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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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IoT-Aided Charity-Redistribution of Excess Food 2

IoT-Aided Charity-Redistribution of Excess Food

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Yogananth, V.S. Balasubramani, M. Vellore Institute of Technology Department of Computer Applications Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The utilization of the Internet of Things (IOT) has shown significant potential in various aspects of daily life, yet its application in addressing social issues remains underdeveloped. India, with a substantial number of orphanages, faces challenges in efficiently distributing crucial information, hindering donors from obtaining accurate data on food reserves. This project proposes an IOT-based system to tackle this issue, using Node MCU IOT devices connected to sensors that relay data through a network, accessible and monitorable via web applications. The proposed system architecture aims to bridge the gap between those who waste food and those in poverty who desperately need it. We intend to facilitate food donations by implementing smart containers equipped with IOT technology. These containers can detect excess food waste and share this information via a GSM module with trusted charitable organizations. This application is designed to serve three main stakeholders: service providers, donors, and food recipients in need. By creating smart containers, we ensure that donated food reaches the right individuals. We prioritize collaborating with trusted charities to oversee the distribution process. Only authorized charity members can access and distribute the food stored in these containers, ensuring that it reaches those who need it most. © 2024 IEEE.

关键词： Containers

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The Survival Analysis of Mental Fatigue Utilizing the Estimator of Kaplan-Meier and Nelson-Aalen 4th

The Survival Analysis of Mental Fatigue Utilizing the Esti...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Eswar Reddy, R. Santhi, K. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9783031488870

The aim of this study is to investigate mental fatigue using the Kaplan-Meier and Nelson-Aalen estimators in survival analysis. Mental fatigue is a common occurrence when the mind becomes tired from regular tasks, and it can have a negative impact on an employee’s operational functions and job efficiency. To detect mental fatigue, the shallow Kaplan-Meier method is employed by analyzing data from employee burnout evaluations. Both the Kaplan-Meier and Nelson-Aalen estimators have proven to be effective in automatically analyzing various features from raw data. However, they often impose a significant burden on system resources during training and predictions. Therefore, alternative methods of analysis are necessary to derive the survival curve. In this paper, we provide a mathematical foundation for the Kaplan-Meier method and explain the concept of censoring, including right censoring, interval censoring, and left censoring. Furthermore, we construct a Kaplan-Meier survival curve, which represents the probability of survival over time. The Kaplan-Meier survival curve is considered the most reliable and is recommended for predicting the variable under investigation, particularly in the fields of public health and medical research. The findings of this research can also be utilized to develop interventions and strategies aimed at reducing mental fatigue and improving employee morale. Enhancing employee morale can positively impact an organization as a whole, as mental fatigue has been associated with lower job satisfaction and an increased likelihood of employee turnover, both of which can be further explored in future studies. Overall, this study sheds light on the significance of understanding and addressing mental fatigue in the workplace, and it provides valuable insights that can contribute to the well-being of employees and the success of organizations. © 2024, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Job satisfaction

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A Collaborative Approach for Secured Routing in Mobile Ad-Hoc Network

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Intelligent Automation & Soft Computing 2023年第2期35卷 1337-1351页

作者： W.Gracy Theresa A.Gayathri P.Rama Department of Computer Science and Engineering Panimalar Institute of TechnologyChennai600123Tamil NaduIndia Department of Information Technology Vellore Institute of TechnologyVellore632014Tamil NaduIndia Department of Computer Science and Engineering Loyola Institute of TechnologyChennai600123Tamil NaduIndia

Mobile computing is the most powerful application for network com-munication and connectivity,given recent breakthroughs in theﬁeld of wireless networks or Mobile Ad-hoc networks(MANETs).There are several obstacles that effective networks confront and the networks must be able to transport data from one system to another with adequate *** most applications,a frame-work must ensure that the retrieved data reﬂects the transmitted *** driv-ing to other nodes,if the frame between the two nodes is deformed in the data-link layer,it must be *** link-layer protocols immediately disregard the frame and enable the high-layer protocols to transmit it *** other words,because of asset information must be secured from threats,information is a valu-able *** MANETs,some applications necessitate the use of a network method for detecting and blocking these *** a secure intrusion detection system in the network,which provides security to the nodes and route paths in the network,is a major difﬁculty in *** on the network can jeopardize security issues discovered by the intrusion detection system engine,which are then blocked by the network’s intrusion prevention *** bringing the Secure Intrusion Detection System(S-IDS)into the network,a new technique for implementing security goals and preventing attacks will be *** Secure Energy Routing(SER)protocol for MANETs is introduced in this *** protocol addresses the issue of network security by detecting and preventing attacks in the *** data transmission in the MANET is forwarded using Elliptical Curve Cryptography(ECC)with an objective to improve the level of *** Simulator–2 is used to simulate the network and experiments are compared with existing methods.

关键词： Mobile ad-hoc network(MANET) intrusion detection system secure energy routing(SER) elliptical curve cryptography(ECC) security

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Protein Sequence Classification Using Bidirectional Encoder Representations from Transformers (BERT) Approach

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SN computer science 2023年第5期4卷 481页

作者： Balamurugan, R. Mohite, Saurabh Raja, S.P. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore 632014 India

Proteins play a vital role by booming out a number of activities within an organism to withstand its life. The field of Natural Language Processing has successfully adapted deep learning to get a better insight into the semantic nature of languages. In this paper, we propose semantic approaches based on deep learning to work with protein amino acid sequences and compare the performances of these approaches with traditional classifiers to predict their respective families. The Bidirectional Encoder Representations from Transformers (BERT) approach was tested over 103 protein families from UniProt consortium database. The results show the average prediction accuracy to 99.02%, testing accuracy to 97.70%, validation accuracy to 97.69%, Normalized Mutual Information (NMI) score on overall data to 98.45, on test data to 96.99, on validation data 96.93 with high weighted average F1 scores of 99.02 on overall data, 97.72 on test data and 97.70 on validation data, and high macro average F1 scores of 99.00 on overall data, 98.00 on test data and 98.00 on validation data. From the results, it is justified that our proposed approach is outperforming well when compared to the existing approaches. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Document embedding Logistic Regression Natural Language Processing Protein family

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Real-Time Implementation of an Automated Student Attendance Monitoring System with computer Vision technology 5

Real-Time Implementation of an Automated Student Attendance ...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Lakshmi, M. Vidhya SivaPriya, M.S. Manivannan, Kirankumar Vivekanandan, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350379945

computer vision and the Internet of Things have led to significant advancements in student attendance monitoring. In this work, an innovative system has been designed to facilitate real-time, automated student attendance tracking in educational environments. Utilizing face recognition algorithms and IOT. The aim of the system is to optimize attendance management in educational institutions. IoT-connected, high-resolution cameras are strategically positioned across classrooms to capture visual data for analysis. By utilizing advanced computer vision algorithms, the system identifies and tracks students in real time, facilitating precise and efficient attendance monitoring. Student Faces are detected using a Haar cascade classifier and recognized using the LBPH (Local Binary Pattern Histogram) Algorithm. Then, Histogram data is checked against an established dataset. If a Match is found, the device automatically labels attendance. The Integration of IoT helps in seamless data transmission, storage and ensures accessibility and analysis of attendance records by administrators and educators. The primary objective is to offer an intelligent, automated solution for attendance management, that will significantly reduce manual workload and enhances accuracy. This system addresses the limitations of conventional attendance-taking methods, which are often laborintensive, error-prone, and time-consuming. © 2024 IEEE.

关键词： Local binary pattern

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Human Detection and Tracking for Drone based Marine Surveillance 15

Human Detection and Tracking for Drone based Marine Surveill...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Micheal, Ancy A. Sivaramakrishnan, Sneha School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350370249

Effective object detection and tracking are essential for prompt and precise rescue operations in water-based emergency response scenarios. Acknowledging this necessity, this paper introduces an innovative method using deep learning methodologies. At the heart of this project is the MobDrone dataset, selected to document various situations that occur during water crises. By carefully reannotating a subset of this dataset, new labels of swimmers, rescue victims, and deceased individuals were generated, making it more useful for training and assessing. In this context, the research assesses the effectiveness of YOLOv9 using this specific dataset. By conducting thorough experiments, the study highlights YOLOv9’s exceptional accuracy and resilience, particularly in comparison with its predecessor YOLOv8. This research highlights the possibility of combining advanced object detection algorithms such as YOLOv9 with effective tracking methods like BoT-SORT to transform autonomous systems in water-based emergency response, leading to improved and quicker rescue operations. © 2024 IEEE.

关键词： Object tracking

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An optimal selection of virtual machine for e-healthcare services in cloud data centres

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International Journal of Cloud Computing 2022年第1期11卷 79-88页

作者： Prathap, R. Mohanasundaram, R. School of Computer Science and Engineering Vellore Institute of Technology TN Vellore India

In recent times, in the handling of health facilities, cloud computing performs a big part. Such title is electronic healthcare, used to enhance the efficiency of cloud healthcare. A virtual device for health services is a significant selection and placement factor and one of the cloud's problems. The data centre processes the medical application with huge concentrations. This would maximise the use of resources and reduce the time the cloud data centre requires to execute the medical request. Multiple methods for solving cloud-based problems are employed. In this article, a multi-target grey-wolf optimisation hybrid application (ROMGW) algorithm is provided to address healthcare requirements effectively in cloud data centres. The suggested algorithm has been evaluated in the cloud data centres and contrasted to the well-known VM algorithm benchmark. Furthermore, cloud performance effectiveness improves the cloud use of the electronic health services scheme. Inaccuracy, the hybrid algorithm is able to interact with customers at the highest rate. This is one of the leading designs that improves the use of resources for cloud health facilities. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Cloud computing

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Hand-Gesture Based Emoji Identification and Chat Integration 5

Hand-Gesture Based Emoji Identification and Chat Integration

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Tare, Sanskar S Patil, Madhav J Alam, Adnan Swarnalatha, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798350321425

Every aspect of human life is slowly getting computerized and gradually turning electronic. This has made a rangeof tasks a lot easier and accessible for people from all walks of life. Unfortunately, this rapid computerization and the ever changing landscape of electronics has given birth to a new classof computer - illiterate individuals. Such individuals exist due to the lack of accessibility to latest electronic gadgets, or humansocial problems like old age. There is a need to make technology fun and easier to use for individuals belonging to such classes. The very primary use of internet-based networking is text-based communications namely: e-mails, texts chats, blogs, etc. The text based interaction is mainly facilitated using a keyboard which makes it the most focal human-computer interaction involved. The modern usage of this text-based communication involves extensive usage of emoticons. While most handheld devices feature a customised emoji-keyboard, the emoji keyboard is difficult to access in personal computers. This can adversely affect the interactions the aforementioned computer-illiterates partake in. This project aims to deal with this problem by creating a hand-detection based emoji detection algorithm in python through OpenCV and Mediapipe for detection of hand landmarks and implementing it in one such communication channel. The algorithm consists of two modules: a Hand-Tracking Moduleand an emoji detector capable of matching the tracked hand to existing emoji database. The resulting application is consistent, allow frequent users to use shortcuts and make the user feel more in control. The work also strives to integrate error prevention and simple error handling in existing emoji detection technologies along with managing short term memory load. © 2023 IEEE.

关键词： Palmprint recognition

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Enhancing Ophthalmic Diagnostics: A New Horizon for OCT Retinal Analysis

Enhancing Ophthalmic Diagnostics: A New Horizon for OCT Reti...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Priyanka, S. Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350361537

Ophthalmic diagnostics play a critical role in the early detection and management of various ocular diseases. Among the advanced imaging modalities employed in ophthalmology, Optical Coherence Tomography (OCT) has emerged as a cornerstone technology for non-invasive, high-resolution imaging of ocular structures. This paper analyzes the efficiency of three deep learning models such as VGG16, ResNet50, and InceptionV3, in Optical Coherence Tomography (OCT) images. This study compares the efficiency of these deep learning models in classifying four different pathological retinal conditions: Normal Retina (NR), Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), and Early Age-Related Macular Degeneration (EAMD). In addition, we have performed two preprocessing operations, image enhancement and data augmentation, to improve the image quality and to over the data imbalance, respectively. We assessed the performance of these deep learning models using a benchmark dataset. After the experiments, we found that the VGG16 outperforms ResNet50 and InceptionV3 by accomplishing an accuracy of 98.7%. © 2024 IEEE.

关键词： Optical coherence tomography

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