Abstract. The World Wide Web has shortened the distances between people but it is still hard to find a general user interface for all the users. Because people living in different areas of the world have different cultures, religions, and traditions. Designing a user interface according to the culture of the user is important. Different minds have different views about cultural effects in user interface. This paper presents a detailed review on the recent work and research in cultural effects on metaphor design. This paper also explores the problems and issues regarding localizing metaphors in different cultures.

In order to forecast the need for bike-sharing services, this paper suggests a rule-based regression model. Commuters and tourists alike are taking advantage of public bike sharing programs because of the convenience and low carbon footprint they provide. Used information from the UCI Machine Learning Repository. Repeated cross-validation was used to fine-tune the hyper-parameters of five statistical models. Conditional Inference Tree, K-Nearest Neighbor Analysis, Regularized Random Forest, Classification and Regression Trees, and CUBIST. The predictive accuracy of the regression models was measured by calculating the Root Mean Squared Error, R-Squared, Mean Absolute Error, and Coefficient. For both the Seoul Bike and Capital Bikeshare programs, the rule-based model CUBIST was able to account for 95 and 89% of the Variance (R2), respectively. All models built from the two datasets using WEKA v3.8.6, and are used a variable significance analysis to establish which variables were most crucial. The most important factors in determining the hourly demand for bike rentals are the weather and the time of day.

Abstract— Sales predictions or forecasting can help in analyzing the current and future sales trends of a big mart company. Based on the sales prediction or forecast, a retailer company can plan its production, marketing and promotional activities. Using several machine learning techniques, the obtained data may then be utilized to predict possible sales for retailers. This paper investigates that which machine learning regression algorithm best predicts big marts sales and which technique has the highest correlation coefficient value and the lowest values of mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), and root relative squared error (RRSE). A comparative analysis of various machine learning regression algorithms such as SMO regression, simple linear regression, linear regression, additive regression, multi-layer perceptron, random forest, and M5P will be provided in this paper. After the experiments are completed, a comparison of various cross validations and splitting ratios for training and testing data will be given.

The geographic information system (GIS) is rapidly becoming the part of current technology trends. GIS can be used to identify the factors that become the reason for an individual to adopt a field or subject. We used GIS as a major tool with the other technologies to identify the key factors. This research has analyzed that mostly people used to migrate to other cities due to unavailability of resources in their own region. Collection of data was done with the help of Survey 123 through which we were able to collect location coordinates of participants. After that, Pilot study approach used to conduct this research. Results show` that mostly user preferred to move to other cities due to unavailability of programs in local institutes. The overall idea can be used to improvement of local institutes and this research can also be used for proper and efficient allocation of facilities and resources in a region, which in turn can save money and time.