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Intelligent patient monitoring through hybrid consensus algorithm based blockchain technology

Multimedia Tools and Applications 2025年第18期84卷 19579-19600页

作者： Priyadharshini, A. Nithiya, S. Archana, H.A. Jaganathan, Subash Chandra Bose School of Engineering and Technology Bangaluru India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Agronomy SRM College of Agricultural Science Chengalpattu603201 India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India

This research aims to integrate IoT with blockchain technology to securely manage and monitor sensitive patient health data in critical care environments, thereby improving the reliability and efficiency of patient monitoring systems. A hybrid blockchain architecture is employed to store ICU patient data securely, linking each patient's information with their respective doctors' details. This system operates without third-party intermediaries, ensuring trusted communication channels between patients and doctors. The blockchain ensures authorized access and secure transmission of medical histories and treatment plans, even allowing for remote care by doctors outside hospital premises. The integration of blockchain technology with IoT devices facilitates rapid and secure data transmission, enhancing the speed and accuracy of critical care delivery. The decentralized nature of blockchain ensures data integrity and prevents unauthorized access, while the hybrid approach allows for efficient and trusted collaboration among healthcare professionals. By harnessing the power of hybrid blockchain technology, this system not only safeguards sensitive medical information but also improves the overall quality of critical care through timely and informed medical intervention. The proposed solution holds the promise of reducing patient mortality rates by ensuring secure, efficient, and collaborative healthcare delivery. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

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Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Mendel

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Mendel 2023年第2期29卷 111-130页

作者： Chang, Wee Chea Sangodiah, Anbuselvan Faculty of Technology and Applied Science Open University Malaysia Malaysia School of Computing Faculty of Computing and Engineering Quest International University Malaysia

Semantic Web is the vision to make Internet data machine-readable to achieve information retrieval with higher granularity and personalisation. Semantic annotation is the process that binds machine-understandable descriptions into Web resources such as text and images. Hence, the success of Semantic Web depends on the wide availability of semantically annotated Web resources. However, there remains a huge amount of unannotated Web resources due to the limited annotation capability available. In order to address this, machine learning approaches have been used to improve the automation process. This Systematic Review aims to summarise the existing state-of-the-art literature to answer five Research Questions focusing on machine learning driven semantic annotation automation. The analysis of 40 selected primary studies reveals that the use of unitary and combination of machine learning algorithms are both the current directions. Support Vector Machine (SVM) is the most-used algorithm, and supervised learning is the predominant machine learning type. Both semi-automated and fully automated annotation are almost nearly achieved. Meanwhile, text is the most annotated Web resource;and the availability of third-party annotation tools is in-line with this. While Precision, Recall, F-Measure and Accuracy are the most deployed quality metrics, not all the studies measured the quality of the annotated results. In the future, standardising quality measures is the direction for research. © 2023, Brno University of technology. All rights reserved.

关键词： Automation

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Nanophononic metamaterials induced proximity effect in heat flux regulation

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Frontiers of physics 2024年第2期19卷 149-156页

作者： Jian Zhang Haochun Zhang Gang Zhang School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China Institute of High Performance Computing Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore

Recent studies have shown that the construction of nanophononic metamaterials can reduce thermal conductivity without affecting electrical properties,making them promising in many fields of application,such as energy conversion and thermal ***,although extensive studies have been carried out on thermal conductivity reduction in nanophononic metamaterials,the local heat flux characteristic is still *** this work,we construct a heat flux regulator which includes a silicon nanofilm with silicon *** regulator has remarkable heat flux regulation ability,and various impacts on the regulation ability are ***,even in the region without nanopillars,the local heat current is still lower than that in pristine silicon nanofilms,reduced by the neighboring nanopillars through the thermal proximity *** combine the analysis of the phonon participation ratio with the intensity of localized phonon modes to provide a clear *** findings not only provide insights into the mechanisms of heat flux regulation by nanophononic metamaterials,but also will open up new research directions to control local heat flux for a broad range of applications,including heat management,thermoelectric energy conversion,thermal cloak,and thermal concentrator.

关键词： nanophononic metamaterials proximity effect

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Enhancing Asthma Diagnosis Through Machine Learning: A Stacking Ensemble Approach 1

Enhancing Asthma Diagnosis Through Machine Learning: A Stack...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Goel, Manas Gupta, Manya Jothikumar, C. Shanmugam, S. Ashokkumar, C. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Chennai603203 India

ISBN: (纸本)9798331518943

Asthma diagnosis remains a major clinical concern since it has diverse symptoms and is caused by various contributory causes. Machine learning was applied in this study to solve these complications in order to analyze such a broad dataset of 2,392 patients with the capture of important variables such as demographics, environmental exposures, allergies, medical history, and clinical indicators. Several machine learning models were implemented, including those such as Random Forest, XGBoost, Decision Tree, LGBM, CatBoost, and AdaBoost. Next, the stacking ensemble strategy was taken to further optimize predictive performance, using the Random Forest model meta-classifier. Our approach proves that stable ensemble learning takes place with an amazing 97% accuracy rate when it comes to difficult clinical data. Recall, precision, and F1-score represent some of the most important measures for model performance evaluation. These results might point out the machine learning potential in improving asthma diagnosis accuracy and might be helpful for a doctor in coping with this complicated disease. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Attack-Resilient Distributed Cooperative Control of Virtually Coupled High-Speed Trains via Topology Reconfiguration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1066-1068页

作者： Shunyuan Xiao Xiaohua Ge Qing Wu the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the School of Engineering and Technology Central Queensland UniversityRockhampton QLD 4701Australia

Dear Editor,This letter addresses the resilient distributed cooperative control problem of a virtually coupled train convoy under stochastic disturbances and cyber *** main purpose is to achieve distributed coordinati... 详细信息

关键词： prescribed Speed trains

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YOLOv4-Powered Traffic Intelligence: From Detection to Optimization

YOLOv4-Powered Traffic Intelligence: From Detection to Optim...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sanjiv, Sinha Tharunn, E. Baskar, M. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350382693

The Traffic Analysis System utilizes machine learning for real-time traffic insights at a junction. It provides instant stats like vehicle count, density, and FPS, while summarizing average vehicle crossings, densities, stop times, and identifying peak minutes. Machine learning techniques include object detection, tracking, and lane segmentation. The system offers a user-friendly interface with graphical representations, aiding traffic management decisions. It not only ensures efficient traffic control but also allows for predictive analytics and potential integration with traffic light systems for adaptive signal adjustments. Continuous model training and future enhancements promise improved accuracy and adaptability to evolving traffic scenarios. © 2024 IEEE.

关键词： Machine learning

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Revolutionizing Lion Conservation with EfficientNet B7: Real-Time Detection and Health Classification 3

Revolutionizing Lion Conservation with EfficientNet B7: Real...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Koradaa, Sampath Kumar Potnuru, Syam Virat Gupta AshokKumar, C. Kumar, C. Jothi Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331532420

This research introduces a cutting-edge framework leveraging advanced AI and IoT technologies to revolutionize lion conservation. The primary focus is on real-time lion detection, individual identification using unique facial features, gender classification, and health condition assessment, including malnutrition, injuries, and illnesses. By integrating GPS modules and IoT sensors, the system facilitates noninvasive and ethical monitoring of lions and their habitats. The study employs YOLO (You Only Look Once) for real-time detection of lions in video feeds and EfficientNet B7 for multitask classification, enabling precise identification of individual lions and their health status. DeepSORT is utilized for tracking lion movements over time, ensuring continuous observation and territorial mapping. These models are deployed on edge devices, such as drones and camera traps, to deliver instantaneous results while minimizing latency. The proposed system offers numerous advantages, including improved conservation efforts through real-time alerts and actionable insights. Its ability to monitor health conditions and movement patterns enables proactive interventions to safeguard lion populations. Moreover, the integration of IoT sensors ensures comprehensive environmental monitoring, supporting a holistic understanding of the ecosystem. By employing cloud platforms for data analytics and storage, the system provides accessible dashboards and detailed reports, empowering conservationists and wildlife authorities with valuable insights. This framework not only enhances the efficiency of lion conservation but also ensures ethical practices, setting a benchmark for integrating AI and IoT in wildlife preservation. The research underscores the potential of technology to address critical ecological challenges and promote sustainable conservation efforts. © 2024 IEEE.

关键词： Mammals

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Detection of Alzheimer’s Disease from Brain MRI Images Using Convolutional Neural Network 6th

Detection of Alzheimer’s Disease from Brain MRI Images Usin...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Santosh, Nomula Khan, Patan Imran Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819774227

Alzheimer's disease (AD) ranks as the predominant factor leading to the onset of dementia. AD leads to a steady decline in memory, reasoning, behavior, and social abilities. These changes affect a person's personality. Alzheimer's disease is most common in older adults. Conventional diagnostics methods for Alzheimer's disease face challenges like invasiveness, cost, imprecision, and patient discomfort. The aim is to develop an innovative AI model offering a non-invasive, cost-effective solution. In this suggested system, we use a convolutional neural network to classify AD into four classes: Normal Phase, First Phase, Second Phase, and Third Phase. The dataset size is 6000 MRI (Magnetic Resonance Imaging) images. AD causes the brain regions to shrink, and connections between the network of neurons may break down. Hence, accurate and timely Alzheimer's disease classification is crucial for effective treatment and better patient outcomes. The primary goal of the suggested model is to classify AD with greater accuracy using a deep learning model. The work follows several preprocessing steps for the input images, such as image resizing, image pixel resizing, and batching images to enhance the input features, and for disease classification, the Convolutional Neural Network model is used. The suggested model was evaluated using Kaggle dataset images, and a maximum accuracy of 97.25% was obtained using the Kaggle dataset. This research addresses the critical need for precise AD classification, potentially revolutionizing patient outcomes through early detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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