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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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Age-Based Automatic Voice Conversion Using Blood Relation for Voice Impaired

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Computers, Materials & Continua 2022年第2期70卷 4027-4051页

作者： Palli Padmini C.Paramasivam G.Jyothish Lal Sadeen Alharbi Kaustav Bhowmick Department of Electronics&Communication Engineering Amrita School of EngineeringBengaluruAmrita Vishwa VidyapeethamIndia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringCoimbatoreAmrita Vishwa VidyapeethamIndia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Electronics and Communication Engineering PES UniversityBengaluruIndia

The present work presents a statistical method to translate human voices across age groups,based on commonalities in voices of blood *** age-translated voices have been naturalized extracting the blood relation features e.g.,pitch,duration,energy,using Mel Frequency Cepstrum Coefficients(MFCC),for social compatibility of the *** system has been demonstrated using standard English and an Indian *** voice samples for resynthesis were derived from 12 families,with member ages ranging from 8–80 *** voice-age translation,performed using the Pitch synchronous overlap and add(PSOLA)approach,by modulation of extracted voice features,was validated by perception *** translated and resynthesized voices were correlated using Linde,Buzo,Gray(LBG),and Kekre’s Fast Codebook generation(KFCG)*** translated voice targets,a strong(θ>∼93%andθ>∼96%)correlation was found with blood relatives,whereas,a weak(θ<∼78%andθ<∼80%)correlation range was found between different families and different gender from same *** study further subcategorized the sampling and synthesis of the voices into similar or dissimilar gender groups,using a support vector machine(SVM)choosing between available voice ***,∼96%,∼93%,and∼94%accuracies were obtained in the identification of the gender of the voice sample,the age group samples,and the correlation between the original and converted voice samples,*** results obtained were close to the natural voice sample features and are envisaged to facilitate a near-natural voice for speech-impaired easily.

关键词： Blood relations KFCG LBG MFCC vector quantization correlation speech samples same-gender dissimilar gender voice conversion PSOLA SVM

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A Real-Time Oral Cavity Gesture Based Words Synthesizer Using Sensors

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Computers, Materials & Continua 2022年第6期71卷 4523-4554页

The present system experimentally demonstrates a synthesis of syllables and words from tongue manoeuvers in multiple languages,captured by four oral sensors *** an experimental demonstration of the system used in the oral cavity,a prototype tooth model was *** on the principle developed in a previous publication by the author(s),the proposed system has been implemented using the oral cavity(tongue,teeth,and lips)features alone,without the glottis and the *** positions of the sensors in the proposed system were optimized based on articulatory(oral cavity)gestures estimated by simulating the mechanism of human *** system has been tested for all English alphabets and several words with sensor-based input along with an experimental demonstration of the developed algorithm,with limit switches,potentiometer,and flex sensors emulating the tongue in an artificial oral *** system produces the sounds of vowels,consonants,and words in English,along with the pronunciation of meanings of their translations in four major Indian languages,all from oral cavity *** experimental setup also caters to gender mapping of *** sound produced from the hardware has been validated by a perceptual test to verify the gender and word of the speech sample by listeners,with∼98%and∼95%accuracy,*** a model may be useful to interpret speech for those who are speech-disabled because of accidents,neuron disorder,spinal cord injury,or larynx disorder.

关键词： English vowels and consonants oral cavity proposed system sensors speech-disabled speech production vocal tract model

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DMD-CGR: Dynamic Mode Decomposition-based Novel Features for DNA sequence classification

DMD-CGR: Dynamic Mode Decomposition-based Novel Features for...

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Intelligent Technologies (CONIT), International Conference on

作者： Aadharsh Aadhithya A Anirudh Edpuganti Sachin Kumar S Neethu Mohan Soman K. P Amrita Vishwa Vidyapeetham Center for Computational Engineering and Networking (CEN) Coimbatore India

Accurate classification and analysis of pathogenic organisms are essential for understanding the evolutionary relationships between different viruses, as well as for developing effective treatments and preventive measures. In this paper, we propose the use of machine learning models for pathogen classification, specifically for classifying a novel pathogen at a genus level. We propose a novel feature extraction technique called Dynamic Coherent Features through Progressive Rank Approximations (DCFPRA) for extracting relevant features from genomic sequences. We then evaluate the performance of several machine learning algorithms, including Support Vector Machines (SVM), and K-Nearest Neighbours (KNN), using these features. Our results demonstrate that machine learning models trained on DCFPRA features outperform traditional alignment-based methods for pathogen classification. Furthermore, our proposed method is computationally efficient and can handle large-scale datasets, making it a valuable tool for future research in pathogen classification.

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Covid Cough Identification using Machine Learning and Deep Learning Networks

Covid Cough Identification using Machine Learning and Deep L...

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Intelligent Technologies (CONIT), International Conference on

作者： Anand Vinod Neethu Mohan Sachin Kumar S Soman K P Center for Computational Engineering and Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India

The COVID-19 pandemic engulfed the entire world. RT-PCR assessment nowadays is a metric golden standard for contemplating COVID. However, this method takes time and violates social distancing and thus necessary steps have to be taken for its prediction in the future due to its communicable property. Cough is an important bio-marker and it contains micro patterns and audio fingerprints that can be used for classification purposes. Thus in this paper, the main focus is given to the cough data for the classification of COVID-19 diseases. The necessary features such as ZCR, MFCC, chroma STFT, roll-off, spectral centroid, and spectral bandwidth are extracted from the cough data. Traditional machine learning models as well as various deep learning models are implemented in order to find out the model with maximum accuracy for COVID-19 identification.

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A Fast Iterative Filtering Method for Efficient Denoising of Phonocardiogram Signals

A Fast Iterative Filtering Method for Efficient Denoising of...

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Intelligent Technologies (CONIT), International Conference on

作者： N Sai Satwik Reddy V Poorna Muni Saisdhar Reddy Venkata Siva Manoj A Neethu Mohan Sachin Kumar S Soman K P Center for Computational Engineering and Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India

The need for computer-aided diagnosis (CAD) in the automatic monitoring of cardiac diseases has significantly increased in the current medical arena. This paper presents a novel denoising approach based on Fast Iterative Filtering (FIF) for CAD in the automatic monitoring of cardiac diseases. FIF efficiently eliminates unwanted noise from phonocardiogram (PCG) signals, preserving information for precise diagnosis. The comparative analysis highlights the exceptional performance of the FIF method in enhancing cardiac signal denoising tasks in automated heart monitoring systems. The proposed framework integrates a robust phonocardiography system with suitable hardware and a computer, utilizing the FIF technique to denoise PCG signals. The system provides a visual representation of the denoised waveform to the user and sends cardiac information to physicians for further analysis.

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Sentiment Analysis at Document level of Telugu Data from Multi-Domains

Sentiment Analysis at Document level of Telugu Data from Mul...

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Intelligent Technologies (CONIT), International Conference on

作者： Katipally Vighneshwar Reddy Sachin Kumar S KP Soman Center for Computational Engineering and Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India

Finding common threads of optimism or negative in text is the goal of sentiment analysis. In the business world, it is used to learn about a product's reception, a customer's identity, and their expectations of a firm by monitoring the tone of online conversations on forums like Reddit and Twitter. Sentiment analysis in various languages has arisen as a separate topic of NLP as businesses seek for client feedback in previously untapped markets. The stakes could not be greater, since Telugu is a Dravidian language spoken by almost 82 million people. Little effort, such as annotated data and software, is put towards supporting Telugu, hence the language is often overlooked. To better understand the sentiment of the data, we conducted our research using the "Sentirama" dataset and, as you'll see in the paper, we used a variety of Machine Learning models (including SVM-linear, SVM-quadratic, SVM-polynomial, Random Forest, Naive Bayes, and KNN) and Featured concepts (including word2vec+ (CBOW or skip-gram), TF-IDF, and Fastext). To find the best Deep-learning model for Telugu sentiment analysis, we also tried out LSTM, Bidirectional-LSTM, and 1D-CNN.

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Multi-Focus Image Fusion using 2D Compact Variational Mode Decomposition

Multi-Focus Image Fusion using 2D Compact Variational Mode D...

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Intelligent Technologies (CONIT), International Conference on

作者： Swarnamuhi K A Swetha Tirumalai Neethu Mohan Sachin Kumar S Soman K.P Center for Computational Engineering and Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India

Fusion is the technique to integrate noteworthy information from different devices such as sensors using mathematical techniques. The major motive of image fusion is to generate an image that has maximized information and clarity while maintaining integrity. Image fusion aims to reduce redundant data and to construct image more eligible and comprehensible for perception. Fusion is now an indispensable part of technical and industrial applications. This paper investigate the significance of 2D Compact Variational Mode Decomposition (2D-CVMD) algorithm for multi focus fusion of image. The methodology proposed emphasises on multi-focus images. Through extensive experiments it is validated that the proposed methodology generates final fused image with maximum information and better explanation of the view than the original images. Further the accuracy of estimation is validated using standard quality metrics on different multi-focus fusion images.

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A Review of Earth or land surface use and cover Classification using the Landsat8 Remote Sensing data for Dehradun Region

A Review of Earth or land surface use and cover Classificati...

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2023 International Conference on computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Sachin Memoria, Minakshi Kumar, Rajiv Joshi, Kapil Anandaram, Harishchander Gupta, Amit Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India Centre for Computational Engineering Networking Amrita School of Artificial Intelligence Coimbatore India Graphic Era Hill University Deptt. of Cse Dehradun India

ISBN: (纸本)9798350323917

In accordance with economic and industrial development, the number of land use or land-cover or (ESU/ESC) deviations has increased tremendously with uncontrolled population growth, particularly in emerging nations in the late 20th and primary 21st periods. In the best efficient way to understand the impact of ESU/ESC and control the effects of land change, ESU/ESC change has become a major concern for ecologists and policymakers to understand how different machine learning classifiers significantly support the estimation of ESU/ESC maps from remote sensing data. Prime purpose of the study is to categorize ESU/ESC applying different machine learning algorithms on the Google Earth Engine platform, specifically using the Random Forest, Support Vector Machine, Classification and Regression Trees to compare the effectiveness of each algorithm using accuracy estimation. Future sustainable development and a knowledge of the dynamics of our changing environment will undoubtedly benefit from ESU/ESC categorization study. © 2023 IEEE.

关键词： Neural networks

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Learning (With) Distributed Optimization

arXiv

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arXiv 2023年

作者： Aadhithya, Aadharsh A. Abinesh, S. Akshaya, J. Jayanth, M. Radhakrishnan, Vishnu Sowmya, V. Soman, K.P. Center for Computational Engineering and Networking School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

This paper provides an overview of the historical progression of distributedoptimization techniques, tracing their development from early duality-basedmethods pioneered by Dantzig, Wolfe, and Benders in the 1960s to the emergenceof the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN)algorithm. The initial focus on Lagrangian relaxation for convex problems anddecomposition strategies led to the refinement of methods like the AlternatingDirection Method of Multipliers (ADMM). The resurgence of interest indistributed optimization in the late 2000s, particularly in machine learningand imaging, demonstrated ADMM\'s practical efficacy and its unifying *** overview also highlights the emergence of the proximal center method andits applications in diverse domains. Furthermore, the paper underscores thedistinctive features of ALADIN, which offers convergence guarantees fornon-convex scenarios without introducing auxiliary variables, differentiatingit from traditional augmentation techniques. In essence, this work encapsulatesthe historical trajectory of distributed optimization and underscores thepromising prospects of ALADIN in addressing non-convex optimization challenges. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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