A Neuro-fuzzy approach for user behaviour classification and prediction

Abstract

Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users' interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users' temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user's 360-degree feedback is also utilized. Various rules have been implemented to address the company's policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

“…Thus, the OSNs provide an unprecedented amount of data, reflecting the behavior of the users [7] . However, analyzing the behavior of the users in an OSN is a complex task [8] , and thus, some models to detect anomalies in the user behavior have been studied [9] . Some studies have focused particularly on the domain of user behavior analysis on social media for instance in the contexts of political events [10] , [11] , a diverse range of recommendation systems [12] – [14] , public health [2] , [15] , communication network recommendations [16] , and prediction of urban traffic trends [12] , [17] , among others.…”

A Novel Data Mining Algorithm and Its Applications in Basketball Match Technique and Tactical Analysis

“…Data mining technology can screen useful tournament information in a large amount of data so that fans can easily understand the team's tactics and combinations through edited data, which is more conducive to discussion and analysis. e results of basketball matches are uncertain, and the usual in uences include subjective and objective factors [35]. ese factors, such as the weather at that time, the altitude of the venue, climate, and so on, may also be due to the athlete's character, mood, interpersonal relationship, and so on.…”

Deep Learning for User Behaviour Prediction Using Streaming Analytics

“…They found that LSTM showed highest accuracy. Atta-ur-Rahman et al [11] proposed a neuro-fuzzy approach for supervised learning of user behaviour prediction. They used temporal logs of user's interaction with web site as dataset and found that their approach performed well.…”