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.

Prediction of Flood in Barak River using Hybrid Machine Learning Approaches: A Case Study

Abstract: Flooding causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Therefore, flood prediction is of prime importance for reducing loss of life and devastation to property. To model complex nature of hydrologic processes artificial neural network (ANN) tool is effectively being utilized for modelling different nonlinear relationships, and has proved to be an appropriate method for flood prediction. Present study investigated relative accuracy of radial basis function neural network (RBFNN) and support vector machine (SVM) models combined with Firefly Algorithm (FA) in predicting river flood discharge and contrasted with that of regular ANN, RBFNN and SVM models. Monthly river flow data of Silchar and Dholai stations located in Cachar district of Assam, India are utilized for the present study. For assessing model performance, coefficient of determination (R2), mean square error (MSE) and root mean square error (RMSE) were measured. Evaluation of outcomes shows that both RBF-FA (radial basis function — firefly algorithm) and SVM-FA (support vector machine — firefly algorithm) hybrid models give more precise forecasting results than RBFNN, FFBPNN (feed forward back propagation neural network) and SVM models. Yet, it can be observed that SVM-FA model give better forecasting outputs with R2 value 0.9818 than RBF-FA model. Results also reveal that simple SVM model performs marginally better than simple ANN model.

Graphene-based 1D defective photonic crystal biosensor for real-time detection of cancer cells

Abstract: Miniaturized biosensor with fabrication feasibility is of tremendous interest for point-of-care testing of cancer cells. With this objective, we propose a novel biophotonic sensor based on graphene embedded defect 1D photonic crystal (PhC) for real-time detection of different cancerous cells like basal, cervical and breast cancer cells. The transfer matrix method is employed to study the transmittance and absorption spectra of different cells under consideration. The cornerstone of this work is based on an the assay of shift in resonant mode wavelength formed within the transmittance and absorption spectra, by infiltrating the defect layer with the normal and cancer cells. The proposed structure is optimized vis-a-vis the selection of materials, thickness of dielectric layers, period of dielectric layers, thickness of defect layer and angle of incidence to envisage efficient cancer sensor. The sensor is characterized with high sensitivity, high signal-to-noise ratio, large quality factor, high figure of merit, high dynamic range, low resolution (R) of 290 nm/RIU, 52.96, 2270.74, 1074.04 1/RIU, 1179.94, and 0.0668 nm, respectively, which ensures reliable and accurate detection of cancer cells. The significance of this research is proved by comparing the results with previously published works. Moreover, the simple structure, cost-effective fabrication methods and label-free detection of cancer cells make the proposed sensor a promising challenger for biosensing applications.

On existence theorems for some generalized nonlinear functional-integral

Abstract: In the present paper, utilizing the techniques of suitable measures of noncompactness in Banach algebra, we prove an existence theorem for nonlinear functional-integral equation which contains as particular cases several integral and functional-integral equations that appear in many branches of nonlinear analysis and its applications. We employ the fixed point theorems such as Darbo’s theorem in Banach algebra concerning the estimate on the solutions. We also provide a nontrivial example that explain the generalizations and applications of our main result is also included.