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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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A universal approach to high-performance thermoelectric module design for power generation (vol 9, 101818, 2025)

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JOULE 2025年第4期9卷

作者： Cheng, Jinxuan Xue, Wenhua Zhang, Tianyu Li, Xiaofang Zhu, Yichen Yin, Li Yao, Honghao Fu, Zixuan Wu, Longzhi Chen, Chen Zhao, Peng Ma, Xiaojing Jiang, Feng Wang, Xiaodong Li, Mingyu Mao, Jun Wang, Yumei Cao, Feng Zhang, Qian School of Materials Science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen 518055 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Science Beijing 100190 China School of Science Harbin Institute of Technology Shenzhen 518055 China Sauvage Laboratory for Smart Materials School of Materials Science and Engineering Harbin Institute of Technology Shenzhen 518055 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin 150001 China School of Physical Sciences Great Bay University Dongguan 523000 China Institute of Special Environments Physical Sciences Harbin Institute of Technology Shenzhen 518055 China Beijing Branch of Songshan Lake Materials Laboratory Dongguan 523808 China

Thermoelectric modules fabricated by traditional welding fall short of achieving optimal conversion efficiencies, primarily due to performance degradation of materials at high temperatures, severe elemental diffusion, and residual thermal stress at the interface. Transient liquid phase bonding enables the realization of joints with high-melting-point compounds at low bonding temperatures, providing a promising solution for achieving "low-temperature bonding and high-temperature service." Owing to the low eutectic point of the solder and high melting points of compounds, we optimize the design of a germanium-telluride-based module at 533 K, which is successfully applied at the hot-side temperature of 773 K. Attributed to high-performance materials and reliable joints, the module realizes a high conversion efficiency of ∼15.1% and remains stable throughout 150 h of service. By adopting the same approach, the low- (Bi2Te3) and high-temperature (half-Heusler) modules are assembled. We provide a promising and general route for the assembly of full-temperature-range thermoelectric devices.

关键词： transient liquid phase bonding thermoelectric module intermetallic compounds GeTe

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Adaptive sail fish optimizer based optimized region selection for robust video steganography

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International Journal of Information technology (Singapore) 2025年第4期17卷 2417-2427页

作者： Babu, J. Suresh Niranjana, G. Ramana, Kadiyala Department of Computing Science & Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology (Autonomous) Hyderabad India

Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometimes fail to identify the best pixels for encoding hidden data, resulting in lowerquality and robustness. To solve this issue, this article proposes an innovative steganography technique that improves the security and quality of data hiding. The proposed method takes cover video and secret images as input. The Structural Similarity Index Measurement (SSIM) first collects key frames from the input video. The adaptive sailfish optimizer (ASFO) is then used to find the best regions within these keyframes to insert the secret data in. To improve security, the discrete wavelet transform (DWT) is applied to the specified region, and the LL band is used for the hiding process. The secret image is encrypted by Adaptive elliptical curve cryptography (AECC) and transformed to binary bits, which then get embedded in the chosen band. This method leads to the construction of a stego-video. The extraction procedure is repeated using the same approach, which ensures secure and robust data transmission. Performance metrics, including PSNR, BER, NAE, TAF, and NC are analyzed to compare each method's efficacy. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Adaptive elliptical curve cryptography And stego-video Discrete wavelet transform Structural similarity index measurement Video steganography

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Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight

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Computer Modeling in Engineering & sciences 2025年第4期143卷 875-909页

作者： Iman S.Al-Mahdi Saad M.Darwish Magda M.Madbouly Department of Information Technology Institute ofGraduate Studies and ResearchAlexandriaUniversityAlexandria21526Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria5432042Egypt

Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare *** problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical *** challenges often lead to inefficient and less *** predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational *** work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel *** features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction *** hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the *** enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional *** proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over ***,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medi

关键词： Heart disease prediction feature selection ensemble deep learning optimization genetic algorithm(GA) ensemble deep learning tunicate swarm algorithm(TSA) feature selection

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Integrated Farm Security System

Integrated Farm Security System

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

作者： Franciska, W Arockia Harshavardhini, C. Arockia Shermila, S. Beulah Patturose, J Gold St.Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798331529833

India's agriculture, the nation's largest industry, faces significant challenges due to current economic conditions, with crop loss from man-wildlife conflicts being a pressing issue. Traditional methods like employing guards have proven inadequate and unsustainable. This technical abstract proposes a technology-driven solution to safeguard crops, wildlife, and human welfare. By deploying an advanced setup integrating machine learning and computer vision, the system can detect and identify animals and humans in agricultural areas, enabling proactive measures to prevent crop damage. Sophisticated algorithms differentiate between various threats, allowing targeted, ethical responses that prioritize coexistence. This approach addresses immediate challenges while contributing to the long-term sustainability of agriculture and biodiversity conservation. Continuous monitoring and collaboration among farmers, wildlife conservationists, and technology experts aim to establish a holistic framework for managing man-wildlife conflicts in Indian agriculture, ensuring the well-being of both humans and wildlife. © 2025 IEEE.

关键词： Smart agriculture

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Predicting LinkedIn Post Reactions: A Comparative Analysis of Machine Learning Models

Predicting LinkedIn Post Reactions: A Comparative Analysis o...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Jayashree, S. Livedha, S. Ragupathi, T. Hariharan, Kumaran Sanjana, R. Sagar, Shrddha Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India Galgotias University Scse India

ISBN: (纸本)9798331508685

Social media platforms have become a critical space for businesses to interact with their audience. LinkedIn, a professional networking platform, offers metrics like reactions, comments, and shares to gauge post engagement. This paper investigates a data-driven approach for predicting the number of responses to a LinkedIn post. Various features such as post content, company statistics (e.g., followers and connections), and media type are analyzed using machine learning models. The study compares the performance of multiple regression models and explores the importance of features, yielding valuable insights for optimizing LinkedIn post engagement. © 2025 IEEE.

关键词： Contrastive Learning

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SARA: A Sparsity-Aware Efficient Oblivious Aggregation Service for Federated Matrix Factorization 25th

SARA: A Sparsity-Aware Efficient Oblivious Aggregation Servi...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Zheng, Yifeng Xiong, Tianchen Ouyang, Huajie Wang, Songlei Hua, Zhongyun Gao, Yansong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Data61 CSIRO Sydney Australia

ISBN: (纸本)9789819605668

Federated matrix factorization (FedMF) has recently emerged as a privacy-friendly paradigm which runs matrix factorization (MF) in a federated learning (FL) setting and enables users to keep their individual rating data local in the training process. In FedMF, users only need to share out item gradients for aggregation. However, prior work has shown that the item gradients, if directly exposed, can still leak users’ rating data. Meanwhile, the rating data are typically sparse and simply uploading the gradients of rated items will leak which and how many items a user rates. In light of the above, in this paper, we present SARA, a new sparsity-aware efficient oblivious aggregation service for FedMF. SARA protects the confidentiality of item gradients as well as hides which and how many items a user rates, through a custom sparsity-aware design that delicately builds on differential privacy and lightweight cryptography. Extensive experiments over real-world datasets demonstrate that SARA is utility-preserving and can bring the users significant savings (up to 98.28%) in the number of transmitted item gradients, compared to the baseline of fully transmitting gradients for all possible items. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Personalized Dementia Prediction Using Machine Learning Techniques

Personalized Dementia Prediction Using Machine Learning Tech...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Jayashree, S. Livedha, S. Ragupathi, T. Hariharan, Kumaran Sanjana, R. Arora, Ginni Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9798331508685

Early-stage dementia detection is a significant challenge in the healthcare field. Many individuals remain unaware of their cognitive decline until the condition progresses. This research aims to develop a personalized approach for predicting dementia. Multiple machine learning algorithms were applied to the dataset. Impressively, Random Forest model achieved a perfect accuracy of 98.75%. © 2025 IEEE.

关键词： Support vector regression

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DFRC Waveform Design for Ground-Air System Under Inaccurate Target Angle

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IEEE Signal Processing Letters 2025年 32卷 2119-2123页

作者： Wang, Fengyi Wang, Shilian Chen, Weiyu Sun, Yifu Niyato, Dusit National University of Defense Technology College of Electronic Science and Technology Changsha410073 China National University of Defense Technology Sixty-third Research Institute Nanjing210007 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

In ground-air networks, dual-function radar and communication (DFRC) enables a base station (BS) to sense and send communication signals to unmanned aerial vehicles (UAVs) simultaneously, which has enormous potential. The emphasis of this letter is on the design of DFRC waveform for ground-air system under inaccurate target angle. The desired covariance matrix is considered to have the best radar performance in the presence of inaccurate target angle and the waveform design objective is to make the corresponding covariance matrix approximated to it. The constraints conclude multi-user interference (MUI) energy for high quality of communication and constant modulus for avoiding signal distortion. To address the problem that minimizes the covariance matrix difference under the constraints, we propose a block coordinate descent (BCD) method. Moreover, to ensure the problem feasible, we design an initial value acquisition strategy using bisection-based alternating direction method of multipliers (ADMM). Numerical results indicate that better communication and radar performance are achieved by the proposed method compared with existing methods. © 1994-2012 IEEE.

关键词： Micro air vehicle (MAV)

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On Generalization Bounds for Neural Networks with Low Rank Layers 36

On Generalization Bounds for Neural Networks with Low Rank L...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Pinto, Andrea Rangamani, Akshay Poggio, Tomaso Center for Brains Mind and Machines Massachusetts Institute of Technology United States Department of Data Science New Jersey Institute of Technology United States

While previous optimization results have suggested that deep neural networks tend to favour low-rank weight matrices, the implications of this inductive bias on generalization bounds remain underexplored. In this paper, we apply a chain rule for Gaussian complexity (Maurer, 2016a) to analyze how low-rank layers in deep networks can prevent the accumulation of rank and dimensionality factors that typically multiply across layers. This approach yields generalization bounds for rank and spectral norm constrained networks. We compare our results to prior generalization bounds for deep networks, highlighting how deep networks with low-rank layers can achieve better generalization than those with full-rank layers. Additionally, we discuss how this framework provides new perspectives on the generalization capabilities of deep networks exhibiting neural collapse. © 2025 A. Pinto, A. Rangamani & T. Poggio.

关键词： Deep neural networks

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