An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior

The technological development in the devices and services provided via the Internet and the availability of modern devices and their advanced applications, for most people, have led to an increase in the expansion and a trend towards electronic commerce. The large number and variety of goods offered on e-commerce websites sometimes make the customers feel overwhelmed and sometimes make it difficult to find the right product. These factors increase the amount of competition between global commercial sites, which increases the need to work efficiently to increase financial profits. The recommendation systems aim to improve the e-commerce systems performance by facilitating the customers to find the appropriate products according to their preferences. There are lots of recommendation system algorithms that are implemented for this purpose. However, most of these algorithms suffer from several problems, including: cold start, sparsity of user-item matrix, scalability, and changes in user interest. This paper aims to develop a recommendation system to solve the problems mentioned before and to achieve high realistic prediction results this is done by building the system based on the customers’ behavior and cooperating with the statistical analysis to support decision making, to be employed on an e-commerce site and increasing its performance. The project contribution can be shown by the experimental results using precision, recall, F-function, mean absolute error (MAE), and root mean square error (RMSE) metrics, which are used to evaluate system performance. The experimental results showed that using statistical methods improves the decision-making that is employed to increase the accuracy of recommendation lists suggested to the customers.

Pythagorean fuzzy time series model based on Pythagorean fuzzy c-means and improved Markov weighted in the prediction of the new COVID-19 cases

Time series is an extremely important branch of prediction and the research on it plays an important guiding role in production and life. To get more realistic prediction results, scholars have explored the combination of fuzzy theory and time series. Although some results have been achieved so far, there are still gaps in the combination of n-Pythagorean fuzzy sets and time series. In this paper, a pioneering n-Pythagorean fuzzy time series model (n-PFTS) and its forecasting method (n-IMWPFCM) is proposed to employ a n-Pythagorean fuzzy c-means clustering method (n-PFCM) to overcome the subjectivity of directly assigning membership and non-membership values, thus improving the accuracy of the partition the universe of discoure. A novel improved Markov prediction method is exploited to enhance the prediction accuracy of the model. The proposed prediction method is applied to the yearly University of Alabama enrollments data and the new COVID-19 cases data. The results show that compared with the traditional fuzzy time series forecasting method, the proposed method has better forecasting accuracy. Meanwhile, it has the characteristics of low computational complexity and high interpretability and demonstrates the superiority of this model from a realistic perspective.

Fluid Dynamic Approaches for Prediction of Spray Drift from Ground Pesticide Applications: A Review

Spray drifts have been studied by mathematical models and computer simulations as an essential complement to lab and field tests, among which are fluid dynamic approaches that help to understand the transport of spray droplets in a turbulent atmosphere and their potential impacts to the environment. From earlier fluid mechanical models to highly computational models, scientific advancement has led to a more realistic prediction of spray drift, but the current literature lacks reviews showing the trends and limitations of the existing approaches. This paper is to review the literature on fluid-mechanical-based modelling of spray drift resulting from ground spray applications. Consequently, it provides comprehensive understanding of the transition and development of fluid dynamic approaches and the future directions in this research field.

Laser diode to single-mode graded index fiber coupling via cylindrical microlens on the fiber tip: evaluation of coupling efficiency by ABCD matrix formalism

We use ABCD matrix formalism for estimation of the coupling optics involving laser diode to cylindrical lensed circular core graded index fiber excitation. The investigation has been carried on for some typical step, parabolic, and triangular index fibers as examples of graded index fiber. It is relevant to mention in this connection that we have shown that the coupling optics is excellent along the vertical plane only while the coupling along the horizontal plane produces poor performance. Analytic expressions for coupling efficiencies are prescribed. The investigations have been made for two commonly used wavelengths, namely 1.3 and 1.5 µm. Our formulations require little computation for the necessary execution. The wavelength of 1.3 µm has been found to be more efficient in respect of coupling. Our formalism takes care of the limited aperture allowed by the cylindrical lens and thus provides the realistic prediction of coupling optics. The results will prove extremely useful to the designers and packagers working with cylindrical microlens.

Numerical Simulation and Design Optimization of Baffle Perforations for Better Noise Reduction

An increasing vehicle population always pressurizes to focus on environmental concerns for reduced noise level and harmful emissions. Even though many qualitatively optimized exhaust systems are developed during last two decades, still researchers focus on noise reduction and emission control. Abundant research and development activities are carried out to get a reasonable back pressure drop along with a significant noise reduction in the muffler. The development in computational coding leads to a realistic prediction of flow and acoustic characteristics inside the muffle. Hence the present research work focuses on computational simulation of muffler with single and multi-chambers separated by baffles along with porous pipes. The present work analyses a conventional single baffle muffler configuration and compares the flow behaviour with multi chambers separated by multiple baffle configurations. The shape of the baffle holes is also optimised to get better acoustic performance based on the transmission losses. The flow behaviour and its details are studied inside the muffler to identify the possibility of design improvements. Among the muffler configurations with single and multiple chambers, the overall pressure losses are calculated and compared to get an optimized design with reasonable sound attenuation.

Oblique geographic coordinates as covariates for digital soil mapping

Decision tree algorithms, such as random forest, have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit spatial trends into these algorithms has proven problematic. Using x and y coordinates as covariates gives orthogonal artifacts in the maps, and alternative methods using distances as covariates can be inflexible and difficult to interpret. We propose instead the use of coordinates along several axes tilted at oblique angles to provide an easily interpretable method for obtaining a realistic prediction surface. We test the method on four spatial datasets and compare it to similar methods. The results show that the method provides accuracies better than or on par with the most reliable alternative methods, namely kriging and distance-based covariates. Furthermore, the proposed method is highly flexible, scalable and easily interpretable. This makes it a promising tool for mapping soil properties with complex spatial variation.

General Model for COVID-19 Spreading With Consideration of Intercity Migration, Insufficient Testing, and Active Intervention: Modeling Study of Pandemic Progression in Japan and the United States

Background The coronavirus disease (COVID-19) began to spread in mid-December 2019 from Wuhan, China, to most provinces in China and over 200 other countries through an active travel network. Limited by the ability of the country or city to perform tests, the officially reported number of confirmed cases is expected to be much smaller than the true number of infected cases. Objective This study aims to develop a new susceptible-exposed-infected-confirmed-removed (SEICR) model for predicting the spreading progression of COVID-19 with consideration of intercity travel and the difference between the number of confirmed cases and actual infected cases, and to apply the model to provide a realistic prediction for the United States and Japan under different scenarios of active intervention. Methods The model introduces a new state variable corresponding to the actual number of infected cases, integrates intercity travel data to track the movement of exposed and infected individuals among cities, and allows different levels of active intervention to be considered so that a realistic prediction of the number of infected individuals can be performed. Moreover, the model generates future progression profiles for different levels of intervention by setting the parameters relative to the values found from the data fitting. Results By fitting the model with the data of the COVID-19 infection cases and the intercity travel data for Japan (January 15 to March 20, 2020) and the United States (February 20 to March 20, 2020), model parameters were found and then used to predict the pandemic progression in 47 regions of Japan and 50 states (plus a federal district) in the United States. The model revealed that, as of March 19, 2020, the number of infected individuals in Japan and the United States could be 20-fold and 5-fold as many as the number of confirmed cases, respectively. The results showed that, without tightening the implementation of active intervention, Japan and the United States will see about 6.55% and 18.2% of the population eventually infected, respectively, and with a drastic 10-fold elevated active intervention, the number of people eventually infected can be reduced by up to 95% in Japan and 70% in the United States. Conclusions The new SEICR model has revealed the effectiveness of active intervention for controlling the spread of COVID-19. Stepping up active intervention would be more effective for Japan, and raising the level of public vigilance in maintaining personal hygiene and social distancing is comparatively more important for the United States.

General Model for COVID-19 Spreading with Consideration of Intercity Migration, Insufficient Testing and Active Intervention: Application to Study of Pandemic Progression in Japan and USA

A new Susceptible-Exposed-Infected-Confirmed-Removed (SEICR) model with consideration of intercity travel and active intervention is proposed for predicting the spreading progression of the 2019 New Coronavirus Disease (COVID-19). The model takes into account the known or reported number of infected cases being fewer than the actual number of infected individuals due to insufficient testing. The model integrates intercity travel data to track the movement of exposed and infected individuals among cities, and allows different levels of active intervention to be considered so that realistic prediction of the number of infected individuals can be performed. The data of the COVID-19 infection cases and the intercity travel data for Japan (January 15 to March 20, 2020) and the USA (February 20 to March 20, 2020) are used to illustrate the prediction of the pandemic progression in 47 regions of Japan and 50 states (plus a federal district) in the USA. By fitting the model with the data, we reveal that, as of March 19, 2020, the number of infected individuals in Japan and the USA could be twenty-fold and five-fold as many as the number of confirmed cases, respectively. Moreover, the model generates future progression profiles for different levels of intervention by setting the parameters relative to the values found from the data fitting. Results show that without tightening the implementation of active intervention, Japan and the USA will see about 6.55% and 18.2% of the population eventually infected, and with drastic ten-fold elevated active intervention, the number of people eventually infected can be reduced by up to 95% in Japan and 70% in the USA. Finally, an assessment of the relative effectiveness of active intervention and personal protective measures is discussed. With a highly vigilant public maintaining personal hygiene and exercising strict protective measures, the percentage of population infected can be further reduced to 0.23% in Japan and 2.7% in the USA.