The most prominent source of drinking water is groundwater, followed by lakes and reservoirs. Hydrological parameters like temperature, dissolved oxygen, pH, conductivity, ORP, and turbidity often change due to waste dumping into natural drinking water sources, particularly in densely populated areas. As a result, the water quality must be tested before public consumption to ensure healthy living in society. This research collected water samples from 129 wells in the Kanchipuram district in Tamil Nadu, India. An efficient integrated machine-learning-based prediction model has been proposed and modeled to determine the groundwater quality index (GQI). Several machine learning models were used to predict the water’s quality, including the naïve Bayes model, the KNN classifier, and the XGBoost classifier. Water quality predictions in 2024 were made using a combination of classification algorithms and models based on long short-term memory (LSTM) neural networks. The projected water quality characteristics were analyzed using geographical information system (GIS) technology to better understand and visualize the results. The XGBoost classifier model outperforms prior findings in the literature, with an accuracy of roughly 94.6%. The classification and prediction model was validated using collected and tested current data samples from a selected well. The findings were accurate within the 5% error range, promoting sustainability.

Classification algorithms are very widely used algorithms for the study of various categories of data located in multiple databases that have real-world implementations. The main purpose of this research work is to identify the efficiency of classification algorithms in the study of breast cancer analysis. Mortality rate of women increases due to frequent cases of breast cancer. The conventional method of diagnosing breast cancer is time consuming and hence research works are being carried out in multiple dimensions to address this issue. In this research work, Google colab, an excellent environment for Python coders, is used as a tool to implement machine learning algorithms for predicting the type of cancer. The performance of machine learning algorithms is analyzed based on the accuracy obtained from various classification models such as logistic regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree and Random forest. Experiments show that these classifiers work well for the classification of breast cancers with accuracy>90% and the logistic regression stood top with an accuracy of 98.5%. Also implementation using Google colab made the task very easier without spending hours of installation of environment and supporting libraries which we used to do earlier.

Farmers and terrace gardeners use PlantMed, a sophisticated mobile application, to track ailments and monitor plant health through the use of machine learning and computer vision. The API focuses on various plants, and users can upload pictures of their plants to diagnose diseases quickly using K-means and CNN network (KNN) algorithms. The application utilizes an open API (Application Programming Interface) for well-being and a linear regression model to forecast production figures for various climatic conditions, including rainfall, temperature, humidity, and wind, while also integrating current weather information. The application offers suggestions for managing agro-health care, such as using pesticides, linking GP (Gram Panchayat) outage measures with government schemes, and registering the soil condition in their vicinity using the Google Maps API. Eva, the integrated multilingual Chabot, encourages users across languages by teaching them how to grow plants, prevent them from diseases, and use the app. Additionally, the application provides educational content in the form of articles, videos, and news about current agricultural trends. Some upcoming enhancements include the incorporation of market demand, the implementation of growing crops where there is a demand for them, and the ability to search nearby locations for agricultural equipment and supplies, thereby transforming plant medicine into a remedy for enhancing crop productivity.