Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chakravarthy, V.J.; Thontadari, C.; Basavaraj G.N.; Sowndharya, R.; Aravindan, N.; Sangeetha, V. Title A name given to the resource Neural network-assisted carbon nanotube electrochemical sensors for automated environmental risk assessment Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Microchemical Journal;Volume;218;Issue;;Article No.;115413; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.microc.2025.115413" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.microc.2025.115413</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017332098?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017332098?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chakravarthy V.J., Faculty of Computer Applications, Dr. M. G. R. Educational and Research Institute, Maduravoyal, Chennai, 95, India; Thontadari C., Department of Computer Science, Christ University, Bangalore, India; Basavaraj G.N., Department of Information Science and Engineering, BMS Institute of Technology &amp; Management, Karnataka, Bengaluru, India; Sowndharya R., Department of Computer Science and Engineering, Sona College of Technology, Salem, India; Aravindan N., Department of Fire Engineering, National Fire Service College, Nagpur, India; Sangeetha V., Faculty of Computer Applications, Dr. M. G. R. Educational and Research Institute, Chennai, 95, India Description An account of the resource The current research proposes an intelligent network system that continuously tracks the quality of water flow, with particular attention to pollutants frequently occurring in runoff water from agricultural practices. It deploys high-performance electrochemical sensors based on carbon nanotubes (CNT) combined with a small neural network that functions directly on the built-in microcontroller. It is deployed on floating buoys powered by solar energy, where it can detect some critical contaminants in the rural water bodies, including nitrates, phosphates, atrazine, cadmium, and lead. The sensors work by transmitting their electrical signals through the sensors to the neural network, which provides precise identification of the level of pollutants as one of three risk levels: safe (below detection levels), manageable (within regulatory levels), and hazardous (exceeding regulations). Regarding power performance, results can be delivered over a relatively small-time delay (1.2 milliseconds per reading) and with low memory usage (1.8 MB), making it ideal for remote and low-powered sensors. It is more accurate (93.6 %) than typical machine learning models. Should pollutants exceed the above-prescribed limits, an automated warning will be generated, and the information will be immediately uploaded to a cloud-based dashboard. The dashboard will be closely monitored via remote control, and trend analysis will be conducted. By eliminating the need for manual water sampling, the system offers a scalable and energy-saving method for autonomous environmental testing, particularly in inaccessible locations. In the future, the study will focus on the use of federated learning, a technique that retains data locally to protect privacy, enabling more intelligent and collaborative conclusions across sensor networks. This prepares the ground for more intelligent and secure environmental surveillance systems in the future. 2025 Elsevier B.V. Subject The topic of the resource Carbon nanotube sensors; Electrochemical sensors; Environmental monitoring; Neural networks; Water quality monitoring Publisher An entity responsible for making the resource available Elsevier Inc. Relation A related resource ISSN: 0026265X; CODEN: MICJA Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online