Faculty Publications
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Item Diagnostic classification of undifferentiated fevers using artificial neural network(American Institute of Physics Inc. subs@aip.org, 2020) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.K.; Mahabala, C.; Dakappa, P.H.; Prasad, K.Accurate diagnosis of undifferentiated fever case at the earliest is a challenging effort, which needs extensive diagnostic tests. Prediction of undifferentiated fever cases at an early stage will help in diagnosing the disease in comparatively lesser time and more effectively. The aim of the present study was to apply Artificial Intelligence (AI) algorithm using temperature information for the prediction of major categories of diseases among undifferentiated fever cases. This was an observational study carried out in tertiary care hospital. Total of 103 patients were involved in the study and 24-hour continuous temperature recording was done. Analysis was done using Artificial Neural Network (ANN) model based on the temperature data of each patients and its statistical parameters. Temperature datasets were labeled with the help of experienced physicians. Levenberg Marquardt error back-propagation algorithm was used to train the network. A good relation was found between the target data set and output data set, purely based on the observed 24 hr continuous tympanic temperature of the patients. An accuracy of 98.1% was obtained from ANN prediction model. The study concluded that a single noninvasive temperature parameter is sufficient to predict the major categories of diseases using ANN algorithms, from the undifferentiated fever cases. © 2020 Author(s).Item Flower Phenotype Recognition and Analysis using YoloV5 Models(Grenze Scientific Society, 2022) Naik, P.M.; Rudra, B.Real time detection of flowers based on their growth cycle is called as phenotype. It is one of the most important methods for judging the maturity of flowers and to estimate their yield. Traditional method involves flower detection and classification of varied species. In this paper, we introduce a new dataset based on flower phenotype. The dataset has images of flowers, classified into bud, fresh and stale. The work will help for identification and localization of classes based on flower phenotype. The detection of flowers at various stages of their life will be more important to harvesting in floriculture field. We propose a state of art deep learning-based approach using YOLOV5 model is used for identifying flowers based on flower phenotype. The images are subsequently augmented using rotation transformation, color balance transformation, brightness transformation and blur processing. The augmented images are used for preparing training sets. Using different versions of YoloV5 the flower image dataset is trained and tested. Primarily, flower phenotype considered has stages like bud formation, blossoming and stale flower. © Grenze Scientific Society, 2022.Item Recent Advancements and Challenges in FinTech(Institute of Electrical and Electronics Engineers Inc., 2023) Girish, K.K.; Bhowmik, B.The rapid advancement of technology in recent years has brought about numerous changes in various industries, and the financial sector is no exception. The rise of financial technology (FinTech) has disrupted traditional banking and financial services by offering more convenient, accessible, and personalized services to customers. Contrarily, financial services have become more efficient, cost-effective, and secure with FinTech, enabling people to manage their finances with just a few clicks, even on their smartphones. FinTech has also created new opportunities for financial inclusion, making it possible for people who were previously unbanked or underbanked to access financial services. Despite its many benefits, the rise of FinTech has also brought about several challenges. This paper gives an overview of FinTech, its progress, and its importance. Following this, significant challenges of FinTech are highlighted to ensure its long-term success and continued growth. The recent literature shows the way how it is transforming our perceptions. © 2023 IEEE.Item Anomaly Detection in Electric Powertrain System Software Behaviour(Institute of Electrical and Electronics Engineers Inc., 2023) Vyas, A.; Ghorpade, V.; Kamble, S.; Johnson, P.S.; Kamath, A.; Rawat, K.A software-in-loop (SIL) testing is a method of early testing of control software of a car in virtual environment. A system level testing is carried out on regular basis and it is important to see, if system is behaving as expected or unexpected. For unexpected behaviors, which test engineers not easily notice, modern techniques such as machine learning can give an advantage. This paper presents an application of machine learning algorithms that helps in identifying the abnormal patterns in time series data generated from electric powertrain system testing done in SIL environment for a Mercedes Benz Electric Car. Output of the SIL testing, results in time series data that is a collection of observations that are ordered chronologically and can be used to analyze trends, patterns, and changes over time. Anomaly detection in time series data is a process in machine learning that identifies data points, events, and observations that deviate from a dataset's normal behavior. By monitoring the expected and unexpected behavior of the electric powertrain system, anomaly detection can be a valuable tool for identifying potential issues. This study aims at coming up with an efficient process for anomaly detection in SIL. In order to get this process, various anomaly detection techniques are compared to detect a defined anomaly in time series data. Data pre-processing methods are also discussed before training the model. At the end, we conclude a best-fit method for identified anomaly. With finally identified method, a model was trained and used further in application. © 2023 IEEE.Item A Review on Application of Soft Computing Techniques in Geotechnical Engineering(Springer Science and Business Media Deutschland GmbH, 2024) Thotakura, T.V.; Sireesha, M.; Sunil, B.M.; Alisha, S.S.Numerous test results, mathematical relationships, and in-the-moment analysis and design are all components of geotechnical issues. Additionally, due to smart infrastructure and materials, the research trend in engineering nowadays is shifting toward intelligent tools and their ability to tackle engineering problems. Artificial neural networks (ANN), support vector machines (SVM), genetic algorithms (GA), and particle swarm optimization algorithms (PSO), among other soft computing techniques, have made significant progress in recent years in solving geotechnical issues. Based on a review of more than 800 published research, this study discusses the applicability of soft computing techniques in the current environment. Traditional methods, such as regression analysis and trial-and-error techniques, take time and could be more effective. Additionally, most geotechnical designs require considerable experimental data and may require laborious work. A novel methodology for soft computing approaches has emerged to solve the problems mentioned above. This paper presents soil problems and geotechnical challenges while examining recent developments and the potential applications of soft computing. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Kumar, S.; Bhowmik, B.Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum Machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.Item Improving CNN-Based Breast Cancer Detection Integrating Quantum Layers(Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Bhowmik, B.Breast cancer continues to be a significant burden on global healthcare systems, as early and accurate diagnosis is crucial for improving patient outcomes. Conventional methods used for diagnosis include mammography and biopsy; although they do supply critical information, they often have poor accuracy and are operator-dependent. Artificial Intelligence(AI), particularly Convolutional Neural Networks, presents a promising tool for analyzing medical images; however, conventional CNNs face significant challenges in generalizing from one dataset to another. This paper presents a hybrid Quantum Convolutional Neural Networks(QCNN) framework by integrating the classical feature extraction models VGG16, VGG19, and InceptionV3 with a Quantum Convolutional Layer (QCL). It uses the principles of quantum, such as superposition and entanglement, which process high-dimensional data for capturing non-linear patterns. Therefore, it improves the model's accuracy, sensitivity, and specificity. This hybrid framework presents a scalable and robust solution for the early detection of breast cancer, thereby advancing automated diagnostic systems to enhance reliability and adaptability. © 2025 IEEE.Item Self-healing bacterial concrete: A bibliometric overview and state-of-the-art review on fundamentals, techniques and future perspectives(Elsevier Ltd, 2025) Ranjith, A.; Das, B.B.The augmentation of micro-cracks in concrete is unavoidable, and under varying external ambience, such cracks have the potential to cause concrete deterioration due to the ingress of deleterious substances. The utilization of bacteria enabled self-healing methods displayed promising outcomes in the sealing of such minute cracks, offering considerable benefits and reducing the need for human intervention. From this point of view, this article aims to provide comprehensive review of the existing literature on bacteria based self-healing concrete using Bibliometric analysis. The critical evaluation of the significant features that have a notable impact on the self-healing efficacy of cementitious composites incorporating bacteria is presented. These factors encompass primary aspects involving bacteria selection, healing conditions, influence of crack width, effect of pre cracking, influence of carrier compounds etc., emphasizing their significance on the healing performance. Also, the improvement in mechanical and durability properties through the utilization of bacteria-enabled cementitious composites for self-healing purposes is scrutinized. Furthermore, the performance of bacteria based self-healing concrete in aggressive environments like corrosion and carbonation exposure are critically reviewed. Additionally, this article delves into research on the application of Artificial Intelligence (AI) and Machine Learning (ML) in relation to bacteria enabled self-healing concrete. Finally, suggestions for future research directions and practical implementations in this domain are put forward. © 2025 Elsevier LtdItem 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.