K PARAMESHA

@vvce.ac.in

Professor/CSE

K PARAMESHA


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https://researchid.co/paramesha.k

RESEARCH INTERESTS

AI, TEXT MINING, NLP
13

Scopus Publications

160

Scholar Citations

6

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Automated brain tumor detection in MRI images using CNN and ResNet architectures
    Annapurna V K, Asha N, K Paramesha, Shabana Sultana, Kirankumar Humse
    Journal of Integrated Science and Technology, 2026
    Deep learning has shown significant potential in medical image analysis, particularly for disease detection using MRI scans. Accurate and early diagnosis of brain tumors remains challenging due to the complexity of brain structures and reliance on manual interpretation. This work presents an automated deep learning–based approach for brain tumor detection from MRI images using Convolutional Neural Networks and Residual Networks. Transfer learning is applied with two pretrained architectures, ResNet18 and ResNet50, to classify MRI scans into tumor and non-tumor categories. Experiments are conducted on a dataset of 3,929 brain MRI images, evaluating the impact of model depth and fine-tuning strategies. The results show that ResNet18 achieves a higher accuracy of 97% compared to 96% for ResNet50, demonstrating better generalization on limited medical data. The proposed framework enables fast, accurate, and cost-effective brain tumor detection, supporting early diagnosis and clinical decision-making.
  • Emerging Optical Approaches for Sensitive and Selective Detection of Copper in Water: A Comprehensive Review
    B. K. Vinay, K. Paramesha, M. Shrilalitha, T. R. Suranjan, B. M. Vikas
    Critical Reviews in Analytical Chemistry, 2026
    Cu contamination in water poses serious threats to both public health and aquatic ecosystems, necessitating the development of accurate and efficient detection methods. This review study uniquely offers a comprehensive analysis of optical techniques for detecting Cu pollution in water, encompassing both reagent and reagent-less based approaches across various sensing platforms. This work sheds light on the underexplored potential of advanced optical technologies such as Colourimetry, Surface Plasmon Resonance, UV-Vis spectroscopy, and others in detecting various Cu species. The findings highlight the high sensitivity, specificity, and adaptability of optical detection methods. Reagent-based strategies provide outstanding sensitivity and selectivity but often involve elaborate sample preparation and pose risks of secondary contamination. On the other hand, reagent-less approaches offer environmentally friendly alternatives with easier implementation, though they demand careful calibration and system optimization. Future directions include the advancement of miniaturized, cost-effective optical sensors, enhanced speciation capabilities for different Cu oxidation states, and integration with real-time data acquisition, processing, and remote sensing technologies to enable robust field-based monitoring. This study aims to guide researchers, engineers, and policymakers by providing insights into the emerging optical detection technologies and their transformative potential in water quality assessment.
  • Machine Learning and Deep Learning Approaches for Guava Disease Detection
    K. Paramesha, Shruti Jalapur, Shalini Hanok, Kiran Puttegowda, G. Manjunatha, Bharath Kumara
    SN Computer Science, 2025
  • Artificial Intelligence Driven Curriculum Development: Challenges and Modalities
    K Paramesha, M Hamsaveni, B J Soumya
    Annual International Conference on Data Science Machine Learning and Blockchain Technology Aicdmb 2025, 2025
    Generative Artificial Intelligence (GenAI) has the potential to transform the engineering education industry, offering new opportunities for innovation, automation and personalization in content generation. Engineering colleges function through various committees comprising teaching and non-teaching staff with definite roles and responsibilities in documentation. Traditionally, these committees manually execute multiple types of documentation by capturing the facts and figures of different facets of the college and referring to statutory bodies and other universities and colleges for relevant content. The curriculum design and development by the academic committee involves documentation of curriculum components. Automating the documentation using sophisticated GenAI is indeed more feasible and powerful than ever before and offers several benefits to the end users. In this paper, we are scoping the extent of possible integration of GenAI for the automation of curriculum development for programs in engineering colleges and its implementation modalities. It also explores the implications of integrating generative AI into the curriculum syllabus development process, addressing key considerations, challenges, and strategies for effective implementation. Leveraging GenAI, the content-based recommendations offer ample content and insights that aid the college committees tasked with formulating the documentation content. This type of automation in the education domain has several benefits in saving lot of manual work perusing documents for the required information.
  • A novel image cryptosystem for biomedical images and secured storage by randomized chaotic encryption scheme
    K Paramesha, V Karthik, Polosatkin S.V., Sathisha M.S., Bhargav H.K., et al.
    Journal of Integrated Science and Technology, 2025
    Medical images transfer sensitive elements about diagnosis along with patient information across public networks between doctors and hospitals and patients. Secure storage and transmission methods must be implemented for image protection which addresses patient privacy. The proposed system introduces an elaborate image encryption method that uses chaotic and rotational systems for performing inter-block shuffling operations. A 2D sine map system with random behavior allows the algorithm to transform target images by performing scaling and rotational transformations and randomly reshuffling arrays. The chaotic system applies scaling and rotation operations as its first step before processing the medical image to reduce pixel dependencies. Permutation of the image produces an encrypted file through the S-box which applies a diffusion operation to the rearranged data. A chaotic system generates both unpredictability and sensitive initial condition reactions that produces a large key space that makes brute-force attacks less successful. Real-time operations are possible because the algorithm operates with fast data processing while using minimal resources. Evaluation results show that grayscale medical images perform better under security tests as the NPCR and UACI value reached above 98.53% and 34.33% throughout the testing phase.
  • A Machine Learning-Based Approach for Crop Price Prediction
    H. L. Gururaj, V. Janhavi, H. Lakshmi, B. C. Soundarya, K. Paramesha, B. Ramesh, A. B. Rajendra
    Journal of Circuits Systems and Computers, 2024
    Agriculture is associated with the production of essential food crops for decades and is the one which is playing an important role in the economy of a country as well as in life of an individual. Due to various uncertain variations in the climatic conditions such as rain and other affecting factors, crop prices vary in an unusual pattern. This variation of prices without the knowledge of the farmer may lead to losses in the economy of the individual who is involved in agriculture. In this paper, we have discussed a well-designed system which accurately predicts the crop prices of future months. We have used a Supervised Machine Learning algorithm that is Decision Tree Regression technique for the design of the prediction model as the data is of continuous form. The parameters, which are considered in the dataset, include crop name, month, year, rainfall and wholesale price index (WPI). We have considered the data of 22 crops in total with 4 parameters. We have developed a user-friendly user interface consisting of 22 crop profiles with the predicted prices. Our results show that the regression model achieved an accuracy of 97.32% which will help the farmer on decision of future crop selection for the growth and also hyperinflation can be avoided.
  • Revolution in agriculture sector using blockchain technology
    Abdul Latif Saleem, K. Paramesha, M.G. Vinay
    Recent Trends in Computational Sciences Proceedings of the 4th Annual International Conference on Data Science Machine Learning and Blockchain Technology Aicdmb 2023, 2024
    In recent years, blockchain technology has gotten a lot of attention. The introduction of blockchain smart contracts which enable the implementation of decentralized apps in trust-free contexts is one explanation for this new trend. As smart contracts become more widely used attacks exploiting their flaws will surely increase. Several ways have been investigated to mitigate these assaults and avoid breaches such as documenting vulnerabilities or model testing using formal verification. These methods however fall short of capturing the blockchain and user behaviour features. Agriculture is gradually becoming into a significant financial industry with global implications. Real-time monitoring of the weather and the condition of the soil, along with improved food quality, are what are driving the technological, ecological, and economical trend. The study explores the exciting notion of fusing smart agriculture with smart contract technology to produce not only better agricultural products but also better supply chains and agricultural logistics, resulting in a variety of advantages for all parties involved. With the use of numerical and possibly categorical data, the emphasis is on calculating similarity metrics for tuples that reflect soil and climate conditions. In addition, Solidity a high-level language for creating smart contracts meant for the Ethereum Virtual Machine is provided with a sample implementation of one such measure. The final stage for smart contracts that depend on physical objects is to highlight elements of agricultural asset digitalization.
  • Sentiment analysis on cross-domain textual data using classical and deep learning approaches
    K. Paramesha, H. L. Gururaj, Anand Nayyar, K. C. Ravishankar
    Multimedia Tools and Applications, 2023
  • Fragmentation aware heuristic algorithm for routing and wavelength assignment in optical networks
    Hamsaveni Mogannaiah, Savita Choudhary, Paramesha Kenchappa
    Indonesian Journal of Electrical Engineering and Computer Science, 2023
    Wavelength division multiplexing (WDM) is one of the dominating technologies with high-capacity back bone networks. The cost associated with the high-capacity networks given more importance. The major issue is allocating and managing the available resources. To achieve this most efficient algorithms has to be used. We are considering the routing of lightpath and wavelength assignment problem, called as the routing and wavelength assignment (RWA) problem. The optimization of wavelength fragmentation in the WDM network is very much important in resource utilization. Wavelength fragmentation is one of the most important challenges in the area of the WDM network. Where it leads to some serious issues for the operators, such as the rejection of new requests. We are using integer linear program (ILP), here the problem is based on the node link formation. It is based on the multilayer concept and the original WDM network consists of several layers. We propose an efficient heuristic approach to solve this problem of finding the shortest path and assigning a wavelength without wavelength conversion. The model achieves better performance with fragmentation aware wavelength allocation strategy that minimizes fragmentation.
  • Analysis of Preventive Measures Against DDoS Attacks in Smart Grid
    H. L. Gururaj, B. H. Swathi, R. Trupti, Urs R. Darshan, A. B. Rajendra, K. Paramesha
    Journal of the Institution of Engineers India Series B, 2023
    In recent times, the number of cyberattacks has escalated quickly and predominantly. There is no particular victim of this attack as it has outrageously tampered with all the domains in society. Cogitating this issue, the paper focuses on the cyberattacks on smart grids and endeavors some prevention techniques for the DDOS attacks. The researchers are amidst a paradigm shift, and the factors affecting this must be addressed and dealt with. It talks about a radical transformation of the medieval grids to smart grids and the technologies used and some of the possible solutions.
  • Decentralized Blockchain-Based Infrastructure for Numerous IoT Setup
    C. Balarengadurai, C. R. Adithya, K. Paramesha, M. Natesh, H. Ramakrishna
    Smart Innovation Systems and Technologies, 2023
  • Applications of Machine Learning in Biomedical Text Processing and Food Industry
    K. Paramesha, H.L. Gururaj, Om Prakash Jena
    Machine Learning for Healthcare Applications, 2021
  • Exploiting dependency relations for sentence level sentiment classification using SVM
    K Paramesha, K C Ravishankar
    Proceedings of 2015 IEEE International Conference on Electrical Computer and Communication Technologies Icecct 2015, 2015

RECENT SCHOLAR PUBLICATIONS

  • Artificial Intelligence Driven Curriculum Development: Challenges and Modalities
    K Paramesha, M Hamsaveni, BJ Soumya
    2025 Annual International Conference on Data Science, Machine Learning and … , 2025
    2025
  • Machine learning and deep learning approaches for guava disease detection
    K Paramesha, S Jalapur, S Hanok, K Puttegowda, G Manjunatha, ...
    SN Computer Science 6 (4), 361 , 2025
    2025
    Citations: 23
  • A novel image cryptosystem for biomedical images and secured storage by randomized chaotic encryption scheme
    KP K Paramesha, Karthik V, Prashanth M.V., Sathisha M.S., Bhargav H.K ...
    Journal of Integrated Science and Technology 13 (5(2025)), 1103 , 2025
    2025
    Citations: 2
  • A machine learning-based approach for crop price prediction
    HL Gururaj, V Janhavi, H Lakshmi, BC Soundarya, K Paramesha, ...
    Journal of Circuits, Systems and Computers 33 (03), 2450054 , 2024
    2024
    Citations: 3
  • Revolution in agriculture sector using blockchain technology
    AL Saleem, K Paramesha, MG Vinay
    Recent Trends in Computational Sciences, 177-184 , 2023
    2023
  • Sentiment analysis on cross-domain textual data using classical and deep learning approaches
    K Paramesha, HL Gururaj, A Nayyar, KC Ravishankar
    Multimedia Tools and Applications 82 (20), 30759-30782 , 2023
    2023
    Citations: 14
  • Analysis of preventive measures against ddos attacks in smart grid
    HL Gururaj, BH Swathi, R Trupti, UR Darshan, AB Rajendra, ...
    Journal of The Institution of Engineers (India): Series B 104 (1), 297-303 , 2023
    2023
    Citations: 7
  • Decentralized Blockchain-Based Infrastructure for Numerous IoT Setup
    C Balarengadurai, CR Adithya, K Paramesha, M Natesh, H Ramakrishna
    International Conference on Soft Computing and Signal Processing, 401-408 , 2022
    2022
    Citations: 1
  • Neural networks for detecting cardiac arrhythmia from PCG signals
    AM Athreya, K Paramesha, HS Avani, Pooja, S Madhu
    Intelligent Vision in Healthcare, 103-115 , 2022
    2022
    Citations: 3
  • Optimized clustering approach for automated detection of abnormalities in MRI brain images
    KV Sudheesh, A Geethashree, K Paramesha, DC Vinutha, SJ Sushma
    Int J Health Sci 6 (s2), 6531 , 2022
    2022
    Citations: 3
  • Applications of machine learning in biomedical text processing and food industry
    K Paramesha, HL Gururaj, OP Jena
    Machine learning for healthcare applications, 151-167 , 2021
    2021
    Citations: 50
  • Detection of Cardiac Arrhythmia using Machine Learning Algorithms
    M Athreya A, A H S, Pooja, M S, K Paramesha
    10.35940/IJRTE.D4249.118419 8 (4), 11704-11707 , 2019
    2019
    Citations: 2
  • A Perspective on Analyzing IPL Match Results using Machine Learning
    KP Gagana S
    International Journal for Scientific Research & Development 7 (3), 1476-1479 , 2019
    2019
    Citations: 11
  • Sentiment Analysis of News Articles using Probabilistic Topic Modeling
    KCR Gaurav M Pai, Paramesha K
    International Journal of Engineering Research in Computer Science and … , 2018
    2018
  • Big Data Revolution in Health Care Sector
    PK Gagana S
    3rd National Conference on Image Processing, Computing, Communication … , 2018
    2018
    Citations: 1
  • An approach for efficient utilisation of public cloud storage and securing data
    M Prabhu, K Paramesha
    International Research Journal of Engineering and Technology (IRJET) 4 (5 … , 2018
    2018
    Citations: 3
  • Prediction of Heart Disease Based on Decision Trees
    J Lakshmishree, K Paramesha
    International Journal for Research in Applied Science & Engineering … , 2017
    2017
    Citations: 6
  • A perspective on sentiment analysis
    K Paramesha, KC Ravishankar
    arXiv preprint arXiv:1607.06221 , 2016
    2016
    Citations: 4
  • Analysis of opinionated text for opinion mining
    K Paramesha, KC Ravishankar
    arXiv preprint arXiv:1607.02576 , 2016
    2016
    Citations: 4
  • An Efficient Approach For Sentiment Analysis Of Health Posts
    SA Ahmed, P Ravi, K Paramesha, K Ravishankar
    International Journal For Technological Research In Engineering 3 , 2016
    2016
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Applications of machine learning in biomedical text processing and food industry
    K Paramesha, HL Gururaj, OP Jena
    Machine learning for healthcare applications, 151-167 , 2021
    2021
    Citations: 50
  • Machine learning and deep learning approaches for guava disease detection
    K Paramesha, S Jalapur, S Hanok, K Puttegowda, G Manjunatha, ...
    SN Computer Science 6 (4), 361 , 2025
    2025
    Citations: 23
  • Sentiment analysis on cross-domain textual data using classical and deep learning approaches
    K Paramesha, HL Gururaj, A Nayyar, KC Ravishankar
    Multimedia Tools and Applications 82 (20), 30759-30782 , 2023
    2023
    Citations: 14
  • Optimization of cross domain sentiment analysis using SentiWordNet
    K Paramesha, KC Ravishankar
    arXiv preprint arXiv:1401.3230 , 2013
    2013
    Citations: 14
  • A Perspective on Analyzing IPL Match Results using Machine Learning
    KP Gagana S
    International Journal for Scientific Research & Development 7 (3), 1476-1479 , 2019
    2019
    Citations: 11
  • Analysis of preventive measures against ddos attacks in smart grid
    HL Gururaj, BH Swathi, R Trupti, UR Darshan, AB Rajendra, ...
    Journal of The Institution of Engineers (India): Series B 104 (1), 297-303 , 2023
    2023
    Citations: 7
  • Prediction of Heart Disease Based on Decision Trees
    J Lakshmishree, K Paramesha
    International Journal for Research in Applied Science & Engineering … , 2017
    2017
    Citations: 6
  • Exploiting dependency relations for sentence level sentiment classification using SVM
    K Paramesha, KC Ravishankar
    2015 IEEE International Conference on Electrical, Computer and Communication … , 2015
    2015
    Citations: 6
  • A perspective on sentiment analysis
    K Paramesha, KC Ravishankar
    arXiv preprint arXiv:1607.06221 , 2016
    2016
    Citations: 4
  • Analysis of opinionated text for opinion mining
    K Paramesha, KC Ravishankar
    arXiv preprint arXiv:1607.02576 , 2016
    2016
    Citations: 4
  • A machine learning-based approach for crop price prediction
    HL Gururaj, V Janhavi, H Lakshmi, BC Soundarya, K Paramesha, ...
    Journal of Circuits, Systems and Computers 33 (03), 2450054 , 2024
    2024
    Citations: 3
  • Neural networks for detecting cardiac arrhythmia from PCG signals
    AM Athreya, K Paramesha, HS Avani, Pooja, S Madhu
    Intelligent Vision in Healthcare, 103-115 , 2022
    2022
    Citations: 3
  • Optimized clustering approach for automated detection of abnormalities in MRI brain images
    KV Sudheesh, A Geethashree, K Paramesha, DC Vinutha, SJ Sushma
    Int J Health Sci 6 (s2), 6531 , 2022
    2022
    Citations: 3
  • An approach for efficient utilisation of public cloud storage and securing data
    M Prabhu, K Paramesha
    International Research Journal of Engineering and Technology (IRJET) 4 (5 … , 2018
    2018
    Citations: 3
  • An Efficient Approach For Sentiment Analysis Of Health Posts
    SA Ahmed, P Ravi, K Paramesha, K Ravishankar
    International Journal For Technological Research In Engineering 3 , 2016
    2016
    Citations: 3
  • A novel image cryptosystem for biomedical images and secured storage by randomized chaotic encryption scheme
    KP K Paramesha, Karthik V, Prashanth M.V., Sathisha M.S., Bhargav H.K ...
    Journal of Integrated Science and Technology 13 (5(2025)), 1103 , 2025
    2025
    Citations: 2
  • Detection of Cardiac Arrhythmia using Machine Learning Algorithms
    M Athreya A, A H S, Pooja, M S, K Paramesha
    10.35940/IJRTE.D4249.118419 8 (4), 11704-11707 , 2019
    2019
    Citations: 2
  • Decentralized Blockchain-Based Infrastructure for Numerous IoT Setup
    C Balarengadurai, CR Adithya, K Paramesha, M Natesh, H Ramakrishna
    International Conference on Soft Computing and Signal Processing, 401-408 , 2022
    2022
    Citations: 1
  • Big Data Revolution in Health Care Sector
    PK Gagana S
    3rd National Conference on Image Processing, Computing, Communication … , 2018
    2018
    Citations: 1
  • Artificial Intelligence Driven Curriculum Development: Challenges and Modalities
    K Paramesha, M Hamsaveni, BJ Soumya
    2025 Annual International Conference on Data Science, Machine Learning and … , 2025
    2025