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arXiv 2024年

作者： Goldenstein, Nelson Sulam, Jeremias Romano, Yaniv Department of Electrical and Computer Engineering The Technion Israel Biomedical Engineering Department The Mathematical Institute for Data Science Johns Hopkins University United States Department of Electrical and Computer Engineering The Department of Computer Science The Technion Israel

Sparse auto-encoders are useful for extracting low-dimensional representations from high-dimensional data. However, their performance degrades sharply when the input noise at test time differs from the noise employed during training. This limitation hinders the applicability of auto-encoders in real-world scenarios where the level of noise in the input is unpredictable. In this paper, we formalize single hidden layer sparse auto-encoders as a transform learning problem. Leveraging the transform modeling interpretation, we propose an optimization problem that leads to a predictive model invariant to the noise level at test time. In other words, the same pre-trained model is able to generalize to different noise levels. The proposed optimization algorithm, derived from the square root lasso, is translated into a new, computationally efficient auto-encoding architecture. After proving that our new method is invariant to the noise level, we evaluate our approach by training networks using the proposed architecture for denoising tasks. Our experimental results demonstrate that the trained models yield a significant improvement in stability against varying types of noise compared to commonly used architectures. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Survey of Covid Vaccine Hesitancy 15

Survey of Covid Vaccine Hesitancy

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Tripathi, Kirti Carter, Vivian Fergerson, An'Darius Prakash, Duraisamy Dutt, Varun Lloyd Institute of Engineering and Technology Electronics and Communication Engineering Uttar Pradesh India Tuskegee University Department of Psychology & Sociology AL United States University of Wisconsin-Green Bay Department of Computer Science WI United States Indian Institute of Technology Mandi School of Computing and Electrical Engineering Himachal Pradesh India

ISBN: (纸本)9798350370249

Vaccination serves as vital for strengthening immunity to illnesses and diseases that occurred amid the COVID-19 pandemic. There is a significant reluctance on the part of the general public as well as medical professionals to get vaccinated despite the affordability and availability of numerous licensed vaccines. This reluctance mainly causes worries about possible problems and negative effects. The keyword 'Vaccine hesitancy during COVID-19' was the primary search keyword on the Google Scholar platform. Seventeen studies that were published recently on the COVID19 pandemic that reported vaccine reluctance were found via our search, and these have been included in this review. During this survey, we found that the acceptance or rejection of vaccines was influenced by several criteria, such as income, education, gender, age, politics, religion, work position, and ethnicity. The vaccine was frequently refused for several reasons, which involve a general dislike of vaccinations, safety concerns or the belief that a rapidly developed vaccine may be too risky, the belief that immunity already exists or that the vaccine is unnecessary because COVID-19 is relatively harmless, a general lack of trust, doubts about the vaccine's efficacy, and distrust about the vaccine's origin. This review emphasizes the factors influencing COVID-19 vaccine acceptance and participation in clinical trials globally. It also presents strategies to enhance attitudes toward vaccination drives in the future. Additionally, governments and health authorities need to improve communication efforts and build public confidence. © 2024 IEEE.

关键词： Covid-19 Ethnicity Healthcare Vaccine Acceptance Vaccine Hesitancy

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Security in the World of Internet of Things (IoT): Overcoming Challenges

Security in the World of Internet of Things (IoT): Overcomin...

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2024 International Conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Gannavaram, Venkat Tulasi Krishna Desu, Sravya Jujjuru, Venkata Rohith Renikunta, Sri Vatsa Venkata Tejaswi Gajula, Arun Kumar Reddy, Ganta Raghotham School of Electrical Computer and Energy Engineering Arizona State University Tempe United States School of Computing and Augmented Intelligence Arizona State University Tempe United States Department of Electronics and Communication Engineering Kakatiya Institute of Technology and Science Warangal India

ISBN: (纸本)9798350355338

Internet of Things (IoT) is turning out to be more prominent in our everyday existence, with smart home appliances and industrial control systems serving as examples. But the proliferation of IoT devices and internet connectivity increased the potential of security breaches. Botnet attacks, injection attacks, Domain Name Systems (DNS) spoofing attacks, firmware assaults, zero-day attacks, brute-force attacks, and cryptographic attacks may be used against IoT devices. These assaults may result in the loss of data, illegal access, and even physical damage to infrastructure and people. There are various potential courses of action. Machine Learning subpart of Artificial Intelligence have the capacity for finding and prevent dangers in real time. With blockchain technology, an IoT network can have a secure and unbreakable a record of every exchange and data transfers. Hardware-based security techniques, such as secure boot and trusted execution environments, may protect IoT devices against attacks that seek to compromise the underlying hardware or firmware. Standardizing security procedures may ensure that all devices can work together safely and achieve a given degree of security. The use of privacy-enhancing technology, IoT data may be kept confidential and sensitive information disguised. © 2024 IEEE.

关键词： Firmware

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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data-Driven with IoT Sensing and Deep Learning Model for Dynamic Skin Cancer Diagnosis 2

Data-Driven with IoT Sensing and Deep Learning Model for Dyn...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumar, S. Mohan Thenmozhi, R. Balaji Damodhar, T.S. Epsiba, P. Malathi, N. Meenakshi, B. Cmr University Karnataka Bangalore India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chengalpattu India Madanapalle Institute of Technology & Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

This research introduces an innovative approach to continuously assess the condition of the skin barrier by combining advanced deep learning algorithms with Internet of Things (IoT) sensor technologies. For the system to function, it constantly uses IoT sensors to collect real-time data from various skin metrics, including moisture levels, pH balance, and environmental conditions. A complex analysis, made possible by deep learning, may be built using this vast amount of information. A type of deep learning technique that works well with data, recurrent neural networks (RNNs) are at the heart of this system. RNNs capture subtle changes over time in skin health by capitalizing on the temporal dependsequentialencies in skin data. Training RNNs iteratively on historical data covering a wide range of skin types and conditions allows them to detect patterns indicating healthy and damaged skin barriers. The proposed system has several benefits, such as individualized skincare suggestions, real-time information to guide adaptive skincare regimens, and continuous monitoring to identify skin concerns early. The system can discover complex patterns and correlations in the skin data, which results in better skincare solutions. © 2024 IEEE.

关键词： Recurrent neural networks

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Intrusion Detection System in Industrial Cyber-Physical System Using Clustered Federated Learning

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SN computer science 2023年第5期4卷 452页

作者： Jayagopal, Vellingiri Elangovan, Mohanraj Singaram, Saranya Sathasivam Shanmugam, Kavitha Bharathi Subramaniam, Balu Bhukya, Srinivasarao School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Artificial Intelligence and Data Science K.S.Rangasamy College of Technology Tamil Nadu Tiruchengode 637215 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Technology Kongu Engineering College Tamil Nadu Perundurai Erode 638060 India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamil Nadu Tiruchengode Namakkal 637205 India Department of Computer Science and Engineering Koneru Lakshmayya Education Foundation Andhra Pradesh Vaddeswaram 520002 India

The quick convergence in the industrial infrastructure with computing and networking has proliferated the attacks of industry-based Cyber-Physical Systems (CPS). Protecting such massive, sophisticated, and varied industrial CPSs from cyber-attacks is difficult. This research proposed a novel Clustered Federated Learning (CFL) technique that identifies cyber-attacks against industrial CPSs. A CFL architecture enables many industrial CPS to create an extensive model for maintaining privacy. The contribution of the research is to identify the intrusion in the CPS using CFL that is highly effective in terms of accuracy, error rate, and MCC. CFL is a Federated Multi-Task Learning (FMTL) paradigm that groups the attacks into clusters with simultaneously trainable data distributions by taking advantage of geometric features of the Federated Learning (FL) loss surface. Extensive tests on an industrial CPS dataset show their proposed CFL technique's high efficacy in identifying cyber-attacks on industrial CPSs and its advantages over cutting-edge solutions. Experiments are conducted on gas pipelining system-based datasets. In this research study, the data resource is split into two main categories: training is 80%, and testing is 20%. The proposed CFL method is effective by attaining the highest accuracy of 99.3%, the minimal error rate of 86%, and the highest Mathews Coefficient Correlation (MCC) of 0.7994 for communication round 10. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry 4 Learning Privacy Security Smart objects

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Security in the World of Internet of Things (IoT): Overcoming Challenges 4

Security in the World of Internet of Things (IoT): Overcomin...

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Gannavaram, Venkat Tulasi Krishna Desu, Sravya Jujjuru, Venkata Rohith Renikunta, Sri Vatsa Venkata Tejaswi Gajula, Arun Kumar Reddy, Ganta Raghotham School of Electrical Computer and Energy Engineering Arizona State University Tempe United States School of Computing and Augmented Intelligence Arizona State University Tempe United States Kakatiya Institute of Technology and Science Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350360165

Internet of Things (IoT) is turning out to be more prominent in our everyday existence, with smart home appliances and industrial control systems serving as examples. But the proliferation of IoT devices and internet connectivity increased the potential of security breaches. Botnet attacks, injection attacks, Domain Name Systems (DNS) spoofing attacks, firmware assaults, zero-day attacks, brute- force attacks, and cryptographic attacks may be used against IoT devices. These assaults may result in the loss of data, illegal access, and even physical damage to infrastructure and people. There are various potential courses of action. Machine Learning subpart of Artificial Intelligence have the capacity for finding and prevent dangers in real time. With blockchain technology, an IoT network can have a secure and unbreakable a record of every exchange and data transfers. Hardware-based security techniques, such as secure boot and trusted execution environments, may protect IoT devices against attacks that seek to compromise the underlying hardware or firmware. Standardizing security procedures may ensure that all devices can work together safely and achieve a given degree of security. The use of privacy-enhancing technology, IoT data may be kept confidential and sensitive information disguised. © 2024 IEEE.

关键词： Firmware

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Real-time Stream Processing in IoT Environments 9

Real-time Stream Processing in IoT Environments

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Rajasekar, P. Bhosale, Rupali Shekar Indhumathi, C. Sandeep, K.V. Prasanthi Kumari, N. Rajendiran, M. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Kattankulathur603203 India Bharati Vidyapeeth Institute of Technology Department of Electronics And Telecommunication Engineering Maharashtra Navi Mumbai400614 India Rmk College of Engineering And Technology Department of Computer Science And Business Systems Tamil Nadu Puduvoyal601206 India Dayananda Sagar College of Engineering Department of Electronics & Telecommunication Engineering Karnataka Bengaluru560111 India School of Computer Sciences University of Petroleum And Energy Studies Uttarakhand 248007 Dehradun India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions. © 2024 IEEE.

关键词： Edge computing

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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

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Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Air Purification Robotics using Cloud and Deep Q Networks for Autonomous Systems 1

Air Purification Robotics using Cloud and Deep Q Networks fo...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Reddy, Elangovan Guruva Damodhar, T. S. Balaji Yuvaraj, S. Vathani, B. Santha Sivakumar, S. Malathi, N. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India Madanapalle Institute of Technology and Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues. © 2024 IEEE.

关键词： Robotics

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