Enhancing precision in agriculture: A smart predictive model for optimal sensor selection through IoT integration Praveen Sankarasubramanian Smart Agricultural Technology, 2025 The rapid advancement in communication technology has sparked a transformative wave across various domains, significantly enhancing comfort and convenience in daily life. Addressing the escalating global demand for food, coupled with the need to alleviate the efforts of farmers, technology, particularly the Internet of Things (IoT), has emerged as a pivotal force. Precisely predicting variations in climatestrictures, ground conditions, and dirt attributes has emerged as a formidable challenge in the realm of agricultural IoT. In this paper, we introduce a smart optimal prediction model for sensors based on IoT-enabled precision agriculture. Initially, we enhance the THAM index (temperature, humidity, air- and water-quality measurement) by using the modified Wild Geese (MWG) algorithm to predict environmental conditions accurately. The deployment of IoT sensor nodes using quantum deep reinforcement learning (QDRL) to determine the idealamount of devices required for effective coverage of the target agricultural field to improving communication. Furthermore, we compute the production yield rate, consider various attributes such as fertilizer regulatory measures, temperature quotient, and agronomy by using the improved prairie dog optimization (IPDO) algorithm. Finally, we assess the performance of MWG-QDRL-IPDO model using test samples collected from the Meteorology Bureau through the related sensor middleware. Our findings reveal a checking efficacy of 96.35 %, even with a reduced amount of devices covering a hugezone. Similarly, the accuracy of IoT sensor node deployment reaches 91.47 %, contributive to reduce the irrelevant data generation and processing time.
Protection of Hazardous Places in Industries using Machine Learning Praveen Sankarasubramanian 2023 International Conference on Emerging Smart Computing and Informatics Esci 2023, 2023 Extreme precautions must be observed to handle toxic wastes, radioactive substances, chemical raw materials, chemical wastes, and bio-products in different industries. Any malfunction in a dangerous traffic network can lead to serious accidents, deaths and / or serious damage. Direct monitoring and analysis, and preventive measures to prevent the spread of failures, can significantly reduce the recurrence of adverse effects. Current research suggests that detailed publicity and information on the latest developments in pipeline monitoring and research may help modernize the oil industry in the future. We also propose a framework to detect timely leakage in pipelines, especially in oil and gas sector.
Artificial intelligence-based detection system for hazardous liquid metal fire Praveen Sankarasubramanian, E. Ganesh Proceedings of the 2021 8th International Conference on Computing for Sustainable Global Development Indiacom 2021, 2021 Liquid metals are commonly used in chemical industries and nuclear reactors. Since liquid metals may be hazardous, they should be handled very carefully. Careless handling might cause an adverse effect and even disasters. Corrosion and pressure can deteriorate the structure that handles the liquid metals. Leakage of liquid metals can result in ecological disasters and can lead to a humanitarian crisis. Early warning systems, detection of the accident, and prompt steps taken after the incident are the three important phases of monitoring. Continuous monitoring and timely detection of risk reduce the impact caused by the leakage of liquid metal. At present, industries have sensors-based detection. This paper proposes an enhanced version of the existing system. Here, continuous monitoring uses sensors, the Internet of things (IoT), and an artificial intelligence-based system. In this paper, the conventional system is integrated with AI to identify indoor and open-air fire situations. This paper discusses different data collected and investigated data from the videos, sensors, other monitoring systems. And the false-positive results are reduced by using the proposed methodology.
Realtime Pipeline Fire Smoke Detection Using a Lightweight CNN Model Vaishnav Kumar Suresh Kumar, Praveen Sankarasubramanian Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies Icmlant 2021, 2021 Fire disasters due to pipeline leaks result in loss to life and property. Therefore, an expeditious model to detect smoke and fire is much needed. Even though there have been several types of research done on fire and smoke detection, most of these focuses on very generic datasets that boast good performance; the case of monitoring pipelines remotely necessitates a focused approach for enhanced performance for early detection. This paper attempts to develop a model specifically for this purpose and can be deployed more confidently in a pipeline environment. We have also customized the existing dataset by adding images of pipelines to bias the dataset and give a more confidence rate while predicting. Our proposed neural network architecture achieves higher accuracy, precision, recall, and F-measure. Also, the lightweight makes it easily deployable on embedded platforms as well. The performance of our model is evaluated against FireNet on our biased dataset, sounds very promising.
IoT based prediction for industrial ecosystem International Journal of Engineering and Advanced Technology, 2019
RECENT SCHOLAR PUBLICATIONS
An efficient crack detection and leakage monitoring in liquid metal pipelines using a novel BRetN and TCK-LSTM techniques P Sankarasubramanian Multimedia Tools and Applications 84 (23), 26067-26095 , 2025 2025.0 Citations: 2
Enhancing precision in agriculture: A smart predictive model for optimal sensor selection through IoT integration P Sankarasubramanian Smart Agricultural Technology 10, 100749 , 2025 2025.0 Citations: 14
ARTIFICIAL INTELLIGENCE AND NATURAL ALGORITHM P Sankarasubramanian 2024.0
An optimal segmentation framework for early crack and fire detection using Potoo swarm optimization algorithm P Sankarasubramanian, E Ganesh Journal of Harbin Engineering University 44 (7), 217-233 , 2023 2023.0 Citations: 1
A Study Guide to Uncover Industrial Hazards P Sankarasubramanian, EN Ganesh Research and Developments in Engineering Research Vol. 2 2, 71-82 , 2023 2023.0
Protection of hazardous places in industries using machine learning P Sankarasubramanian 2023 International Conference on Emerging Smart Computing and Informatics … , 2023 2023.0 Citations: 5
IoT based Optimal Liquid Metal Pipeline Damage Detection Using Hybrid Soft Computing Techniques P Sankarasubramanian, EN Ganesh NeuroQuantology 20 (5), 773 , 2022 2022.0 Citations: 2
CNN based intelligent framework to predict and detect fire P Sankarasubramanian, EN Ganesh NeuroQuantology 20 (5), 755-772 , 2022 2022.0 Citations: 4
Adaptive fire detection using cnn and image processing P Sankarasubramanian, EN Ganesh International Journal of Mechanical Engineering 7 (4) , 2022 2022.0 Citations: 2
Realtime pipeline fire & smoke detection using a lightweight CNN model VKS Kumar, P Sankarasubramanian 2021 IEEE International Conference on Machine Learning and Applied Network … , 2021 2021.0 Citations: 10
Effective Handling of Fluids and Liquid Metals using IoT P Sankarasubramanian International Journal of Institution of Safety Engineers (India) 3 (1) , 2020 2020.0 Citations: 5
Prevent, Detect, Respond, Mitigate Liquid Sodium Leakage, and Fire Accidents using AI P Sankarasubramanian, EN Ganesh International Journal of Engineering and Advanced Technology 9 (5), 7-11 , 2020 2020.0 Citations: 3
Fire investigation and assessment using CNN and image processing P Sankarasubramanian, EN Ganesh Journal of Critical Reviews 7 (19), 9825-9830 , 2020 2020.0 Citations: 2
Industrial accident report analysis using natural language processing P Sankarasubramanian International Journal of Scientific & Technology Research , 2020 2020.0 Citations: 7
Real Tıme AI, Computer Vısıon Based Framework To Detect And Prevent Lıquıd Metal Fıre Hazards P Sankarasubramanian, EN Ganesh International Journal of advanced science and technologies 28 (8), 3796-3085 , 2020 2020.0 Citations: 5
Ganesh. EN," Algorithm to Identify the Connection between Sentences," P Sankarasubramanian International Journal Of Information And Computing Science 6 (7), 158-162 , 2019 2019.0 Citations: 3
Dr. EN Ganesh,“IoT Based Prediction for Industrial Ecosystem” P Sankarasubramanian International Journal of Engineering and Advanced Technology (IJEAT) ISSN … , 2019 2019.0 Citations: 130
Data Security and Replication on Cloud P Sankarasubramanian 2017.0 Citations: 1
ARTIFICIAL INTELLIGENCE AND NATURAL ALGORITHM P Sankarasubramanian
Fire Detection and Prediction Framework Using Vision Based System and Convolutional Neural Network P Sankarasubramanian, EN Ganesh Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Dr. EN Ganesh,“IoT Based Prediction for Industrial Ecosystem” P Sankarasubramanian International Journal of Engineering and Advanced Technology (IJEAT) ISSN … , 2019 2019.0 Citations: 130
Artificial Intelligence-Based Detection System for Hazardous Liquid Metal Fire P Sankarasubramanian, EN Ganesh Proceedings of the 15th INDIACom; INDIACom-2021; IEEE Conference ID: 51348 … , 0 Citations: 130
Enhancing precision in agriculture: A smart predictive model for optimal sensor selection through IoT integration P Sankarasubramanian Smart Agricultural Technology 10, 100749 , 2025 2025.0 Citations: 14
Realtime pipeline fire & smoke detection using a lightweight CNN model VKS Kumar, P Sankarasubramanian 2021 IEEE International Conference on Machine Learning and Applied Network … , 2021 2021.0 Citations: 10
Industrial accident report analysis using natural language processing P Sankarasubramanian International Journal of Scientific & Technology Research , 2020 2020.0 Citations: 7
Protection of hazardous places in industries using machine learning P Sankarasubramanian 2023 International Conference on Emerging Smart Computing and Informatics … , 2023 2023.0 Citations: 5
Effective Handling of Fluids and Liquid Metals using IoT P Sankarasubramanian International Journal of Institution of Safety Engineers (India) 3 (1) , 2020 2020.0 Citations: 5
Real Tıme AI, Computer Vısıon Based Framework To Detect And Prevent Lıquıd Metal Fıre Hazards P Sankarasubramanian, EN Ganesh International Journal of advanced science and technologies 28 (8), 3796-3085 , 2020 2020.0 Citations: 5
CNN based intelligent framework to predict and detect fire P Sankarasubramanian, EN Ganesh NeuroQuantology 20 (5), 755-772 , 2022 2022.0 Citations: 4
Prevent, Detect, Respond, Mitigate Liquid Sodium Leakage, and Fire Accidents using AI P Sankarasubramanian, EN Ganesh International Journal of Engineering and Advanced Technology 9 (5), 7-11 , 2020 2020.0 Citations: 3
Ganesh. EN," Algorithm to Identify the Connection between Sentences," P Sankarasubramanian International Journal Of Information And Computing Science 6 (7), 158-162 , 2019 2019.0 Citations: 3
An efficient crack detection and leakage monitoring in liquid metal pipelines using a novel BRetN and TCK-LSTM techniques P Sankarasubramanian Multimedia Tools and Applications 84 (23), 26067-26095 , 2025 2025.0 Citations: 2
IoT based Optimal Liquid Metal Pipeline Damage Detection Using Hybrid Soft Computing Techniques P Sankarasubramanian, EN Ganesh NeuroQuantology 20 (5), 773 , 2022 2022.0 Citations: 2
Adaptive fire detection using cnn and image processing P Sankarasubramanian, EN Ganesh International Journal of Mechanical Engineering 7 (4) , 2022 2022.0 Citations: 2
Fire investigation and assessment using CNN and image processing P Sankarasubramanian, EN Ganesh Journal of Critical Reviews 7 (19), 9825-9830 , 2020 2020.0 Citations: 2
An optimal segmentation framework for early crack and fire detection using Potoo swarm optimization algorithm P Sankarasubramanian, E Ganesh Journal of Harbin Engineering University 44 (7), 217-233 , 2023 2023.0 Citations: 1
Data Security and Replication on Cloud P Sankarasubramanian 2017.0 Citations: 1
Fire Detection and Prediction Framework Using Vision Based System and Convolutional Neural Network P Sankarasubramanian, EN Ganesh Citations: 1
ARTIFICIAL INTELLIGENCE AND NATURAL ALGORITHM P Sankarasubramanian 2024.0
A Study Guide to Uncover Industrial Hazards P Sankarasubramanian, EN Ganesh Research and Developments in Engineering Research Vol. 2 2, 71-82 , 2023 2023.0
