Yiea-Funk Te

Towards a crowdsourcing approach for crime prevention

With the rising level of criminal activities, crime is becoming one of the main problems of modern society. To address this issue, we implement a mobile application for crime prevention. We focus on the usage intention and motivations for content creation and consumption. Our results indicate that people are willing to use the app for acquiring and sharing crime-related information, but not on a daily basis. In addition, participation on the platform was found to be driven by affective and rational motivations, to contribute to the neighborhood safety and in return receive help for maintaining personal safety.

Design of a Small and Medium Enterprise Growth Prediction Model Based on Web Mining

Small and medium enterprises (SMEs) play an important role in the economy of many countries. Still, due to the highly turbulent business environment, SMEs experience more severe challenges in maintaining and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying web mining (WM) to build growth prediction models. WM enables automatic and large-scale collection and analysis of potentially valuable data from various online platforms, thus bearing a great potential for extracting SME growth factors, and enhancing existing SME growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and machine learning (ML) techniques.