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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Bhaskaranaik, S. Sreedevi, M. Rafick, S. Ravivarman, G. Rajendiran, M. Maranan, Ramya SVU College commerce management & Computer Science Department of Computer Science Andara Pradesh Tirupati India RVS College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Poonamallee Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This article presents the design, development, implementation, and evaluation of a system for patient health monitoring by synergistically combining machine learning models, mobile ad hoc networks (MANET), and Internet of Things (IoT). In this patient health monitoring system, data are gathered from numerous sensors installed on IoT devices in real-time to support dynamic and continuous health monitoring of patients within predefined constraints. The data are sent over MANET, which has nodes that send data to cloud storage for a centralized management. The data collected from the patients are used to train various Machine Learning (ML) models to learn when the patient is likely to experience a health problem. The analysis carried out in this study reveal that the Support Vector Machine (SVM) is found to be the most accurate model for predicting patient health problems, with an accuracy of 91.23%. The Artificial Neural Network (ANN), Random Forest (RF), and Naive Bayes (NB) rank next, respectively, with accuracy of 88.7%, 86.5%, and 84.4% respectively, following SVM. Recall, accuracy, and F1 score are given to provide comparative information on the relative merits of these models. This integrated healthcare monitoring system, with its timely alert system, real-time monitoring, and powerful predictive analytics, provide patients with personalized and proactive care. It enhances healthcare practices by showing that the data-driven strategic approach and its associated technologies are transforming patient outcomes in the emerging field of healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Social Networks on WEB 3.0 12th

Social Networks on WEB 3.0

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Cidri, Anirudh AshaRedddy, M. Bhaskar, T. Vijaya Krishna, A. Suryanarayana, G. Department of Information Technology Vardhaman College of Engineering Hyderabad India Department of Computer Science & Engineering-AIML CMR College of Engineering Hyderabad India Department of Information Technology G. Narayanamma Institute of Technology and Science Hyderabad India

ISBN: (纸本)9789819601462

The "Social Network on WEB3.0" project is poised to revolutionize online social dynamics by leveraging blockchain technology to create a decentralized platform. This initiative prioritizes user control and security, aiming to address the shortcomings of existing Web 2.0 networks. Users will have unprecedented control over their data and privacy, with blockchain ensuring transaction irreversibility. Key goals include user empowerment, enhanced data security, decentralization, incentivized participation, and adherence to legal and privacy standards. The project incorporates a token-based incentive system to stimulate user engagement and collaboration, fostering a vibrant online community. Leveraging Ethereum and MetaMask for transactions and smart contracts highlights the project’s adaptability and scalability. With a commitment to ongoing enhancement and user feedback, the initiative heralds a significant shift toward a digital landscape characterized by user-centricity, heightened security, and privacy awareness, offering individuals unparalleled authority over their online interactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Style Transfer in 3D Object Generation using Generative Adversarial Networks 1

Style Transfer in 3D Object Generation using Generative Adve...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sabah, Hawraa Ali Singh, Sandeep Al-Farouni, Mohammed Pareek, Shashank Verma, Deeksha Dey, Protyay National University of Science and Technology College of Technical Engineering Dhi Qar Iraq Bahra University Department of Civil Engineering School of Engineering and Technology Waknaghat India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Department of Computer Science Engineering Mohali India Nims University Nims School of Computing Science and Artificial Intelligence Jaipur India

ISBN: (纸本)9798350352931

This paper uses style transfer techniques with GANs to improve the variety and quality of generated 3D objects. By outlining the numerous advantages and highlighting the key obstacles, we show that it is feasible to merge style transfer with 3D object generation. It has been shown that GANs, originally thought of in terms of 2D domains, can be easily extended to 3D settings;however, certain issues have been identified with the modeling of 3D shapes and the training of models. Mode collapse and high computational complexity are two of the main obstacles that prevent GANs from being used for 3D objects. Additionally, it clarifies why a distinct and more intricate architecture is necessary to address the issue of 3D gan and neural implicit representations in modeling. Additional emphasis was placed on enhancing training stability through the use of superior methods, such as the progressive growing and WassersteinGANs with gradient penalty method. The study does, however, detail the difficulties of putting these models into practice, as well as some environmentally friendly ways to improve computation, such as by trimming networks and making use of clouds. These are the research vulnerabilities that have been identified: Researchers should expand their focus in their current studies of the aforementioned model to include domains that adapt and transfer learning, and they should also take into account the reality of operating in VR and AR settings. If these holes are filled, we can remove the current limitations and use style transfer to create 3D objects to a far greater extent. So, it's clear that style transfer intermittently into 3D GANs can work, but there are still a lot of obstacles to overcome and limits to these methods, so researchers are working hard to make them more effective and realistic. © 2024 IEEE.

关键词： 3D object generation generative adversarial networks (GANs) mode collapse style transfer training stability

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Deep Fake Detection Using computer Vision-Based Deep Neural Network with Pairwise Learning

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Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning-based approaches are applied successfully in manyﬁelds such as deepFake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： Deep fake deep belief network fuzzy clustering feature extraction pairwise learning

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Design of a Machine Learning based Data Analytics Engine to Improve Decision Making Processes 5

Design of a Machine Learning based Data Analytics Engine to ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Pethe, Yoginee Surendra Dandekar, Pranali Rahul Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9781665474672

Analytical Hierarchy Processing (AHP) assists in estimation of input-specific weights for improving system-level decision making operations. The process initially decides a goal, and then uses different criterion & alternatives to achieve it. But original AHP model is not efficient for large-scale systems, which limits its capability under real-time use cases. To overcome this issue, various Machine Learning based techniques are proposed, and each of them has their own set of limitations. Techniques with higher efficiency also showcase high complexity, while simplistic optimization techniques have limited performance levels. To overcome these limitations, this text proposes design of an efficient AHP model with low complexity via integration of (GWO), and iterative learning (IL) operations. The proposed model initially performs context-based correlation between input parameter sets. These correlation values are used to modify the AHP decisions, which are tuned by GWO process. Results of Grey Wolf optimization this GWO process are optimized via IL based tuning, which incrementally modifies the correlation values for optimum efficiency levels. Due to which the proposed model showcases low complexity, while maintaining higher efficiency when compared with the original AHP process. The proposed model was evaluated on multiple datasets including housing, image processing, and employment scenarios. Based on this evaluation it was observed that the proposed model showcased 8.5% higher accuracy, 6.4% higher precision, with 4.5% 1ower delay when compared with original AHP and standard AHP-optimization techniques. Due to this improvement, the proposed model is capable of deployment for a wide variety of real-time scenarios. © 2023 IEEE.

关键词： Machine learning

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IoT-Enhanced Smart Carry to Prevent Spinal Cord Injury by Weight Monitoring

IoT-Enhanced Smart Carry to Prevent Spinal Cord Injury by We...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Varshitha, D.N. Shreya, B.S. Nidhi Shree, S.P. Vinutha, D.C. Gourav, R. Chetan, P. Mysuru India Vidyavardhaka College of Engineering Computer Science Engineering Department Mysuru India Vidyavardhaka College of Engineering Mechanical Engineering Department Mysuru India

ISBN: (纸本)9798350306927

The necessity for students to carry heavy backpacks, laden with books and study materials, is prevalent in numerous educational institutions worldwide, particularly in remote areas where walking long distances is common. However, this practice poses significant health risks, leading to issues like improper posture, neck, shoulder, and back strain, and potentially severe spinal damage. To address these concerns, our study proposes an IoT-based solution. Utilizing components such as Arduino 101 processing board, a Bluetooth module, accelerometers, force-sensitive resistors, backpacks, and a mobile application, our solution aims to alert students when their backpack weight exceeds a predetermined threshold. This paper discusses the methodology, results, and relevant literature associated with our proposed solution. © 2023 IEEE.

关键词： Accelerometers

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A semantics-based validation approach for enhancing QoS in distributed real-time DBMS

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International Journal of Intelligent Information and Database Systems 2025年第1期17卷 124-142页

作者： Achour, Fehima Bouazizi, Emna Jaziri, Wassim MIRACL Laboratory The Higher Institute of Computer Science and Multimedia P.B. 242 Sfax3021 Tunisia The College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Faisal University Al Ahsa Saudi Arabia

Distributed real-time database management systems (DRTDBMS) are essential for critical applications such as financial trading and healthcare, where timely processing is vital. Quality of service (QoS) approaches, like the distributed feedback control scheduling (DFCS) architecture, are favoured for improving DRTDBMS performance. However, transactions scheduling remains a challenge in such systems, impacting user’s experience. Recently, the advanced earliest deadline first based on transactions aggregation links and data semantic links (AEDF-TAL-DSL) protocol emerged as a scheduling solution in a centralised real-time context. This paper focuses on exploring the adaptability of the AEDF-TAL-DSL protocol to the DFCS architecture, offering a significant opportunity to optimise DRTDBMS performance. The novelty of our approach is to enable advanced transaction scheduling while considering transaction aggregation links and data semantic links aiming to maximise the number of satisfied transactions. We illustrate the contributions of our approach through simulation results, demonstrating its effectiveness to enhance the QoS in DRTDBMS. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Quality of service

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A New Offloading Method in Cloud Platforms: Reducing Latency Based on Data Compression 7

A New Offloading Method in Cloud Platforms: Reducing Latency...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Yang, Zhiqin Zhou, Zhennan Dong, Han Feng, Xiaoning Southampton Ocean Engineering Joint Institute Harbin Engineering University Harbin China College Of Computer Science And Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798350361445

This paper introduces a novel approach to reduce computational latencies in cloud platforms by integrating data compression with Non-Orthogonal Multiple Access (NOMA) technologies. We propose a computational task offloading scheme that optimizes resource allocation to minimize maximum user task completion latency by utilizing an adaptive step size Artificial Fish Swarm Algorithm. Experiments conducted using Python simulation demonstrate that our approach significantly lowers task completion latencies compared to existing methods that rely solely on either data compression or NOMA technologies. © 2024 IEEE.

关键词： Data compression

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Social Media Networking Analytics and Growth Perspectives 8th

Social Media Networking Analytics and Growth Perspectives

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Sabla, Yaashie Gour, Sanjay Mukesh Patel School of Technology Management and Engineering Mumbai India Department of Computer Science and Engineering Jaipur Engineering College and Research Centre Jaipur India

ISBN: (纸本)9789819994854

In the digital age, the significance of social media networking cannot be overstated. Individuals, businesses, and organizations have embraced these platforms as essential tools for communication, brand promotion, and community building. Social media analytics involve the systematic collection and analysis of data to measure the performance of one’s presence on platforms like Facebook, Instagram, Twitter, and LinkedIn. Key performance indicators (KPIs) such as engagement, reach, impressions, and conversion rates are pivotal in assessing the effectiveness of social media strategies. Additionally, understanding audience demographics, content performance, and competitor analysis informs data-driven decision-making. Growth perspectives in social media networking encompass strategies to expand reach, engage audiences, and foster sustainable growth. Consistency in posting, delivering high-quality content, and engaging with users are fundamental principles. Paid advertising, collaborations, and community building are strategies that extend one’s social media reach. Furthermore, data-driven optimization, trend monitoring, and user experience enhancements are indispensable for staying relevant in a dynamic digital landscape. This article is an attempt to provide a concise overview of the critical components related to social media networking along with analytics and growth perspectives. The article encapsulates the essence of social media networking analytics and growth perspectives, offering a foundation for individuals, businesses, and organizations to harness the full potential of these platforms, connect with their target audiences, and achieve enduring growth in the ever-evolving realm of social media. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification 2

Revolutionizing Influenza A Detection in Humans: Unleashing ...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Nikhil, V. Sahaaya Arul Mary, S.A. Natarajan, B. Bajulunisha, A. Anuradha, R.S. Venkatraman, K. Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VIT University School of Computer Science Engineering Chennai India M.A.M College of Engineering and Technology Department of Artificial Intelligence & Data Science Tiruchirappalli621105 India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350383287

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model's capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems. © 2024 IEEE.

关键词： Classification (of information)

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