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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Demystifying XAI: Requirements for Understandable XAI Explanations

Demystifying XAI: Requirements for Understandable XAI Explan...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Stodt, Jan Reich, Christoph Knahl, Martin Institute for Data Science Cloud Computing and IT Security Furtwangen University Furtwangen Germany

ISBN: (纸本)9781643685335

This paper establishes requirements for assessing the usability of Explainable Artificial Intelligence (XAI) methods, focusing on non-AI experts like healthcare professionals. Through a synthesis of literature and empirical findings, it emphasizes achieving optimal cognitive load, task performance, and task time in XAI explanations. Key components include tailoring explanations to user expertise, integrating domain knowledge, and using non-propositional representations for comprehension. The paper highlights the critical role of relevance, accuracy, and truthfulness in fostering user trust. Practical guidelines are provided for designing transparent and user-friendly XAI explanations, especially in high-stakes contexts like healthcare. Overall, the paper's primary contribution lies in delineating clear requirements for effective XAI explanations, facilitating human-AI collaboration across diverse domains. © 2024 The Authors.

关键词： Requirements engineering

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Generative Adversarial Network Art Generator for Sculpture Analysis

Generative Adversarial Network Art Generator for Sculpture A...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Kalita, Swagat Subhash Mahajan, Parag Sharanya, S. Srm Institute Of Science And Technology Data Science And Business System School Of Computing Chennai India

ISBN: (纸本)9798350352689

This study examines computer-generated sculpture using a hybrid architecture of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). CNNs are essential for visual data analysis and processing, while GANs iteratively blur the line between synthesis and authenticity. A dynamically interacting generator and discriminator network form the GAN framework. The discriminator, trained to distinguish authentic sculptures from computer-generated ones, guides the generator toward greater realism and complexity with each iteration. This study analyzes the complicated dynamics of GAN-enabled sculpture production, examining several elements that affect the result. We intend to illuminate the complex relationship between composition, texture, and shape of digitally generated sculptures by analyzing sculptures using GANs and CNNs. We want to understand their formation methods as well as their aesthetic value. We also consider how GAN-generated sculptures may be transformed beyond traditional artistic usage. The sculptures offer unique opportunities for artistic expression, scholarly study, and practical use. They can enhance art, education, architectural design, and historical preservation. This thorough inquiry using GANs and CNNs aims to push the limitations of traditional art creation and explore unknown sculpture interpretation and analysis zones. © 2024 IEEE.

关键词： Generative adversarial networks

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Two genetic algorithms for final exam scheduling 24

Two genetic algorithms for final exam scheduling

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6th Asia Conference on Machine Learning and computing, ACMLC 2024

作者： Mixer, Mark Morrow, Steven Sukherman, Lev School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9798400710018

The scheduling of final exams at a university is a problem which can be improved with artificial intelligence techniques. In this paper we explain and compare two algorithms used to solve the exam scheduling problem at Wentworth institute of Technology. The first technique is an implementation of a pure genetic algorithm, while the second combines some graph theoretical algorithms with two variations of a genetic algorithm. We will compare the performance of these approaches to each other and to historical results. © 2024 Copyright held by the owner/author(s).

关键词： Scheduling algorithms

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Diabetic Retinopathy Detection 4

Diabetic Retinopathy Detection

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4th IEEE International Conference on data Engineering and Communication Systems, ICDECS 2024

作者： Sharanya, S. Rai, Arnav Vinod Vedanth, Sai Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems India

ISBN: (纸本)9798350393354

In this pivotal study, we delve into the imperative realm of Diabetic Retinopathy (DR), a sight-threatening eye disease, introducing a nuanced and comprehensive approach to its detection through cutting-edge deep learning techniques. Our focus centers on the meticulous comparison of four distinguished convolutional neural network (CNN) architectures: CNN, VGG16, ResNet50, and EfficientNetB5. The dataset comprises five meticulously curated image categories, delineating various DR stages. The discerning outcomes illuminate the consistent superiority of the CNN and VGG16 algorithms, showcasing a commendable edge in accuracy. To amplify DR detection precision, we introduce an innovative hybrid model amalgamating the robust attributes of both CNN and VGG16. This hybrid model triumphs with an extraordinary accuracy rate of 90.5%, surpassing individual performances of the four algorithms. In summation, our groundbreaking study unequivocally establishes the efficacy of a hybrid CNN-VGG16 model for Diabetic Retinopathy detection, underscoring its substantial superiority in accuracy when compared to each of the individual algorithms, advocating for the strategic integration of diverse deep learning models to fortify the accuracy and dependability of medical image analysis. The repercussions of our findings promise to revolutionize the landscape of early detection and holistic management of DR, fulfilling an unmet healthcare need. © 2024 IEEE.

关键词： Convolutional neural networks

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Big data analytics for privacy through ND-homomorphic encryption

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Journal of Control and Decision 2023年第1期10卷 64-71页

作者： S.Saravanan N.Poornima Department of CSE SRM Institute of Science and TechnologyChennaiIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathurIndia

Rapidly rising the quantity of Big data is an opportunity to flout the privacy of people. Whenhigh processing capacity and massive storage are required for Big data, distributed networkshave been used. There are several people involved in these activities, the system may contributeto privacy infringements frameworks have been developed for the preservation of privacy atvarious levels (e.g. information age, information the executives and information preparing) asfor the existing pattern of huge information. We plan to frame this paper as a literature surveyof these classifications, including the Privacy Processes in Big data and the presentation of theAssociate Challenges. Homomorphic encryption is particularised aimed at solitary single actionon the ciphered information. Homomorphic enciphering is restrained to an honest operation onthe encoded data. The reference to encryption project fulfils many accurate trading operationson coded numerical data;therefore, it protects the written in code-sensible information evenmore.

关键词： Security and confidentiality vast statistics data encryption nondeterministic fully homomorphic encryption

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Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-27页

作者： Liu, Jiao Gupta, Abhishek Ong, Yew-Soon College of Computing and Data Science Nanyang Technological University Singapore Singapore School of Mechanical Sciences Indian Institute of Technology Goa Goa India

Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of tradeoff solutions is sought under tight evaluation budgets. In this article, we introduce a novel concept of inverse transfer in multiobjective optimization. Inverse transfer stands out by employing Bayesian inverse Gaussian process models to map performance vectors in the objective space to population search distributions in task-specific decision space, facilitating knowledge transfer through objective space unification. Building upon this idea, we introduce the first Inverse Transfer Evolutionary Multiobjective Optimizer (invTrEMO). A key highlight of invTrEMO is its ability to harness the common objective functions prevalent in many application areas, even when decision spaces do not precisely align between tasks. This allows invTrEMO to uniquely and effectively utilize information from heterogeneous source tasks as well. Furthermore, invTrEMO yields high-precision inverse models as a significant byproduct, enabling the generation of tailored solutions on-demand based on user preferences. Empirical studies on multi-and many-objective benchmark problems, as well as a practical case study, showcase the faster convergence rate and modeling accuracy of the invTrEMO relative to state-of-The-Art evolutionary and Bayesian optimization algorithms. The source code of the invTrEMO is made available at https://***/LiuJ-2023/invTrEMO. © 2024 Copyright held by the owner/author(s).

关键词： Budget control

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An Efficient Capsule Based Neural Network for Detection of Grape Plant Leaf Disease and Disease Stages 5

An Efficient Capsule Based Neural Network for Detection of G...

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5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Vasudevan, N. Praveenkumar, S. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamilnadu kattankulathur India

ISBN: (纸本)9798331522100

Plant infections destroy and impair the quality of crops, and the pesticides used to treat them pollute the soil, rendering it unfit for planting. Image processing and deep learning technologies may be used to identify disease spots on grape leaves. The detection's precision and efficiency, on the other hand, remain problems. In this paper, the performance of a few well-known CNN models implemented utilizing transfer learning, such as ResNet18, VGG16, and GoogleNet were compared with capsule network. The local entity characteristics are first found by the Capsule layers. For the purpose of collecting aggregate data like "disease type"and "disease stage,"the capsule layer makes use of geographic information and the frequency of local-level characteristics. Colored pictures of strong and unhealthy leaves were taken from the public dataset and then used to train the models. The proposed work focused on the novelty concept of identifying the stages of diseases. The Capsule based classification technique can give competitive benefits with an effective classification of accuracy and stages of leaf disease related to other models. © 2025 IEEE.

关键词： Agricultural engineering classifications diseases image processing neural networks

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Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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Large Graphs with Small Burning Numbers

Large Graphs with Small Burning Numbers

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International Conference on Computational Engineering, ICCE 2023

作者： Rajasingh, Angelin Jemima Parthiban, N. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

In this paper, we have determined the burning number of graphs with universal vertices and graphs that are generated using the graph operation namely ‘join of graphs’. © The Author(s), under exclusive license t... 详细信息

ISBN: (纸本)9783031777639

关键词： Burning number Join of graphs Universal vertex

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