Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
Browse
4 results
Search Results
Item An Enhanced Protein Fold Recognition for Low Similarity Datasets Using Convolutional and Skip-Gram Features with Deep Neural Network(Institute of Electrical and Electronics Engineers Inc., 2021) Bankapur, S.; Patil, N.The protein fold recognition is one of the important tasks of structural biology, which helps in addressing further challenges like predicting the protein tertiary structures and its functions. Many machine learning works are published to identify the protein folds effectively. However, very few works have reported the fold recognition accuracy above 80% on benchmark datasets. In this study, an effective set of global and local features are extracted from the proposed Convolutional (Conv) and SkipXGram bi-gram (SXGbg) techniques, and the fold recognition is performed using the proposed deep neural network. The performance of the proposed model reported 91.4% fold accuracy on one of the derived low similarity (< 25%) datasets of latest extended version of SCOPe_2.07. The proposed model is further evaluated on three popular and publicly available benchmark datasets such as DD, EDD, and TG and obtained 85.9%, 95.8%, and 88.8% fold accuracies, respectively. This work is first to report fold recognition accuracy above 85% on all the benchmark datasets. The performance of the proposed model has outperformed the best state-of-the-art models by 5% to 23% on DD, 2% to 19% on EDD, and 3% to 30% on TG dataset. © 2002-2011 IEEE.Item Enhanced protein structural class prediction using effective feature modeling and ensemble of classifiers(Institute of Electrical and Electronics Engineers Inc., 2021) Bankapur, S.; Patil, N.Protein Secondary Structural Class (PSSC) information is important in investigating further challenges of protein sequences like protein fold recognition, protein tertiary structure prediction, and analysis of protein functions for drug discovery. Identification of PSSC using biological methods is time-consuming and cost-intensive. Several computational models have been developed to predict the structural class; however, they lack in generalization of the model. Hence, predicting PSSC based on protein sequences is still proving to be an uphill task. In this article, we proposed an effective, novel and generalized prediction model consisting of a feature modeling and an ensemble of classifiers. The proposed feature modeling extracts discriminating information (features) by leveraging three techniques: (i) Embedding – features are extracted on the basis of spatial residue arrangements of the sequences using word embedding approaches; (ii) SkipXGram Bi-gram – various sets of skipped bi-gram features are extracted from the sequences; and (iii) General Statistical (GS) based features are extracted which covers the global information of structural sequences. The combined effective sets of features are trained and classified using an ensemble of three classifiers: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). The proposed model when assessed on five benchmark datasets (high and low sequence similarity), viz. z277, z498, 25PDB, 1189, and FC699, reported an overall accuracy of 93.55, 97.58, 81.82, 81.11, and 93.93 percent respectively. The proposed model is further validated on a large-scale updated low similarity (?25%) dataset, where it achieved an overall accuracy of 81.11 percent. The proposed generalized model is robust and consistently outperformed several state-of-the-art models on all the five benchmark datasets. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.Item Binary class and multi-class plant disease detection using ensemble deep learning-based approach(Inderscience Publishers, 2022) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Providing food for the exponentially growing global population is a highly challenging task. Owing to the demand and supply gap may diminish food production due to diseases in plants, such as bacterial disease, viral disease, and fungal diseases. Early recognition of such diseases and applying an appropriate pesticide or fertiliser can improve crop yield. Accordingly, early plant disease detection necessitates continuous crop monitoring from its initial stages. Recently some research works have been proposed as remedial measures. However, such methodologies utilise costly equipment that is infeasible for small-scale farmers. Thus, there is a need for a cost-effective plant-disease-detection approach. This study embellishes the challenges and opportunities in plant disease detection. Correspondingly, this research proposes an ensemble deep learning-based plant disease diagnosis approach using a combination of AlexNet, ResNet50, and VGG16 deep learning-based models. It effectively ascertains plant diseases by analysing the plant leaf images. A broad set of experiments were conducted using different plant leaf image datasets such as cherry, grape, maize, pepper, potato, strawberry, and cardamom to evaluate the robustness of the proposed approach. Experiential results demonstrated that the proposed approach attained a maximum detection accuracy of 100% for binary and 99.53% for multi-class datasets. © © 2022 Inderscience Enterprises Ltd.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.