Faculty Publications
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Item Role of Genomics in Smart Era and Its Application in COVID-19(Taylor and Francis, 2023) Kumar, S.; Bhowmik, B.Genomics is a rapidly developing field that aims to understand the whole inherited traits of an organism, including its structure, function, and evolution. The purpose of genomics is to gain a detailed understanding of the biological basis for human disease, to explore the genetic variation of several species and humans, and also to enhance rural livelihoods and farming practices. The motivation to completely comprehend the complex biological processes that regulate life on earth and to put this knowledge to enhance people’s lives, improve food security, and safeguard the environment has driven the growth of genomics technologies. The discovery of the genetic roots of human diseases and other complex traits is one of the main goals of genomics, which may lead to the development of treatments and medications. Researchers can find similar genetic pathways and mechanisms to develop drugs and medicines for a broad range of diseases by comparing the genomes of many species. With the introduction of new technologies and advancements in deoxyribonucleic acid sequencing, genomics has evolved into a powerful tool for solving life’s riddles and transforming the lives of people from all over the world. By comparing the genomes of DNA sequencing disorders, researchers can uncover the genes responsible for desirable characteristics such as improved genetics, disease resistance, and better efficiency. This information is essential to develop populations of organisms better adaptable to evolving biological conditions. This chapter provides an overview of genomics, including its background, key attributes, and various types and application areas. The numerous challenges in genomics are also addressed in this chapter, including dealing with large genomes, sequencing and retrieving genetic data, comprehending the features of potential pathogens, and analyzing pathogen sequence trends. The chapter also addresses recent advances in genomics, such as its involvement in the COVID-19 pandemic and the most sophisticated techniques used in the discipline. The development of artificial intelligence in genomics and its usage in COVID-19 research are also discussed in this chapter. Moreover, this chapter provides a comprehensive insight into the evolution, present condition, and future potential of genomics research. Overall, the purpose of the chapter is to understand the problems and accomplishments in genomics and how it may assist healthcare systems. © 2024 Scrivener Publishing LLC.Item Reducing False Prediction on COVID-19 Detection Using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.; Varna, S.A.; Kumar, A.; Kumar, R.This paper proposes a custom deep neural network-based scheme for coronavirus disease 2019 (COVID-19) detection. The proposed method takes X-ray images that use transfer learning techniques on pre-trained models. One objective of this work is to quickening the detection of the virus. Another goal is to reduce the number of falsely detected cases by a significant margin. The experimental setup demonstrates promising results on the selected dataset, which achieve up to 99.74%, 99.69%, 98.80% as classification, precision, and recall accuracy. © 2021 IEEE.Item COVID-19 Waves and Their Impacts to Society(Institute of Electrical and Electronics Engineers Inc., 2023) Kumar, S.; Bhowmik, B.The COVID-19 pandemic has led to a global medical crisis and significant disruptions to daily life since its emergence in December 2019. Rapidly, it spread to 218 countries affecting more than 754 million people. The virus appears in different variants bringing significant implications at all societal levels. Recently, different variants of the virus have emerged, which have caused significant consequences in society. This paper presents the state-of-the-art on other waves caused by COVID-19 variants, their impacts on society, and challenges. The paper also details recent advancements to combat this disease. © 2023 IEEE.Item Verification of COVIFIND Test Kit for COVID-19(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.; Rathore, R.; Bhowmik, B.The ongoing global challenges of infectious diseases, particularly COVID-19, highlight the critical need for accurate and timely diagnostic tools. Rapid antigen test kits have become essential for swiftly detecting COVID-19 infections, enabling healthcare professionals to make prompt decisions based on quick results. Due to their complexity and the potential impact on public health, these kits require thorough validation. This paper presents a novel formal verification approach using predicate logic and state transition tables to validate the performance of COVIFIND COVID-19 antigen self-test kit. The proposed methodology encompasses sensitivity, specificity, and operational parameters, incorporating logical formulae and state transition rules to describe and verify test states and transitions. The frame-work has been applied to various test components, including sample pads, conjugate pads, and control lines, demonstrating its effectiveness in maintaining adherence to specified requirements and performance standards. The results affirm the robustness of the COVIFIND test kit in providing reliable results across diverse conditions. This work enhances the quality assurance processes for rapid diagnostic tests, lays the groundwork for further development, and is crucial for maintaining high standards in public health diagnostics. © 2024 IEEE.Item Detecting COVID-19 Infection Using Customized Convolutional Neural Network(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.; Kisku, B.; Vardhan K, S.H.; Kumar, S.; Bhowmik, B.The COVID-19 pandemic has affected 775 million people globally, with an estimated death toll of 7 million. Detection methods like reverse transcription polymer chain reaction (RT-PCR) face multiple challenges, including false positive cases, time-consuming, and high cost. A rapid, precise, affordable screening alternative is essential to expedite COVID-19 detection. Various efforts have focused on expediting COVID-19 detection due to the high costs and logistical challenges associated with traditional methods. This paper proposes a customized deep-learning framework architecture for automatically identifying COVID-19 infection in chest X-ray (CXR) images. Multiple neural networks extract deep features from the CXR images, including popular models such as VGG19, DenseNet201, EfficientNet, MobileNetV2, and InceptionV3. The proposed model undergoes training and testing using the QaTa-COVID-19 dataset. The proposed model achieves classification accuracy of 97.06%, with precision, recall, and F1 score rates for COVID-19 cases recorded at 97.34%, 96.36%, and 97.01%, respectively, for the 4-class cases (COVID vs. Normal vs. Pediatric Bacterial Pneumonia vs. Pediatric Viral Pneumonia). © 2024 IEEE.Item Automated Segmentation of COVID-19 Infected Lungs via Modified U-Net Model(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.; Bhowmik, B.The COVID-19 pandemic has led to significant outbreaks in more than 220 countries worldwide, profoundly impacting the public health and lives. As of February 2024, over 774 million cases have been reported, with more than 7,035,337 deaths recorded. Therefore, there is a significant need for automated image segmentation to serve as clinical decision support. This paper presents a novel automated segmentation framework that dynamically generates distinct and randomized image patches for training using preprocessing techniques and extensive data augmentation. The proposed architecture employs a semantic segmentation approach, ensuring accuracy despite limited data availability. Experimental assessment comprises a visual inspection of the predicted segmentation outcomes. Quantitative evaluation of segmentation includes standards performance metrics such as precision, recall, Dice score, and Intersection over Union (IoU). The results exhibit a remarkable Dice coefficient score of 98.3% and an IoU rate surpassing 96.8%, demonstrating the model's robustness in identifying COVID-19-infected lung regions. © 2024 IEEE.Item AutoCov22: A Customized Deep Learning Framework for COVID-19 Detection(Springer, 2023) Bhowmik, B.; Varna, S.; Kumar, A.; Kumar, R.The novel coronavirus disease 2019 (COVID-19) spill has spread rapidly and appeared as a pandemic affecting global public health. Due to the severe challenges faced with the increase of suspected cases, more testing methods are explored. These methods, however, have several disadvantages, such as test complexity and associated problems—sensitivity, reproducibility, and specificity. Hence, many of them need help to achieve satisfactory performance. Motivated by these shortcomings, this work proposes a custom deep neural network framework named “AutoCov22” that detects COVID-19 by exploiting medical images—chest X-ray and CT-scan. First, multiple neural networks extract deep features from the input medical images, including popularly used VGG16, ResNet50, DenseNet121, and InceptionResNetV2. Then, the extracted features are fed to different machine-learning techniques to identify COVID-19 cases. One objective of this work is to quicken COVID-19 detection. Another goal is to reduce the number of falsely detected cases by a significant margin. Comprehensive simulation results achieve a classification accuracy of 99.74%, a precision of 99.69%, and a recall of 98.80% on exercising chest X-ray images. Extended experiment results in accuracy, precision, and recall up to 87.18%, 84.98%, and 85.66%, respectively, in processing CT-scan images. Thus, the AutoCov22 approach demonstrates a promising and plausible best solution over several methods in the state-of-the-art. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item ADConv-Net: Advanced Deep Convolution Neural Network for COVID-19 Diagnostics Using Chest X-Ray and CT Images(Springer, 2025) Kumar, S.; Bhowmik, B.The worldwide COVID-19 epidemic has emerged as a significant concern, affecting daily lives and underscoring the importance of early diagnosis for effective treatment in medical and healthcare settings. Current diagnostic testing for COVID-19 is sluggish, typically requiring hours to get results. Detection of COVID-19 from medical imaging presents a challenging task that has gained substantial interest from experts worldwide. Essential imaging modalities for diagnosing COVID-19 include chest X-rays and computed tomography (CT) scans. By contrast, most of the chest radiography can be completed in within fifteen minutes. Thus, employing chest radiography gives a possibility for early and reliable diagnosis of COVID-19, intending to relieve therapeutic obstacles for patients and speed up the diagnostic process. Recently, deep learning (DL) techniques have been shown to be effective in image-based diagnostics. This paper proposed an advanced deep convolution neural network (ADConv-Net) for COVID-19 detection and categorization using chest X-ray and CT images. The proposed technique is not only capable of recognizing critical connections and similarities in image classification, but also leads to improved diagnostic accuracy. The proposed model undergoes thorough evaluation for standard performance metrics. After evaluation, the ADConv-Net model achieves high accuracies of 98.84% and 97.25% in training and testing for X-ray images and 99.41% and 98.87% in training and testing for CT images, respectively. Additionally, the proposed model demonstrates strong performance, with AUC values of 0.993 and 0.996 for X-ray and CT images, respectively. Further, the model also introduces a heatmap approach for displaying COVID-19 disease areas. Subsequently, radiologists can find COVID-19 disorders in chest X-ray and CT images with this approach. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.Item EffiCOVID-net: A highly efficient convolutional neural network for COVID-19 diagnosis using chest X-ray imaging(Academic Press Inc., 2025) Kumar, S.; Bhowmik, B.The global COVID-19 pandemic has drastically affected daily life, emphasizing the urgent need for early and accurate detection to provide adequate medical treatment, especially with limited antiviral options. Chest X-ray imaging has proven crucial for distinguishing COVID-19 from other respiratory conditions, providing an essential diagnostic tool. Deep learning (DL)-based models have proven highly effective in image diagnostics in recent years. Many of these models are computationally intensive and prone to overfitting, especially when trained on limited datasets. Additionally, conventional models often fail to capture multi-scale features, reducing diagnostic accuracy. This paper proposed a highly efficient convolutional neural network (CNN) called EffiCOVID-Net, incorporating diverse feature learning units. The proposed model consists of a bunch of EffiCOVID blocks that incorporate several layers of convolution containing (3×3) filters and recurrent connections to extract complex features while preserving spatial integrity. The performance of EffiCOVID-Net is rigorously evaluated using standard performance metrics on two publicly available COVID-19 chest X-ray datasets. Experimental results demonstrate that EffiCOVID-Net outperforms existing models, achieving 98.68% accuracy on the COVID-19 radiography dataset (D1), 98.55% on the curated chest X-ray dataset (D2), and 98.87% on the mixed dataset (DMix) in multi-class classification (COVID-19 vs. Normal vs. Pneumonia). For binary classification (COVID-19 vs. Normal), the model attains 99.06%, 99.78%, and 99.07% accuracy, respectively. Integrating Grad-CAM-based visualizations further enhances interpretability by highlighting critical regions influencing model predictions. EffiCOVID-Net's lightweight architecture ensures low computational overhead, making it suitable for deployment in resource-constrained clinical settings. A comparative analysis with existing methods highlights its superior accuracy, efficiency, and robustness performance. However, while the model enhances diagnostic workflows, it is best utilized as an assistive tool rather than a standalone diagnostic method. © 2025 Elsevier Inc.Item Machine Learning Framework for Classification of COVID-19 Variants Using K-mer Based DNA Sequencing(John Wiley and Sons Inc, 2025) Kumar, S.; Raju, S.; Bhowmik, B.Accurate classification of viral DNA sequences is essential for tracking mutations, understanding viral evolution, and enabling timely public health responses. Traditional alignment-based methods are often computationally intensive and less effective for highly mutating viruses. This article presents a machine learning framework for classifying DNA sequences of COVID-19 variants using K-mer-based tokenization and vectorization techniques inspired by Natural Language Processing (NLP). DNA sequences corresponding to Alpha, Beta, Gamma, and Omicron variants are obtained from the Global Initiative on Sharing All Influenza Data (GISAID) database and encoded into feature vectors. Multiple classifiers, including Extra Trees, Random Forest, Support Vector Classifier (SVC), Decision Tree, Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), Ridge Classifier, Stochastic Gradient Descent (SGD), and XGBoost, are evaluated based on accuracy, precision, recall, and F1-score. The Extra Trees model achieved the highest accuracy of 93.10% (Formula presented.) 0.42, followed by Random Forest with 92.60% (Formula presented.) 0.38, both demonstrating robust and balanced performance. Statistical significance tests confirmed the robustness of the results. The results validate the effectiveness of K-mer-based encoding combined with traditional machine learning models in classifying COVID-19 variants, offering a scalable and efficient solution for genomic surveillance. © 2025 Wiley Periodicals LLC.