RESEARCH OUTPUT
Publications
38 publications and patents in optimization, machine learning, cloud computing, IoT and intelligent systems.
10 results · Clear filters
2023
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Bell pepper leaf disease classification with LBP and VGG-16 based fused features and RF classifier
International Journal of Information Technology (Singapore), vol. 15(1), pp. 465–475, 2023
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Efficient feature selection using BoWs and SURF method for leaf disease identification
Multimedia Tools and Applications, vol. 82(18), pp. 28187–28211, 2023
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Human brain tumor classification and segmentation using CNN
Multimedia Tools and Applications, vol. 82(5), pp. 7599–7620, 2023
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Hybrid Methods for Increasing Security of IoT and Cloud Data
Lecture Notes in Electrical Engineering, pp. 571–586, 2023
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Mode Search Optimization Algorithm for Traffic Prediction and Signal Controlling Using Bellman–Ford with TPFN Path Discovery Model Based on Deep LSTM Classifier
SN Computer Science, vol. 4(5), 2023
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Performance and security analysis using B-128 modified blowfish algorithm
Multimedia Tools and Applications, vol. 82(17), pp. 26661–26678, 2023
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Performance evaluation of PCA based reduced features of leaf images extracted by DWT using random Forest and XGBoost classifier
Multimedia Tools and Applications, vol. 82(17), pp. 26225–26254, 2023
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Securing of Cloud Storage Data Using Hybrid AES-ECC Cryptographic Approach
Journal of Mobile Multimedia, 2023
DOI Link BibTeX Cited by 12Abstract
Internet has revolutionized the world in a way no one could have ever imagined. It paved the way for various different technologies, that have transformed the world exceptionally. Internet enabled cloud technology which provides cost-effective, scalable, on-demand computing resources with little to no downtime. Cloud storage allows its users to store and access private data from anywhere in the world without needing any high-end computing system. Cloud storage isn’t always secure, but that doesn’t imply it isn’t. The security of a data depends on the security policies followed by the provider along with the security of the communication channel via which the data is being sent. Encryption is used to obfuscate the data so that it can only be viewed when correct credentials, known as encryption keys, are provided. Following study proposes an encryption technique using ECC (Elliptic Curve Cryptography) along with AES (Advance Encryption Standard) to provide data confidentiality in an efficient way for securing data on cloud and hence, protect the personal information of user from any adversary. This new method is more effective, and the results are superior as a consequence.
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Stability analysis of mathematical model for spread of pest in tea plant by RKM-4 and ABM-2
Journal of Difference Equations and Applications, vol. 29(2), pp. 121–142, 2023
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Traffic Prediction and Signal Controlling Using Mode-search Optimization Based Deep Long Short Term Memory Classifier
International Journal on Artificial Intelligence Tools, vol. 32(06), 2023
DOI Link BibTeXAbstract
Aim: The research aims at developing a traffic prediction and signal controlling model based on deep learning technique in order to provide congestion-free transportation in Intelligent Transport System (ITS). Need for the Research: Recent technical advancements in the ITS, industrialization, and urbanization increase traffic congestion, which leads to high fuel consumption and health issues. This signifies the need for a dynamic traffic management system to handle the traffic congestion issues that negatively affect the transportation service. Methods: For promoting congestion-free transportation in the ITS, this research aims to devise a traffic prediction and control system based on deep learning techniques that effectively controls the traffic during peak hours. The proposed mode-search optimization effectively clusters the vehicles based on the necessity. In addition, the mode-search optimization tunes the optimal hyperparameters of the deep Long Short Term Memory classifier, which minimizes the training loss. Further, the traffic signal control system is developed through the mode-search-based deep LSTM classifier for predicting the path of the vehicles by analyzing the attributes, such as velocity, acceleration, jitter, and priority of the vehicles. Result: The experimental results evaluate the efficacy of the traffic prediction model in terms of quadratic mean of acceleration (QMA), jitter, standard deviation of travel time (SDTT), and throughput, for which the values are found to be 37.43, 0.23, 8.75, and 100 respectively. Achievements: The proposed method attains the performance improvement of 5% to 42% when compared with the conventional methods.