E-commerce plays a vital role in today’s economy, and website usability is crucial for attracting customers and achieving higher conversion rates. Process mining offers significant advantages in analyzing User Behavior, providing insights into typical User paths and identifying deviations. This study aims to enhance the usability of e-commerce websites by leveraging User Behavioral data and process mining tools. User Behavioral data from a cosmetics e-commerce website were collected and preprocessed. Various metrics were established to evaluate usability, revealing low task completion rates and high bounce rates. Bottlenecks were identified where Users faced delays, indicating areas for improvement. Recommendations included redirecting Users who remove items from their cart to the homepage, suggesting similar products, and addressing payment page issues. These suggestions aim to improve User experience and increase conversion rates. Despite limitations, such as the lack of detailed data, this study demonstrates the potential of process mining tools in enhancing website usability.

In today's age, the Internet has become the most important source of information and its supply for many goods and services due to its ease of use, wide range and high speed. The unique characteristics of the Internet and its complete superiority over other markets have led many organizations to expand their services and products in the Internet markets. Therefore, the Internet environment has become a very competitive environment for organizations. In the process of buying and checking User and consumer Behavior, goods or services will be exchanged, and these goods or services can be information, physical or virtual products, and even emotions. Many kinds of research have been conducted in the analysis of Users' Behavior, but these researches were not highly accurate, therefore, in this research, an attempt was made to provide a solution for recommending items based on Users' past Behaviors to improve the accuracy of past methods. This proposed method can eliminate the noise in the data by using fuzzy logic and hierarchical analysis which is used for the phase of selecting the effective features and also works on more effective data to increase the accuracy. The tree proposed in this research has the lowest possible height and this causes the computational overhead to be greatly reduced. The proposed method was compared with several competing methods and the results indicate the superiority of the proposed method over the compared methods.

One of the challenges of any network is User traffic analysis and prediction. Mobile networks have achieved notable growth in recent years. Traffic information on these networks play a crucial role in service quality control, User access control, and optimization. There are various methods for traffic prediction, including harmonic analysis and mathematical transformations, time series methods, and machine learning. Due to the increasing volume of User data on mobile networks, machine learning methods have gained popularity in recent decades. In this paper, we introduce a probabilistic Behavioral model and propose a new method that focuses on human Behavior by categorizing Users through clustering and utilizing their similarities. Assuming a history of past User data is available, Users with similar Behavior are grouped into categories using clustering methods, and each category is assigned a label. The average of each cluster represents the traffic in that category. To predict the traffic of new Users, our proposed method utilizes classification functions to determine the most appropriate category. Subsequently, weighted averages are used to calculate the overall network traffic. We compare our proposed method with time series and Fourier transform methods through three different scenarios. The results indicate that our method exhibits significant superiority over the other methods.