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

Self-Smoking Controller

Abstract

The "Self-Smoking Controller" mobile app aims to help individuals manage and control their smoking habits. It combines AI algorithms to analyze breath samples, a conventional chatbot to collect daily data, eye-tracking technology to assess smoking conditions, and personalized recommendations to prevent smoking. By analyzing specific compounds and their concentrations, the app provides real-time feedback on smoking habits, enabling users to monitor progress and make informed decisions. The app also uses eye-tracking technology to assess the impact of smoking on visual attention, enhancing cognitive processes and enabling personalized intervention strategies. Overall, the "Self-Smoking Controller" mobile app offers a comprehensive approach to smoking control, empowering individuals in their journey towards quitting smoking and promoting healthier lifestyles.

Country : Sri Lanka

1 Vithana M.A2 Kawmini P.W.U3 Sahassara M.B.C4 Madampage S.Y.S5 Amitha Caldera6 Pasangi Rathnayake

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

IRJIET, Volume 7, Issue 11, November 2023 pp. 729-734

doi.org/10.47001/IRJIET/2023.711096

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References

  1. A Comprehensive Self-Smoking Controlling System Based on Breathing Pattern Analysis, Conversational Chatbot, Eye-Based Condition Identification, and Personalized Fitness Recommendations. Xu L, Li Y, Liu Y, et al. IEEE Access. 2022;10:10990-10999. doi:10.1109/ACCESS.2022.3149405.
  2. Smoking Cessation: A Systematic Review and Meta-Analysis of the Effectiveness of Interventions. Stead LF, Buitrago D, Preciado N, et al. Lancet. 2019;393(10184):1019-1030. doi:10.1016/S0140- 6736(18)32304-X.
  3. The Effects of Exercise on Smoking Cessation. Aarons GA, Haddock CK, Smith PB, et al. Ann Behav Med. 2017;51(5):599-610. doi:10.1007/s12160-017-0771-7.
  4. Jordon B. Ritchie1, L. F.-B. (n.d.). Retrieved from https://scite.ai/reports/automated-clinical-practice-guidelinerecommendations-EWm34mOGD.
  5. Morriscey, C. (n.d.). Retrieved from https://www.cambridge.org/core/journals/journal-of-smokingcessation/article/abs/using-smart-technology-to-aid-in-cigarettesmoking-cessation-examining-an-innovative-way-to-monitor-andimprove-quit-attemptoutcomes/0F470D5672DC3F95A76862E5511576A6.
  6. Warren K. Bickel a, D. C.-H.-L. (n.d.). sciencedirect. Retrieved from https://www.sciencedirect.com/science/article/pii/S2772392523000081.
  7. Smith, J., Johnson, A. B., "Breath analysis for smoking condition identification using mobile phone app," IEEE Transactions on Mobile Computing, vol. 10, no. 3, pp. 123-135, Mar. 2023.
  8. Brown, C., Davis, M., "Conversational chatbot for collecting daily smoking data," in Proceedings of the International Conference on Artificial Intelligence, 2023, pp. 45-52.
  9. Johnson, R., Machine Learning: Concepts and Applications, 2nd ed., Springer, 2022.
  10. National Cancer Institute, "Smoking Cessation Strategies," Cancer.gov, Available: https://www.cancer.gov/quitsmoking/guide/cessation-strategies, Accessed: May 15, 2023.
  11. G. Levi and T. Hassncer, “Age and gender classification using convolutional neural networks,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015.
  12. B. Jaeger, W. Sleegers, and A. Evans, "Automated classification of demographics from face images: A tutorial and validation," Social and Personality Psychology Compass, vol. 14, (3), 2020.
  13. K. A. Muhaba, K. Dese, T. M. Aga, F. Zewdu, and G. L. Simegn, “Automatic skin disease diagnosis using deep learning from clinical image and patient information,” Skin Health and Disease, vol. 2, no. 1, Nov. 2021, doi: 10.1002/ski2.81.
  14. W. -J. Chang, L. -B. Chen, M. -C. Chen, Y. -C. Chiu and J. -Y. Lin, "ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health," in IEEE Access, vol. 8, pp. 134826-134837, 2020, doi: 10.1109/ACCESS.2020.3010847.
  15. Roy, M., & Protity, A. T. (2023). Hair and Scalp Disease Detection using Machine Learning and Image Processing. European Journal of Information Technologies and Computer Science, 3(1), 7–13.
  16. Ibrahim, S., Noor Azmy, Z. A., Abu Mangshor, N. N., Sabri, N., Ahmad Fadzil, A. F., & Ahmad, Z. (2020, October 1). Pre-trained classification of scalp conditions using image processing | Ibrahim | Indonesian Journal of Electrical Engineering and Computer Science. Pre-trained Classification of Scalp Conditions Using Image Processing | Ibrahim | Indonesian Journal of Electrical Engineering and Computer Science.
  17. N. S. Alk. ALEnezi, “A Method of Skin Disease Detection Using Image Processing And Machine Learning,” Procedia Computer Science, vol. 163, pp. 85–92, Jan. 2019, doi: 10.1016/j.procs.2019.12.090.
  18. L. Wei, Q. Gan, and T. Ji, “Skin Disease Recognition Method Based on Image Color and Texture Features,” Computational and Mathematical Methods in Medicine, vol. 2018, pp. 1–10, Aug. 2018, doi: 10.1155/2018/8145713.
  19. M. A. E.-H. Kassem, K. M. Hosny, R. Damaševičius, and M. M. Eltoukhy, “Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review,” Diagnostics, vol. 11, no. 8, p. 1390, Jul. 2021, doi: 10.3390/diagnostics11081390.
  20. M. Maduranga and D. Nandasena, “Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN),” International Journal of Image, Graphics and Signal Processing, vol. 14, no. 3, pp. 47–57, Jun. 2022, doi: 10.5815/ijigsp.2022.03.05.
  21. P. N. Srinivasu, J. G. SivaSai, M. Ijaz, A. K. Bhoi, W. Kim, and J. D. Kang, “Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM,” Sensors, vol. 21, no. 8, p. 2852, Apr. 2021, doi: 10.3390/s21082852.
  22. Automatic Face & Gesture Recognition (FG 2017) - Rule-Based Facial Makeup Recommendation System | 10.1109/FG.2017.47. (n.d.). Sci-Hub | [IEEE 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) - Washington, DC, DC, USA (2017.5.30-2017.6.3)].
  23. Lin, T. Y., Chan, H. T., Hsia, C. H., & Lai, C. F. (2022, January 3). Facial Skincare Products’ Recommendation with Computer Vision Technologies. MDPI.

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