Ecosystems for Healthcare

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3rd International Conference on Intelligent Communication and Computational techniques, ICCT 2023

作者： Shirgur, Pranav Chaurasia, Sandeep Manipal University Jaipur B. Tech Computer Science and Engineering Jaipur Rajasthan India Manipal University Jaipur Department of Computer Science and Engineering Jaipur Rajasthan India

ISBN: (纸本)9781665453578

The Internet of Things (IoT) is a rapidly developing field of technology which entails a network of smart devices connected to each other and the internet. The TATCRb2industry is anticipated to increase by 18% to 14.4 billion active connections in 2022. There will likely be about 27 billion linked IoT devices by 2025 as supply limitations, brought about by the current global semiconductor and chip shortage - loosen and demand quickens. IoT has quickly penetrated the healthcare industry, this paper defines a framework that enables the development of secure and scalable IoT healthcare platforms/applications. These platforms will also allow for secure cloud storage and analysis of patient data, helping professionals recognize latent parameters such as patient behavioral patterns that contribute to an ailment. This ultimately will enable the study of social and economic impact of a particular disease. This will greatly cull the survivorship bias in the health care industry - especially in testing times like a pandemic. © 2023 IEEE.

关键词： Health care

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Parametrized Optimization based on an Investigation of Musical Similarities Using SPARK and Hadoop

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SN computer science 2024年第1期5卷 78页

作者： Chaudhary, Savita Karthik, V. Shankar, R. Taranum, Ayesha Naresh, E. Department of Computer Science and Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Bengaluru India Department of Information Science and Engineering Ramaiah Institute of Tech B’lore India Department of Computer Science and Engineering BMS Institute of Technology and Engineering Bengaluru India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. by computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion. © 2023, The Author(s).

关键词： big data Hadoop Music information retrieval Optimization Spark

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Comparative Analysis of Vision Transformers and CNN-based Models for Enhanced brain Tumor Diagnosis

Comparative Analysis of Vision Transformers and CNN-based Mo...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Asmi Mangesh Kocharekar Satavisha Datta Padmanaban Rajakumar R. Final Year B.Tech Computer Science VIT Chennai Chennai India SCOPE VIT Chennai Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Patient care quality is directly linked to the accuracy and success of diagnostic radiography, notably in the diagnosis and treatment of brain tumors. Central to both medical treatments used for diagnosis and treatment, segmentation serves as a fundamental precursor. Recent advancements in machine learning aim to improve the effectiveness of diagnosis by transitioning from specialized applications to generalized algorithms. Unsupervised anomaly detection plays a crucial role in identifying possible areas of anomaly, particularly those not observed during instruction. Autoencoders based on convolutional neural networks (CNN) and variational autoencoders have emerged as standard approaches for segmenting anomalies based on reconstruction. However, CNNs' constrained receptive fields impose limitations on their ability to capture global context, thereby hindering semantic understanding when large sections of the image are filled with anomalies. We investigate the possibilities of vision transformers (ViTs) in this research as a viable alternative to CNNs for brain magnetic resonance imaging abnormality segmentation. ViTs depend on self-attention mechanisms to establish relationships between image patches, offering a more comprehensive view of the image. We present a comparative study wherein five transformer-based models are developed to reconstruct coherent and realistic images for anomaly segmentation. Our findings demonstrate that these ViT-based models achieve segmentation effectiveness that is on par with or surpassing cutting-edge CNN-based methods. by harnessing the capabilities of ViTs, we pave the way for improved anomaly detection and segmentation in brain MRI, thereby enhancing diagnostic accuracy and patient care in radiology practice.

关键词： Image segmentation computer vision Accuracy Magnetic resonance imaging Medical treatment Machine learning Radiology Transformers brain modeling Convolutional neural networks

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Digital Cottage Hub for Empowering Artisans, Fostering Connectivity, and Promoting 'Make in India' 2

Digital Cottage Hub for Empowering Artisans, Fostering Conne...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Hiremani, Vani Praveen Gujjar, J. Shinde, Sumit Devadas, Raghavendra M. Anser Pasha, C.A. Ambreen, Lubna Pune Campus Lavale Pune412115 India Bengaluru India B. Tech Computer Science and Engineering ITM Skills University Navi Mumbai India Bengaluru India Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

Aims to redefine the landscape of the cottage industry with an innovative social web application designed to simplify the exchange of handcrafted items. At its heart, the platform aims to bridge the gap between producers and customers by providing a thriving marketplace for one-of-a-kind, handcrafted things. With a focus on encouraging job possibilities, particularly for women, the application corresponds with the wider purpose of supporting "women empowerment" and contributing to national efforts such as "Make in India. "The system study highlights the existing holes in present systems, emphasizing the necessity for a complete platform tailored exclusively to the cottage sector. An architecture diagram and extensive use case overviews for customers, producers, and administrators are included in the system design. Implementation specifics include login, identity, purchasing, and administrative operations, among others. The initiative aims to promote not just economic activity in the cottage industry, but also social issues like as empowering women and inclusive employment. The dedication to ongoing development is obvious in the plans for the future, which include tightening security measures, notably for online financial transactions. Finally, the social web application aspires to build a dynamic and safe digital area for artisans, producers, and customers, therefore leaving a lasting impression on the cottage industry in the age of technology. The chatbot is implemented using the rasa open-source framework along with the digital platform. The results of the chatbot are discussed in detail in the results and discussion section. © 2024 IEEE.

关键词： Sales

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DLMNN based Heart Disease Prediction with PD-SS Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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Adaptable Individualized Investment Recommendation System

Adaptable Individualized Investment Recommendation System

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sabiyath Fatima, N. Noor Alleema, N. Mahesh, C. Umanesan, R. Senthil, K. Santhosh Kumar, P. B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available. © 2024 IEEE.

关键词： Decentralized finance

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Implementing blockchain based Security in EHR Using Ganache

Implementing Blockchain Based Security in EHR Using Ganache

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Kumar Satyam Ayush Sharma Runumi Devi B.Tech in Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

A blockchain-based electronic health record (EHR) system has been proposed to manage patient data and maintain computerized records across different healthcare institutions. The main objective of this system is to provide immediate access to patient health information while addressing concerns about privacy and security. It is developed on the Ethereum network using programming languages, tools, and technologies such as Ganache, Solidity, and ***. The system uses smart contracts to provide security and privacy, and transactions are verified and propagated to the entire decentralized network. The system includes a cryptocurrency wallet (MetaMask) with a centralized privacy system that allows authorized parties to access and secure records quickly. The proposed system aims to achieve several objectives such as allowing users to receive the same data simultaneously, increasing efficiency, building trust, and reducing barriers. It secures data storage by restricting user access and enables the secure transfer of patient medical records. The solution allows for better transparency and ownership of sensitive data, which can be recorded and safeguarded via blockchain technology, therefore assisting the healthcare sector. This paper describes a medical record system and a new protocol that can be used quickly and safely, paving the way for more secure and efficient healthcare systems. Overall, the proposed system offers potential solutions to the challenges associated with traditional EHR systems and has the potential to revolutionize the healthcare industry.

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Cross-Layer Management Framework for Enhancing XR-based System Security in Zero-Trust Wireless Communications

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2011-2024页

作者： Ghourab, Esraa M. Azab, Mohamed Gracanin, Denis Al-Qutayri, Mahmoud Muhaidat, Sami Khalifa University 6G Center Department of Computer Science Abu Dhabi127788 United Arab Emirates Virginia Military Institute Department of Computer and Information Sciences VA United States Virginia Tech Department of Computer Science BlacksburgVA24060 United States Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Khalifa University 6G Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency requirements is challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense. Driven by deep reinforcement learning and real-time monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user’s quality of experience (QoE), reduce the overall latency, and minimize the attacker’s intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE, outperforming conventional Q-learning algorithms. © 1983-2012 IEEE.

关键词： Resource allocation

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