RESEARCH OUTPUT
Publications
38 publications and patents in optimization, machine learning, cloud computing, IoT and intelligent systems.
21 results · Clear filters
2026
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Predictive workload forecasting models for energy-efficient resource provisioning in cloud data centres
Next research., vol. 11, pp. 102001, 2026
2024
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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices
Computers, Materials and Continua, vol. 78(1), pp. 31–63, 2024
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A comprehensive review on detection and classification of dementia using neuroimaging and machine learning
Multimedia Tools and Applications, vol. 83(17), pp. 52365–52403, 2024
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Optimized transfer learning approach for leaf disease classification in smart agriculture
Multimedia Tools and Applications, vol. 83(20), pp. 58103–58123, 2024
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Performance enhancement of kernelized SVM with deep learning features for tea leaf disease prediction
Multimedia Tools and Applications, vol. 83(13), pp. 39117–39134, 2024
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SHC: 8-bit Compact and Efficient S-Box Structure for Lightweight Cryptography
IEEE Access, vol. 12, pp. 39430–39449, 2024
DOI Link BibTeX Cited by 39Abstract
The AES (Advance Encryption Standard) has made the development of new block ciphers unnecessary; it is now the de facto standard for most uses of block ciphers. However, the AES is still not well-suited for very limited contexts like RFID (Radio-Frequency Identification) tags and WSN(Wireless Sensor Networks), despite recent implementation advancements. In this study, we present SHC (Simple Hybrid Cipher), a new block cipher that uses a 64-bit block length and a 128-bit key length. It offers a hardware implementation that efficiently uses limited resources, making it ideal for use as a sensor in a WSN or an RFID tag. The core function of SHC depends on S-Box-based, composite field arithmetic technology, as it consumes relatively low cost on hardware implementation while still providing sufficient security as a solid encryption algorithm. The hardware implementation of SHC-64 requires 949 LUTs; it generates a maximum operating frequency of 515.995 MHz on the Xilinx-powered Artix-7 Field Programmable Gate Array (FPGA) development board. At the same time, the National Institute of Standards and Technology (NIST) recommended standard algorithm AES consumes 3645 LUTs and generates a maximum operating frequency of 277.369 MHz. The SHC-64 cipher also shows resistance against known cryptanalytics attacks.
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Self-adaptive search optimization-based vehicle path prediction and traffic light controller in vehicular ad hoc network
International Journal of Communication Systems, vol. 37(1), 2024
DOI Link BibTeX Cited by 3Abstract
Summary In recent days, VANET is considered as the main hopeful equipment in the system of transportation since traffic congestion arises regularly and thus occurred road accidents very easily. Besides, the network traffic is increasing due to the huge quantity of information generated in region of urban. Therefore, one of the primary challenges faced by Intelligent Transportation System (ITS) is ensuring the accurate transmission of information in both vehicle‐to‐vehicle (V2V) and vehicle‐to‐road (V2R) sensing unit communications. So, here, an adaptive routing controller (ARC) is utilized to improve the data transmission between vehicle and road sensing unit (RSU) and to minimize the traffic density in VANET, and the self‐adaptive search optimization is developed in VANET clustering to prioritize vehicles in the lane, in which the multi‐objective function is intended depending on the energy of the node, acceleration, jitter, priority, velocity, and trust factors. The traffic light control is done to facilitate the effective communication in the network. Thus, the proposed technique is calculated in terms of throughput, jitter, quadratic mean of acceleration (QMA), and spatially distributed travel time (SDTT), which acquired the values of 0.53 s, 36.07 kmph, 3.472 s, and 43.572%, respectively, while using 50 vehicles at 50 s.
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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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.
2022
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A comprehensive survey on leaf disease identification & classification
Multimedia Tools and Applications, vol. 81(23), pp. 33897–33925, 2022
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An Automatic Lung Nodule Classification System Based on Hybrid Transfer Learning Approach
SN Computer Science, vol. 3(4), 2022
2021
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TPA Auditing to Enhance the Privacy and Security in Cloud Systems
Journal of Cyber Security and Mobility, 2021
DOI Link BibTeX Cited by 36Abstract
Over the last decade, many enterprises around the world migrating from traditional infrastructure to cloud resources in order to cut down operational and capital expenditure. With cloud computing, huge amount of data transactions is communicated between cloud consumers and cloud service providers. However, this cloud computing enables surplus security challenges associated to unauthorized access and data breaches. We proposed in this paper a trusted third-party auditor (TPA) model which uses lightweight cryptographic system and lightweight hashing technique to ensure data security and data integrity to audit the cloud users outsourced data from cloud service providers. With our proposed system, we solve the concern of data reliability using data correctness and verification analysis and error recovery analysis. The time complexity of our proposed system is less as compared with other TPA model. Our proposed system also shows resistance against various known cryptanalytic attacks, the performance and extensive compression technique of our proposed system are probably secure and highly proficient.
2017
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Bi-objective virtual machine placement using hybrid of genetic algorithm and particle swarm optimization in cloud data center
International Journal of Applied Engineering Research, 2017
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Multi-objective virtual machine placement using improved teaching learning based optimization in cloud data centers
International Journal of Applied Engineering Research, 2017