In this paper, we present the development of a Web-based expert system, StrategyAdvisor Cloud, to support strategic management decision-making. The system was developed using a multistage methodology that builds upon knowledge graphs, where knowledge acquisition and rule base construction by project members with different roles, capabilities, and skills can be facilitated through customized visual languages. The methodology systematizes knowledge acquisition and knowledge representation for each stage, coupled with algorithms for the transformation of knowledge graphs between successive stages. The developed expert system and the development process are described in detail in the paper and its supplement, to serve as guidance in the development of similar systems in future.

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Please cite the paper as follows:

Irdesel, l., Ertek, G., Demirelli, A., Kailas, L., Lekesiz, A., Shuvo, R.U. (2023). Development of a Web-Based Strategic Management Expert System Using Knowledge Graphs. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 693. Springer, Singapore. https://doi.org/10.1007/978-981-99-3243-6_48

Online food delivery (OFD) has become a popular and profitable e-business category due to the rising demand for online food delivery. People are increasingly ordering food online, especially in urban areas and on college campuses. Using data from online food delivery services, one can analyze and predict the values of key performance indicators (KPIs). In the study presented in this paper, we developed a systematic methodology to analyze and predict such KPIs using various classification and regression algorithms. We found that, for the case study we analyzed, Random Forest (RF) consistently ranked as the best algorithm for regression and classification in predicting most of the KPIs. The methodology we introduce and illustrate in the paper can be adapted and extended to similar problems to reveal potential operational issues and identify the possible root causes of such problems.

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Please Cite as Follows:
M. A. Akasheh, N. Eleyan and G. Ertek, “A Predictive Data Analytics Methodology for Online Food Delivery,” 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), Milan, Italy, 2022, pp. 1-7, doi: 10.1109/SNAMS58071.2022.10062613.

Due to the digitalization, there exists an increased amount of user-generated content on the Internet, where people express their opinions on various topics. Sentiment analysis is the statistical and analytical examination of human emotions and opinions regarding a certain subject. Our study extends the literature by developing a data analytics methodology for the benchmarking of sentiment scoring algorithms in the context of online customer reviews. We demonstrate the applicability of the methodology using Amazon product reviews as the source data. Analyzing text-based content such as Amazon customers’ reviews through text analytics and sentiment analysis can help Amazon and other online retailers to discover valuable actionable insights regarding their products. The contributions of this study are twofolds: to examine the predictive power of machine learning (ML) algorithms with respect to predicting sentiment scores and to analyze patterns in the differences between scores obtained from different sentiment scoring algorithms.

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Please Cite as Follows:

Abou-Kassem, T., Alazeezi, F.H.O., Ertek, G. (2023). A Data Analytics Methodology for Benchmarking of Sentiment Scoring Algorithms in the Analysis of Customer Reviews. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 693. Springer, Singapore. https://doi.org/10.1007/978-981-99-3243-6_46