Quality of Citizen Reporting Tools at Municipal Level
Hana Kopackova, Petra Libalova
J INFORM SYSTEMS ENG, Volume 4, Issue 3, Article No: em0092
https://doi.org/10.29333/jisem/5891
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
Quality of life in cities is highly dependent on the state of public infrastructure. Maintenance of the infrastructure is managed by the local government that needs to be informed about issues on the infrastructure. Although the duty of maintenance staff is to check the state of the infrastructure, it is difficult to know about all the problems in the city. Therefore, citizens can take the initiative and participate by reporting any issue on public infrastructure. Today, citizens have many options on how to report such issues, due to modern communication technologies. Citizen reporting systems, which are a special type of participatory technology, deserve deeper research due to its novelty in the information system research. In this article, we introduce the study of 13 Czech regional capitals and their reporting systems. In this study, we evaluate the existence of different types of communication channels (phone, e-mail, electronic form, WebGis, and mobile application) and their quality based on four criteria (searchability, coverage of issues, visual geolocation, and display of sent reports). Moreover, the results of regional capitals will be compared with previous research on cities in these regions.
Keywords: citizen reporting, smart cities, non-emergency, e-participation
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Decision Support System for Business Ideas Competitions
Diana Martins, Rui Assis, Ricardo Coelho, Fernando Almeida
J INFORM SYSTEMS ENG, Volume 4, Issue 3, Article No: em0093
https://doi.org/10.29333/jisem/5892
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
Business ideas competitions have gained increasing importance in stimulating entrepreneurial activity mainly among highly qualified graduates. However, the operating model of these competitions is quite heterogeneous, complex and often confusing, since the perception of the merit of each project is assessed differently by each jury member. Therefore, it is important to propose a decision support system that simplifies the evaluation process of competing projects and ensures all the opinions of the jury members are considered and have the same importance. The developed application uses C# and Windows Forms technologies and the AHP method to serialize competing projects according to the individual evaluation of each jury member. The results of the study allowed testing the application considering four scenarios in which the relative importance of each criterion and the performance of each project according to these criteria are changed and evaluated.
Keywords: support decision system, AHP, business competition, ideas competition, entrepreneurship
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An Ensemble Predictive Model Based Prototype for Student Drop-out in Secondary Schools
Neema Mduma, Khamisi Kalegele, Dina Machuve
J INFORM SYSTEMS ENG, Volume 4, Issue 3, Article No: em0094
https://doi.org/10.29333/jisem/5893
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
When a student is absent from school for a continuous number of days as defined by the relevant authority, that student is considered to have dropped out of school. In Tanzania, for instance, drop-out is when a student is absent continuously for a period of 90 days. Despite the fact that several efforts have been made to improve the overall status of education at secondary level, the student drop-out problem still persists. Taking advantage of advancement in technology, several studies have used machine learning to address the student drop-out problem. However, most of the conducted studies have used datasets from developed countries, while developing countries are facing challenges on generating public datasets to be used to address this problem. Using a dataset from Tanzania which reflect a local scenario; this study presents an ensemble predictive model based prototype for student drop-out in secondary schools. The deployed model was developed by soft combining a tuned Logistic Regression and Multi-Layer Perceptron models. A feature engineering experiment was conducted to obtain the most important features for predicting student drop-out. Furthermore, a visualization module was integrated to assist interpretation of the machine learning results and we used flask framework in the development of the prototype.
Keywords: student drop-out, predictive model, machine learning, prototype, feature engineering experiment, visualization module
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