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Queue-Based Analytical Modeling for Capacity Estimation of Wearable eHealth Systems

IEEE Transactions on Network science and Engineering 2025年第3期12卷 2033-2042页

作者： Pathak, Nidhi Mukherjee, Anandarup Misra, Sudip Indian Institute of Technology Kharagpur Advanced Technology Development Center Kharagpur721302 India University of Cambridge Institute for Manufacturing CambridgeCB3 0FS United Kingdom Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

The heterogeneity in IoT-based health monitoring devices and communication protocols leads to diverse system designs and configurations resulting in uncertain outcomes. Most present-day eHealth approaches focus on optimizing the performance of already designed systems (hardware, network, and software) rather than optimizing the system design itself. A reliable eHealth monitoring system must use appropriate protocols and hardware to avoid data loss and delays. Identifying the magnitude of performance changes when integrating new devices into an already functional eHealth system is also critical. This work addresses these issues by adopting a queuing theory-based analytical model to characterize an eHealth wearable system. This approach acts as a guiding framework for system and network capacity estimation by simulating the dynamics of an eHealth wearable system with selected communication protocols before deployment. The simulation tool identifies the system's limits in terms of delays, the number of simultaneously supported wearables, and packet loss. We identify and examine some well-known wireless connectivity protocols as possible candidates for eHealth wearable systems. The output of the analytical model is compared vis-à-vis data from a real-life proof-of-concept eHealth wearable system. The proposed approach estimates the numbers of served packets and blocked packets with approximate errors of 0.02% and 0.2%, respectively. © 2013 IEEE.

关键词： Queueing theory

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support 2

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Kattankulathur Department of Computational Intelligence Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Deep Learning-Driven SIoT Security: Detecting Anomalies and Community Attacks with SPY IoT Devices

Deep Learning-Driven SIoT Security: Detecting Anomalies and ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anciline Jenifer J Piramu Preethika. S. K. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The rapid growth of Social Internet of Things (SIoT) networks, new security vulnerabilities have evolved, including irregularity detection and community-based hacks. Given the large degree of decentralization in SIoT systems, traditional security methods are frequently ineffective. The study employs a deep learning (DL)-based security solution that detects anomalies and community attacks in real time using SPY IoT sensors. The proposed approach employs AutoEncoders(AEs) for SIoT behaviour, Convolutional Neural networks(CNN)+Long Short Term Memory(LSTM) for spatial and temporal features and Reinforcement Learning(RL) for adaptive Learning. The novel approach described in this study improves the detection of various attack situations, hence increasing the overall security and resilience of SIoT networks. The findings of the proposed study outperforms with an accuracy of 97.2 compared to other state o art methods. This study is related to building and developing intelligent and self-adjusting safety procedures for the future Internet of Things.

关键词： Deep learning Adaptive learning Adaptation models Accuracy Social Internet of Things Safety Security Convolutional neural networks Anomaly detection Standards

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Machine Learning based Anomaly and Threat Detection System in Real Time Social Internet of Things using Dimensionality Reduction Techniques

Machine Learning based Anomaly and Threat Detection System i...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Anciline Jenifer J Preethika S.K. Piramu Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The Social Internet-of-Things (SIoT) is an innovative model developing in the struggle to safeguard privacy and increase trust inside the Internet-of-Things(IoT). The revolutionary notion of SIoT combines the IoT with social platforms, allowing inanimate items to develop relationships with one another. The fast growth of SIoT has transformed networked networks, enabling effortless interaction between devices. However, integrating these systems creates major security issues, including anomalies and attacks that may compromise system integrity. This study presents a machine learning(ML) methods like Isolation Forest (IF), One class support vector Machine(SVM) and Kernel density estimation(KDE) for in SIoT networks using the Principal component Analysis(PCA) and Correlation Analysis as dimensionality reduction techniques. The findings shows that the IF method provides an accuracy of 93% with the dimensionality reduction techniques applied on RT-IoT dataset. By utilizing powerful machine learning algorithms and contextual analytics, the framework improves detection accuracy and enables strong device collaboration in highly dynamic contexts..

关键词： Dimensionality reduction Performance evaluation Correlation Machine learning algorithms Real-time systems Vectors Threat assessment Social Internet of Things Security Kernel

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A Perspective on Artificial Intelligence for Molecular Pathologists

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Journal of Molecular Diagnostics 2025年第5期27卷 323-335页

作者： O'Leary Timothy J. O'Leary Brendan J. O'Leary Dianne P. Office of Research and Development Veterans Health Administration Washington District of Columbia United States Independent Consultant Rockville Maryland United States Department of Computer Science and Institute for Advanced Computing Studies University of Maryland College Park MD United States

The widespread adoption of next-generation sequencing technology in molecular pathology has enabled us to interrogate the genome as never before. The huge quantities of data generated by sequencing, the enormous complexity of human and microbial genetics, and the need for fast answers demand increasing use of automation as we diagnose disease and guide patient treatment. Much of this automation is based on tools that fall under umbrellas that have come to be known as machine learning and artificial intelligence. This review outlines some of the broad ideas that underpin these complex computational methods. It discusses the roles of pathologists and data scientists in generating new tools and factors to keep in mind when adopting these systems for use in molecular pathology. It pays special attention to regulatory and professional society guidance for validating them in individual institutions and to possible sources of bias. Finally, it briefly discusses ongoing efforts in computer science that may dramatically impact artificial intelligence in the future. © 2025 Association for Molecular Pathology and American Society for Investigative Pathology

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Exploring The Socio-Cognitive Determinants of Antibiotic Compliance: The Mediating Role of Antimicrobial Resistance Awareness

Exploring The Socio-Cognitive Determinants of Antibiotic Com...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Fathima Rumaiza S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavarm Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Antimicrobial resistance (AMR) is a meaningful and growing public health concern that is largely driven by improper antibiotic use. An important factor contributing to AMR is the failure to complete prescribed antibiotic courses, which allows resistant bacteria to survive as well as spread. This study examines the effect of awareness of AMR on the likelihood that people will adhere to their prescribed antibiotic regimens. Using a chi-square test for independence, the analysis identifies a statistically meaningful association between AMR awareness and the completion of antibiotic courses. Results indicate that people who are aware of AMR and its risks are more likely to complete their prescribed courses than those with lower awareness levels. The study explores several factors influencing adherence, including personal health beliefs, access to adequate medical advice and common misconceptions about antibiotic use, along with this key finding. Many people discontinue antibiotics after symptoms subside, completely unaware of the meaningful risks linked with incomplete treatment. Effectively dealing with these issues is important for considerably reducing the spread of AMR. The study suggests multiple public health interventions to improve antibiotic compliance, which include educational campaigns, increased patient counseling and stricter prescription guidelines. These strategies can considerably help curb the rising threat of AMR by importantly increasing awareness and promoting responsible antibiotic use.

关键词： Employee welfare Antibiotics Bacteria Public healthcare Immune system Guidelines

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PQS-AKA: Post-Quantum Secure Lightweight Authenticated Lattice-Based Key Agreement for IoT Communications 4th

PQS-AKA: Post-Quantum Secure Lightweight Authenticated Latti...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Singh, Ajeet Tentu, Appala Naidu Rao, K. Gangadhara Department of Computer Science and Engineering Acharya Nagarjuna University Andhra Pradesh Guntur522 510 India C.R. Rao Advanced Institute of Mathematics Statistics and Computer Science University of Hyderabad Campus Telangana Hyderabad500 046 India

ISBN: (纸本)9789819757022

As the Internet of Things (IoT) ecosystem expands, the need for secure and private communication over public networks becomes increasingly crucial. A fundamental component of this security paradigm is the establishment of a secret session key among IoT devices, enabling the secure transmission of confidential data over the Internet. In this paper, we introduce a Post-Quantum Secure Lightweight Authenticated Lattice-based Key Agreement (PQS-AKA) protocol, specifically developed for the rapidly evolving IoT ecosystem. Traditional Authenticated Key Agreement (AKA) protocols, predominantly based on concepts like Diffie–Hellman or prime factorization, are becoming obsolete due to their vulnerability to quantum computing advancements and their inefficiency in the resource-constrained IoT environment. Current quantum-resistant methods, although leveraging lattice cryptography for security, are impeded by issues such as high certificate management overheads and substantial communication and computational costs. PQS-AKA not only offers a defense against the threats posed by quantum computing through the inherent hardness of lattice problems but also simplifies the process by removing the need for extensive certificate management. The core security strength of our protocol is thoroughly evaluated under the random-natured oracle model, demonstrating its robustness against both present and future quantum computing attacks. Performance evaluations reveal that the proposed protocol offers a significant improvement over existing solutions in terms of efficiency and practicality, making it a viable and secure choice for industrial IoT applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum computers

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Machine Learning based Prediction of Parkinson's Diseases

Machine Learning based Prediction of Parkinson's Diseases

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Arockiya Selvi S T. Kamalakannan Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Parkinson's disease(PDs) is a neurological disorder characterized by accidental or uncontrollable movements such as shaking, rigidity, and trouble with balance and coordination. Symptoms are usually minor at first and intensify over time. PD datasets tend to be compact and inadequately diversified. Several datasets contain incorrect values or discrepancies as a result of diagnostic procedure inconsistencies. Datasets frequently have fewer PD patients than healthy individuals, resulting in biased models. SVM is a common method for PD prediction due to its ability to handle tiny, high-dimensional data sets and its robust classification skills. SVM performs effectively even with small medical datasets, unlike deep learning models, which require enormous volumes of data. SVM minimizes overfitting, particularly when working with tiny datasets, making it more dependable for use in real-world medical settings. The study employed SVM algorithms to predict Parkinson's disease. Additionally, disease data is classified and compared using SVM with different kernel parameters. The study compares the performance of four alternative SVM models Linear SVM (LSVM), Quadratic SVM (QSVM), Cubic Gaussian SVM (CGSVM), and Medium Gaussian SVM (MGSVM) using five assessment metrics: accuracy, sensitivity, specificity, precision, and F1-Score. MGSVM is the most effective model for predicting PDs, with the highest precision and consistency.

关键词： Support vector machines Deep learning Sentiment analysis Accuracy Sensitivity Parkinson's disease Predictive models Prediction algorithms Classification algorithms Medical diagnostic imaging

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Prediction of Covid and Covid-19 and Omicron data with ESPRINT, ERF, CSDLM and HMC-19HRP Models

Prediction of Covid and Covid-19 and Omicron data with ESPRI...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： V. Ramya K.S. Thirunavakkarasu Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

SARS-CoV-2, also known as the Covid-19 Corona Virus, has caused widespread damage globally, and the situation is only becoming worse. Officials from all across the world are using a number of COVID-19 outbreak prediction models to help them make choices and implement pertinent control measures. Simple epidemiological and statistical models are well-liked by the media and have garnered more attention from authorities among the conventional models for COVID-19 global pandemic prediction. It's a pandemic illness that spreads daily from person to person. As a result, it's critical to monitor the total patient population impacted. The way that the computerised data is now provided by the system makes it extremely difficult to analyse and forecast the spread of disease both locally and globally. An enhanced Sprint algorithm, Enhanced Random forest algorithm, Customized sequential Deep learning Model (CSDLM), and HMC-19HRP model is proposed in this study and analysed each with two dataset that is Covid dataset and OMICRON dataset. Finally results obtained from all the method are compared. On overall comparison HMC-19HRP outperforms other methods on both the dataset with precision of 94.92 and 98.26, Recall of 99.03 and 95.86, F1-score of 96.93 and 97.04, Specificity of 78.71 and 97.06 and Accuracy of 94.98 and 96.30 on covid data and Omicron dataset respectively.

关键词： COVID-19 Measurement Deep learning Accuracy Pandemics Computational modeling Predictive models Prediction algorithms Data models Random forests

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Emotional Contribution to Parkinson's Disease Diagnosis Using Machine Learning for EEG Dataset Generation

Emotional Contribution to Parkinson's Disease Diagnosis Usin...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： S. Arockiya Selvi T. Kamalakannan Department of Computer science VEL'S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Emotion is the root cause of nearly all harmful illnesses in humans. Emotion affects blood pressure, heart rate, kidney function, and a few disorders of the nervous system. Parkinson's disease, which causes impairment of movement, is one of the brain disorders. This issue is caused by a reduction in dopamine release in the cortex of the brain. Dopamine production deviations have been related to essential hypertension. Dopamine has a crucial role in anxiety modulation in multiple regions of the brain. Patients with Parkinson's disease frequently indicate that acute stress worsens their motor symptoms, especially gait freezing, dyskinesia, and tremors and the emotions play a substantial role in Parkinson's disease. This study focuses on the Classification Learner algorithms and the Electro Encephalogram (EEG) dataset, both of which covered the diagnostic range of PD development. The classifiers were evaluated using key performance indicators such as error rate, accuracy, sensitivity, specificity, and precision. The findings illustrate the convergence of emotional analysis, machine learning (ML), and neuroscience in improving diagnostic capabilities by demonstrating how a combination of EEG datasets with KNN algorithms offers a reliable and accurate approach for early Parkinson's disease identification.

关键词： Machine learning algorithms Accuracy Sensitivity Parkinson's disease Error analysis Machine learning Nearest neighbor methods Electroencephalography Classification algorithms Reliability

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