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Privacy-preserving deep learning approaches for effective utilization of wearable health data

Measurement: Sensors 2024年 33卷

作者： Deepak, Gerard Sharma, Priti Jayachitra, S. Chepur, Jyothi Srihari, T. Judgi, T. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Information Technology Jagannath International Management School New Delhi India Department of ECE PSNA College of Engineering and Technology Tamil Nadu Dindigul India School of Management Studies Chaitanya Bharathi Institute of Technology Hyderabad India Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

Wearable technology can significantly impact human existence by allowing for innovative perspectives on the outside world, such as through the use of augmented reality (AR) programs. Electronic gadgets that are worn as clothing, gadgets, or even incorporated into our bodies are called wearable gadgets. While there are many prospective advantages to smartwatches, their widespread and continuous use raises several privacy problems as well as complex data protection hurdles. Among the newer areas of healthcare informatics involves cognitive computation, which uses biological as well as physical information to autonomously track an individual's emotional status in mobility settings. While effective computation technologies hold great promise for enhancing everyday life, privacy problems and challenges must be addressed before analyzing physical information. Federated knowledge is a viable option for creating high-performing algorithms while protecting people's confidentiality. We used federated training to analyze cardiac activity information that was gathered from smart wristband tracking of anxiety levels during various situations. By protecting the confidentiality of information, we were able to accomplish promising outcomes when implementing federated training in Internet of Things-based wearable physiological tracking devices. © 2024 The Authors

关键词： Deep learning

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Revolutionizing Healthcare: A Smart Pill Detection System Empowered by Deep Learning for Intelligent Drug Recognition

Revolutionizing Healthcare: A Smart Pill Detection System Em...

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Trends in Quantum Computing and Emerging Business technologies (TQCEBT), International Conference on

作者： A. Dhayanithi Shivakumar Dalali B Madhavi Govindu Surla Bhaskar Marapelli Arun Kumar Arigela Department of Information Technology Sri Sairam Engineering College Department of Computer Science and Engineering PES University Department of Information Technology SRKR Engineering College Bhimavaram Department of CSE Lakireddy Bali Reddy College of Engineering (A) Mylavaram Andhra Pradesh Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of CSIT Marri Laxman Reddy Institute of Technology and Management (MLRITM) Hyderabad Telangana India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

In the dynamic landscape of healthcare, the accurate identification of medications stands as a critical pillar for patient safety and effective medical management. Traditional methodologies for identifying pills often rely on manual processes, placing a considerable burden on healthcare professionals and leaving room for errors, particularly when patients are unable to furnish precise prescription details. Compounding this challenge is the common practice of patients discarding medication containers, further complicating the identification process. At its core, the system harnesses the cutting-edge capabilities of artificial intelligence, specifically delving into advanced deep learning techniques. The overarching goal is to automate the intricate task of pill detection from images while simultaneously providing intelligent recognition of various medicinal *** by the robust Python programming language and underpinned by the sophisticated MobileNet architecture, the system aspires to achieve unprecedented accuracy and efficiency in the identification of pills and recognition of medicinal drugs. The training and testing processes unfold across a diverse dataset, encompassing a myriad of pill *** underscore the system's adaptability and real-world applicability within healthcare scenarios. The underlying ambition of this project is to furnish healthcare professionals with a state-of-the-art tool that not only mitigates the risk of medication errors but also optimizes time and resource utilization. Beyond its clinical applications, the system extends its impact to patients, affording them access to comprehensive information about their prescribed medications, thereby enhancing overall treatment comprehension and adherence. Over the ensuing sections, this project unfurls its narrative, delving into the intricate details of its methodology, implementation strategies, and the meticulous evaluation that underscores its potential to revolutionize healthcare t

关键词： Drugs Deep learning Training Adaptation models Image recognition Medical services Safety

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Leakage-Resilient Anonymous Multi-Receiver Outsourced Revocable Certificateless Encryption

Leakage-Resilient Anonymous Multi-Receiver Outsourced Revoca...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Yuh-Min Tseng Hung-Yu Chien Ruo-Wei Hung Tung-Tso Tsai Department of Mathematics National Changhua University of Education Changhua Taiwan Department of Information Management National Chinan University Nantou Taiwan Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan

To reduce the complexity of managing certificates in public-key infrastructure (PKI)-based public key systems (PKI-PKS) and solve the key escrow issue in identity (ID)-based public-key systems (ID-PKS), certificateless public-key systems (CL-PKS) are proposed and play an important role in public-key cryptography. But, a CL-PKS setting should consider the revocation issue to revoke intruded members so that a revocable certificateless public-key setting (RCL-PKS) is used to resolve this issue. Recently, side-channel attacks have been realized and endangered the existing cryptographic schemes/protocols. Fortunately, the research of leakage-resilient cryptographic schemes/protocols resisting such attacks have attracted the attention of cryptologists. Indeed, several leakage-resilient certificateless encryption (LR-CLE) schemes have been proposed. However, these LR-CLE schemes have some deficiencies. In the paper, the first leakage-resilient anonymous multi-receiver outsourced revocable certificateless encryption (LRAMR-ORCLE) scheme is proposed. As compared with several previous schemes, the proposed scheme possesses several merits: (1) The revocation functionality is performed by an outsourced revocation authority; (2) It guards against side-channel attacks and possesses leakage-resilient property; (3) An authorized receiver's identity is anonymous for other receivers; (4) It is a secure one-to-many encryption scheme.

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Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

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Journal of Network and computer Applications 2024年 227卷

作者： Lin, Chun-Cheng Deng, Der-Jiunn Hsieh, Li-Tsung Pan, Pei-Tzu Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua500 Taiwan

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities. © 2024 Elsevier Ltd

关键词： 5G mobile communication systems

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Adversarial attacks and defenses in physiological computing:a systematic review

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National science Open 2023年第1期2卷 62-90页

作者： Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China

Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

关键词： physiological computing brain-computer interfaces health informatics biometrics machine learning adversarial attack

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Hybrid high-performance computing enhanced machine learning framework for nano-thermal conductivity in MWNT-oil-based solar cooking systems

Journal of Engineering and Applied Science

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Journal of Engineering and Applied science 2025年第1期72卷 1-22页

作者： Mungle, Nischal P. Kumar, Sumit Mate, Dnyaneshwar M. Mankar, Sham H. Patil, Tejas R. Padwad, Hirkani Kakade, Niteen T. Shelke, Nilesh Isleem, Haytham F. Vairagade, Vikrant S. Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Electronics and Computer Science RTMNU Nagpur Nagpur India Department of Mechanical Engineering JSPM’s Rajarshi Shahu College of Engineering Pune India Department of Mechanical Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Pune India Department of Civil Engineering Priyadarshini Bhagwati College of Engineering Nagpur India Department of Computer Science & Engineering and Emerging Technologies Shri Ramdeobaba College of Engineering and Management Ramdeobaba University Nagpur India Department of Mechanical Engineering Tulsiramji Gaikwad Patil College of Engineering and Technology Nagpur India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science University of York York UK Department of Civil Engineering Priyadarshini College of Engineering Nagpur India

Solar cooking is a very pertinent alternative for energy-challenged regions, but conventional systems are miserably lacking in heat retention and efficiency, as well as adaptability to changing solar conditions. The presented theoretical study proposes an HPC hybrid model designed to optimize nano-thermal behavior in oil-based solar cooking systems with MWNTs. The proposed scheme is an amalgamation of five advanced modules: adaptive multiphase heat transfer modeling (AMPHTM), topological data analysis (TDA), graph-theoretic heat flow optimization (GTHFO), fractal-based multi-scale thermal transport modeling (FB-MTTM), and thermo-optical spectral mapping (TOSM-HA). These modules together provide real-time corrections for MWNT dispersion, topological clustering, and spectral mismatch while enhancing thermal transport at multiple scales. Modeling and simulation predicted enhanced effective thermal conductivity under dynamic solar conditions, ranging from 0.49 to 1.27 W/mK in the 0.01%–0.1% MWNT volume fraction. Reduction of heat loss by 45% and improvement of cooking efficiency by 25% to 30% in 30 min compared to baseline methods. The topological instability in MWNT dispersion was diminished using a clustering index of 0.027, and spectral absorption in the near-infrared region saw a 2.85-fold enhancement compared with base fluids. The very multi-paradigms adaptive thermosystem presents new horizons in precision thermal control for solar cooking and puts into perspective a real-time field-scalable envision for nano-thermal optimization in sustainable energy technologies in process.

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Heart Rate Variability as a Noninvasive Sensor for Acute Mental Stress and Cardiac Abnormalities: Comparative Study

IEEE Sensors Reviews

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IEEE Sensors Reviews 2025年 2卷 199-208页

作者： Azhar Shadab Purnendu Shekhar Pandey Sanjeev Kumar Raghuwanshi Shashank Awasthi Santosh Kumar Department of Electronics and Communication Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Department of Electronics Engineering Indian Institute of Technology (Indian School of Mines) Dhanbad India Department of Computer science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Centre of Excellence for Nanotechnology Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

The electrocardiogram (ECG) is an essential diagnostic tool with a broad spectrum of clinical applications. Heart rate variability (HRV) is a well-known and widely used indicator for identifying diseases affecting the heart and related systems. Continuous monitoring and appropriate action are crucial for stress management, as mental stress has emerged as a significant health concern in today’s society. An increasing variety of HRV measurements has been created, and HRV has been suggested as a viable option for ecological instantaneous assessment of acute mental stress. However, additional research into the suitable metrics for monitoring the transient change of mental stress is required due to the inconsistent results of previous studies. The proposed study utilizes a noninvasive approach to compare ECG data from a normal sample with those from subjects affected by arrhythmia. The same test was conducted for two subjects (living in different atmospheres), and their mental stress was monitored with different scenarios. HRV analysis serves as the foundation for the comparison. The results are derived from the extraction or calculation of certain aspects of the HRV signal, and a comparison is then performed for the two classes, the cardiac and the normal samples. This work proposes an experimental demonstration of the ECG signal and verification with BIOPAC.

关键词： Heart rate variability Electrocardiography Heart rate Human factors Real-time systems Monitoring Feature extraction Time-domain analysis Software Resonant frequency

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Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent 20

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcem...

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Hunor Toth, Szilard Janos Viharos, Zsolt Bardos, Adam Institute of Computer Science and Control Research Laboratory on Engineering Management Intelligence Intelligent Processes Research Group Budapest Hungary John von Neumann University Faculty of Economics Kecskemét Hungary Budapest University of Technology and Economics Department of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665497046

Self-driving vehicles have become a more and more important field in recent years. Supported by the techniques of Artificial Intelligence (AI), the current tendency of positive results in applications is making it a promising area to focus further research. Additionally, Reinforcement Learning (RL) is already proved to be an efficient approach for complex problems, e.g. robots, industrial systems and also games (like chess, Go), *** is a driving technique at handling limits where the driver intentionally oversteers, with loss of traction, while driving the vehicle through the entirety of a corner. It is a very challenging control task and often results in an accident when it occurs on public roads, consequently, the efficient control of this motion is especially important in the safety of autonomous *** paper reports novel research results whose main goal is to develop a self-driving agent for drift motion control based on vehicle simulation by Matlab/Simulink. Longitudinal and lateral velocity together with the yaw rate formed the state representation of the vehicle. The agent action space consists of two continuous actuator values: pedal ratio and roadwheel angle. The goal of the agent is twofold: first, it has to jump into a drifting state, second, it has to keep the vehicle in *** simulation results show that the proposed Soft Actor-Critic (SAC) RL agent is capable of learning to approach a pre-determined drift equilibrium from cornering and staying in this drift situation as well. For the training, the solution excluded using any kind of prior data, it only works with information gained from the simulation model, which is a remarkable difference from the actual state-of-the-art RL-based solutions. © 2022 IEEE.

关键词： Training Roads Simulation Reinforcement learning Mathematical models Data models Artificial intelligence

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Twitter Spam Detection Using Word Vector and Binary Classifier

Twitter Spam Detection Using Word Vector and Binary Classifi...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Ramaprabha Marimuthu Gurusigaamani Ayyanar Muthulingam Vinoth N.A. S Sivasathiya Ganesan Kardeepa Ponnuchamy Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Artificial Intelligence and Data Science Easwari Engineering College Ramapuram Chennai India

In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links for redirecting to phishing websites. Researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitter datasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics.

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A new (p;q)- rung orthopair fuzzy SIR method with a multi-criteria decision-making approach

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Granular Computing 2024年第2期9卷

作者： Shahzadi, Gulfam Luqman, Anam Shahzadi, Sundas Deveci, Muhammet Department of Mathematics University of Management and Technology Sialkot Campus Pakistan Division of Science and Technology Department of Mathematics University of Education Lahore Pakistan Department of Industrial Engineering Turkish Naval Academy National Defence University Istanbul Tuzla34942 Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

In comparison to Fermatean, Pythagorean, and intuitionistic fuzzy sets, (p,q)-rung orthopair fuzzy sets have a wider range of displaying membership grades and can therefore provide more uncertain situations. In this work, the accuracy of (p,q)-rung orthopair fuzzy numbers is investigated using sine trigonometric functions. First, the (p,q)-rung orthopair fuzzy data are extended to the sine trigonometric operational laws (STOLs). In this study, we suggest a novel (p,q)-rung orthopair fuzzy superiority and inferiority ranking (SIR) approach to address the uncertainty group multiple-attribute decision-making (MADM) problem. This strategy handles unclear information, incorporates individual perspectives into group viewpoints, makes conclusions based on many criteria, and ultimately structures a specific decision map. The proposed SIR method utilizes two kinds of information, the superiority and the inferiority information, to obtain two types of flows, including the superiority and the inferiority flows. Then, these flows are utilized to rank the set of alternatives partially or completely. Using sine trigonometric functions and the flexibility of (p,q)-rung orthopair fuzzy sets, novel STOLs have been created. Further, we conduct a case study of the selection of the best journal to demonstrate the feasibility and applicability of the developed technique. The main contributions of this article are as follows: (1) The aggregation operators for (p,q)-rung orthopair fuzzy numbers and their characteristics have been studied under sine trigonometric functions. (2) The SIR approach has been developed under (p,q)-rung orthopair fuzzy sets. The proposed technique is explained through a step-by-step Algorithm. (3) Then, a case study of journal selection is considered to apply the developed technique. (4) The results obtained have been compared with the ranking obtained through various existing techniques. © The Author(s), under exclusive licence to Springer Nature Switzerland AG

关键词： Decision making

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