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"Singh, Dharmendra"
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Concepts and techniques of graph neural network
\"This book will aim to provide stepwise discussion; exhaustive literature review; detailed analysis and discussion; rigorous experimentation results, application-oriented approach that will be demonstrated with respect to applications of Graph Neural Network (GNN). It will be written to develop the understanding of concepts and techniques on GNN and to establish the familiarity of different real applications in various domains for GNN. Moreover, it will also cover the prevailing challenges and opportunities\"-- Provided by publisher.
Book
Personality matters: does an individual's personality affect adoption and continued use of green banking channels?
PurposeTechnology has revolutionized banking, and “green banking” has been the most recent phenomenon to have caught the financial world's attention. In this paper, the authors look at how personality traits of individuals influence their adoption and continued use of green banking channels. The authors also propose a comprehensive model integrating the “big five” personality traits (conscientiousness, agreeableness, extraversion, openness and neuroticism) into the Technology Acceptance Model (TAM), along with expectation confirmation theory. The integrated proposed model is used in this longitudinal study to predict the continued use of green banking channels once adopted.Design/methodology/approachThe authors collected data during two time periods about 24 weeks apart from 826 green banking channel users from different regions in India. The data were analyzed using Structural Equation Modeling.FindingsThe authors found that traits of agreeableness, conscientiousness and extraversion favor an individual adopting green banking channels, while conscientiousness and openness were only associated with its perceived usefulness (PU).Research limitations/implicationsThe results offer valuable insights for understanding the adoption and use behavior of people regarding green banking channels. This study would help develop effective segmentation strategies for promoting green banking channels.Originality/valueBy incorporating the big five, along with TAM and Expectation Confirmation Model (ECM), coupled with “trust” as an additional construct, we believe that our study enlarges the boundaries of Information Technology (IT) theories, especially in the context of green banking channels. This study also contributes to advancing the personality theory by exploring how personality traits significantly relate to adopting and using green banking channels.
Journal Article
A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images
Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.
Journal Article
Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model
In today’s world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with principal component analysis (PCA) for dimensionality reduction, and to extract the most important features, Harris hawks optimization algorithm is used further to optimize the classification and feature extraction process. The results shown by the deep learning model with respect to specificity, precision, accuracy, and recall are very much satisfactory compared to the existing systems.
Journal Article
Genome wide transcriptome analysis reveals vital role of heat responsive genes in regulatory mechanisms of lentil (Lens culinaris Medikus)
The present study reports the role of morphological, physiological and reproductive attributes viz. membrane stability index (MSI), osmolytes accumulations, antioxidants activities and pollen germination for heat stress tolerance in contrasting genotypes. Heat stress increased proline and glycine betaine (GPX) contents, induced superoxide dismutase (SOD), ascorbate peroxidase (APX) and glutathione peroxidase (GPX) activities and resulted in higher MSI in PDL-2 (tolerant) compared to JL-3 (sensitive).
In vitro
pollen germination of tolerant genotype was higher than sensitive one under heat stress.
In vivo
stressed pollens of tolerant genotype germinated well on stressed stigma of sensitive genotype, while stressed pollens of sensitive genotype did not germinate on stressed stigma of tolerant genotype.
De novo
transcriptome analysis of both the genotypes showed that number of contigs ranged from 90,267 to 104,424 for all the samples with N
50
ranging from 1,755 to 1,844 bp under heat stress and control conditions. Based on assembled unigenes, 194,178 high-quality Single Nucleotide Polymorphisms (SNPs), 141,050 microsatellites and 7,388 Insertion-deletions (Indels) were detected. Expression of 10 genes was evaluated using quantitative Real Time Polymerase Chain Reaction (RT-qPCR). Comparison of differentially expressed genes (DEGs) under different combinations of heat stress has led to the identification of candidate DEGs and pathways. Changes in expression of physiological and pollen phenotyping related genes were also reaffirmed through transcriptome data. Cell wall and secondary metabolite pathways are found to be majorly affected under heat stress. The findings need further analysis to determine genetic mechanism involved in heat tolerance of lentil.
Journal Article
Glycine betaine modulates chromium (VI)-induced morpho-physiological and biochemical responses to mitigate chromium toxicity in chickpea (Cicer arietinum L.) cultivars
Chromium (Cr) accumulation in crops reduces yield. Here, we grew two chickpea cultivars, Pusa 2085 (Cr-tolerant) and Pusa Green 112 (Cr-sensitive), in hydroponic and pot conditions under different Cr treatments: 0 and 120 µM Cr and 120 µM Cr + 100 mM glycine betaine (GB). For plants grown in the hydroponic media, we evaluated root morphological attributes and plasma membrane integrity via Evans blue uptake. We also estimated H
+
-ATPase activity in the roots and leaves of both cultivars. Plants in pots under conditions similar to those of the hydroponic setup were used to measure growth traits, oxidative stress, chlorophyll contents, enzymatic activities, proline levels, and nutrient elements at the seedling stage. Traits such as Cr uptake in different plant parts after 42 days and grain yield after 140 days of growth were also evaluated. In both cultivars, plant growth traits, chlorophyll contents, enzymatic activities, nutrient contents, and grain yield were significantly reduced under Cr stress, whereas oxidative stress and proline levels were increased compared to the control levels. Further, Cr uptake was remarkably decreased in the roots and leaves of Cr-tolerant than in Cr-sensitive cultivars. Application of GB led to improved root growth and morpho-physiological attributes and reduced oxidative stress along with reduced loss in plasma membrane integrity and subsequently increase in H
+
-ATPase activity. An increment in these parameters shows that the exogenous application of GB improves the Cr stress tolerance in chickpea plants.
Journal Article
Transcriptome analysis of lentil (Lens culinaris Medikus) in response to seedling drought stress
Background
Drought stress is one of the most harmful abiotic stresses in crop plants. As a moderately drought tolerant crop, lentil is a major crop in rainfed areas and a suitable candidate for drought stress tolerance research work. Screening for drought tolerance stress under hydroponic conditions at seedling stage with air exposure is an efficient technique to select genotypes with contrasting traits. Transcriptome analysis provides valuable resources, especially for lentil, as here the information on complete genome sequence is not available. Hence, the present studies were carried out.
Results
This study was undertaken to understand the biochemical mechanisms and transcriptome changes involved in imparting adaptation to drought stress at seedling stage in drought-tolerant (PDL-2) and drought-sensitive (JL-3) cultivars. Among different physiological and biochemical parameters, a significant increase was recorded in proline, glycine betaine contents and activities of SOD, APX and GPX in PDL-2 compared to JL-3while chlorophyll, RWC and catalase activity decreased significantly in JL-3. Transcriptome changes between the PDL-2 and JL-3 under drought stress were evaluated using Illumina HiSeq 2500 platform. Total number of bases ranged from 5.1 to 6.7 Gb. Sequence analysis of control and drought treated cDNA libraries of PDL-2 and JL-3 produced 74032, 75500, 78328 and 81523 contigs, respectively with respective N50 value of 2011, 2008, 2000 and 1991. Differential gene expression of drought treated genotypes along with their controls revealed a total of 11,435 upregulated and 6,934 downregulated transcripts. For functional classification of DEGs, KEGG pathway annotation analysis extracted a total of 413 GO annotation terms where 176 were within molecular process, 128 in cellular and 109 in biological process groups.
Conclusion
The transcriptional profiles provide a foundation for deciphering the underlying mechanism for drought tolerance in lentil. Transcriptional regulation, signal transduction and secondary metabolism in two genotypes revealed significant differences at seedling stage under severe drought. Our finding suggests role of candidate genes for improving drought tolerance in lentil.
Journal Article
Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques
Nowadays, healthcare is the prime need of every human being in the world, and clinical datasets play an important role in developing an intelligent healthcare system for monitoring the health of people. Mostly, the real-world datasets are inherently class imbalanced, clinical datasets also suffer from this imbalance problem, and the imbalanced class distributions pose several issues in the training of classifiers. Consequently, classifiers suffer from low accuracy, precision, recall, and a high degree of misclassification, etc. We performed a brief literature review on the class imbalanced learning scenario. This study carries the empirical performance evaluation of six classifiers, namely Decision Tree, k-Nearest Neighbor, Logistic regression, Artificial Neural Network, Support Vector Machine, and Gaussian Naïve Bayes, over five imbalanced clinical datasets, Breast Cancer Disease, Coronary Heart Disease, Indian Liver Patient, Pima Indians Diabetes Database, and Coronary Kidney Disease, with respect to seven different class balancing techniques, namely Undersampling, Random oversampling, SMOTE, ADASYN, SVM-SMOTE, SMOTEEN, and SMOTETOMEK. In addition to this, the appropriate explanations for the superiority of the classifiers as well as data-balancing techniques are also explored. Furthermore, we discuss the possible recommendations on how to tackle the class imbalanced datasets while training the different supervised machine learning methods. Result analysis demonstrates that SMOTEEN balancing method often performed better over all the other six data-balancing techniques with all six classifiers and for all five clinical datasets. Except for SMOTEEN, all other six balancing techniques almost had equal performance but moderately lesser performance than SMOTEEN.
Journal Article
Impact of Service Quality on Customer Loyalty and Customer Satisfaction in Islamic Banks in the Sultanate of Oman
This study attempts to examine the impact of service quality on customer loyalty and customer satisfaction using the SERVQUAL model for four main Islamic banks in the Sultanate of Oman. This is a quantitative nature of a study, which involved a structured, self-administered questionnaire based on a convenience sampling method gathering data from 120 customers of Islamic banks in Oman. The study data were analyzed using SPSS, and the reliability coefficient (Cronbach’s alpha) was established. The correlation analysis examined the significant relationships among the study variables. The impact of service quality dimensions on customer satisfaction was captured through regression analysis. The key findings of the study revealed that the respondents showed on average an “Agree” response in the five areas, namely, tangibles, responsiveness, reliability, assurance, and empathy. The correlation results depicted a significant relationship between the three variables: service quality, customer satisfaction, and customer loyalty. Similarly, regression results demonstrated that empathy and responsiveness dimensions have a significant positive impact on customer satisfaction. It is, therefore, recommended that banks should focus more on empathy and responsiveness considering the significant relationship of these two variables on customer satisfaction. However, banks should not neglect the importance of other variables such as reliability, assurance, and tangibles that are revealed as important by responses of the participants for the bank’s provisions.
Journal Article