Kazi Kutubuddin Sayyad Liyakat

@bmitsolapur.org

Professor, Department of Electronics and Telecommunication Engineering
Brahmdevdada Mane institute of Technology, Solapur

Kazi Kutubuddin Sayyad Liyakat


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IR. Dr. Kazi Kutubuddin Sayyad Liyakat has completed his B.E., M.E., Ph.D. in E&TC Engineering and Post Doctorate in “IoT in Healthcare Applications”, and is nowadays working as a Professor & Head of Department, E&TC Engineering Department and was Dean R&D. His area of Interest is IoT, IoRT, IoBT, AI, ML, and AIIoT. He had suggested KSK Approach, KSK1 Approach, KK Approach, KVS Approach and DL Approach in the arena of IoT and its security. He has published more than 250+ papers, including Book chapters with various renowned publishers like IGI Global, CRC, NOVA publishers. Also he had received various 56 Patents from Indian Patent House (Utility, Design and Copyright), UK Design Grant Patents, South African Design Grant Patents and Canadian Copyright Patents. He received funds for various projects on IoT applications. He published 78 books under different National and International publishers. He worked as Editor for 2 book. He worked as a Reviewer for Scopus Conferences and Journal a

EDUCATION

Post- Doctorate in IoT in Healthcare Applications

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Health Informatics, Nuclear Energy and Engineering, Agricultural and Biological Sciences
31

Scopus Publications

4927

Scholar Citations

36

Scholar h-index

164

Scholar i10-index

Scopus Publications

  • IoT Sensors in a Wireless Environment for Healthcare Monitoring: A Framework for Fault Detection
    Altaf O. Mulani, Kazi Kutubuddin Sayyad Liyakat, Nilima S. Warade, Alaknanda Patil, Mahesh T. Kolte, Kishor Kinage, Manish Rana, Shweta Sadanand Salunkhe, Vaishali Satish Jadhav, Megha Nagrale
    Journal of Pharmacology and Pharmacotherapeutics, 2026
    Background The Internet of Things (IoT) and wireless sensor networks (WSNs) are now being explored and used in various sectors, thanks to recent technological advancements. Purpose The healthcare sector is one of the areas we will examine in this research. This study’s primary focus will be on the fault detection framework (FDF) for healthcare monitoring employing IoT sensors in a wireless environment. Methods Because isolating defects yields more pertinent information about the issues, fault detection first finds weaknesses in a system or process before isolating the intricate process or variable. Results and Discussion The outcome demonstrates that the suggested strategy achieves an acceptable level of 80% accuracy in problem identification, in addition to the greater number of patients recorded. Conclusion The outcomes show that the defect-detection system for wireless IoT sensor-based healthcare monitoring is efficient.
  • AI-Driven-IoT (AIIoT) decision-making system for hepatitis disease patient healthcare monitoring: KSK1 approach for hepatitis patient monitoring
    Kutubuddin Sayyad Liyakat Kazi
    Navigating Innovations and Challenges in Travel Medicine and Digital Health, 2025
    The KSK1 strategy is positioned to transform decision-making processes and create a more intelligent and efficient world as AI and IoT continue to expand. This model was specifically designed to satisfy the criteria of the task that is being suggested. These classifiers are used in the case of disease datasets during the classification process, particularly in regions like those that relate to Hepatitis diseases. Three fundamental indications are considered to assess how well the classifiers are performing. It is crucial to remember that accuracy, precision, and recall are the measurements being discussed here. Through the application of the proposed KSK1 approach, an accuracy rate ranging from 85% to 91% can be obtained for every sickness. The suggested KSK1 approach's accuracy, precision, and recall are displayed. As a result, the KSK1 method approach has 93.4% accuracy, precision of 91.2%, and recall of 91.2%.
  • KSK Approach in LOVE Health: AI-DrivenIoT(AIIoT) based Decision Making System in LOVE Health for Loved One's
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • AI-Driven-IoT (AIIoT)-Based Jawar Leaf Disease Detection: KSK Approach for Jawar Disease Detection
    Kutubuddin Sayyad Liyakat Kazi
    Modern Intelligent Techniques for Image Processing, 2025
    This study evaluated the efficacy of three different models for identifying diseases that affect jawar leaves: Decision Trees (DT), K-Nearest Neighbours (K-NN), and Artificial Neural Networks (ANN). Both healthy and sick specimens of jawar leaves might be included in the dataset of jawar leaf photographs that could be obtained. A number of different image processing techniques were utilised in order to extract characteristic information from the photographs. Once the features had been extracted, the ANN, DT, and K-NN models were trained and evaluated using the information that was obtained. In addition to having the best accuracy (96.5%), the KSK technique had the highest recall (95.8%) and precision (97.2%) measurements. In comparison to the DT and K-NN models, the ANN model performed significantly better. This was due to the fact that it was able to analyse the data and identify subtle non-linear relationships.
  • Use of Machine Learning Approach for Tongue based Health Monitoring: A Review
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Kidney Diseases Patient Healthcare Monitoring using AI-Driven-IoT(AIIoT) - An KSK1 Approach
    Kazi Kutubuddin Sayyad Liyakat, Suhas B Khadake, Babasaheb R Ingale, Daphale D. D., Swapnil S Sudake, Maina Machindra Awatade
    Proceedings of the 7th International Conference on Intelligent Sustainable Systems Iciss 2025, 2025
    Monitoring kidney disorders with the assistance of trained medical professionals are one of the most important aspects of managing and treating these conditions. Imaging investigations, blood tests, urine tests, blood pressure monitoring, and routine check-ups are critical for tracking the evolution of the condition and making any required changes to treatment plans. All of these are essential components. Patients with renal abnormalities can enhance their quality of life and lower their risk of developing complications if they receive proper and timely monitoring. This is due to their enhanced abilities to manage their conditions. The KSK1 strategy, which integrates IoT and artificial intelligence, is transforming decision-making. This improves data accuracy and reliability, enabling real-time decision-making, and makes the solution more cost-effective. However, in order to fully realise the promise of this method, businesses must identify and resolve the challenges that arise while utilising it. With the continued development of artificial intelligence (AI) and the internet of things (IoT), the KSK1 method has enormous potential to revolutionise decision-making procedures and create a more intelligent and efficient environment. This model was designed expressly to meet the criteria of the work being offered. These classifiers are used to classify disease datasets, notably in sectors such as renal disease datasets. Three main indicators are used to judge how effectively the classifiers function. It is critical to understand that the measurements under discussion here are accuracy, precision, and recall. If the suggested KSK1 approach is used, an accuracy rate of 87% to 95% can be attained for each and every disease. Accuracy of 92.5%, precision of 94.5%, recall of 94.5%.
  • AI-Powered-IoT (AIIoT) for Decision Making - KSK Approach in Smart Agriculture
    Kazi Kutubuddin Sayyad Liyakat, Suhas B Khadake, Abhijeet S Dhavale, Raviraj Madhekar, Patil Karan Babaso, Amol B Chounde
    3rd International Conference on Emerging Applications of Material Science and Technology Iceamst 2025, 2025
  • AI-Powered-IoT (AIIoT) based Bridge Health Monitoring using Sensor Data for Smart City Management- A KSK Approach
    Kazi Kutubuddin Sayyad Liyakat, Suhas B Khadake, Kamal Galani, Karan Babaso Patil, Abhijeet Dhavale, Sagar D Sarik
    Proceedings of the 7th International Conference on Intelligent Sustainable Systems Iciss 2025, 2025
    The incorporation of smart city initiatives with bridge health monitoring marks a big step forward in urban infrastructure management. Cities can ensure the safety and resilience of critical infrastructure while also improving sustainability and increasing citizen engagement by leveraging cutting-edge technologies and real-time data. As metropolitan areas grow and change, the deployment of widespread health monitoring systems will be critical in creating smarter, safer, and more environmentally friendly cities for future generations. Resolving the integration challenges will be critical to realising the full potential of smart city technologies as we move forward. The extraordinarily quick growth of technology has resulted in significant advancements in a wide range of sectors and businesses, notably infrastructure and construction. In recent years, there has been a growing emphasis on making use of the Internet of Things (IoT) for bridge monitoring and maintenance. To improve safety and extend bridge lifespans, a new sector called as "Bridge Health Monitoring through AIIoT" has emerged. Although the notion of monitoring bridge health wasn't novel, the Internet of Things has revolutionised the field. Normative bridge health monitoring depends on manual inspections, which were costly, time-consuming, and frequently delayed the detection of structural problems. The Internet of Things (IoT) enables the installation of sensors on various bridge sections to collect data on temperature, strain, vibration, and corrosion levels. The data is subsequently transferred to a centralised server, allowing for real-time analysis. Any anomalies or possible concerns can be discovered early, and maintenance or repair operations can be arranged accordingly. One of the most significant benefits of an IoT-based bridge health monitoring system is the ability to provide real-time data. This allows for the early detection of any alterations in the bridge's condition and the implementation of essential steps to avoid potential calamities. The system can forecast when a bridge needs maintenance or repairs by gathering and analysing data on a continual basis. The KNN makes choices on bridge usage. For ordinary operations, we keep the water below 48 degrees Celsius and at a depth of 25 meters. During monsoon months such as July and August, the water level surged by more than thirty meters. In addition, the average yearly high in mid-April and mid-May exceeds 48 degrees Celsius. Once a KNN makes a decision, an actuator opens the gate, allowing traffic to use the bridge in smart city planning.
  • AI-Driven IoT based Decision Making for Hepatitis Diseases Patient's Healthcare Monitoring: KSK Approach for Hepatitis Patient Monitoring
    Kazi Kutubuddin Sayyad Liyakat, Suhas B Khadake, Pravin S More, Renuka J Shinde, Komal P Kondubhairi, Shashikant S Kamble
    Proceedings of the 7th International Conference on Intelligent Sustainable Systems Iciss 2025, 2025
    Making decisions is a crucial part of the medical treatment of hepatitis patients. The KSK technique transforms decision-making by fusing the power of artificial intelligence (AI) with the internet of things (IoT). It improves data accuracy and reliability, enables real-time decision-making, and increases the solution's cost-effectiveness. However, businesses must address the issues associated with its use if they are to fully realise the potential of this approach. The KSK strategy has the potential to transform decision-making procedures and create a more intelligent and efficient world as AI and IoT continue to expand. This particular model was created specifically to meet the demands of the task that is being offered. These classifiers are applied to disease datasets during the classification process, particularly those related to hepatitis conditions. Three fundamental indicators are taken into account when assessing how well the classifiers are performing. Note that the metrics of accuracy, precision, and recall are being discussed here. The proposed KSK approach can be used to obtain an accuracy rate ranging from at least 85% to as much as 91% for all illnesses. The suggested KSK approach's accuracy, precision, and recall are displayed. As a result, the KSK method has 91.3% accuracy, 90.1% precision, and 90.6% recall.
  • ML-powered Internet of Medical Things Structure for Heart Disease Prediction
    Altaf O. Mulani, Kazi Kutubuddin Sayyad Liyakat, Nilima S. Warade, Alaknanda Patil, Mahesh T. Kolte, Kishor Kinage, Manish Rana, Shweta Sadanand Salunkhe, Vaishali Satish Jadhav, Megha Nagrale
    Journal of Pharmacology and Pharmacotherapeutics, 2025
    Background Machine Learning-powered Internet of Medical Things (MLIoMT) is a burgeoning framework poised to transform healthcare, particularly in the timely identification of heart disease. Purpose This article proposes an innovative MLIoMT structure aimed at leveraging machine learning (ML) algorithms for heart disease detection. Methods Through the integration of wearable sensors, mobile applications, cloud computing, and advanced ML techniques, MLIoMT enables continuous monitoring of vital signs and cardiac health indicators in real time. By analyzing this data stream, abnormalities indicative of heart disease can be detected early, facilitating timely intervention and personalized healthcare recommendations. The MLIoMT framework employs diverse ML methods such as deep learning and ensemble techniques to enhance the accuracy and reliability of heart disease prediction models. Results The proposed structure holds promise for revolutionizing preventive healthcare, enabling proactive management of cardiac health and ultimately reducing the burden of heart disease. Results in terms of accuracy, precision, recall and F1 score show that the proposed system has better performance and efficiency. Conclusion Overall, MLIoMT represents a significant advancement in healthcare technology, with the potential to improve patient outcomes and enhance overall quality of life.
  • Machine learning-driven internet of medical things (ML-IoMT)-based healthcare monitoring system
    Kutubuddin Sayyad Liyakat Kazi
    Responsible AI for Digital Health and Medical Analytics, 2024
  • Malicious node detection in IoT networks using artificial neural networks: A machine learning approach
    Kazi Kutubuddin Sayyad Liyakat
    Intelligent Networks Techniques and Applications, 2024
  • Iot based air, water, and soil monitoring system for pomegranate farming
    Annals of Agri Bio Research, 2024
  • Corrigendum to “AI in public-private partnership for IT infrastructure development” [The Journal of High Technology Management Research 35 (2024) 1–10/100496] (Journal of High Technology Management Research (2024) 35(1), (S1047831024000051), (10.1016/j.hitech.2024.100496))
    K. Rajendra Prasad, Santoshachandra Rao Karanam, D. Ganesh, Kazi Kutubuddin Sayyad Liyakat, Vamsidhar Talasila, P. Purushotham
    Journal of High Technology Management Research, 2024
  • IoT technologies for the intelligent dairy industry: A new challenge
    Kutubuddin Sayyad Liyakat Kazi
    Designing Sustainable Internet of Things Solutions for Smart Industries, 2024
  • Heart health monitoring using IoT and machine learning methods
    Kazi Kutubuddin Sayyad Liyakat
    AI Powered Advances in Pharmacology, 2024
  • AI in public-private partnership for IT infrastructure development
    K. Rajendra Prasad, Santoshachandra Rao Karanam, D. Ganesh, Kazi Kutubuddin Sayyad Liyakat, Vamsidhar Talasila, P. Purushotham
    Journal of High Technology Management Research, 2024
  • Explainable AI in healthcare
    Explainable Artificial Intelligence in Healthcare Systems, 2024
  • A Novel Approach on ML based Palmistry
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks
    Kazi Kutubuddin Sayyad Liyakat
    Lecture Notes in Networks and Systems, 2024
  • IoT based Boiler Health Monitoring for Sugar Industries
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • AI-Driven-IoT(AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring
    Kutubuddin Sayyad Liyakat Kazi
    AI Driven Innovation in Healthcare Data Analytics, 2024
  • Machine Learning for Predicting Wind Turbine Output Power in Wind Energy Conversion Systems
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • Machine Learning-Driven Internet of Things (MLIoT)-Based Healthcare Monitoring System
    Kutubuddin Sayyad Liyakat Kazi, Mahesh Ashok Mahant
    Digitalization and the Transformation of the Healthcare Sector, 2024
  • Vehicle Health Monitoring System (VHMS) by Employing IoT and Sensors
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring
    Peddinti Neeraja, R. G. Kumar, M. S. Kumar, Kazi Kutubuddin, Sayyad Liyakat, M. S. Vani
    Proceedings International Conference on Computing Power and Communication Technologies Ic2pct 2024, 2024
  • AI-Driven-IoT(AIIoT) Based Decision-Making- KSK Approach in Drones for Climate Change Study
    Kazi Kutubuddin Sayyad Liyakat, Suhas B Khadake, Dipti. A. Tamboli, Vijay A. Sawant, Mallad H. M., Sushil Sathe
    Proceedings of the 4th International Conference on Ubiquitous Computing and Intelligent Information Systems Icuis 2024, 2024
  • AI-Driven-IoT(AIIoT) Based Decision Making System for High-Blood Pressure Patient Healthcare Monitoring
    Kazi Kutubuddin Sayyad Liyakat, Suhas B Khadake, Amol B Chounde, Avinash Anil Suryagan, Mallad H. M., Mauli R Khadatare
    5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
  • Detecting Malicious Nodes in IoT Networks Using Machine Learning and Artificial Neural Networks
    Kazi Kutubuddin Sayyad Liyakat
    2023 International Conference on Emerging Smart Computing and Informatics Esci 2023, 2023
  • Implementation and Recognition of Waste Management System with Mobility Solution in Smart Cities using Internet of Things
    Kishori Kasat, Naim Shaikh, Vijay Kumar Rayabharapu, Manjushree Nayak, Kazi Kutubuddin Sayyad Liyakat
    Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2023, 2023
  • HEECCNB: An Efficient IoT-Cloud Architecture for Secure Patient Data Transmission and Accurate Disease Prediction in Healthcare Systems
    C. Veena, Mulagundla Sridevi, Kazi Kutubuddin Sayyad Liyakat, Bishal Saha, Sheri Ramchandra Reddy, N Shirisha
    Proceedings of the IEEE International Conference Image Information Processing, 2023

RECENT SCHOLAR PUBLICATIONS

  • Iot sensors in a wireless environment for healthcare monitoring: A framework for fault detection
    AO Mulani, KKS Liyakat, NS Warade, A Patil, MT Kolte, K Kinage, M Rana, ...
    Journal of Pharmacology and Pharmacotherapeutics 17 (1), 94-100 , 2026
    2026
    Citations: 1
  • Intelligent Trajectories: Harnessing Artificial Intelligence for Next Generation Missile and Propellant Design
    KKS Liyakat
    International Journal of Energetic Materials 12 (1), 20-26 , 2026
    2026
  • A Survey on Hydrogen Storage System using Alloys
    KKS Liyakat
    International Journal of Energetic Materials 12 (1), 13-19 , 2026
    2026
  • A Technical Overview of Nanorobots Using Nanotechnology
    HT Shaikh
    International Journal of Nanomaterials and Nanostructures 12 (1), 31–38 , 2026
    2026
  • An Overview on Nanomaterial-Enabled Electronic Skin for Physiological Sensing and Biomedical Use
    KKS Liyakat
    International journal of Nanobiotechnology 12 (1) , 2026
    2026
  • An investigation into the use of nanotechnology in medical-military applications
    HT Shaikh
    International journal of Nanobiotechnology 12 (1) , 2026
    2026
  • Nano-Chemical Revolution in Vaccinology: A Study
    KKS Liyakat
    Research & Reviews: A Journal of Immunology 16 (1), 26–38 , 2026
    2026
  • A Study on AI-driven Security Concerns in the Wireless Ecosystem
    HT Shaikh
    Research & Review: Electronics and Communication Engineering, 27 -38 , 2026
    2026
  • Optimization of Pesticide Requirement Calculations for IoT- Operated Hexacopter Delivery Systems.
    HT Shaikh
    International Journal on Drones 2 (1), 8-14 , 2026
    2026
  • A Study on AI-driven Security Concerns in the Wireless Ecosystem
    HT Shaikh
    Research & Review: Electronics and Communication Engineering 3 (1), 27-38 , 2026
    2026
  • A Study of Self-Healing Polymer Nanocomposites with Filler Effect
    KKS Liyakat
    International Journal of Applied Nanotechnology 12 (1), 26-35 , 2026
    2026
  • An Overview on Intelligent Operating Systems (iOS)
    HT Shaikh
    Journal of Operating Systems Development & Trends 13 (1), 21-28 , 2026
    2026
  • Sensors-Based Electric Machine Design for Industry
    HT Shaikh
    International Journal of Electrical Machine Analysis and Design 4 (1), 1-10 , 2026
    2026
  • An Overview on Quantum dot Technology in Temperature Sensor Design
    KKS Liyakat
    Journal of Electronic Design Technology 17 (1), 10-17 , 2026
    2026
  • The Future of Farming with IoT-Operated Drones
    HT Shaikh
    International Journal on Drones 2 (1), 20-26 , 2026
    2026
  • Optimization of Pesticide Requirement Calculations for IoT- Operated Hexacopter Delivery Systems
    KKSL Heena T. Shaikh
    International Journal on Drones 2 (1), 8-14 , 2026
    2026
  • An Overview of Transforming IoT with Millimeter-Wave,
    HT Shaikh
    Journal of RF and Microwave Communication Technologies 3 (1), 18-28 , 2026
    2026
  • T-Flip-Flop Implementation using Quantum-dot Cellular Automata‖
    KKS Liyakat
    Journal of Electronics Design and Technology 3 (1), 24-32 , 2026
    2026
  • An Overview on Energy Harvesting Using Piezoelectric Material for Wi-Fi Systems
    HT Shaikh
    International Journal of Electro-Mechanics and Material Behavior 4 (1), 56-63 , 2026
    2026
  • A Study of Self-Healing Polymer Nanocomposites with Filler Effect
    KKS Liyakat
    International Journal of Applied Nanotechnology 12 (1), 26-35 , 2026
    2026

MOST CITED SCHOLAR PUBLICATIONS

  • Trends of Artificial Intelligence for online exams in education
    MM Babitha, C Sushma, VK Gudivada
    International Journal of Early Childhood Special Education 14 (01), 2457-2463 , 2022
    2022
    Citations: 103
  • A path towards child-centric Artificial Intelligence based Education
    JS Devi, MB Sreedhar, P Arulprakash, K Kazi, R Radhakrishnan
    International Journal of Early Childhood 14 (3), 9915-9922 , 2022
    2022
    Citations: 99
  • AI in public-private partnership for IT infrastructure development
    KR Prasad, SR Karanam, D Ganesh, KKS Liyakat, V Talasila, ...
    The Journal of High Technology Management Research 35 (1), 100496 , 2024
    2024
    Citations: 94
  • Deep convolution neural network based solution for detecting plant diseases
    MS Kumar, D Ganesh, AV Turukmane, U Batta, KK Sayyadliyakat
    Journal of Pharmaceutical Negative Results 13 (1) , 2022
    2022
    Citations: 90
  • Smart agriculture system using IoT
    WA Devanand, RD Raghunath, AS Baliram, K Kazi
    Int. J. Innov. Res. Technol 5 (10) , 2019
    2019
    Citations: 77
  • IoT based air, water, and soil monitoring system for pomegranate farming
    AO Mulani, AV Bang, GB Birajadar, AB Deshmukh, HM Jadhav, ...
    Annals of Agri-Bio Research 29 (2), 71-86 , 2024
    2024
    Citations: 74
  • Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks
    KKS Liyakat
    In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and … , 2023
    2023
    Citations: 71
  • Detecting malicious nodes in IoT networks using machine learning and artificial neural networks
    KKS Liyakat
    2023 International Conference on Emerging Smart Computing and Informatics … , 2023
    2023
    Citations: 62
  • Computer-Aided Diagnosis in Ophthalmology: A Technical Review of Deep Learning Applications.
    KKS Liyakat
    In M. Garcia & R. de Almeida (Eds.), Transformative Approaches to Patient … , 2024
    2024
    Citations: 60
  • AI-driven IoT (AIIoT) in healthcare monitoring
    K Kazi
    Using Traditional Design Methods to Enhance AI-Driven Decision Making, 77-101 , 2024
    2024
    Citations: 57
  • Student health detection using a machine learning approach and IoT
    M Pradeepa, K Jamberi, S Sajith, MR Bai, A Prakash
    2022 IEEE 2nd Mysore sub section International Conference (MysuruCon), 1-5 , 2022
    2022
    Citations: 57
  • Implementation and recognition of waste management system with mobility solution in smart cities using Internet of Things
    K Kasat, N Shaikh, VK Rayabharapu, M Nayak, KKS Liyakat
    2023 Second International Conference on Augmented Intelligence and … , 2023
    2023
    Citations: 53
  • Fruit Grading, Disease Detection, and an Image Processing Strategy
    Sultanabanu Kazi, Kazi Kutubuddin
    Journal of Image Processing and Artificial Intelligence 9 (2), 17 - 34 , 2023
    2023
    Citations: 52
  • ChatGPT: An automated teacher's guide to learning
    KSL Kazi
    AI Algorithms and ChatGPT for Student Engagement in Online Learning, 1-20 , 2024
    2024
    Citations: 45
  • Machine Learning for Predicting Wind Turbine Output Power in Wind Energy Conversion Systems
    Prashant K Magadum
    Grenze International Journal of Engineering and Technology 10 (1), 2074-2080 , 2024
    2024
    Citations: 45
  • Monitoring fresh fruit and food using Iot and machine learning to improve food safety and quality
    PM Nerkar, SS Shinde, KKS Liyakat, S Desai, SSL Kazi
    Tuijin Jishu/Journal of Propulsion Technology 44 (3), 2927-2931 , 2023
    2023
    Citations: 44
  • Artificial intelligence (AI)-driven IoT (AIIoT)-based agriculture automation
    KSL Kazi
    Advanced computational methods for agri-business sustainability, 72-94 , 2024
    2024
    Citations: 43
  • Modelling and Simulation of Electric Vehicle for Performance Analysis: BEV and HEV Electrical Vehicle Implementation Using Simulink for E-Mobility Ecosystems
    K Kazi
    E-Mobility in Electrical Energy Systems for Sustainability, 295-320 , 2024
    2024
    Citations: 43
  • Arduino Based Weather Monitoring System
    KSSLKKS Liyakat
    Journal of Switching Hub 8 (3), 24-29 , 2023
    2023
    Citations: 43
  • Predictive data analytics framework based on heart healthcare system (HHS) using machine learning
    PM Nerkar, BU Dhaware, KSS Liyakat
    Journal of Advanced Zoology 44 (2) , 2023
    2023
    Citations: 43