18,575 research outputs found
A validation measure for computational scheduler activity-based transportation models based on sequence alignment methods
No full textIn recent decades, activity-based transportation models have gained growing attention, due to their strong foundation in behavioral theory and ability to model the response of individuals to travel demand management policies. Hence, researchers have become increasingly interested in analyzing and predicting individuals' decisions about activity participation. This paper investigates the reliability and uncertainty of computational process activity-based models. The design of the scheduling process model is experimented with by introducing an alternative decision sequence. The results provide additional information to better understand the process model's reliability and behavior. Furthermore, the findings show that the current sequence of decision steps in the process model in ALBATROSS achieves satisfactory work activity schedules. Finally, the study concludes that using a decision tree model achieves a better performance than using diverse data mining approaches
Deployment of MOOCs in Virtual Joint Academic Degree Programs
No full textThis research paper investigates the readiness of students to opt for MOOC courses in universities offering a joint master degree international programme. A study is conducted on two joint academic study programs offered by the University of Hasselt in Belgium and Princess Sumaya University for Technology in Jordan. The study examines the readiness of students to take MOOC courses and their acceptance by universities' management staff and professors. The study reveals promising results as the results suggest that such virtual study programs are readily accepted in both universities by professors and students. On the other hand, management staff and some professors expressed concerns on the approval of the equivalence of a MOOC onto courses.Sammour, G (corresponding author), Princess Sumaya Univ Technol PSUT, Dept Business Informat Technol, Amman, Jordan
Using Fuzzy Cognitive Maps to predict the economic sustainability of Jordan Social Security
No full textFuzzy Cognitive Maps are emerging as an important new tool in economic modeling. This study investigates the use of fuzzy cognitive maps with their learning algorithms, based on genetic algorithms, for the purposes of economic prediction. The case study data are extracted from the Jordanian social sociality revenues and expanse for the last 120 months; The Real-Code genetic algorithm and structure optimization algorithm were chosen for their ability to select the most significant relationships between the concepts and to predict future development of the Jordanian social sociality revenues and expenses. Furthermore, fuzzy cognitive maps are able to calculate prediction errors accurately. The study shows that fuzzy cognitive maps models clearly predict the future of a complex financial system with incoming and outgoing flows. Consequently, this research confirms the benefits of fuzzy cognitive maps applications as a tool for scholarly researchers, economists and policy makers
A Fuzzy Cognitive Map Approach to Investigate the Sustainability of the Social Security System in Jordan
No full textFuzzy Cognitive Maps are emerging as an important new tool in economic modelling. The aim of this study is to investigates the use of fuzzy cognitive maps with their learning algorithms, based on genetic algorithms, for the purposes of prediction of economic sustainability. A Case study data are extracted from the Jordanian Social Security system for the last 120 months; The Real-Code genetic algorithm and structure optimization algorithm were chosen for their ability to select the most significant relationships between the concepts and to predict future development of the Jordanian social security revenues and expenses. The study shows that fuzzy cognitive maps models clearly predict the future of a complex financial system with incoming and outgoing flows. Therefore, this research confirms the benefits of fuzzy cognitive maps applications as a tool for scholarly researchers, economists and policy makers
E-learning readines in organisations, the case of hospitals
No full textE-learning is a good opportunity for companies to up-skill their employees and meet the demands of lifelong learning but its implementation needs to be well prepared and managed because it often requires high investment costs. That is why it is important for a company to know if it is e-ready. E-readiness is already well covered in literature and several models are suggested. We used these models to develop an e-learning readiness measurement instrument. We used our instrument to check whether the Flemish hospitals are e-ready for e-learning
Using K-Means Clustering and Data Visualization for Monetizing logistics Data
No full textLogistics companies possess collect large amount of data on the shipments they perform while at the same time facing a challenge to understand their complicated market better. They can extract useful market knowledge by using data mining technologies such as visualization and clustering. The detailed results of such big data analytics methods can also be monetized under certain circumstances. We studied the data on the transactions of a logistics company in the Middle East. K-Means clustering of their data proved to generate deeper insight into several clusters of customers having different profiles. The results propose a best fit model for the clustering. Since the clustering and visualization results are relevant, reliable and anonymous they fit the monetization criteria as well. Improved data driven marketing applications are possible for the customers
Using Fuzzy Cognitive Maps to predict the economic sustainability of Jordan Social Security
No full textFuzzy Cognitive Maps are emerging as an important new tool in economic modeling. This study investigates the use of fuzzy cognitive maps with their learning algorithms, based on genetic algorithms, for the purposes of economic prediction. The case study data are extracted from the Jordanian social sociality revenues and expanse for the last 120 months; The Real-Code genetic algorithm and structure optimization algorithm were chosen for their ability to select the most significant relationships between the concepts and to predict future development of the Jordanian social sociality revenues and expenses. Furthermore, fuzzy cognitive maps are able to calculate prediction errors accurately. The study shows that fuzzy cognitive maps models clearly predict the future of a complex financial system with incoming and outgoing flows. Consequently, this research confirms the benefits of fuzzy cognitive maps applications as a tool for scholarly researchers, economists and policy makers
Using visual analytics and K-means clustering for monetising logistics data, a case study with multiple e-commerce companies
No full textLogistics companies possess and collect a large amount of data on the shipments they perform while at the same time facing a challenge to understand their complicated market better. Therefore, investigating whether large databases gathered by logistics companies on their e-commerce partners could be monetised as a business service and how this could eventually be achieved is an important research venture. In this paper we used visual analytics and k-means clustering to see whether the data could be structured and presented in a monetisable way, while at the same time adhering to the quality characteristics necessary for doing so: reliable, accurate, relevant, segmented, secured and anonymized. Results show that is clearly the case for the database we investigated and contained 85989 transactions. Using a semi-structured interview with several key managers of both the logistics company and its e-commerce partners, a business-model canvass was developed that indicates the necessary elements for this venture and the right mindset to manage the process. We can confidently conclude that all elements are present to answer the monetisability question positively and to pretend that given the right visualization and confidence between the companies the process could very well be profitable
Using visual analytics and K-means clustering for monetising logistics data, a case study with multiple e-commerce companies
No full textLogistics companies possess and collect a large amount of data on the shipments they perform while at the same time facing a challenge to understand their complicated market better. Therefore, investigating whether large databases gathered by logistics companies on their e-commerce partners could be monetised as a business service and how this could eventually be achieved is an important research venture. In this paper we used visual analytics and k-means clustering to see whether the data could be structured and presented in a monetisable way, while at the same time adhering to the quality characteristics necessary for doing so: reliable, accurate, relevant, segmented, secured and anonymized. Results show that is clearly the case for the database we investigated and contained 85989 transactions. Using a semi-structured interview with several key managers of both the logistics company and its e-commerce partners, a business-model canvass was developed that indicates the necessary elements for this venture and the right mindset to manage the process. We can confidently conclude that all elements are present to answer the monetisability question positively and to pretend that given the right visualization and confidence between the companies the process could very well be profitable
Data Mining Techniques to Improve the Response Rate of E-mail campaigns and Customer Loyalty
No full textThe efficiency of e-mail campaigns is a big challenge for any e-commerce venture in terms of the response rate of e-mail campaigns and customer seg-mentation based on loyalty. Data mining techniques are useful tools to ex-tract customer information related to response rate from e-mail campaigns data. This study aims at predicting customer loyalty and improving the re-sponse rate of e-mail campaigns, specifically open rate and click through rate, using data mining techniques such as logistic regression and clustering.
The models are trained using chi square and logistic regression techniques to detect the effect of customers’ loyalty based on their demographic and be-havioural characteristics. Furthermore, a clustering technique is used to seg-ment customers based on their behavioural characteristics . The models re-ported satisfactory results in predicting customer loyalty based on open rate and click through rate values. In addition, the clustering of customers suggest that companies will have a better understanding of their customers in terms of their demographic and behavioural characteristics. The response rates also increase at the preferred moment at which e-mails should be send to custom-ers in email campaigns
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