Advance Urban Flood Control System Using Fuzzy Logic and Internet of Things (IoT) for Smart City

City flood control is a significant concern everywhere due to the constantly changing environment. The modern world needs smart cities with smart infrastructure to manage or control floodwaters. The research objective of this study is to design real time urban flood control methodology, develop the working model and testing the model with result analysis in controlled environment. This research paper proposes a smart water control model based on fuzzy inference system. The research is advancement in the Water Sensitive Storm Water Management System by creating a prototype model and then evaluating it in real-world scenarios using input parameters as rainfall intensity, water flow rate, and water level. The method relies on water catchment flooding data that was collected in real-time using sensors and an autonomous smart controller. The system considers the real-time sensor data from all catchments to make collective decision, which also optimize the use of actuators by conserving the power used by the actuators. In terms of early floodwater control, the recommended approach optimizes the use of actuators with utilizing the existing drainage system. The average water reduction rate at the medium level is 34.8%. At high levels, the average water reduction rate is 61.43%, and at extremely high levels, it 73.63%. A significant reduction of water level achieved in the most inundated area by 73.9 % in high and extreme input parameter value

Effective Feature Selection Methods for User Sentiment Analysis using Machine Learning

Text classification is the method of allocating a particular piece of text to one or more of a number of predetermined categories or labels. This is done by training a machine learning model on a labeled dataset, where the texts and their corresponding labels are provided. The model then learns to predict the labels of new, unseen texts. Feature selection is a significant step in text classification as it helps to identify the most relevant features or words in the text that are useful for predicting the label. This can include things like specific keywords or phrases, or even the frequency or placement of certain words in the text. The performance of the model can be improved by focusing on the features that are most important to the information that is most likely to be useful for classification. Additionally, feature selection can also help to reduce the dimensionality of the dataset, making the model more efficient and easier to interpret. A method for extracting aspect terms from product reviews is presented in the research paper. This method makes use of the Gini index, information gain, and feature selection in conjunction with the Machine learning classifiers. In the proposed method, which is referred to as wRMR, the Gini index and information gain are utilized for feature selection. Following that, machine learning classifiers are utilized in order to extract aspect terms from product reviews. A set of customer testimonials is used to assess how well the projected method works, and the findings indicate that in terms of the extraction of aspect terms, the method that has been proposed is superior to the method that has been traditionally used. In addition, the recommended approach is contrasted with methods that are currently thought of as being state-of-the-art, and the comparison reveals that the proposed method achieves superior performance compared to the other methods. In general, the method that was presented provides a promising solution for the extraction of aspect terms, and it can also be utilized for other natural language processing tasks