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Blockchain with drug traceability in medical supply chain with Henry gas solubility optimisation algorithm

International Journal of Wireless and Mobile Computing 2025年第2期28卷 135-148页

作者： Devi, P. Yamini Sriramya, P. Department of Computer Science and Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai India Department of Data Science Saveetha School of Engineering SIMATS Tamil Nadu Chennai India

The drug traceability model is used for ensuring drug quality and its safety for customers in the medical supply chain. The healthcare supply chain is a complex network, which is susceptible to failures and leakage of information because of cyber security attacks. Hence, this research aims to design a blockchain to perform traceability of drugs using Henry Gas Solubility Optimisation (HGSO). The entities include distributors, wholesalers, retailers, blockchain and patients. This model comprises initialisation, registration, key generation, authentication, drug distribution with traceability and key generation phases. Any exchange of medicines needs the approval of both sender and receiver. The drugs are registered in the blockchain, which then generates a secret key that is obtained using HGSO. The verification and authentication of drugs are performed after the authentication process. The implemented model outperformed with the response time, memory, normalised variance, and privacy of 32.678 sec, 84 MB, 0.958 and 0.958. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： authentication blockchain drug traceability key generation medical supply chain

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Identification of Human Activity from Video Streaming Smartphone data Using Intensified VGG16

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International Journal of engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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Improving Multi-model Anomaly Traffic Detection in MEC Networks with Large-Model-powered Continuous Learning

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IEEE Network 2025年第3期39卷 56-62页

作者： Zhang, Junjie Chen, Zheyi Cheng, Hongju Li, Jie Min, Geyong Fuzhou University College of Computer and Data Science China Shanghai Jiao Tong University Department of Computer Science and Engineering China University of Exeter Department of Computer Science China

Anomaly traffic detection offers essential technical support for securing Mobile Edge Computing (MEC) networks. The emerging Large Model (LM) has attracted much attention for their excellent data generation and processing capabilities, but it is difficult to deploy LM-based detection models in resource-constrained MEC networks. Existing solutions usually compress large models into tiny ones, but they tend to be impacted by data drift, resulting in decreased detection accuracy. To address this key challenge, we propose CL4Det, a novel multi-model anomaly traffic detection framework with LM-powered continuous learning, where the tiny models deployed in MEC networks can achieve the desired performance comparable to the large models via continuous retraining. Specifically, CL4Det periodically evaluates the model performance degradation caused by data drift in MEC networks and decides whether to generate retraining tasks and their configurations. Meanwhile, CL4Det schedules all traffic detection and retraining tasks with proper resource allocation, aiming to ensure real-time detection and maximize model accuracy. A case study with real-world traffic datasets verifies the effectiveness and superiority of CL4Det. Finally, we outline the challenges and future directions to fully exploit the collaborative potentials of MEC networks and LM in anomaly traffic detection. © 1986-2012 IEEE.

关键词： Resource allocation

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A 3 Degrees-of-Freedom Lightweight Flexible Twisted String Actuators (TSAs)-based Exoskeleton for Wrist Rehabilitation

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IEEE Robotics and Automation Letters 2025年第6期10卷 6520-6527页

作者： Dragusanu, Mihai Guinet, Nicolas Suthar, Bivraj Baldi, Tommaso Lisini Prattichizzo, Domenico Malvezzi, Monica University of Siena Department of Information Engineering and Mathematics Siena Italy School of Artificial Intelligence and Data Science India

This paper introduces a lightweight, three-degrees-of-freedom exoskeleton for wrist rehabilitation powered by Twisted String Actuators (TSAs), specifically designed to support flexion/extension, radial/ulnar deviation, and pronation/supination movements. Leveraging the high power-to-weight ratio of TSA actuation system, the exoskeleton ensures effective, comfortable, and personalized rehabilitation exercises. The device comprises five TSAs arranged in a tendon-driven configuration, enabling precise control and adaptability to various user anatomies. The experimental evaluations was conducted on a prototype demonstrating the device's ability to accurately replicate wrist movements guided by a physiotherapist, achieving low tracking errors (RMSE 1°). The exoskeleton effectively achieves the desired wrist range of motion-115° for flexion/extension, 70° for radial/ulnar deviation, and 150^{\circ } for pronation/supination-with torque capabilities suitable for rehabilitation purposes (0.35 Nm for flexion/extension and radial/ulnar deviation, and 0.06 Nm for pronation/supination). © 2016 IEEE.

关键词： Exoskeleton (Robotics)

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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An Unconstrained dataset for Face Recognition Across Distance, Pose, and Resolution 27th

An Unconstrained Dataset for Face Recognition Across Distan...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rathore, Udaybhan Agarwal, Akshay Data Science and Engineering Department IISER Bhopal Bhopal India

ISBN: (纸本)9783031783401

Face, a non-intrusive recognition modality, is an ideal candidate for identifying criminals or performing general-purpose person identification. On top of that, faces are not only related to identity but other essential attributes such as age and gender can also be extracted. Due to such potential, face recognition has received tremendous attention, yet face recognition from a distance remains challenging. To empower the face recognition research, we have collected a novel unconstrained video face dataset namely MobileFaces across various distances using mobile phones. Utilizing the proposed dataset, we have performed extensive experiments on face recognition, including verification and identification using state-of-the-art (SOTA) deep face recognition networks. Further, we have evaluated the robustness of current SOTA deep face attributes prediction networks to demonstrate whether the challenge lies in face recognition only or whether the existing algorithms are vulnerable in predicting facial attributes such as age and gender. The results suggest that the existing algorithms are ineffective not only in identifying the identity of the subjects but also fail to detect face attributes when the images are captured in unconstrained environments. For example, deep face networks yield the best macro average accuracy of 65% and an F-1 score of 0.48 when asked to predict gender on the collected dataset at a distance of 10 m. Based on the comparison with existing unconstrained face datasets and analysis of the effectiveness of image super-resolution techniques, it is showcased that the proposed dataset is significantly challenging compared to them, and hence, we believe that the presence of our dataset can advance the development of unconstrained face recognition algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Face Recognition MobileFaces Unconstrained Videos dataset

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Streamlining Kubernetes Deployments through GitOps Methodologies

Streamlining Kubernetes Deployments through GitOps Methodolo...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Kurrewar, Samiksha Dhomane, Sejal Dahake, Aditya Yadav, Ram Kumar Wyawahare, Nikhil Morris, Nekita Chavhan Ghrce IoT and Cyber Security Department of Data Science Nagpur India Manipal University Department of Data Science and Engineering Jaipur India

ISBN: (纸本)9798331529833

A clear process for automating the deployment of apps on Kubernetes using GitOps concepts is shown, with an emphasis on tools like Helm and Argo CD. The method demonstrates how to improve the effectiveness and reliability of application delivery by utilizing techniques like infrastructure- as-code, YAML configuration files, and CI/CD pipelines through GitHub Actions. The entities can accomplish reliable and repeatable deployments while developing teamwork among development teams by considering Git as the primary source of information. Applications remain in their original state with the support of Argo CD's continuous delivery, and monitoring tools provide data that is insightful on resource use and performance. By optimizing the management of cloud-native apps, our research helps people remain effective in the face of rapidly evolving technology. All things considered, the results demonstrate how applying GitOps techniques can streamline deployment procedures and improve application management in Kubernetes systems. © 2025 IEEE.

关键词： Information management

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A parallel solver framework for fully implicit monolithic fluid-structure interaction

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Acta Mechanica Sinica 2025年第2期41卷 88-115页

作者： Yujie Sun Qingshuang Lu Ju Liu Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and TechnologyShenzhen 518055China

We propose a suite of strategies for the parallel solution of fully implicit monolithic fluid-structure interaction(FSI).The solver is based on a modeling approach that uses the velocity and pressure as the primitive variables,which offers a bridge between computational fluid dynamics(CFD)and computational structural *** spatiotemporal discretization leverages the variational multiscale formulation and the generalized-αmethod as a means of providing a robust discrete *** particular,the time integration scheme does not suffer from the overshoot phenomenon and optimally dissipates high-frequency spurious modes in both subproblems of *** on the chosen fully implicit scheme,we systematically develop a combined suite of nonlinear and linear solver *** a block factorization of the Jacobian matrix,the Newton-Raphson procedure is reduced to solving two smaller linear systems in the multi-corrector *** first is of the elliptic type,indicating that the algebraic multigrid method serves as a well-suited *** second exhibits a two-by-two block structure that is analogous to the system arising in *** by prior studies,the additive Schwarz domain decomposition method and the block-factorization-based preconditioners are invoked to address the linear *** the number of unknowns matches in both subdomains,it is straightforward to balance loads when parallelizing the algorithm for distributed-memory *** use two representative FSI benchmarks to demonstrate the robustness,efficiency,and scalability of the overall FSI solver *** particular,it is found that the developed FSI solver is comparable to the CFD solver in several aspects,including fixed-size and isogranular scalability as well as robustness.

关键词： Fluid-structure interaction Monolithic coupling Implicit time integration Iterative methods Vortex-induced vibration

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