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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Prajwala Priyanka, M. Kumar, Sheo Lakshmi Bhargav, A. Department of Computer Science Engineering CMR Engineering College Hyderabad India Department of Computer Science Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions Hyderabad India

ISBN: (纸本)9789819971367

Heart attack is a catch-all term for a variety of conditions affecting the heart. Analysis of large amounts of data and comparisons between them are essential for the prediction, prevention, and management of cardiovascular illnesses including heart attacks. This paper aims to develop an Intelligent System using various ML techniques such as Naive Bayes, KNN, Random Forest and Decision Tree. Users of this web-based software will answer a prepared series of questions. Data analytics is crucial for organizing, analyzing, and controlling large data sets. It has shown beneficial in the diagnosis, prevention, and treatment of cardiovascular disease. With this in mind, we want to apply SVM- and GA-based data analytics to the challenge of identifying cardiac problems. A comparison of these algorithms’ prediction models indicates which one is the most developed. It may aid physicians in making more educated diagnoses of heart illness than would have been possible with older, more traditional methods of decision assistance. By providing efficient treatments, it contributes to cost-effective healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Heart

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Towards Intelligent Attendance Monitoring for Scalable Organization with Hybrid Model Using Deep Learning

Towards Intelligent Attendance Monitoring for Scalable Organ...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Srivastava, Akhilesh Kumar Mishra, Chandrahas Mishra, Anurag Srivastava, Atul Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science KIET Group of Institutions Ghaziabad India Amity School of Engineering and Technology AUUP Lucknow India

ISBN: (纸本)9789819706402

Face recognition technology using AI has seen paradigm shift in the evolving world. Automatic attendance monitoring using real-time face identification is a solution to handle attendance in any small/large as well as scalable organization. Traditional methods in the organization involve calling names or signing sheets with individuals, which is a very time-consuming process and provides insurance. This is also subjected to manual errors. Automation of attendance recording and monitoring through face recognition is a process of identifying the face for taking attendance by using the image of the human face as biometric parameter captured through a surveillance camera in the premise. This article presents an effective way of attendance monitoring by making use of deep learning technology and compares its results with the state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Yoga Pose Identification Using Deep Learning 1

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5th International Conference on Data and Information sciences, ICDIS 2023

作者： Verma, Ashutosh Kumar Sharma, Divyanshu Aggarwal, Himanshu Chauhan, Naveen Department of Computer Science and Engineering KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Yoga is an ancient practice that has gained popularity all over the world. It is a holistic approach to maintaining physical and mental health. Identifying yoga poses can be a challenging task for beginners and even experienced practitioners. In this research paper, we present a deep learning-based approach for identifying yoga poses from images. We put up a convolutional neural network (CNN) architecture that can appropriately classify 20 distinct yoga poses. Using a dataset of 5000 images, our suggested method had an accuracy of 97.3%. The results show that deep learning techniques can be used to accurately identify yoga poses from images, which can be used to develop an intelligent yoga training system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Convolutional neural networks

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Leveraging ground truth data and GIS technologies for reliable crop analysis and agricultural production optimization

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5247-5259页

作者： Shah, Jayneel Kothari, Smiti Verma, JaiPrakash Papakostas, George A. Department of Computer Science and Engineering Institute of Technology Nirma University Ahmadabad India MLV Research Group Department of Computer Science Democritus University of Thrace Kavala 65404 Greece

With an increasing global population, food security and crop data analysis are critical. Accurate ground-truth data is essential for reliable crop analysis and agricultural production optimisation. Geographic Information System (GIS) technologies play an important role in this regard, enabling the exact extraction, analysis, and visualisation of spatial data, allowing users to make informed choices. This research focuses on the significance of crop analysis and optimising agricultural production through the mapping of ground truth data to raster data and use of machine learning algorithms to predict crop yield. While mapping, a divergence of 45% was obtained between ground truth data and raster data. In the proposed solution, an expert system is employed to map raster data from the USGS Earth Explorer with ground truth data from drones using ArcGIS software, and a clustering algorithm is employed to forecast the yield from the generated NDVI image. Ground truth data collected using drones provides high-resolution imaging, flying flexibility, real-time data capture, and precise control over data collection elements, all of which improve results accuracy. The yield percentage was 66%, which meant 66% of the crops would yield well. A classification model trained on these images also provided a yield percentage of 66.8%, which was consistent with the findings. This research can help farmers make more educated decisions about irrigation, fertiliser application, and crop management, resulting in increased agricultural output. It can also help policymakers allocate resources and devise policies to increase food production and distribution, ultimately contributing to global food security. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ArcGIS Food security GIS Ground truth mapping Remote sensing

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Peer to Peer Communication using Web Sockets and Web RTC

Peer to Peer Communication using Web Sockets and Web RTC

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Sharma, Vineet Pandey, Mridul Kumar Gupta, Jayant Kishan, Hem KIET Group of Institutions Department of Computer Science and Engineering Ghaziabad Delhi-NCR India

ISBN: (纸本)9798350376470

In the rapidly expanding information era, the Internet has become an indispensable part of peoples' lives, and in this rapidly globalizing world, communication amongst people is paramount. Users can now access websites and other online resources because of the HTTP (Hypertext Transfer Protocol) request-response protocol, which is used to transfer data over the internet. However, one of its disadvantages is that clients cannot access servers' real-time data. Therefore, real-time communication is not possible with technologies like HTTP streaming, long polling, and polling. As a result, WebRTC, or Web Real-Time Communication, and the Web Socket Protocol were used. The latter enables browser-to-browser sharing of media and data in real time. With the help of this peer-to-peer technology, web browsers can now communicate in real time. Almost all contemporary browsers have this technology, which allows them to talk to each other rather than just web servers. Thus, this paper covers the importance of communicating in real-time, the limitations of traditional web real-time communication, an overview of web sockets, and the differences between HTTP and Web Socket Protocol. The paper then explains the significance of audio and video conferencing in the modern world and provides an overview of Web Real-Time Communication (WebRTC), including its architecture and the use of a web-RTC-based system. © 2024 IEEE.

关键词： HTTP

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Unmasking the Illusion: Deepfake Detection through MesoNet 5

Unmasking the Illusion: Deepfake Detection through MesoNet

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Akanksha Pandey, Dilkeshwar Kiet Group of Institutions Department of Computer Science and Engineering Delhi- NCR Ghaziabad India

ISBN: (纸本)9798350383522

In today's era of vast digital manipulation, the rise of deepfake technology poses a significant challenge to genuineness of multimedia content and introduces a profound risk to privacy, cybersecurity, and information integrity. Our research contributes to the ongoing discussion on deepfake detection, with a particular focus on assessing effectiveness of MesoNet model. It focuses on analysis of face micro-expressions and makes use of the Face2Face deepfake dataset, known for its adeptness in facial reenactment. Objectives of the research include evaluating MesoNet's efficacy by scrutinizing its performance across various parameters, fine-tuning the model for improved results, and gaining nuanced insights into its capabilities. Results reveal a notable advancement, with MesoNet achieving an accuracy of 90.4%, surpassing the previous 89.1%. Improved results after careful adjustment of activation functions and regularization parameters underscores the significance of hyperparameter optimization in deep learning models. © 2024 IEEE.

关键词： CNN deepfake expression swap face swap Face2Face MesoNet

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Stock Market Prediction Approach: An Analysis

Stock Market Prediction Approach: An Analysis

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Joshi, Mayank Goel, Gaurav Chandigarh Group of College Department of Computer Science and Engineering India

ISBN: (纸本)9798350322309

Data mining is a method that can glean meaningful information from a wealth of data. The data mining method known as prediction analysis uses current data to forecast potential future outcomes. Pre-processing the image, to extract the features, and classify the image are just a few of the operations used in the prediction analysis methodology. The stock market forecast is the foundation of this review essay. This study reviews a number of classification-based stock market prediction systems. Python is used to put the stock market forecasting concepts into practice. © 2023 IEEE.

关键词： Financial markets

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