Tejaswini H

@sode-edu.in

Assistant Professor
Shri Madhwa Vadiraja Institute of Technology and Management

Tejaswini H


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https://researchid.co/tejaswinih

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Aquatic Science
4

Scopus Publications

32

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • A Review on Utilization and Impact of Prominent Datasets in Fisheries Research
    H. Tejaswini, M. M. Manohara Pai, Radhika M. Pai
    SN Computer Science, 2025
    The utilization of image datasets has become a crucial aspect of fisheries research lately, revolutionizing conventional techniques and improving humans’ comprehension of aquatic ecosystems. Of all the aquatic creatures, fish is thought to be the most nutritious for the large class of nonvegetarian consumers worldwide. With more than 32,000 varieties, fish are differentiated by visual characteristics such as shape, texture, patterns, and color, which makes identification difficult for a common user. Machine learning (ML) and deep learning (DL) based models are being developed to automate fish related studies by utilizing their visual characteristics. The automated DL-based applications related to fisheries research include, but are not limited to, fish length estimation, behavioral analysis, fish detection, and classification. These applications rely on image datasets, but several challenges hinder their effectiveness. Existing datasets often suffer from limitations such as lack of defined information, uneven image quality, limited geographic coverage, and insufficient species variety. Additionally, the absence of benchmark datasets and inconsistencies in data collection, annotation accuracy, and environmental variability may affect model performance and generalizability. These gaps limit the development of robust DL-based fisheries applications. This review systematically examines the utilization of fish image datasets in fisheries research, focusing on applications, species studied, study regions, image characteristics, and associated metadata. By identifying key research gaps, the study highlights the need for improved dataset quality, standardization, and comprehensive metadata to enhance automated fisheries research. Addressing these challenges can facilitate more accurate fisheries research, monitoring, and conservation efforts, ultimately supporting sustainable aquaculture and ecosystem management.
  • Automatic Estuarine Fish Species Classification System Based on Deep Learning Techniques
    H. Tejaswini, M. M. Manohara Pai, Radhika M. Pai
    IEEE Access, 2024
    Fish classification (FC) is crucial in various domains, including fishery management and ecological research. Traditional FC methods rely mainly on morphological criteria such as body shape and patterns. Although these methods are useful, they require expert knowledge and are prone to subjective interpretation. Recent advances in technology and the availability of datasets have allowed deep learning (DL) techniques to be used in fish species classification. These methods automatically extract relevant features from fish images and categorize them into species groupings. Traditional DL models, however, have difficulties capturing long-range dependencies and require fixed input sizes, making them less adaptive when working with images with varying proportions. The Vision Transformer (ViT) addresses these constraints by utilizing the transformer model’s self-attention mechanisms. So, in this study, a ViT is used to solve the FC problem. The performance of ViT is assessed against pre-trained models, VGG16, VGG19, DenseNet121, ResNet50v2, InceptionV3, InceptionResNetV2, and Xception. The experiments make use of a curated Estuarine Fish species dataset (EFD). In this study, ViT outperformed state-of-the-art literature by achieving 99.04% and 100% accuracy without and with augmentation, respectively. The presented research is tailored to the task of recognizing estuarine fish species that are useful in the aquaculture domain. Additionally, our research aligns with the objectives of Sustainable Development Goals (SDGs) 2 and 14. This emphasises the broader societal and environmental implications of our work, emphasizing its potential to positively impact food security and aquaculture ecosystem sustainability.
  • Knowledge Graph for Aquaculture Recommendation System
    Tejaswini H, Manohara Pai M M, Radhika M Pai
    2021 IEEE Mysore Sub Section International Conference Mysurucon 2021, 2021
    Aquaculture is a growing industry. It would be beneficial to the fish farmers if the data about the fish, such as the ecosystem, food and related information are available to them to increase the fish yield. Most of the fish species data of the aquaculture domain are stored using relational databases. However, the relational tables work well only for structured data. It would help the fishermen if the data can be visualized and provided with a suitable recommendation system that recommends the best species and best ecosystem. In this paper, an approach to store the details of fish species of the brackish water of the west coast of Karnataka, India using Neo4j is presented. Further, a recommendation system to retrieve the best fish species for a particular ecosystem is proposed. The data relating to fish species, names, threatened status, taxonomy, fish species location, type of water they survive in are stored as a connected graph in the Neo4j graph database. This helps the aquatic scientists and aquaculture users visualize the relationship among the fish species and get suitable recommendations on fish species based on their interests. The proposed system is scalable and is capable of processing any complex relationship for providing recommendations.
  • An ontology-based decision support system for nutrition deficiency
    H. Tejaswini, M. M. Manohara Pai, Radhika M. Pai, Girija Attigeri, Revathi P. Shenoy
    2020 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2020 Proceedings, 2020
    Storing the patient's clinical test reports for analysis differs from clinic to clinic as most clinics store the details in customized software or freely available spreadsheets. In the nutrition test report, the test results show the levels, thresholds that doctors analyze and diagnose the type of deficiency. In many situations, the patients have a dilemma about the doctor's advice that results in a second opinion. Hence a simple decision support system is a necessity to help the doctor to analyze the laboratory test report data and prescribe the right treatment. This research proposes a nutrition deficiency decision support framework that models a biochemistry test report using an ontology and automatic nutrition deficiency classification. The resulting system is useful in hospitals for the automatic classification of nutritional deficiency.

RECENT SCHOLAR PUBLICATIONS

  • A Review on Utilization and Impact of Prominent Datasets in Fisheries Research
    H Tejaswini, MM Manohara Pai, RM Pai
    SN Computer Science 6 (8), 1027 , 2025
    2025
    Citations: 1
  • Automatic estuarine fish species classification system based on deep learning techniques
    H Tejaswini, MMM Pai, RM Pai
    IEEE Access 12, 140412-140438 , 2024
    2024
    Citations: 23
  • Knowledge graph for aquaculture recommendation system
    H Tejaswini, MP MM, RM Pai
    2021 IEEE Mysore Sub Section International Conference (MysuruCon), 366-371 , 2021
    2021
    Citations: 3
  • An ontology-based decision support system for nutrition deficiency
    H Tejaswini, MMM Pai, RM Pai, G Attigeri, RP Shenoy
    2020 IEEE International Conference on Distributed Computing, VLSI … , 2020
    2020
    Citations: 5
  • Cascade ciphering technique for confidential, authenticated and integrity preserved transmission of images over a network
    H Tejaswini, R Dodmane
    2017 International Conference On Smart Technologies For Smart Nation … , 2017
    2017

MOST CITED SCHOLAR PUBLICATIONS

  • Automatic estuarine fish species classification system based on deep learning techniques
    H Tejaswini, MMM Pai, RM Pai
    IEEE Access 12, 140412-140438 , 2024
    2024
    Citations: 23
  • An ontology-based decision support system for nutrition deficiency
    H Tejaswini, MMM Pai, RM Pai, G Attigeri, RP Shenoy
    2020 IEEE International Conference on Distributed Computing, VLSI … , 2020
    2020
    Citations: 5
  • Knowledge graph for aquaculture recommendation system
    H Tejaswini, MP MM, RM Pai
    2021 IEEE Mysore Sub Section International Conference (MysuruCon), 366-371 , 2021
    2021
    Citations: 3
  • A Review on Utilization and Impact of Prominent Datasets in Fisheries Research
    H Tejaswini, MM Manohara Pai, RM Pai
    SN Computer Science 6 (8), 1027 , 2025
    2025
    Citations: 1
  • Cascade ciphering technique for confidential, authenticated and integrity preserved transmission of images over a network
    H Tejaswini, R Dodmane
    2017 International Conference On Smart Technologies For Smart Nation … , 2017
    2017