An Ensemble Learning-Driven AI-IoT Model for Sustainable Water Quality Prediction Thirupathi Regula, S Gouri Kiran Kumar, Madhusudhana Rao Battina, Shaima Al-Amri, K.N. Brahmaji Rao, Sathwik Manikumar Raj Regula Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications Icimia 2025, 2025 Drought and water pollution are the key problems in arid regions where intelligent tools play an important role in sustainable water management. This paper introduces DeepSense-Water, a novel AI–IoT integrated framework for real-time water quality prediction and monitoring. By taking advantage of the cross-impact between the Internet of Things (IoT) sensor network and the ensemble learning algorithm, the developed model obtains these key water quality parameters (pH, turbidity, temperature, and dissolved oxygen) in harsh and resource-limited environments. The processed data is then input into a noise-robust ensemble learning architecture of Random Forest (RF) and Adaptive LSTM networks, which can also make accurate predictions with noise. Real-time analytics over an edge-AI disk cache with low-energy consumption is achieved with lightweight deployment. The system's reliability is confirmed by deployment in a semi-arid water quality setting and with over 95% predictive accuracy for water quality parameters of interest and prescriptive insights for sustainable water management. DeepSense-Water offers a scalable and cost-effective model suitable for arid regions, which supports early environmental decision-making and can contribute to meeting long-term water sustainability objectives.
Artificial Intelligence and Internet of Things-Enabled Framework for Sustainable, Real-Time Water Quality Management and Predictive Resource Optimization in Arid Regions Thirupathi Regula, S Gouri Kiran Kumar, Madhusudhana Rao Battina, Shaima Al-Amri, K.N. Brahmaji Rao, Sathwik Manikumar Raj Regula Proceedings of the 2025 IEEE International Conference on Computation Big Data and Engineering Iccbe 2025, 2025 Water management has become a major challenge in arid regions such as Oman, where the efficient use is critical. Utilizing artificial intelligence (AI) and the Internet of Things (IoT), we developed an integrated system for a clean, efficient, sustainable water supply. The system integrates real-time water quality monitoring, intelligent flow control, and automated pipeline monitoring. Using data and analytics, the system ensures prompt decision-making by providing stakeholders with information on water quality, usage patterns, and system maintenance. In addition to meeting the increasing demands of residential and industrial water quality, the developed system enhances operational efficiency by providing integrated automated treatment solutions for tanks and pipelines. Aside from reducing maintenance costs, incorporating AI models for predictive analytics makes it easier to predict system failures and peak demand, enabling timely intervention. The system aligns with Oman’s sustainability objectives, reducing water scarcity through technological innovation and resource-friendly practices. The system highlights the potential of AI and IoT to transform water management, opening the door for application in other dry areas around the world.
A heuristic ranking of different characteristic mining based mathematical formulae retrieval models CS&SE Department, AUCE, Andhra University, Visakhapatnam, India., K.N.Brahmaji Rao*, Dr.G. Srinivas, CSE Department, GITAM deemed to be University, Visakhapatnam, India., Dr.Prasad Reddy P.V.G. D., Senior Professor, CS&SE Department, AUCE, Vice-Chancellor, Andhra University, Visakhapatnam, India. International Journal of Engineering and Advanced Technology, 2019 The significant difficulty in the present circumstances is how to classify the math related keywords from a given text file and group them in one math file. Through this article a heuristic ranking model was developed and was evaluated on different mathematical formulae retrieval algorithms based on Characteristic mining. Our proposed heuristic ranking model was developed using the performance metrics of exiting retrieval algorithms such as NMF clustering, Levenstein distance, Sequence matcher, Fuzzy-wuzzy and Tensorflow. Performance metrics such as sensitivity, specificity, efficiency, accuracy and retrieval time were used in developing our heuristic ranking model.
An Experimental Study with Tensor Flow for Characteristic Mining of Mathematical Formulae from a Document K. N. Brahmaji Rao, G. Srinivas, P. V. G. D. Prasad Reddy Eai Endorsed Transactions on Scalable Information Systems, 2019 Through this article a deep learning technique is proposed for the extraction and classification of mathematical keywords from textual documents. Extraction of math keywords from textual data is predominant problem as textual documents contain a culmination of mathematical symbols and literals from
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