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.

Review on Feature Selection and Classification using Neuro-Fuzzy Approaches

Abstract: This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro-fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied on standard datasets to demonstrate the applicability of neuro-fuzzy approaches.

CFD Analysis of Flow Physics and Aerodynamic Performance of a Combined Three-Bucket Savonius and Three-Bladed Darrieus Turbine

Abstract: In this paper, the performance of a combined three-bucket Savonius and three-bladed Darrieus turbine was analyzed computationally by using Fluent 6.2 CFD software. Two-dimensional steady-state CFD simulations were performed for without overlap, and with five overlaps namely 16.2%, 20%, 25%, 30%, and 35%. The flow physics of the combined turbine was analyzed with the help of pressure, velocity and vorticity contours. Further, the aerodynamic coefficients were evaluated with respect to angle of attack for various tip speed ratios. It was concluded that the power augmentation of the combined turbine occurred for low overlap in Savonius turbine due to high aerodynamic lift-to-drag coefficient of the Savonius turbine, caused by the increase of dynamic pressure from bucket-vortex interactions on the concave face of the returning bucket. And it also occurred for high aerodynamic lift-to-drag coefficient (14.5) of the Darrieus turbine from the increase of velocity difference across the Darrieus blade with small o...

Stochastic Oblique Impact on Composite Laminates: A Concise Review and Characterization of the Essence of Hybrid Machine Learning Algorithms

Abstract: Due to the absence of adequate control at different stages of complex manufacturing process, material and geometric properties of composite structures are often uncertain. For a secure and safe design, tracking the impact of these uncertainties on the structural responses is of utmost significance. Composite materials, commonly adopted in various modern aerospace, marine, automobile and civil structures, are often susceptible to low-velocity impact caused by various external agents. Here, along with a critical review, we present machine learning based probabilistic and non-probabilistic (fuzzy) low–velocity impact analyses of composite laminates including a detailed deterministic characterization to systematically investigate the consequences of source- uncertainty. While probabilistic analysis can be performed only when complete statistical description about the input variables are available, the non-probabilistic analysis can be executed even in the presence of incomplete statistical input descriptions with sparse data. In this study, the stochastic effects of stacking sequence, twist angle, oblique impact, plate thickness, velocity of impactor and density of impactor are investigated on the crucial impact response parameters such as contact force, plate displacement, and impactor displacement. For efficient and accurate computation, a hybrid polynomial chaos based Kriging (PC-Kriging) approach is coupled with in-house finite element codes for uncertainty propagation in both the probabilistic and non- probabilistic analyses. The essence of this paper is a critical review on the hybrid machine learning algorithms followed by detailed numerical investigation in the probabilistic and non-probabilistic regimes to access the performance of such hybrid algorithms in comparison to individual algorithms from the viewpoint of accuracy and computational efficiency.