Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification(Springer, 2023) Kadaskar, M.; Patil, N.Deep learning has recently advanced in its applicability to computer vision challenges, and medical imaging has become the most used technique in histopathology image analysis. Nuclei instance segmentation, detection, and classification are one such task. Reliable analysis of these image slides is critical in cancer identification, treatment, and care. Researchers have recently been interested in this issue. This study reviews the categorization and investigation of strategies utilized in recent works to improve the effectiveness of automated nuclei segmentation, detection, and classification in histopathology images. It critically examines state-of-the-art deep learning techniques, analyzes the trends, identifies the challenges, and highlights and helps with the future directions for research. The taxonomy includes deep learning techniques, enhancement, and optimization methods. The survey findings will help to overcome the challenges of nuclei segmentation, detection, and classification while improving the performance of models and, thus, aid future research plans. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.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 Characterization of short E-glass fiber reinforcedgraphite and bronze filled epoxy matrix composites(Iran University of Science and Technology Narmak Teheran 16844, 2016) Patil, N.; Krishna, K.The mechanical characterization of short E- glass fiber reinforced, graphite and sintered bronze filled epoxy composite was carried out in this study. The aim of the present study was to develop tribological engineering material. In this study the flexural strength, theoretical and experimental density, Hardness and Impact strength of composites was investigated experimentally. The results showed that the increased percentage of graphite (10 to 15%Vol) and Eglass fiber (10 to 15%Vol) enhanced flexural strength (149 MPa) of the composite and the maximum flexural modulus (13.3 GPa and 13.1 GPa) was obtained for composite C2 and C5 respectively. Maximum hardness (84 on L scale) and impact energy (90 Joule) was obtained for the composite C6 with increased percentage of glass fiber and graphite filler. The metallurgical electron microscopic images were discussed to interpret the effect of graphite and sintered bronze on mechanical characterization of composite. © 2016, Iran University of Science and Technology. All rights reserved.Item Effect of cusn filler on tribological performance of glass fiber-epoxy composite(IAEME Publication, 2017) Patil, N.; Krishna, K.The aim of this research work was to design polymer matrix composite material for sleeve bearing application. The friction and wear performance of short glass fiber reinforced epoxy (CY230,) filled with graphite (C,) and sintered bronze (CuSn) were investigated by pin on disc wear test. The thermal analysis was also carried by TGA (Thermo Gravimetric Analysis,) to study stability of designed composites. It was observed that the friction coefficient and wear was significant at the start of the test. As the normal load, sliding distance and sliding velocity increased it was considerably reduced. Fillers and fiber separation might have led to initial high wear and later-on lubricating film developed on pin surface might helped in reducing friction coefficient and wear. © IAEME Publication.Item EGA-FMC: Enhanced genetic algorithm-based fuzzy k-modes clustering for categorical data(Inderscience Enterprises Ltd., 2018) Narasimhan, M.; Balasubramanian, B.; Kumar, S.D.; Patil, N.Categorical data clustering is the unsupervised technique of grouping similar objects which have categorical attributes. We propose a genetic algorithm-based fuzzy k-modes categorical data clustering algorithm using multi-objective rank-based selection with enhanced elitism operation. Compactness of the clusters and inter-cluster separation were chosen as objectives to be optimised. During elitism, in every iteration, the best parent chromosomes were identified. The entire population was passed through the selection, crossover and mutation steps. The worst children were then replaced by the best parents. Our method was evaluated on three real-world datasets and resulted in clusters of better quality as compared to current methods with a significant reduction in computation time. Additionally, statistical significance tests were conducted to show the superiority of our approach over other clustering solutions. © © 2018 Inderscience Enterprises Ltd.Item A novel filter–wrapper hybrid greedy ensemble approach optimized using the genetic algorithm to reduce the dimensionality of high-dimensional biomedical datasets(Elsevier Ltd, 2019) Gangavarapu, T.; Patil, N.The predictive accuracy of high-dimensional biomedical datasets is often dwindled by many irrelevant and redundant molecular disease diagnosis features. Dimensionality reduction aims at finding a feature subspace that preserves the predictive accuracy while eliminating noise and curtailing the high computational cost of training. The applicability of a particular feature selection technique is heavily reliant on the ability of that technique to match the problem structure and to capture the inherent patterns in the data. In this paper, we propose a novel filter–wrapper hybrid ensemble feature selection approach based on the weighted occurrence frequency and the penalty scheme, to obtain the most discriminative and instructive feature subspace. The proposed approach engenders an optimal feature subspace by greedily combining the feature subspaces obtained from various predetermined base feature selection techniques. Furthermore, the base feature subspaces are penalized based on specific performance dependent penalty parameters. We leverage effective heuristic search strategies including the greedy parameter-wise optimization and the Genetic Algorithm (GA) to optimize the subspace ensembling process. The effectiveness, robustness, and flexibility of the proposed hybrid greedy ensemble approach in comparison with the base feature selection techniques, and prolific filter and state-of-the-art wrapper methods are justified by empirical analysis on three distinct high-dimensional biomedical datasets. Experimental validation revealed that the proposed greedy approach, when optimized using GA, outperformed the selected base feature selection techniques by 4.17%–15.14% in terms of the prediction accuracy. © 2019 Elsevier B.V.Item An efficient parallel row enumerated algorithm for mining frequent colossal closed itemsets from high dimensional datasets(Elsevier Inc. usjcs@elsevier.com, 2019) Vanahalli, M.K.; Patil, N.Mining colossal itemsets from high dimensional datasets have gained focus in recent times. The conventional algorithms expend most of the time in mining small and mid-sized itemsets, which do not enclose valuable and complete information for decision making. Mining Frequent Colossal Closed Itemsets (FCCI) from a high dimensional dataset play a highly significant role in decision making for many applications, especially in the field of bioinformatics. To mine FCCI from a high dimensional dataset, the existing preprocessing techniques fail to prune the complete set of irrelevant features and irrelevant rows. Besides, the state-of-the-art algorithms for the same are sequential and computationally expensive. The proposed work highlights an Effective Improved Parallel Preprocessing (EIPP) technique to prune the complete set of irrelevant features and irrelevant rows from high dimensional dataset and a novel efficient Parallel Frequent Colossal Closed Itemset Mining (PFCCIM) algorithm. Further, the PFCCIM algorithm is integrated with a novel Rowset Cardinality Table (RCT), an efficient method to check the closeness of a rowset and also an efficient pruning strategy to cut down the mining search space. The proposed PFCCIM algorithm is the first parallel algorithm to mine FCCI from a high dimensional dataset. The performance study shows the improved effectiveness of the proposed EIPP technique over the existing preprocessing techniques and the improved efficiency of the proposed PFCCIM algorithm over the existing algorithms. © 2018 Elsevier Inc.Item An efficient dynamic switching algorithm for mining colossal closed itemsets from high dimensional datasets(Elsevier B.V., 2019) Vanahalli, M.K.; Patil, N.The abundant data across a variety of domains including bioinformatics has led to the formation of dataset with high dimensionality. The conventional algorithms expend most of their time in mining a large number of small and mid-sized itemsets which does not enclose complete and valuable information for decision making. The recent research is focused on Frequent Colossal Closed Itemsets (FCCI), which plays a significant role in decision making for many applications, especially in the field of bioinformatics. The state-of-the-art algorithms in mining FCCI from datasets consisting of a large number of rows and a large number of features are computationally expensive, as they are either pure row or feature enumeration based algorithms. Moreover, the existing preprocessing techniques fail to prune the complete set of irrelevant features and irrelevant rows. The proposed work emphasizes an Effective Improvised Preprocessing (EIP) technique to prune the complete set of irrelevant features and irrelevant rows, and a novel efficient Dynamic Switching Frequent Colossal Closed Itemset Mining (DSFCCIM) algorithm. The proposed DSFCCIM algorithm efficiently switches between row and feature enumeration methods based on data characteristics during the mining process. Further, the DSFCCIM algorithm is integrated with a novel Rowset Cardinality Table, Itemset Support Table, two efficient methods to check the closeness of rowset and itemset, and two efficient pruning strategies to cut down the search space. The proposed DSFCCIM algorithm is the first dynamic switching algorithm to mine FCCI from datasets consisting of a large number of rows and a large number of features. The performance study shows the improved effectiveness of the proposed EIP technique over the existing preprocessing techniques and the improved efficiency of the proposed DSFCCIM algorithm over the existing algorithms. © 2019 Elsevier B.V.Item An enhanced protein secondary structure prediction using deep learning framework on hybrid profile based features(Elsevier Ltd, 2020) Kumar, P.; Bankapur, S.; Patil, N.Accurate protein secondary structure prediction (PSSP) is essential to identify structural classes, protein folds, and its tertiary structure. To identify the secondary structure, experimental methods exhibit higher precision with the trade-off of high cost and time. In this study, we propose an effective prediction model which consists of hybrid features of 42-dimensions with the combination of convolutional neural network (CNN) and bidirectional recurrent neural network (BRNN). The proposed model is accessed on four benchmark datasets such as CB6133, CB513, CASP10, and CAP11 using Q3, Q8, and segment overlap (Sov) metrics. The proposed model reported Q3 accuracy of 85.4%, 85.4%, 83.7%, 81.5%, and Q8 accuracy 75.8%, 73.5%, 72.2%, and 70% on CB6133, CB513, CASP10, and CAP11 datasets respectively. The results of the proposed model are improved by a minimum factor of 2.5% and 2.1% in Q3 and Q8 accuracy respectively, as compared to the popular existing models on CB513 dataset. Further, the quality of the Q3 results is validated by structural class prediction and compared with PSI-PRED. The experiment showed that the quality of the Q3 results of the proposed model is higher than that of PSI-PRED. © 2019 Elsevier B.V.Item A fast and novel approach based on grouping and weighted mRMR for feature selection and classification of protein sequence data(Inderscience Publishers, 2020) Kaur, K.; Patil, N.The analysis of protein sequences under bioinformatics has gained wide importance in research area. Newly added protein sequences can be analysed using existing proteins and converting them into feature vector form. However, it emerges as a challenging task to deal with huge number of features obtained using sequence encoding techniques. Since all the features obtained are not actually required, a three-stage feature selection approach has been proposed. In the first stage, features are ranked and most irrelevant features are removed; in the second stage, conflicting features are grouped together; and in third stage, a fast approach based on weighted Minimum Redundancy Maximum Relevance (wMRMR) has been proposed and applied on grouped features. Different classification methods are used to analyse the performance of the proposed approach. It is observed that the proposed approach has increased classification accuracy results and reduced time consumption in comparison to the state-of-the-art methods. © 2020 Inderscience Enterprises Ltd.