Examining and protecting air quality has become one of the most essential activities for the government in many industrial and urban areas today. The meteorological and traffic factors, burning of fossil fuels, and industrial parameters play significant roles in air pollution.With this increasing air pollution,Weare in need of implementing models which will record information about concentrations of air pollutants(so2,no2,etc).The deposition of this harmful gases in the air is affecting the quality of people's lives, especially in urban areas. Lately, many researchers began to use Big Data Analytics approach as there are environmental sensing networks and sensor data available.In this paper, machine learning techniques are used to predict the concentration of so2 in the environment. Sulphur dioxide irritates the skin and mucous membranes of the eyes, nose, throat, and lungs.Models in time series are employed to predict the so2 readings in nearing years or months.
Electric energy consumption is the actual energy demand made on existing electricity supply. However, the mismanagement of its utilisation can lead to a fall in the supply of electricity. It is therefore imperative that everybody should be concerned about the efficient use of energy in order to reduce consumption [1]. The purposes of this research are to find a model to forecast the electricity consumption in a household and to find the most suitable forecasting period whether it should be in daily, weekly, monthly, or quarterly. The time series data in our study is the individual household electric power consumption [4].To explore and understand the dataset I used line plots for series data and histograms for the data distribution. The data analysis has been performed with the ARIMA (Autoregressive Integrated Moving Average) model.
The presented article mainly circumspect the idea for the use and implementation of graph database in the most social media of today. Currently many companies are using neo4j graph database for their workouts for data management. We shall stick with social media for this particular paper. We have used neo4j graph database for maintaining data of LinkedIn user and pages in a very systematic manner. Facebook and Twitter do currently use graph databases so we thought of implementing the same for LinkedIn.
Restaurant Rating has become the most commonly used parameter for judging a restaurant for any individual. A lot of research has been done on different restaurants and the quality of food it serves. Rating of a restaurant depends on factors like reviews, area situated, average cost for two people, votes, cuisines and the type of restaurant. The main goal of this is to get insights on restaurants which people like visit and to identify the rating of the restaurant. With this article we study different predictive models like Support Vector Machine (SVM),Random forest and Linear Regression, XGBoost, Decision Tree and have achieved a score of 83% with ADA Boost.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.