National Institute of Technology, Silchar

About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

Linking resources to career satisfaction through work–family enrichment

Abstract: Using the work–family enrichment (WFE) model and social exchange theory as the theoretical underpinnings, our study investigates WFE as a mediator of the effects of material resources (i.e....

Preliminary Diagnosis of COVID-19 Based on Cough Sounds Using Machine Learning Algorithms

Abstract: The proposed work is focused on COVID-19 classification of cough sounds based on machine learning which is used to differentiate COVID-19 coughs from non COVID-19 and healthy coughs. It follows a non-contact based screening test which is very easy to apply being non-invasive and simply carried out within the boundaries of home so that the medical testing centers are not over flooded with patients and there is an overwhelming pressure because of maintenance of those patients with shortage of adequate infrastructure facilities. The dataset used in this study has been derived from the Coswara database which comprises of around 160 infected and 480 healthy individuals. Therefore, Artificial Intelligence based machine learning classifiers were used as an alternative means of diagnosis. Logistic regression (LR), K- Nearest neighbor (KNN), support vector machines (SVM), decision tree algorithms were used as classifiers in the proposed work. The results of this study show that the SVM classifier turned out to be the best in comparing among the COVID-19 and non COVID- 19 coughs with area under receiver operating characteristic curve (ROC) of 0.98. The novelty in the proposed work includes the collection of dry cough samples which would aid in preliminary diagnosis of the infection. This form of classification can also be implemented in a smart phone after performance evaluation from medical authorities.

Rainfall Forecasting Through ANN and SVM in Bolangir Watershed, India

Abstract: Simulating rainfall efficiently is a major complex problem owing to a high number of interrelated hydrological processes. Here, authors used various artificial neural network (ANN) methods to study rainfall forecast in Bolangir district, Odisha, India, by using past 48 years’ data. Different combinations of past rainfall values were formed as forecasting inputs for evaluating the efficiency of ANN approximation. RNN, SVM, and ANFIS techniques are employed for rainfall analysis. The performance of standardized ANN-based model and SVM has been accessed by taking a peak of observed and simulated floods and computation of coefficient of determination (R2) for the intermediate gauging stations on projected river basin. SVM gives better performance with the coefficient of determination 0.9526 and 0.9738 for both testing and training phase while in case ANFIS it gives 0.9342 and 0.9488. Simultaneously, RNN gives less value of R2 is 0.9039 and 0.9292 for training and testing phase.

Existence Results for Some Nonlinear Functional-Integral Equations in Banach Algebra with Applications

Abstract: In the present manuscript, we prove some results concerning the existence of solutions for some nonlinear functional-integral equations which contains various integral and functional equations that considered in nonlinear analysis and its applications. By utilizing the techniques of noncompactness measures, we operate the fixed point theorems such as Darbo's theorem in Banach algebra concerning the estimate on the solutions. The results obtained in this paper extend and improve essentially some known results in the recent literature. We also provide an example of nonlinear functional-integral equation to show the ability of our main result.