A crop needs regular watering throughout its life to grow well. Irrigation improves food growth. Machines irrigate plants. The dry Sahel, which gets a lot of rain during the summer season but is dry in winter, needs irrigation. When it doesn't rain enough, crops need watering. By constantly monitoring soil moisture, humidity, temperature, and pH, precision agriculture reduces water use and increases crop output. Precision gardening uses less water. In many wealthy nations, efficient farming requires the internet of things (IoT). Particle swarm optimization (PSO) and XGBoost are used in this IoT-based intelligent watering system. Humidity and moisture sensors gather soil data at grass roots. Sensors constantly gather this data. These data are useless for smart watering. PSOselects smart watering data. This reduces central cloud info storage. Then, machine learning methods are trained using soil humidity, moisture, crop, and weather data. These programs can calculate a crop's water requirements. IoT devices control irrigation system water flow and results in saving fresh water. XGBoost algorithm is saving water from 23% to 27% for different crops.

Design and Performance Analysis of Fuzzy Based Hybrid Controller for Grid Connected Solar-Battery Unified Power Quality Conditioner

Online product reviews provide data about the users perspective on the features that were experienced by them. Product features and corresponding opinions form a major part in analyzing the online product reviews. Extracting features from a huge number of reviews is categorized into three main categories such as utilizing language rules, sequence labeling and the topic modeling. Latent Dirichlet Allocation (LDA) is one such topic model which clusters the document words into unsupervised learned topics using Dirichlet priors. The words so clustered are the features and opinion words in the product reviews domain. These clusters contain words which are non features of the product. To identify appropriate product features from these clusters a hierarchical, domain independent Feature Ontology Tree (FOT) is applied to LDA clusters. This improves the accuracy of the features using extracted LDA topic clusters.

The effective item recommendations in the online business websites are largely based on the efficient study of product reviews. This paper describes the data science approach to predict product recommendations that are based on ontological sentiments calculated from consumer reviews. The recommendation strategy is based on the action taken on the discovered insight of deviated product recommendations obtained from the diagnosed machine learning model. It is revealed from the comparative results of Mean Absolute Error that there is vividness in the product recommendations in predicting the customer expected order of products when ontology support is used. This drives the customer purchase decisions on the product in a better manner.