Sensor and AI based Pre-Detection Systems Transfiguring Intoxication & Smoking

Year : 2025 | Volume : 12 | Issue : 03 | Page : 37 50
    By

    Vaishnavi Ashok Desai,

  • Heena Tajoddin Shaikh,

  • Kazi Kutubuddin Sayyad Liyakat,

  1. Student, Department of Electronics and Telecommunication Engineering,Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Asst. Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  3. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

The ubiquitous problems of drunkenness and uncontrolled smoking continue to pose substantial hazards to public health, safety, and productivity in a world that is becoming more linked and protective of its personal safety. The costs to society and the economy are substantial, and they range from automobile accidents caused by impaired driving to accidents that occur in the workplace, and from chronic health disorders that are connected to smoking to the risk of fires. A new frontier in preventative technology is emerging, however, and it is comprised of advanced artificial intelligence and sensor-based systems that are geared for the pre-detection of smoking and intoxication. The use of these cutting-edge solutions has the potential to transform our strategy from reactive mitigation to proactive prevention, thereby protecting lives and habitats through the prevention of potential harm. Intoxication and smoking treatments that are currently available frequently fall short of expectations. After an incident or after a traffic stop, breathalysers are often utilised to investigate the situation. As a result of human enforcement, smoking regulations are frequently disregarded, which causes people to be exposed to secondhand smoke and increases the risk of fire. Due to the limitations of current reactive procedures, there is an urgent requirement for real-time, non-invasive, and intelligent technologies that are able to recognise possible problems before they become more severe. When it comes to our efforts to make communities safer and healthier, the development of artificial intelligence (AI) and sensor-based drunkenness and smoking pre-detection systems represents a huge step forward. The potential to avoid a large number of accidents, health crises, and fatalities is enormous, despite the fact that there are still significant obstacles to overcome, such as ethical considerations and issues of public acceptance.

Keywords: Sensors, Artificial Intelligence, Pre-detection, Intoxication, Smoking

[This article belongs to Journal of Telecommunication, Switching Systems and Networks ]

How to cite this article:
Vaishnavi Ashok Desai, Heena Tajoddin Shaikh, Kazi Kutubuddin Sayyad Liyakat. Sensor and AI based Pre-Detection Systems Transfiguring Intoxication & Smoking. Journal of Telecommunication, Switching Systems and Networks. 2025; 12(03):37-50.
How to cite this URL:
Vaishnavi Ashok Desai, Heena Tajoddin Shaikh, Kazi Kutubuddin Sayyad Liyakat. Sensor and AI based Pre-Detection Systems Transfiguring Intoxication & Smoking. Journal of Telecommunication, Switching Systems and Networks. 2025; 12(03):37-50. Available from: https://journals.stmjournals.com/jotssn/article=2025/view=228686


References

  1.  Mulla NR, Liyakat KKS. Air flow analysis in sensor-based aircraft wings design. Recent Trends in Fluid Mechanics. 2025;12(2):29-39.
  2. Mulla NR, Liyakat KKS. A study on machine learning for metal processing: a new future. Int J Machine Design Technol. 2025;1(1):56-69.
  3. Mulla NR, Liyakat KKS. Sensor-based aircraft wings design using airflow analysis. Int J Image Process Smart Sensors. 2025;1(1):55-65.
  4.  Mulla NR, Liyakat KKS. Node MCU and IoT centered smart logistics. Int J Emerg IoT Technol Smart Electron Commun. 2025;1(1):20-36.
  5.  Todakar RD, Jadhav VK. Kinetic power gyms for revolutionizing fitness. J Telecommun Switching Syst Netw. 2025;12(2):13-21. 6. Kutubuddin. The future is smelling: exploring the potential of e-nose. J Semicond Devices Circuits. 2025;12(1):16-27.
  6. Kutubuddin. e-Skin: a revolutionized technology. J Embedded Syst Appl. 2025;13(1):1- 12.
  7. Babitha M, et al. Trends of artificial intelligence for online exams in education. Int J Early Child Spec Educ. 2022;14(1):2457-63.
  8. Shaikh M. Machine learning in the production process control of metal melting. J Adv Mach. 2023;8(2).
  9.  Mishra SB, et al. Nanotechnology’s importance in mechanical engineering. J Fluid Mech Mech Des. 2024;6(1):1-9.
  10. Mishra SB, et al. Review of the literature and methodological structure for IoT and PLM integration in the manufacturing sector. J Adv Mach. 2024;9(1):1-5.
  11.  Aavula R, et al. Design and implementation of sensor and IoT-based remembrance system for closed one. Telematique. 2022;21(1):2769-78.
  12.  Liyakat S. IoT based substation health monitoring. In: Saikia R, editor. Magnification of research: advanced research in social sciences and humanities. Vol. 2. ISBN: 979- 8864297803; 2023. p. 160-71.
  13. Liyakat S. System for love healthcare for loved ones based on IoT. In: Research exploration: transcendence of research methods and methodology. Vol. 2. ISBN: 979- 8873806584; 2023.
  14. Liyakat S. Smart motion detection system using IoT: a NodeMCU and Blynk framework. J Microelectron Solid State Devices. 2023;10(3).
  15.  Mulani AO, Bang AV, Birajadar GB, Deshmukh AB, Jadhav HM. IoT-based air, water, and soil monitoring system for pomegranate farming. Ann Agri-Bio Res. 2024;29(2):71- 86.
  16. Parihar B, Kiran A, Valaboju S, Rashid SZ, Liz ASLD R. Enhancing data security in distributed systems using homomorphic encryption and secure computation techniques. ITM Web Conf. 2025;76:02010. doi:10.1051/itmconf/20257602010.
  17.  Veena C, Sridevi M, Liyakat KKS, Saha B, Reddy SR, Shirisha N. HEECCNB: an efficient IoT-cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. 2023 Seventh International Conference on Image Information Processing (ICIIP); 2023. p. 407-10. doi:10.1109/ICIIP61524.2023.10537627.
  18. Tamboli DA, Sawant VA, MHM, Sathe S. AI-driven-IoT (AIIoT) based decision- making-KSK approach in drones for climate change study. 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS); 2024. p. 1735-44. doi:10.1109/ICUIS64676.2024.10866450.
  19. Prasad KR, Karanam SR, et al. AI in public-private partnership for IT infrastructure development. J High Technol Manag Res. 2024;35(1):100496. doi:10.1016/j.hitech.2024.100496.
  20. KKS. Malicious node detection in IoT networks using artificial neural networks: a machine learning approach. In: Singh VK, Kumar Sagar A, Nand P, Astya R, Kaiwartya O, editors. Intelligent networks: techniques and applications. CRC Press; 2024. doi:10.1201/9781003541363.
  21.  Kazi KKS. KK approach for IoT security: T-cell concept. In: Kumar R, Peng S, Jain P, Elngar A, editors. Deep learning innovations for securing critical infrastructures. IGI Global; 2025. p. 369-90. doi:10.4018/979-8-3373-0563-9.ch022.
  22. Liyakat KKS. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Shukla PK, Mittal H, Engelbrecht A, editors. Computer vision and robotics. Springer; 2023. doi:10.1007/978-981-99-4577-1_3.
  23. Liyakat. AI-driven-IoT (AIIoT)-based decision making in kidney diseases patient healthcare monitoring: KSK approach for kidney monitoring. In: Polat LÖ, Polat O, editors. AI-driven innovation in healthcare data analytics. IGI Global; 2025. p. 277-306. doi:10.4018/979-8-3693-7277-7.ch009.
  24.  Pradeepa M, et al. Student health detection using a machine learning approach and IoT. 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon); 2022.
  25. Mahant MA. Machine learning-driven Internet of Things (MLIoT)-based healthcare monitoring system. In: Wickramasinghe N, editor. Digitalization and the transformation of the healthcare sector. IGI Global; 2025. p. 205-36. doi:10.4018/979-8-3693-9641- 4.ch007.
  26. Mulani AO, Liyakat KKS, Warade NS, et al. ML-powered Internet of Medical Things structure for heart disease prediction. J Pharmacol Pharmacother. 2025;0(0). doi:10.1177/0976500X241306184.
  27. Odnala S, Shanthy R, Bharathi B, Pandey C, Rachapalli A, Liyakat KKS. Artificial intelligence and cloud-enabled e-vehicle design with wireless sensor integration. SSRN Electron J. 2025. doi:10.2139/ssrn.5107242.
  28. Neeraja P, Kumar RG, Kumar MS, Liyakat KKS, Vani MS. DL-based somnolence detection for improved driver safety and alertness monitoring. 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT); 2024. p. 589-94. doi:10.1109/IC2PCT60090.2024.10486714.
  29. Magadum PK. Machine learning for predicting wind turbine output power in wind energy conversion systems. Grenze Int J Eng Technol. 2024;10(1):2074-80.
  30. Khadake SB, Chounde AB, Suryagan AA, MHM, Khadatare MR. AI-driven-IoT (AIIoT) based decision making system for high-blood pressure patient healthcare monitoring. 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA); 2024. p. 96-102. doi:10.1109/ICSCNA63714.2024.10863954.
  31.  Sayyad. AI-powered-IoT (AIIoT)-based decision-making system for BP patient’s healthcare monitoring: KSK approach for BP patient healthcare monitoring. In: Aouadni S, Aouadni I, editors. Recent theories and applications for multi-criteria decision-making. IGI Global; 2025. p. 205-38. doi:10.4018/979-8-3693-6502-1.ch008.
  32. Upadhyaya AN, Surekha C, Malathi P, Suresh G, Suriyan K, Liyakat KKS. Pioneering cognitive computing for transformative healthcare innovations. SSRN Electron J. 2025. doi:10.2139/ssrn.5086894.
Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 04/08/2025
Accepted 11/08/2025
Published 06/10/2025
Publication Time 63 Days
107

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