Institution
National Institute of Technology, Karnataka
Education•Mangalore, Karnataka, India•
About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Corrosion & Cloud computing. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.
Topics: Corrosion, Cloud computing, Microstructure, Alloy, Heat transfer
Papers published on a yearly basis
Papers
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TL;DR: In this paper, steel slag aggregates, a waste product obtained from iron and steel industry, are incorporated as coarse aggregates in alkali activated slag concrete (AASC) and fly ash concrete by replacing traditional natural aggregates.
165 citations
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TL;DR: This paper proposes a novel classification model, based on heuristic features that are extracted from URL, source code, and third-party services to overcome the disadvantages of existing anti-phishing techniques and outperformed these methods and also detected zero-day phishing attacks.
Abstract: Phishing is a cyber-attack which targets naive online users tricking into revealing sensitive information such as username, password, social security number or credit card number etc. Attackers fool the Internet users by masking webpage as a trustworthy or legitimate page to retrieve personal information. There are many anti-phishing solutions such as blacklist or whitelist, heuristic and visual similarity-based methods proposed to date, but online users are still getting trapped into revealing sensitive information in phishing websites. In this paper, we propose a novel classification model, based on heuristic features that are extracted from URL, source code, and third-party services to overcome the disadvantages of existing anti-phishing techniques. Our model has been evaluated using eight different machine learning algorithms and out of which, the Random Forest (RF) algorithm performed the best with an accuracy of 99.31%. The experiments were repeated with different (orthogonal and oblique) random forest classifiers to find the best classifier for the phishing website detection. Principal component analysis Random Forest (PCA-RF) performed the best out of all oblique Random Forests (oRFs) with an accuracy of 99.55%. We have also tested our model with the third-party-based features and without third-party-based features to determine the effectiveness of third-party services in the classification of suspicious websites. We also compared our results with the baseline models (CANTINA and CANTINA+). Our proposed technique outperformed these methods and also detected zero-day phishing attacks.
162 citations
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01 Aug 2018TL;DR: This paper adopts a slight variation of the convolutional neural network model called LeNet to detect and identify diseases in tomato leaves and has achieved an average accuracy of 94–95 % indicating the feasibility of the neural network approach even under unfavourable conditions.
Abstract: The tomato crop is an important staple in the Indian market with high commercial value and is produced in large quantities. Diseases are detrimental to the plant's health which in turn affects its growth. To ensure minimal losses to the cultivated crop, it is crucial to supervise its growth. There are numerous types of tomato diseases that target the crop's leaf at an alarming rate. This paper adopts a slight variation of the convolutional neural network model called LeNet to detect and identify diseases in tomato leaves. The main aim of the proposed work is to find a solution to the problem of tomato leaf disease detection using the simplest approach while making use of minimal computing resources to achieve results comparable to state of the art techniques. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94–95 % indicating the feasibility of the neural network approach even under unfavourable conditions.
159 citations
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TL;DR: In this article, an attempt has been made in this paper to assess the features of rural electrification in India and the feasibility of Photovoltaic Solar Home Systems (PV SHS).
158 citations
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01 Jan 2011TL;DR: Two evolutionary computing approaches namely differential evolution and opposition based differential evolution combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification.
Abstract: This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input-multi-output system (TRMS) to verify the identification performance.
157 citations
Authors
Showing all 5100 results
Name | H-index | Papers | Citations |
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Ajay Kumar | 53 | 809 | 12181 |
Bhiksha Raj | 51 | 359 | 13064 |
Alexander P. Lyubartsev | 49 | 184 | 9200 |
Vijay Nair | 47 | 425 | 10411 |
Sukumar Mishra | 44 | 405 | 7905 |
Arun M. Isloor | 38 | 261 | 6272 |
Vinay Kumaran | 36 | 262 | 4473 |
M. C. Ray | 30 | 115 | 2662 |
Airody Vasudeva Adhikari | 30 | 119 | 2832 |
Ian R. Lane | 27 | 129 | 2947 |
D. Krishna Bhat | 26 | 95 | 1715 |
Anurag Kumar | 26 | 126 | 2276 |
Soma Biswas | 25 | 127 | 2195 |
Chandan Kumar | 25 | 66 | 1806 |
H.S. Nagaraja | 23 | 90 | 1609 |