International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

ZeGo - Mobile Application for Canine Health Care and Analysis

Abstract

This research article introduces a groundbreaking strategy aimed at improving dog management and care procedures by leveraging a comprehensive mobile application. The primary objective of this study is to facilitate easier access and utilization of pet-related services for dog owners and enthusiasts, with test results indicating its effectiveness. One key focus of this research is the precise identification of pure and mixed dog breeds from images, which has demonstrated promising results. Additionally, it streamlines the process of selling healthy canines that have undergone rigorous health evaluations, as confirmed by our tests. The application also provides valuable tools for calculating the correct dosage of medication based on a dog's breed, weight, and age, with the test results validating its accuracy. Moreover, it empowers users to locate nearby pet stores, check product availability, and make purchases directly through the app, as substantiated by our test findings. The incorporation of a healthcare recommendation chatbot, designed to offer insightful information about dog diseases, behaviors, food, and hygiene, is a valuable component of this application. The chatbot's functionality is supported by data accumulated regarding dog diseases, symptoms, and potential health risks associated with specific breeds and age groups. The study delves into predicting the optimal number of vaccinations required in each district to minimize human fatalities caused by rabies, with test results informing the proposed approach's effectiveness. The severity levels of these fatalities are thoroughly assessed to develop efficient preventive and control measures, a concept that our tests have confirmed as promising. This research paper suggests the creation of a digital vaccination book to ensure accurate and easily accessible records for dog vaccinations, as validated by our test results. This comprehensive approach to dog care and management holds significant potential for enhancing both animal welfare and public health, as attested by the findings of our tests.

Country : Sri Lanka

1 Edirisooriya N.D.2 Ranasinghe R.A.M.M.3 Herath H.M.V.W.K.4 Apurwa W.K.E.5 Sanvitha Kasthuriarachchi6 Thamali Kelegama

  1. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  5. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  6. Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 10, October 2023 pp. 461-469

doi.org/10.47001/IRJIET/2023.710061

Full Paper
Download

References

  1. “The path for efficient and precise breed identification through image analysis has been paved by technological advancements and the proliferation of machine learning techniques,” Liu et al, 2018.
  2. “Deep learning models for breed identification have been utilized in several studies, with Convolutional Neural Networks (CNN) often being leveraged due to their efficacy in handling image data Khatb et al., 2017,” Khatb et al, 2017.
  3. Extensive datasets of dog images, like the Stanford Dogs Dataset, which includes over 20,000 images across 120 breeds. 2011.
  4. Transfer learning techniques applied to pre-trained CNNs were used by Wang and Lyu , achieving impressive accuracy in identifying a wide range of dog breeds.
  5. C. Siettou, I. M. Fraser, and R. W. Fraser, “Investigating some of the factors that influence ‘consumer’ choice when adopting a shelter dog in the United kingdom,” J. Appl. Anim. Welf. Sci., vol. 17, no. 2, pp. 136–147, 2014.
  6. S. G. Brown and R. E. Rhodes, “Relationships among dog ownership and leisure-time walking in Western Canadian adults,” Am. J. Prev. Med., vol. 30, no. 2, pp. 131–136, 2006.
  7. D. O’Neill, “Surveillance: pointing the way to improved welfare for companion animals,” Vet. Rec., vol. 173, no. 10, pp. 240–242, 2013.
  8. G. Diesel, D. Brodbelt, and D. U. Pfeiffer, “Reliability of assessment of dogs’ behavioural responses by staff working at a welfare charity in the UK,” Appl. Anim. Behav. Sci., vol. 115, no. 3–4, pp. 171–181, 2008.
  9. R. Lavan, R. Armstrong, K. Tunceli, and D. Normile, “Dog owner flea/tick medication purchases in the USA,” Parasit. Vectors, vol. 11, no. 1, p. 581, 2018.
  10. Detrimental effects of incorrect dosing, which can range from reduced treatment efficacy to serious health complications. German, 2012.
  11. R. J.-H. Wang, E. C. Malthouse, and L. Krishnamurthi, “How mobile shopping affects customer purchase behavior: A retailer’s perspective,” in Let’s Get Engaged! Crossing the Threshold of Marketing’s Engagement Era, Cham: Springer International Publishing, 2016, pp. 703–704.
  12. S. Miner, L. Laranjo, and A. B. Kocaballi, “Chatbots in the fight against the COVID-19 pandemic,” NPJ Digit. Med., vol. 3, no. 1, p. 65, 2020.
  13. L. Asher, G. Diesel, J. F. Summers, P. D. McGreevy, and L. M. Collins, “Inherited defects in pedigree dogs. Part 1: disorders related to breed standards,” Vet. J., vol. 182, no. 3, pp. 402–411, 2009.
  14. Y. Hsu and J. A. Serpell, “Development and validation of a questionnaire for measuring behavior and temperament traits in pet dogs,” J. Am. Vet. Med. Assoc., vol. 223, no. 9, pp. 1293–1300, 2003.
  15. S. Schmitt et al., “Faecal calcium excretion does not decrease during long-term feeding of a low-calcium diet in adult dogs,” J. Anim. Physiol. Anim. Nutr. (Berl.), vol. 102, no. 2, pp. e798–e805, 2018.
  16. N. M. Radziwill and M. C. Benton, “Evaluating quality of chatbots and intelligent conversational agents,” arXiv [cs.CY], 2017.
  17. M. Kaare et al., “Rabies control in rural Africa: Evaluating strategies for effective domestic dog vaccination,” Vaccine, vol. 27, no. 1, pp. 152–160, 2009.
  18. R. Dedmon, D. Briggs, T. Lembo, and S. Cleaveland, “One health: Collaboration, recent research and developments in the global effort to eliminate Rabies,” Int. J. Infect. Dis., vol. 14, p. e159, 2010.
  19. S. Durr et al., “Effectiveness of dog rabies vaccination programmes: comparison of owner-charged and free vaccination campaigns,” Epidemiol. Infect., vol. 137, no. 11, pp. 1558–1567, 2009.
  20. B. Hatch et al., “Towards canine rabies elimination in south-eastern Tanzania: Assessment of health economic data,” Transbound. Emerg. Dis., vol. 64, no. 3, pp. 951–958, 2017.
  21. M. Hergert and L. H. Nel, “Dog bite histories and response to incidents in canine rabies-enzootic KwaZulu-Natal, South Africa,” PLoS Negl. Trop. Dis., vol. 7, no. 4, p. e2059, 2013.
  22. Chun-Wei Tung, Wei-Lun Chang, and Chia-Hung Yeh. (2017). Automatic Image Search and Retrieval Based on OCR and CBIR. Journal of Information Science and Engineering, 33(6), 1491-1507.
  23. D. M. A. Dissanayaka, K. M. Sifran, J. E. T. A. Thiviya, and D. I. D. S. Wijesinghe, “Skin Disease Detection of Pet Dogs and Identifying Home Remedies Using Machine Learning (SVM, NLP) and AI,” in 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), IEEE, 2020, pp. 137–142.
  24. Yu, W., Huang, J., & Li, H. (2019). Augmented Reality Shopping System Through Image Search and Virtual Shop Generation. Journal of Imaging, 5(10), 86.
  25. S. A. M. Kularatne, D. M. P. U. K. Ralapanawa, K. Weerakoon, U. K. Bokalamulla, and N.Abagaspitiya, ‘Pattern of animal bites and post exposure prophylaxis in rabies: A five year study in a tertiary care unit in Sri Lanka’, BMC Infect. Dis., vol. 16, no. 1, p. 62, Feb. 2016.
  26. D. Meena and L. Agilandeeswari, ‘An efficient framework for animal breeds classification using semi-supervised learning and multi-part convolutional neural network (MP-CNN)’, IEEE Access, vol. 7, pp. 151783–151802, 2019.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies