Faculty Publications
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Publications by NITK Faculty
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Item Recent Trends in Conventional and Nonconventional Bioprocessing(wiley, 2021) Goswami, S.; Raval, K.; Anjana; Bhat, P.This chapter presents the developments that happened in conventional and nonconventional bioprocessing in the past two decades. The first part of the chapter is focused on the upstream bioprocessing, especially bioreactor design and development. The most widely used conventional bioreactors at laboratory scale and production scale are discussed. Single-use technology has emerged winner in recent times of high-value products market and therefore, the second part of the chapter discusses the essential characteristics of successful single-use bioreactors. A brief note on the use of disposable technology in downstream bioprocessing is mentioned at the end of the chapter. © 2022 John Wiley and Sons Ltd.Item Osteosarcoma Bone Cancer Detection(Springer Science and Business Media Deutschland GmbH, 2025) Payani, C.A.; Gupta, C.; Vamsidhar, K.; Bhat, P.; Patil, N.Osteosarcoma is a type of bone cancer commonly found in the elongated bones found in the upper and lower limbs. The precise cause is unknown, but experts believe it’s linked to changes in the DNA of the bones, resulting in the growth of abnormal and harmful bone tissue. If caught early, osteosarcoma is treatable, with about 75% people cured when the cancer hasn’t spread to other body parts. Analyzing biopsy samples can be time-consuming, but there are advanced computer programs, known as supervised deep learning methods, that can help speed up the process and enhance the efficiency of the diagnosis. Previous studies have already evaluated the performance of deep learning models such as VGG16, VGG19, DenseNet201, and ResNet101, among which ResNet101 performed better with 90.36% accuracy. When it comes to understanding complex image features, previous models lack behind. We propose EfficientNetV2, Xception, and InceptionV3 models, among which Xception outperformed other models with 94.5% accuracy on the image dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Exploring Various Data Mining Techniques to Predict Heart Disease(Springer Science and Business Media Deutschland GmbH, 2025) Makam, S.K.; Hiranmayi, M.Y.; Kumar, P.; Bhat, P.; Patil, N.One of the main causes of fatalities in the global population is cardiovascular disease (CVD), commonly called heart disease. Early detection of CVD risks is a major area of interest in clinical data analysis. This study focuses on devising strategies for improving the predictive abilities of CVD risk detection algorithms. We experiment with binary and multiclass classification techniques on public UCI machine learning repository datasets, namely, Cleveland for training and Statlog and Hungarian for evaluation. The techniques include feature selection by best subset generation and data balancing using Binary and Multiclass SMOTE and their variants. Every technique is assessed by tenfold cross-validation on six classifiers: K-Nearest Neighbors (KNNs), Naive Bayes, Logistic Regression (LR), Support Vector Machine (SVM), Neural Network, and Vote (a hybrid technique combining Naïve Bayes and Logistic Regression). Experimental results show a rise in average classifier F1-score of 4.36% after feature selection and Binary SMOTE. Top-performing models include Logistic Regression, Neural Networks, and Voting. KNN shows a significant rise of 8.5 and 5.05% in accuracy, after employing Binary and Multiclass SMOTE techniques, respectively. Multiclass SMOTE results can be used as a benchmark but possess scope for further research and enhancement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Automated Colorization of Grayscale Images Using Superpixels and K-Means Clustering(Springer Science and Business Media Deutschland GmbH, 2025) Kulkarni, B.C.; Teja, B.; Hegde, A.R.; Bhat, P.; Patil, N.The process of transforming grayscale photos into aesthetically pleasing color images is called colorization. Convincing the audience of the realism of the outcome is the primary objective of colorization. Natural scenery makes up the majority of the grayscale photographs that require colorization. A broad range of colorization techniques have been created over the past 20 years; these vary from algorithmically basic procedures that need time and energy due to inevitable human participation to more complex ways that are also more automated. The complex field of automatic conversion mixes deep learning, machine learning, and art. Most of the earlier works which use deep learning, use every pixel values to train the models which is computationally expensive. We present a methodology for colorizing grayscale images using convolutional neural network (CNN), our method uses a combination of superpixel segmentation and K-Means clustering to significantly reduce number of pixel values. The process begins with the conversion of grayscale images to superpixels, which are perceptually uniform regions that aid in efficient colorization. Subsequently, K-Means clustering is applied within each superpixel to identify dominant color clusters, followed by quantization of color information to simplify representation. The prepared input, comprising grayscale images and quantized color information, is then fed into a CNN for colorization, leveraging spatial coherence and semantic context to predict plausible colors for grayscale pixels. The proposed methodology is evaluated on a diverse set of grayscale images, demonstrating its effectiveness in producing vibrant and visually appealing colorized outputs. Through experiments and analysis, we showcase the potential applications and benefits of the proposed approach in historical photograph restoration, movie colorization, and other domains requiring accurate and efficient grayscale image colorization. We use SSIM and PSNR as our evaluation metrics. SSIM is calculated based on the similarity of the luminance and brightness values of the considered and obtained rgb images for the grayscale images, and PSNR is calculated using Mean Squared Error (MSE) of the peak signal values within images. Our methodology’s SSIM and PSNR for the considered flower class is 81.5 and 25.6, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item A Hybrid Weighted Loss Function for Enhanced Protein Interaction Site Prediction(Springer Science and Business Media Deutschland GmbH, 2025) Bhat, P.; Patil, N.Accurately predicting protein interaction sites is crucial for applications such as protein design, drug discovery, and functional protein analysis. However, a significant challenge in this task arises from the inherent class imbalance between interacting and non-interacting sites in protein datasets. While data augmentation techniques are commonly used to mitigate this imbalance, they often introduce noise, potentially reducing prediction accuracy. In this study, we present a novel approach to improve protein interaction site prediction by developing a customized loss function that combines focal loss and cost-sensitive loss, specifically designed to address class imbalance without relying on data augmentation. Our model, which integrates graph convolutional networks (GCNs) to process evolutionary and structural features of proteins, is evaluated using robust performance metrics suited for imbalanced data: Matthews Correlation Coefficient (MCC) and Area Under Precision-Recall Curve (AUPRC). We evaluate the proposed method on the Test_60 dataset, achieving an MCC of 0.342 and an AUPRC of 0.425, providing a modest improvement over the standard cross-entropy loss function. These findings highlight the effectiveness of our tailored loss function in handling class imbalance and improving prediction performance in protein interaction site prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item An exhaustive review of computational prediction techniques for PPI sites, protein locations, and protein functions(Springer, 2023) Bhat, P.; Patil, N.The field of proteomics encompasses a comprehensive examination of proteins, encompassing their structural properties, interactions with other biomolecules, subcellular localization, functional roles, interaction sites, regions of disorder, and exploring novel protein designs. Each of these domains interlinks, contributing valuable information to the study of each other part. Extensive research in most of these areas has given rise to many more challenges that require further exploration. This review mainly concentrates on prediction approaches for protein–protein interaction sites, protein subcellular locations, and protein functions. We provide an exhaustive collection of several latest works in the above three domains, along with a digest of their descriptions in the most recent times. We conclude the review by highlighting the existing challenges and emphasizing the need for a deeper exploration of the research gaps in these studies. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Item Expression of Bacillus licheniformis chitin deacetylase in E. coli pLysS: Sustainable production, purification and characterisation(Elsevier B.V., 2019) Bhat, P.; Pawaskar, G.-M.; Raval, R.; Cord-Landwehr, S.; Moerschbacher, B.; Raval, K.Chitosan obtained by enzymatic deacetylation of chitin using chitin deacetylase (CDA) holds promise primarily due to the possibility to yield chitosan with non-random patterns of acetylation and more environmentally friendly process compared to chemical deacetylation. In the present study, a sustainable bioprocess is reported for over-expression of a bacterial CDA in E. coli pLysS cells. A Bacillus licheniformis CDA gene is identified in the genome of the bacterium, cloned, and expressed, yielding enzymatically active recombinant protein. For enzyme production, a growth medium is formulated using carbon and nitrogen sources, which do not compete with the human food chain. The maximum enzyme activity of 320 ± 20 U/mL is achieved under optimized conditions. The CDA productivity is improved by about 23 times in shake flask culture by optimizing operating conditions and medium components. The CDA is purified and the enzyme kinetic values i.e. Km, Vmax and Kcat are reported. Also the effect of cofactors, temperature, and pH on the enzyme activity is reported. Further, economic yield is proposed for production of CDA through this bioprocess. © 2019 Elsevier B.V.Item Hydroxyapatite—a promising sunscreen filter(Springer, 2020) Pal, A.; Hadagalli, K.; Bhat, P.; Goel, V.; Mandal, S.Exposure to ultraviolet (UV) radiation has been known to cause skin cancer, erythema, and sunburn. Continuous efforts have been made to make sunscreens more efficient and non-toxic. Inorganic sunscreens like TiO2 and ZnO are continued to be used for a few decades, and they are efficient in giving protection against harmful UV radiation, but they are photochemically active as well. They generate free radicals upon irradiation, which leads to reactive oxygen species (ROS) generation which is harmful to the human skin. Hydroxyapatite (HA) is a biocompatible material as it has a composition the same as the mineral content of the human bone; therefore, it is suitable for the dermatological application. Though HA itself does not provide protection against UV, studies on doped HA with various ions showed excellent performance. Pure HA absorbs only between 200 and 340 nm, with an intense band below 247 nm. HA doped with bivalent Zn2+, Fe2+, and trivalent Fe3+ and Cr3+, showed absorbance in the entire UV region. TiO2 provides absorbance in the entire UV range, while ZnO does so only in UVA. Compared to HA (refractive index, n = 1.6), TiO2 (n = 2.6) and ZnO (n = 1.9) have higher refractive index, which gives unwanted whitening effect. Additional properties can be brought in HA composites by adding material while retaining their individual properties. As HA is not photocatalytic, it does not lead to a generation of free radicals. This paper throws light on several aspects of HA-based sunscreen filters as an emerging future cosmetic material, and brief analysis and conclusions. © 2019, Australian Ceramic Society.Item Masculinities, femininities, and the patriarchal family: a reading of The Great Indian Kitchen(Routledge, 2024) Karimpaniyil, R.; Bhat, P.This article seeks to examine the representation of masculinities and femininities in the renowned South Indian drama film The Great Indian Kitchen. The research construes the manner in which the two dominant genders promote and/or modify patriarchal norms within the institution of family. The functioning of women as ancillary members of patriarchy, the interplay between masculinities and femininities, their evolution in contemporary times, etc., are also critically engaged in the paper. The paper argues that the movie The Great Indian Kitchen not only illustrates different masculinities and femininities but also reconstructs the patriarchal family structure which institutionalises gender inequality. It further argues that the movie proposes an alternative image of the family based on gender equality, where men and women live with mutual respect and complementation. © 2023 Informa UK Limited, trading as Taylor & Francis Group.Item Detection of heart arrhythmia with electrocardiography(Springer, 2024) Jat, T.; Patil, N.; Bhat, P.Early detection of cardiac arrhythmia, a prevalent form of cardiovascular disease (CVD) impacting millions globally, is heavily reliant on the accurate analysis of heartbeats. Physicians often recommend that patients wear Holter monitors for 24 h or longer to observe concerning cardiac issues, resulting in the collection of substantial amounts of electrocardiogram (ECG) data. Consequently, there is a need to automate the process of interpreting ECGs to detect cardiac abnormalities efficiently. Current state-of-the-art studies rely on handcrafted feature extraction, which may not effectively capture the intricate temporal relationships inherent in ECG signal data. To address this limitation and facilitate the diagnosis of cardiac diseases, this study proposes a technique that converts electrocardiogram signals into images, subsequently training a deep learning model on the generated images. Image encoding techniques such as Gramian Angular Difference Field (GADF), Gramian Angular Summation Field (GASF) and Markov Transition Field (MTF) are employed to translate the ECG signals into images. The highest accuracy, 96.71%, was achieved by training the Convolutional Neural Network (CNN) model using the concatenation of these three image encoding techniques. The proposed approach is assessed using ECG recordings from the MIT-BIH Arrhythmia Database to detect heart arrhythmia, demonstrating the efficacy of the approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.