Sana Maqbool

The critical issue of water deficiency is worsened by widespread wastage in both domestic and industrial sectors globally. A survey conducted by some international organizations indicates that only Pakistan is wasting ten trillion gallons of water. To address this challenge, our study introduces a comprehensive water usage optimization system by using the machine learning opportunities to help improve the classification and prediction of water quality (WQ). Also, using the sensor meters to collect real-time water quality (WQ) parameters in order to facilitate categorization of water sample. The real-time data collected from the sensor meters would be transferred in the storage for further analysis and usage. Several machine learning algorithms are used for the classification. The classifiers include Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and K-nearest neighbors (KNN). The dataset used for the categorization of water consists of 3277 samples. The water quality parameters used for the categorization were water pH, Turbidity, and Total dissolved solids (TDS) values. The stacked classifiers take the water parameters and provide an appropriate approach for forecasting water quality as accurately as possible. Beyond classification, our system provides targeted recommendations for recycling and reusing water based on its categorized quality. These recommendations are tailored to support sustainable water practices at both household and industrial levels. By combining real-time sensing with intelligent prediction and tailored guidance, the proposed system not only enhances water management but also contributes to long-term conservation efforts. This study serves as a step forward in the integration of smart technologies for sustainable resource utilization.

An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient’s health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient’s condition accurately and sends it to the doctor’s portal.