For years, the concept of electronic skin, or e-skin, has captivated imaginations. Imagine a material that could mimic the sensitivity, flexibility, and even the healing properties of human skin. This isn’t just science fiction anymore. E-skin, a network of flexible sensors and electronic components, is rapidly becoming a reality. And now, thanks to the power of artificial intelligence (AI), this technology is poised to revolutionize various aspects of our lives, from healthcare and robotics to human-computer interaction. AI-powered e-skin is not just a technological advancement; it’s a paradigm shift. This technology has the potential to blur the lines between human and machine interaction, ushering in a future where technology seamlessly integrates with our bodies and enhances our lives in profound ways. While there are still challenges to overcome, the potential rewards make it a field worth watching closely. The era of touch-sensitive, intelligent skin is on the horizon, and it promises to be truly revolutionary. While the technology is still in its early stages, the progress made in recent years has been remarkable. Researchers are actively exploring new materials, developing innovative sensor designs, and refining AI algorithms to overcome these challenges.

Keywords:Artificial Intelligence, e-Skin, data processing, pattern recognition, sensitivity

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1. Kazi Kutubuddin Sayyad Liyakat. Impact of nanotechnology on battlefield welfare: a study. Int J Nanobiotechnol. 2024;10(02):19–32.
2. Kazi Sultanabanu Sayyad Liyakat, Kazi Kutubuddin Sayyad Liyakat. Nanosensors in agriculture field: a study. Int J Appl Nanotechnol. 2024;10(02):12–22. Available from: https://journalspub.com/publication/ijan-v10i02-11625/
3. Kutubuddin Sayyad Liyakat. Nanotechnology in space study. Int J Appl Nanotechnol. 2024;10(02):39–46. Available from: https://journalspub.com/publication/ijan-v10i02-11616/
4. Kazi Kutubuddin Sayyad Liyakat. KSK approach to smart agriculture: utilizing AI-driven Internet of Things (AI IoT). J Microcontroller Eng Appl. 2024;11(03):21–32.
5. Kazi Kutubuddin Sayyad Liyakat. Microwave communication in the Internet of Things: a study. J RF Microwave Commun Technol. 2024:38–49. Available from: https://matjournals.net/ engineering/index.php/JoRFMCT/article/view/1276
6. Kazi Kutubuddin Sayyad Liyakat. Nanorobotics: a review. Int J Appl Nanotechnol. 2023;9(2):36–43. DOI: https://doi.org/10.37628/ijan.v9i2.1019
7. K K Sayyad Liyakat. Impact of nanotechnology on battlefield welfare: a study. Int J Nanobiotechnol. 2024;10(2):19–32.
8. Sultanabanu Sayyad Liyakat. Nanotechnology in healthcare applications: a study. Int J Nanobiotechnol. 2024;10(2):48–58.
9. Kazi Kutubuddin Sayyad Liyakat. Nanotechnology in medical applications: a study. Nano Trends: A J Nanotechnol Its Appl. 2024;26(2):1–11.
10. Kazi Kutubuddin Sayyad Liyakat. Nanotechnology in battlefield: a study. J Nanoscience, Nanoengineering & Appl. 2024;14(2):18–30.
11. Sultanabanu Sayyad Liyakat Kazi. Polymer applications in energy generation and storage: a forward path. J Nanoscience, Nanoengineering & Appl. 2024;14(2):31–39.
12. Kazi Kutubuddin Sayyad Liyakat. Review of biopolymers in agriculture application: an eco-friendly alternative. Int J Composite Constituent Mater. 2024;10(1):50–62.
13. Kazi Kutubuddin Sayyad Liyakat. Nanotechnology in precision farming: the role of research. Int J Nanomaterials Nanostructures. 2023;9(2):1–5. DOI: https://doi.org/10.37628/ijnn.v9i2.1051.
14. Kazi Sultanabanu Sayyad Liyakat. Accepting Internet of Nano-Things: synopsis, developments, and challenges. J Nanoscience, Nanoengineering & Appl. 2023;13(2):17–26. DOI: https://doi.org/10.37591/jonsnea.v13i2.1464.
15. Mishra SB, et al. Nanotechnology’s importance in mechanical engineering. J Fluid Mech Mech Des. 2024;6(1):1–9.
16. K K S Liyakat. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Udgata SK, Sethi S, Gao XZ, editors. Intelligent systems. ICMIB 2023. Lecture Notes in Networks and Systems. Springer, Singapore. 2024;728. DOI: https://doi.org/10.1007/978-981-99-3932-9_12.
17. Pradeepa M, et al. Student health detection using a machine learning approach and IoT. 2022 IEEE 2nd Mysore Subsection International Conference (MysuruCon); 2022. Available from: https://ieeexplore.ieee.org/document/9972445.
18. K K S Liyakat. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. 2023 International Conference on Emerging Smart Computing and Informatics (ESCI); Pune, India, 2023;1–5. DOI: 10.1109/ESCI56872.2023.10099544. Available from: https://ieeexplore.ieee.org/document/10099544/.
19. K Kasat, N Shaikh, V K Rayabharapu, M Nayak. Implementation and recognition of waste management system with mobility solution in smart cities using Internet of Things. 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS); Trichy, India, 2023;1661–5. DOI: 10.1109/ICAISS58487.2023.10250690. Available from: https://ieeexplore.ieee.org/document/10250690/.
20. K K S Liyakat. 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. CVR 2023. Algorithms for intelligent systems. Springer, Singapore. 2023. DOI: https://doi.org/10.1007/978-981-99-4577-1_3.
21. Kazi K. AI-driven IoT (AIIoT) in healthcare monitoring. In: Nguyen T, Vo N, editors. Using traditional design methods to enhance AI-driven decision making. IGI Global. 2024;77–101. DOI: https://doi.org/10.4018/979-8-3693-0639-0.ch003.
22. Kazi K. Modelling and simulation of electric vehicle for performance analysis: BEV and HEV electrical vehicle implementation using Simulink for e-mobility ecosystems. In: Nagpal N, Kassarwani N, Varthanan G, Siano P, editors. E-mobility in electrical energy systems for sustainability. IGI Global. 2024;295–320. DOI: https://doi.org/10.4018/979-8-3693-2611-4.ch014.
23. K S Kazi. Computer-aided diagnosis in ophthalmology: a technical review of deep learning applications. In: Garcia M, de Almeida R, editors. Transformative approaches to patient literacy and healthcare innovation. IGI Global. 2024;112-135. DOI: https://doi.org/10.4018/979-8-3693-3661-8.ch006.
24. Prashant K Magadum. Machine learning for predicting wind turbine output power in wind energy conversion systems. Grenze Int J Eng Technol. 2024;10(1):2074–80. Available from: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=2514&id=8.
25. Priya Mangesh Nerkar, Bhagyarekha Ujjwalganesh Dhaware. Predictive data analytics framework based on heart healthcare system (HHS) using machine learning. J Adv Zool. 2023;44(Special Issue -2):3673–86. Available from:
26. https://jazindia.com/index.php/jaz/article/view/1695. 26. Neeraja P, Kumar RG, Kumar MS, Liyakat K K S, Vani M S. DL-based somnolence detection for improved driver safety and alertness monitoring. 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT); Greater Noida, India, 2024;589–594. DOI: 10.1109/IC2PCT60090.2024.10486714. Available from: https://ieeexplore. ieee.org/document/10486714.
27. Kazi K. Explainable AI in healthcare. In: Anitha Kamaraj A, Acharjya DP, editors. Explainable artificial intelligence in healthcare system. 2024. ISBN: 979-8-89113-598-7. DOI: https://doi.org/10.52305/GOMR8163.
28. Kazi K. ChatGPT: an automated teacher’s guide to learning. In: Bansal R, Chakir A, Hafaz Ngah A, Rabby F, Jain A, editors. AI algorithms and ChatGPT for student engagement in online learning. IGI Global. 2024. pp. 1–20. DOI: https://doi.org/10.4018/979-8-3693-4268-8.ch001.
29. Veena C, Sridevi M, K K S Liyakat, Saha B, Reddy S R, 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); Solan, India, 2023;407–410. DOI: 10.1109/ICIIP61524.2023.10537627. Available from: https://ieeexplore.ieee.org/document/10537627.
30. Rajendra Prasad K, Karanam S. AI in public-private partnership for IT infrastructure development. J High Technol Manag Res. 2024;35(1):100496. DOI: https://doi.org/ 10.1016/j.hitech.2024.100496.
31. K K S Kazi. IoT driven by machine learning (MLIoT) for the retail apparel sector. In: Tarnanidis T, Papachristou E, Karypidis M, Ismyrlis V, editors. Driving green marketing in fashion and retail. IGI Global. 2024. 63–81. DOI: https://doi.org/10.4018/979-8-3693-3049-4.ch004.
32. Kutubuddin Kazi. Machine learning (ML)-based Braille Lippi characters and numbers detection and announcement system for blind children in learning. In: Sart G, editor. Social reflections of human-computer interaction in education, management, and economics. IGI Global. DOI: https://doi.org/10.4018/979-8-3693-3033-3.ch002.
33. K S Kazi. Artificial intelligence (AI)-driven IoT (AIIoT)-based agriculture automation. In: Satapathy S, Muduli K, editors. Advanced computational methods for agri-business sustainability. IGI Global. 2024;72–94. DOI: https://doi.org/10.4018/979-8-3693-3583-3.ch005.
34. Kazi Kutubuddin. Vehicle health monitoring system (VHMS) by employing IoT and sensors. Grenze Int J Eng Technol. 2024;10(2):5367–74. Available from: https://thegrenze.com/index. php?display=page&view=journalabstract&absid=3371&id=8.
35. Kazi Kutubuddin. A novel approach on ML based palmistry. Grenze Int J Eng Technol. 2024;10(2):5186–93. Available from: https://thegrenze.com/index.php?display=page&view= journalabstract&absid=3344&id=8.
36. Kazi Kutubuddin. IoT based boiler health monitoring for sugar industries. Grenze Int J Eng Technol. 2024;10(2):5178–85. Available from: https://thegrenze.com/index. php?display=page&view=journalabstract&absid=3343&id=8.
37. K S Kazi. Machine learning-based pomegranate disease detection and treatment. In: Zia Ul Haq M, Ali I, editors. Revolutionizing pest management for sustainable agriculture. IGI Global. 2024;469–498. DOI: https://doi.org/10.4018/979-8-3693-3061-6.ch019.
38. Liyakat. IoT technologies for the intelligent dairy industry: a new challenge. In: Thandekkattu S, Vajjhala N, editors. Designing sustainable Internet of Things solutions for smart industries. IGI Global. 2025;321–350. DOI: https://doi.org/10.4018/979-8-3693-5498-8.ch012.
39. Liyakat Kazi. Heart health monitoring using IoT and machine learning methods. In: Shaik A, editor. AI-powered advances in pharmacology. IGI Global. 2025;257–282. DOI: https://doi.org/10.4018/979-8-3693-3212-2.ch010.
40. 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;205–238. DOI: https://doi.org/10.4018/979-8-3693-6502-1.ch008.
41. K S Kazi. AI-driven-IoT (AIIoT)-based decision making in drones for climate change: KSK approach. In: Aouadni S, Aouadni I, editors. Recent theories and applications for multi-criteria decision-making. IGI Global. 2025;311–340. DOI: https://doi.org/10.4018/979-8-3693-6502-1.ch011.
42. Liyakat. AI-driven-IoT (AIIoT)-based decision making in kidney diseases patient healthcare monitoring: KSK approach for kidney monitoring. In: Polat L Ö, Polat O, editors. AI-driven innovation in healthcare data analytics. IGI Global. 2025;277–306. DOI: https://doi.org/10.4018/979-8-3693-7277-7.ch009.
43. Mahant M A. 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;205–236. DOI: https://doi.org/10.4018/979-8-3693-9641-4.ch007. 44. Priya Nerkar, Kazi Sultanabanu. IoT-based skin health monitoring system. Int J Biol Pharm Allied Sci (IJBPAS). 2024;13(11):5937–50. DOI:
44. https://doi.org/10.31032/IJBPAS/2024/13.11.8488.
45. Sayyad. AI-Powered IoT (AI IoT) for decision-making in smart agriculture: KSK approach for smart agriculture. In: Aouadni S, Aouadni I, editors. Recent theories and applications for multi-criteria decision-making. IGI Global. 2025;67–96. DOI: https://doi.org/10.4018/979-8-3693-6230-3.ch003.
46. Sayyad. KK approach to increase resilience in Internet of Things: a T-cell security concept. In: Darwish D, Charan K, editors. Analyzing privacy and security difficulties in social media: new challenges and solutions. IGI Global. 2025;87–120. DOI: https://doi.org/10.4018/979-8-3693-9491-5.ch005.
47. K S Kazi. Machine learning-driven Internet of Medical Things (ML-IoMT)-based healthcare monitoring system. In: Soufiene B, Chakraborty C, editors. Responsible AI for digital health and medical analytics. IGI Global. 2025;49–86. DOI: https://doi.org/10.4018/979-8-3693-6294-5.ch003.
48. Kazi Kutubuddin. Vehicle health monitoring system (VHMS) by employing IoT and sensors. Grenze Int J Eng Technol. 2024;10(2):5367–74. Available from: https://thegrenze.com/index. php?display=page&view=journalabstract&absid=3371&id=8.
49. Kazi Kutubuddin. A novel approach on ML based palmistry. Grenze Int J Eng Technol. 2024;10(2):5186–93. Available from: https://thegrenze.com/index.php?display=page&view= journalabstract&absid=3344&id=8.
50. Kazi Kutubuddin. IoT based boiler health monitoring for sugar industries. Grenze Int J Eng Technol. 2024;10(2):5178–85. Available from: https://thegrenze.com/index.php?display= page&view=journalabstract&absid=3343&id=8.
51. Prashant K Magadum. Machine learning for predicting wind turbine output power in wind energy conversion systems. Grenze Int J Eng Technol. 2024;10(1):2074–80. Available from: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=2514&id=8.
52. Altaf O Mulani, Arti V Bang, Ganesh B Birajadar, Amar B Deshmukh, Hemlata M Jadhav. IoT based air, water, and soil monitoring system for pomegranate farming. Annals Agri-Bio Res. 2024;29(2):71–86.
53. K Z K. Transformation of agriculture effectuated by artificial intelligence-driven Internet of Things (AIIoT). In: Garwi J, Dzingirai M, Masengu R, editors. Integrating agriculture, green marketing strategies, and artificial intelligence. IGI Global. 2025;449–484. DOI: https://doi.org/10.4018/979-8-3693-6468-0.ch015.
54. Keerthana R, Vinutha K, Bhagyalakshmi K, Papinaidu M, Venkatesh, Liyakat KKS. Machine learning based risk assessment for financial management in big data IoT credit. 2024. Available from: SSRN: https://ssrn.com/abstract=5086671 or http://dx.doi.org/10.2139/ssrn.5086671.
55. Mulani AO, Liyakat KKS, Warade NS, et al. ML-powered Internet of Medical Things structure for heart disease prediction. J Pharmacology Pharmacotherapeutics. 2025;0(0). DOI: 10.1177/0976500X241306184.
56. Odnala S, Shanthy R, Bharathi B, Pandey C, Rachapalli A, Liyakat. Artificial intelligence and cloud-enabled e-vehicle design with wireless sensor integration. 2024. Available from: SSRN: https://ssrn.com/abstract=5107242 or http://dx.doi.org/10.2139/ssrn.5107242.
57. Upadhyaya AN, Surekha C, Malathi P, Suresh G, Suriyan K. Pioneering cognitive computing for transformative healthcare innovations. 2024. Available from: SSRN: https://ssrn.com/abstract=5086894 or http://dx.doi.org/10.2139/ssrn.5086894.
58. Shinde SS, Nerkar PM, Kazi SS, Kazi VS. Machine learning for brand protection: a review of a proactive defense mechanism. In: Khan M, Amin Ul Haq M, editors. Avoiding ad fraud and supporting brand safety: programmatic advertising solutions. IGI Global. 2025;175–220. DOI: https://doi.org/10.4018/979-8-3693-7041-4.ch007.

For years, the concept of electronic skin, or e-skin, has captivated imaginations. Imagine a material that could mimic the sensitivity, flexibility, and even the healing properties of human skin. This isn’t just science fiction anymore. E-skin, a network of flexible sensors and electronic components, is rapidly becoming a reality. And now, thanks to the power of artificial intelligence (AI), this technology is poised to revolutionize various aspects of our lives, from healthcare and robotics to human-computer interaction. AI-powered e-skin is not just a technological advancement; it’s a paradigm shift. This technology has the potential to blur the lines between human and machine interaction, ushering in a future where technology seamlessly integrates with our bodies and enhances our lives in profound ways. While there are still challenges to overcome, the potential rewards make it a field worth watching closely. The era of touch-sensitive, intelligent skin is on the horizon, and it promises to be truly revolutionary. While the technology is still in its early stages, the progress made in recent years has been remarkable. Researchers are actively exploring new materials, developing innovative sensor designs, and refining AI algorithms to overcome these challenges.

Keywords:Artificial Intelligence, e-Skin, data processing, pattern recognition, sensitivity

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1. Kazi Kutubuddin Sayyad Liyakat. Impact of nanotechnology on battlefield welfare: a study. Int J Nanobiotechnol. 2024;10(02):19–32.
2. Kazi Sultanabanu Sayyad Liyakat, Kazi Kutubuddin Sayyad Liyakat. Nanosensors in agriculture field: a study. Int J Appl Nanotechnol. 2024;10(02):12–22. Available from: https://journalspub.com/publication/ijan-v10i02-11625/
3. Kutubuddin Sayyad Liyakat. Nanotechnology in space study. Int J Appl Nanotechnol. 2024;10(02):39–46. Available from: https://journalspub.com/publication/ijan-v10i02-11616/
4. Kazi Kutubuddin Sayyad Liyakat. KSK approach to smart agriculture: utilizing AI-driven Internet of Things (AI IoT). J Microcontroller Eng Appl. 2024;11(03):21–32.
5. Kazi Kutubuddin Sayyad Liyakat. Microwave communication in the Internet of Things: a study. J RF Microwave Commun Technol. 2024:38–49. Available from: https://matjournals.net/ engineering/index.php/JoRFMCT/article/view/1276
6. Kazi Kutubuddin Sayyad Liyakat. Nanorobotics: a review. Int J Appl Nanotechnol. 2023;9(2):36–43. DOI: https://doi.org/10.37628/ijan.v9i2.1019
7. K K Sayyad Liyakat. Impact of nanotechnology on battlefield welfare: a study. Int J Nanobiotechnol. 2024;10(2):19–32.
8. Sultanabanu Sayyad Liyakat. Nanotechnology in healthcare applications: a study. Int J Nanobiotechnol. 2024;10(2):48–58.
9. Kazi Kutubuddin Sayyad Liyakat. Nanotechnology in medical applications: a study. Nano Trends: A J Nanotechnol Its Appl. 2024;26(2):1–11.
10. Kazi Kutubuddin Sayyad Liyakat. Nanotechnology in battlefield: a study. J Nanoscience, Nanoengineering & Appl. 2024;14(2):18–30.
11. Sultanabanu Sayyad Liyakat Kazi. Polymer applications in energy generation and storage: a forward path. J Nanoscience, Nanoengineering & Appl. 2024;14(2):31–39.
12. Kazi Kutubuddin Sayyad Liyakat. Review of biopolymers in agriculture application: an eco-friendly alternative. Int J Composite Constituent Mater. 2024;10(1):50–62.
13. Kazi Kutubuddin Sayyad Liyakat. Nanotechnology in precision farming: the role of research. Int J Nanomaterials Nanostructures. 2023;9(2):1–5. DOI: https://doi.org/10.37628/ijnn.v9i2.1051.
14. Kazi Sultanabanu Sayyad Liyakat. Accepting Internet of Nano-Things: synopsis, developments, and challenges. J Nanoscience, Nanoengineering & Appl. 2023;13(2):17–26. DOI: https://doi.org/10.37591/jonsnea.v13i2.1464.
15. Mishra SB, et al. Nanotechnology’s importance in mechanical engineering. J Fluid Mech Mech Des. 2024;6(1):1–9.
16. K K S Liyakat. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Udgata SK, Sethi S, Gao XZ, editors. Intelligent systems. ICMIB 2023. Lecture Notes in Networks and Systems. Springer, Singapore. 2024;728. DOI: https://doi.org/10.1007/978-981-99-3932-9_12.
17. Pradeepa M, et al. Student health detection using a machine learning approach and IoT. 2022 IEEE 2nd Mysore Subsection International Conference (MysuruCon); 2022. Available from: https://ieeexplore.ieee.org/document/9972445.
18. K K S Liyakat. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. 2023 International Conference on Emerging Smart Computing and Informatics (ESCI); Pune, India, 2023;1–5. DOI: 10.1109/ESCI56872.2023.10099544. Available from: https://ieeexplore.ieee.org/document/10099544/.
19. K Kasat, N Shaikh, V K Rayabharapu, M Nayak. Implementation and recognition of waste management system with mobility solution in smart cities using Internet of Things. 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS); Trichy, India, 2023;1661–5. DOI: 10.1109/ICAISS58487.2023.10250690. Available from: https://ieeexplore.ieee.org/document/10250690/.
20. K K S Liyakat. 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. CVR 2023. Algorithms for intelligent systems. Springer, Singapore. 2023. DOI: https://doi.org/10.1007/978-981-99-4577-1_3.
21. Kazi K. AI-driven IoT (AIIoT) in healthcare monitoring. In: Nguyen T, Vo N, editors. Using traditional design methods to enhance AI-driven decision making. IGI Global. 2024;77–101. DOI: https://doi.org/10.4018/979-8-3693-0639-0.ch003.
22. Kazi K. Modelling and simulation of electric vehicle for performance analysis: BEV and HEV electrical vehicle implementation using Simulink for e-mobility ecosystems. In: Nagpal N, Kassarwani N, Varthanan G, Siano P, editors. E-mobility in electrical energy systems for sustainability. IGI Global. 2024;295–320. DOI: https://doi.org/10.4018/979-8-3693-2611-4.ch014.
23. K S Kazi. Computer-aided diagnosis in ophthalmology: a technical review of deep learning applications. In: Garcia M, de Almeida R, editors. Transformative approaches to patient literacy and healthcare innovation. IGI Global. 2024;112-135. DOI: https://doi.org/10.4018/979-8-3693-3661-8.ch006.
24. Prashant K Magadum. Machine learning for predicting wind turbine output power in wind energy conversion systems. Grenze Int J Eng Technol. 2024;10(1):2074–80. Available from: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=2514&id=8.
25. Priya Mangesh Nerkar, Bhagyarekha Ujjwalganesh Dhaware. Predictive data analytics framework based on heart healthcare system (HHS) using machine learning. J Adv Zool. 2023;44(Special Issue -2):3673–86. Available from:
26. https://jazindia.com/index.php/jaz/article/view/1695. 26. Neeraja P, Kumar RG, Kumar MS, Liyakat K K S, Vani M S. DL-based somnolence detection for improved driver safety and alertness monitoring. 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT); Greater Noida, India, 2024;589–594. DOI: 10.1109/IC2PCT60090.2024.10486714. Available from: https://ieeexplore. ieee.org/document/10486714.
27. Kazi K. Explainable AI in healthcare. In: Anitha Kamaraj A, Acharjya DP, editors. Explainable artificial intelligence in healthcare system. 2024. ISBN: 979-8-89113-598-7. DOI: https://doi.org/10.52305/GOMR8163.
28. Kazi K. ChatGPT: an automated teacher’s guide to learning. In: Bansal R, Chakir A, Hafaz Ngah A, Rabby F, Jain A, editors. AI algorithms and ChatGPT for student engagement in online learning. IGI Global. 2024. pp. 1–20. DOI: https://doi.org/10.4018/979-8-3693-4268-8.ch001.
29. Veena C, Sridevi M, K K S Liyakat, Saha B, Reddy S R, 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); Solan, India, 2023;407–410. DOI: 10.1109/ICIIP61524.2023.10537627. Available from: https://ieeexplore.ieee.org/document/10537627.
30. Rajendra Prasad K, Karanam S. AI in public-private partnership for IT infrastructure development. J High Technol Manag Res. 2024;35(1):100496. DOI: https://doi.org/ 10.1016/j.hitech.2024.100496.
31. K K S Kazi. IoT driven by machine learning (MLIoT) for the retail apparel sector. In: Tarnanidis T, Papachristou E, Karypidis M, Ismyrlis V, editors. Driving green marketing in fashion and retail. IGI Global. 2024. 63–81. DOI: https://doi.org/10.4018/979-8-3693-3049-4.ch004.
32. Kutubuddin Kazi. Machine learning (ML)-based Braille Lippi characters and numbers detection and announcement system for blind children in learning. In: Sart G, editor. Social reflections of human-computer interaction in education, management, and economics. IGI Global. DOI: https://doi.org/10.4018/979-8-3693-3033-3.ch002.
33. K S Kazi. Artificial intelligence (AI)-driven IoT (AIIoT)-based agriculture automation. In: Satapathy S, Muduli K, editors. Advanced computational methods for agri-business sustainability. IGI Global. 2024;72–94. DOI: https://doi.org/10.4018/979-8-3693-3583-3.ch005.
34. Kazi Kutubuddin. Vehicle health monitoring system (VHMS) by employing IoT and sensors. Grenze Int J Eng Technol. 2024;10(2):5367–74. Available from: https://thegrenze.com/index. php?display=page&view=journalabstract&absid=3371&id=8.
35. Kazi Kutubuddin. A novel approach on ML based palmistry. Grenze Int J Eng Technol. 2024;10(2):5186–93. Available from: https://thegrenze.com/index.php?display=page&view= journalabstract&absid=3344&id=8.
36. Kazi Kutubuddin. IoT based boiler health monitoring for sugar industries. Grenze Int J Eng Technol. 2024;10(2):5178–85. Available from: https://thegrenze.com/index. php?display=page&view=journalabstract&absid=3343&id=8.
37. K S Kazi. Machine learning-based pomegranate disease detection and treatment. In: Zia Ul Haq M, Ali I, editors. Revolutionizing pest management for sustainable agriculture. IGI Global. 2024;469–498. DOI: https://doi.org/10.4018/979-8-3693-3061-6.ch019.
38. Liyakat. IoT technologies for the intelligent dairy industry: a new challenge. In: Thandekkattu S, Vajjhala N, editors. Designing sustainable Internet of Things solutions for smart industries. IGI Global. 2025;321–350. DOI: https://doi.org/10.4018/979-8-3693-5498-8.ch012.
39. Liyakat Kazi. Heart health monitoring using IoT and machine learning methods. In: Shaik A, editor. AI-powered advances in pharmacology. IGI Global. 2025;257–282. DOI: https://doi.org/10.4018/979-8-3693-3212-2.ch010.
40. 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;205–238. DOI: https://doi.org/10.4018/979-8-3693-6502-1.ch008.
41. K S Kazi. AI-driven-IoT (AIIoT)-based decision making in drones for climate change: KSK approach. In: Aouadni S, Aouadni I, editors. Recent theories and applications for multi-criteria decision-making. IGI Global. 2025;311–340. DOI: https://doi.org/10.4018/979-8-3693-6502-1.ch011.
42. Liyakat. AI-driven-IoT (AIIoT)-based decision making in kidney diseases patient healthcare monitoring: KSK approach for kidney monitoring. In: Polat L Ö, Polat O, editors. AI-driven innovation in healthcare data analytics. IGI Global. 2025;277–306. DOI: https://doi.org/10.4018/979-8-3693-7277-7.ch009.
43. Mahant M A. 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;205–236. DOI: https://doi.org/10.4018/979-8-3693-9641-4.ch007. 44. Priya Nerkar, Kazi Sultanabanu. IoT-based skin health monitoring system. Int J Biol Pharm Allied Sci (IJBPAS). 2024;13(11):5937–50. DOI:
44. https://doi.org/10.31032/IJBPAS/2024/13.11.8488.
45. Sayyad. AI-Powered IoT (AI IoT) for decision-making in smart agriculture: KSK approach for smart agriculture. In: Aouadni S, Aouadni I, editors. Recent theories and applications for multi-criteria decision-making. IGI Global. 2025;67–96. DOI: https://doi.org/10.4018/979-8-3693-6230-3.ch003.
46. Sayyad. KK approach to increase resilience in Internet of Things: a T-cell security concept. In: Darwish D, Charan K, editors. Analyzing privacy and security difficulties in social media: new challenges and solutions. IGI Global. 2025;87–120. DOI: https://doi.org/10.4018/979-8-3693-9491-5.ch005.
47. K S Kazi. Machine learning-driven Internet of Medical Things (ML-IoMT)-based healthcare monitoring system. In: Soufiene B, Chakraborty C, editors. Responsible AI for digital health and medical analytics. IGI Global. 2025;49–86. DOI: https://doi.org/10.4018/979-8-3693-6294-5.ch003.
48. Kazi Kutubuddin. Vehicle health monitoring system (VHMS) by employing IoT and sensors. Grenze Int J Eng Technol. 2024;10(2):5367–74. Available from: https://thegrenze.com/index. php?display=page&view=journalabstract&absid=3371&id=8.
49. Kazi Kutubuddin. A novel approach on ML based palmistry. Grenze Int J Eng Technol. 2024;10(2):5186–93. Available from: https://thegrenze.com/index.php?display=page&view= journalabstract&absid=3344&id=8.
50. Kazi Kutubuddin. IoT based boiler health monitoring for sugar industries. Grenze Int J Eng Technol. 2024;10(2):5178–85. Available from: https://thegrenze.com/index.php?display= page&view=journalabstract&absid=3343&id=8.
51. Prashant K Magadum. Machine learning for predicting wind turbine output power in wind energy conversion systems. Grenze Int J Eng Technol. 2024;10(1):2074–80. Available from: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=2514&id=8.
52. Altaf O Mulani, Arti V Bang, Ganesh B Birajadar, Amar B Deshmukh, Hemlata M Jadhav. IoT based air, water, and soil monitoring system for pomegranate farming. Annals Agri-Bio Res. 2024;29(2):71–86.
53. K Z K. Transformation of agriculture effectuated by artificial intelligence-driven Internet of Things (AIIoT). In: Garwi J, Dzingirai M, Masengu R, editors. Integrating agriculture, green marketing strategies, and artificial intelligence. IGI Global. 2025;449–484. DOI: https://doi.org/10.4018/979-8-3693-6468-0.ch015.
54. Keerthana R, Vinutha K, Bhagyalakshmi K, Papinaidu M, Venkatesh, Liyakat KKS. Machine learning based risk assessment for financial management in big data IoT credit. 2024. Available from: SSRN: https://ssrn.com/abstract=5086671 or http://dx.doi.org/10.2139/ssrn.5086671.
55. Mulani AO, Liyakat KKS, Warade NS, et al. ML-powered Internet of Medical Things structure for heart disease prediction. J Pharmacology Pharmacotherapeutics. 2025;0(0). DOI: 10.1177/0976500X241306184.
56. Odnala S, Shanthy R, Bharathi B, Pandey C, Rachapalli A, Liyakat. Artificial intelligence and cloud-enabled e-vehicle design with wireless sensor integration. 2024. Available from: SSRN: https://ssrn.com/abstract=5107242 or http://dx.doi.org/10.2139/ssrn.5107242.
57. Upadhyaya AN, Surekha C, Malathi P, Suresh G, Suriyan K. Pioneering cognitive computing for transformative healthcare innovations. 2024. Available from: SSRN: https://ssrn.com/abstract=5086894 or http://dx.doi.org/10.2139/ssrn.5086894.
58. Shinde SS, Nerkar PM, Kazi SS, Kazi VS. Machine learning for brand protection: a review of a proactive defense mechanism. In: Khan M, Amin Ul Haq M, editors. Avoiding ad fraud and supporting brand safety: programmatic advertising solutions. IGI Global. 2025;175–220. DOI: https://doi.org/10.4018/979-8-3693-7041-4.ch007.