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Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

Computer Systems science & engineering 2023年第8期46卷 2141-2157页

作者： J.Prabakaran P.Selvaraj Department of Networking communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Computing Technologies Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational *** this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and ***,the CT images are captured and transmitted to the fog node through IoT *** the fog node,the image gets modified into a convenient and efficient format for further *** encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating ***,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning *** different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature *** softmax of heterogeneous base classifiers assists to make individual *** a meta-classifier,the ensemble approach is employed to obtain final optimal *** predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the *** proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class *** experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

关键词： Intelligent health care cloud computing fog computing ensemble learning RNN-LSTM

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Graph-Theoretic Approaches in Semantic Web Service Orchestration and Discovery: A Comprehensive Literature Survey 5

Graph-Theoretic Approaches in Semantic Web Service Orchestra...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Vignesh, T.V. Vijaya, K. Minu, R.I. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies School of Computing Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Semantic Web Services (SWS) have become a powerful means for automating the discovery, composition, and orchestration of web services by providing them with semantic descriptions. This paper provides a comprehensive survey of graph-theoretic approaches in the orchestration and discovery of SWS, systematically categorizing and analyzing various methodologies. We examine how graph-based models contribute to enhancing the efficiency, accuracy, scalability, performance, reliability, and predictability of SWS processes. Furthermore, the paper discusses research gaps, challenges, and potential future directions, offering insights into emerging solutions and state-of-the-art techniques in the field. The survey also integrates recent advancements such as hybrid frameworks combining knowledge graphs and semantic web services, service clustering using graph embedding, and enhanced recommendation models. © 2024 IEEE.

关键词： Knowledge graph

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation 29

The Evolution of Zero Trust Architecture (ZTA) from Concept ...

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29th International Conference on Information Technology, IT 2025

作者： Nasiruzzaman, Md Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. Xiamen University Malaysia School of Computing and Data Science Malaysia Universiteti Metropolitan Tirana Faculty of Computer Science and IT Albania The Doctoral College United Kingdom Xiamen University Malaysia Mahdi H. Miraz Is with the School of Computing and Data Science Malaysia Wrexham University School of Computing Faculty of Arts Science and Technology United Kingdom University of South Wales Faculty of Computing Engineering and Science United Kingdom

ISBN: (纸本)9798331517649

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA's benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats. © 2025 IEEE.

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Safe Spaces Analysis for Mental Health 1

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Kelawala, Harsh Shah, Zeal Selvaraj, P. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

Mental health is a paramount concern in contemporary urban environments, necessitating comprehensive approaches to understanding its determinants and formulating effective interventions. This research project adopts a multifaceted methodology to explore the factors influencing mental well-being in urban areas, specifically focusing on cities within Gujarat. By integrating Geographic Information Systems (GIS) technology and predictive modeling techniques, we analyze various socio-environmental factors spanning air quality, noise pollution, green space availability, educational attainment, population density, income levels, and employability. At the same time, the GIS approach encompasses all major cities across India, the Random Forest algorithm is tailored specifically to the cities of Gujarat, reflecting the unique socio-geographical context of the region. Through rigorous data analysis and visualization, we identify spatial patterns and correlations, enabling the identification of areas most vulnerable to mental health challenges and prioritizing targeted interventions. This work presents our research's methodology, findings, and implications, offering valuable insights for policymakers and stakeholders striving to cultivate safe and supportive urban environments conducive to optimal mental health outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Deep Learning Based Intrusion Detection for Internet of Things and Edge Devices

Deep Learning Based Intrusion Detection for Internet of Thin...

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2023 IEEE Asia-Pacific Conference on Computer science and Data engineering, CSDE 2023

作者： Moawad, Mohamad Tze, Vong Wan Hang Hui, Patrick Then Hsien Ming, Jason Yong Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Malaysia

ISBN: (纸本)9798350341072

The rapid expansion of Internet of Things (IoT) and Edge devices has introduced a variety of security challenges, particularly within resource-constrained computing environments. This paper aims to address these challenges by proposing a resource-efficient anomaly-based intrusion detection system tailored for IoT and Edge Devices, leveraging the capabilities of Binary Neural Networks (BNNs). Anomaly-based systems have demonstrated their efficacy in identifying potential cyber-attacks, but their conventional development methods often demand substantial storage and computational resources. Therefore, this research investigates the viability of BNNs for anomaly detection, offering a compelling alternative to full-precision approaches. Three distinct models are developed for this study using TensorFlow and Keras: Full-binary, Binary-weighted, and Full-precision. The models are trained and evaluated on the NSL-KDD dataset and achieved accuracy rates of 82%,87%, and 97% for these respective models. While binary networks experience a minor performance reduction due to aggressive quantization, they maintain notable levels of accuracy and precision. This observation takes into consideration the anticipated reduction in resource requirements, affirming the feasibility of BNNs' utilization for efficient anomaly detection. © 2023 IEEE.

关键词： Intrusion detection

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Fair k-Center Problem with Outliers on Massive Data

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Tsinghua science and Technology 2023年第6期28卷 1072-1084页

作者： Fan Yuan Luhong Diao Donglei Du Lei Liu Beijing Institute for Scientific and Engineering Computing Faculty of ScienceBeijing University of TechnologyBeijing 100124China Faculty of Management University of New BrunswickFrederictonE3B 5A3Canada

The clustering problem of big data in the era of artificial intelligence has been widely *** of the huge amount of data,distributed algorithms are often used to deal with big data *** distributed computing model has an attractive feature:it can handle massive datasets that cannot be put into the main *** the other hand,since many decisions are made automatically by machines in today’s society,algorithm fairness is also an important research area of machine *** this paper,we study two fair clustering problems:the centralized fair k-center problem with outliers and the distributed fair k-center problem with *** these two problems,we have designed corresponding constant approximation ratio *** theoretical proof and analysis of the approximation ratio,and the running space of the algorithm are given.

关键词： machine learning distributed algorithm fairness constraints outlier constraints k-center problem

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Alzheimer's Disease Prediction Using Machine Learning 7

Alzheimer's Disease Prediction Using Machine Learning

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mishra, Shashwat Sharma, Viplav Ramya, G. SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350372816

Alzheimer's disease is a progressive and debilitating neurodegenerative disorder that poses a significant public health challenge worldwide. Early detection and prediction of Alzheimer's disease are critical for effective treatment and care. This research paper presents a novel approach for predicting Alzheimer's disease using machine learning methodologies. Our study leverages a diverse dataset that includes clinical, genetic, and neuroimaging data from individuals at various stages of the disease. Through rigorous preprocessing and feature selection processes, we extract relevant features and apply advanced machine learning algorithms. Our results demonstrate the efficacy of this approach in accurately predicting the onset and progression of Alzheimer's disease. The model's high accuracy, sensitivity, and specificity suggest its potential as a valuable tool for clinicians and researchers. Furthermore, our study highlights key factors contributing to the prediction and offers insights into the underlying biological processes of the disease. This research holds promise for early intervention and personalized patient care, ultimately contributing to improved management of Alzheimer's disease and enhancing the quality of life for affected individuals and their families. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid CNN-BiLSTM Model for Power Quality Disturbance Classification 1

Hybrid CNN-BiLSTM Model for Power Quality Disturbance Classi...

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1st IEEE International Conference on Advanced Power engineering and Energy, APEE 2024

作者： Lin, Jiajian Tavalaei, Jalal Yeo, Lit Yen Zhou, Yutong Afrouzi, Hadi Nabipour Ektesabi, Mehran Motamed Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia College of Mechancial Engineering Taishan University Taian China School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC Australia

ISBN: (纸本)9798350360936

With the evolution of intelligent grid tech, the need for monitoring power quality has markedly risen, essential for the continuous functioning of the power grid. Addressing this matter, this research proposes a combined approach combining Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The model effectively catches spatial or temporary aspects of power quality disruptions using CNN and employs BiLSTM to document long-term or temporal aspects, thus creating a profound correlation between input and output sequences. By substantially decreasing reliance on conventional physical attributes, this technique markedly enhances the precision in detecting power quality anomalies. Crucially, this technique, independent of particular original samples and existing information, holds widespread applicability and is applicable in identifying patterns in various other intricate systems. Research on experiments indicates that, as opposed to conventional machine learning frameworks, deep learning approaches are more effective in detecting disruptions in power quality. Our hybrid model excels in identifying power quality disturbance (PQD) and produces highly accurate recognition outcomes by autonomously analyzing central features of the disturbance indications. © 2024 IEEE.

关键词： Convolutional neural networks

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Crowdfunding Platform Using Decentralised Application (Blockchain)

Crowdfunding Platform Using Decentralised Application (Block...

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2024 International Conference on Communication, Computer sciences and engineering, IC3SE 2024

作者： Kumar, Ayush Utekar, Swarali Viji, D. College Of Engineering And Technology Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350366846

Blockchain technology provides a safe, transparent, and effective platform that might completely transform public financing. Each transaction is tamper-proof since it is permanently stored with unique identities and timestamps. Furthermore, agreements are automated via smart contracts, which simplifies procedures and lowers errors usually related to human verification. This encourages accountability and confidence inside the system. For platforms that receive public financing, transparency is crucial. This need is met by this blockchain-based online service, which offers a safe and decentralized environment. Donors can simply monitor the initiatives they support and make sure their money is going to where it's supposed to. Should a project be cancelled, the program enables contributors to receive their money back right away. The application caters to two user groups: campaign creators and donors. Donors have access to other fundraising campaigns, project updates, and insightful information. The website gives campaign designers the ability to monitor the status of their initiative and compare their results to those of other campaigns. This blockchain-powered online application provides a stable, open, and safe environment for public fundraising. By empowering both creators and donors, it promotes an ecosystem that is more reliable and efficient. © 2024 IEEE.

关键词： Smart contract

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Gesture Recognition for Phrase Interpretation: A Deep Learning Approach

Gesture Recognition for Phrase Interpretation: A Deep Learni...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Puyam, Kelvin Singh, Nishant Aruna, M. SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350375237

Gesture recognition holds paramount significance in facilitating communication for individuals utilizing sign language to convey phrases and expressions. We present an innovative approach to gesture recognition in this article tailored for interpreting hand signs to derive meaningful phrases, such as 'I love you,' 'please,' 'sorry,' and 'goodbye.' Our proposed methodology integrates a multi-box detector (SSD) with dynamic hand gesture analysis to achieve real-time and accurate recognition of hand signs across various languages and cultural contexts. By harnessing the power of deep learning SSD for object detection, our system ensures swift responses while maintaining precision. Additionally, the incorporation of dynamic hand gesture analysis techniques enhances the system's adaptability to diverse sign styles and linguistic nuances, thus fostering better communication access. By means of meticulous experimentation, we establish the effectiveness of our methodology in precisely identifying and deciphering real-time sign language gestures, attaining noteworthy results in terms of both speed and accuracy. This study enhances assistive technology for hard-of-hearing and deaf communities, thereby promoting accessibility and inclusiveness in communication environments that are accommodated to the linguistic requirements of a variety of individuals. © 2024 IEEE.

关键词： Gesture recognition

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