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Institution

National Institute of Technology, Silchar

EducationSilchar, Assam, India
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: Control theory & Electric power system. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors have adopted the MOF assisted method to prepare magnetic CuFe2O4 using the Fe containing-Prussian blue MOF cubes, and the synthesized material has been systematically characterized to evaluate the structural properties by X-ray diffraction studies (XRD), morphological properties by scanning electron microscopy (SEM) and optical properties using UV-vis diffuse reflectance spectroscopy (UV-vis DRS).

19 citations

Journal ArticleDOI
01 Aug 2021-Silicon
Abstract: The present environmental concern has motivated researchers to develop bio-polymer based wear materials. In the recent review article focus has been made for proper utilization of natural resources for developing advanced wear resistance material. In order to develop polymer based composite materials with natural fibre and filler, various chemical treatments are focused in the review article. Apart from this the paper also concerned variety of bio-resources products for synthesis of new epoxy grade polymer material. The developed material can be well utilized in various fields such as automobile brake pad, griping material, building construction, heavy machinery, surface modification and improvement. The review work focused on various parameters that are associated with wear behaviour. i. A brief discussion about natural fibres, polymers, their applications and basics of tribology. ii. Effect of the natural filler/fibre weight percentage, orientation of fibre, fibre length, chemical treatment, fibre physical and mechanical properties and are studied. iii. Wear parameters such as amount applied load, sliding velocity, sliding distance, loading time duration, dry and wet condition are focused. The proper utilization of the each and every unutilized part of the bio-products are studied. The various section of the plant i.e. steam, leaf, roots, skin etc. proper utilization of each part of the plant based on its application are studied with suitable epoxy polymer material. The work will most beneficial for improving the wear material in line with tribological behaviour, with proper utilization of natural waste products.

19 citations

Journal ArticleDOI
01 Nov 2020
TL;DR: An efficient method of deep convolutional neural network (DCNN) is introduced because it automatically detects the important features without any human supervision and can deeply detect plant disease by using a deep learning process.
Abstract: Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. The oil obtained from the groundnut is widely used for cooking and losing body weight, and its fats are widely used for making soaps. The groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of deep convolutional neural network (DCNN) because it automatically detects the important features without any human supervision. The DCNN procedure can deeply detect plant disease by using a deep learning process. Moreover, the DCNN training and testing process demonstrate an accurate groundnut disease determination and classification result. The number of groundnut leaf disease images is chosen from the plant village dataset, and it is used for the training and testing process. The stochastic gradient decent momentum method is used for dataset training, and it has shown the better performance of proposed DCNN. From the comparison analysis, the 6th combined layer of proposed DCNN delivers a 95.28% accuracy value. Ultimately, the groundnut disease classification with its overall performance of proposed DCNN provides 99.88% accuracy.

19 citations

Journal ArticleDOI
TL;DR: In this article, the phase transition, electronic, elastic and optical properties of ZnGa2Te4 defect chalcopyrite (DC) semiconductor at different pressures have been investigated.

19 citations

Journal ArticleDOI
15 Nov 2020-Energy
TL;DR: In this paper, the potential of ethanol as an additive on the performance, combustion and exhaust emission characteristics of a compression ignition engine fueled with adulterated diesel considering the environmental protection agency tier 4 emission mandates was explored.

19 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202335
2022149
2021947
2020742
2019596
2018451