A web-based intelligent report e-learning system using data mining techniques

Blagojević, Marija; Micić, Ivadin

doi:10.1016/j.compeleceng.2012.09.011

Cited by 37 publications

(17 citation statements)

References 5 publications

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“…The ADOMD.NET is an environment of Microsoft.NET that enables communication with Microsoft SQL services for analysis. Figure 4 shows the application architecture which is similar to the one in the research by Blagojević and Micić [24]. Figure 5 shows the appearance of the user interface, and also one use case) with input variables and a result.…”

Section: Resultsmentioning

confidence: 97%

Artificial neural network model for air pollution prediction: case study of Moravica District, Serbia

Blagojević¹,

Papić²,

Vujičić³

et al. 2018

Environ. Protect. Eng.

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An example of artificial neural network model for predicting air pollution has been presented. The research was conducted in Serbia, the Moravica District, on the territory of two municipalities (Lučani and Ivanjica) and the town Čačak. The level of air pollution was classified by a neural network model according to the input data: municipality, site, year, levels of soot, sulfur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter. The model was evaluated using a lift chart and a root mean square error (RMSE) has been determined, whose value was 0.0635. A multilayer perceptron has also been created and trained with a back propagation algorithm. The neural network was tested with the data mining extensions (DMX) queries. The results have been obtained for air pollution based on new input data that can be used to predict the level of pollution in future if new measurements are carried out. A web-based application was designed for displaying the results.

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Section: Resultsmentioning

confidence: 97%

Artificial neural network model for air pollution prediction: case study of Moravica District, Serbia

Blagojević¹,

Papić²,

Vujičić³

et al. 2018

Environ. Protect. Eng.

Self Cite

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show abstract

“…Soon, the digital workspace paradigm is expected to become more and more widespread, progressively replacing the traditional physical workstation paradigm:, thanks to the increasing use of digital communication platforms based on data mining technologies, as well as sharing tools and mobile terminals and applications, the possibility to work from anywhere, to be recognized and measured by systems by objectives, will be extended, by also promoting dialogue and collaboration between people, innovating processes and operating environments. Examples of these solutions are, even if partially, collected by the literature shows, in a wider context, interesting applications of data mining in for learning systems (Blagojević & Micić, 2013) The above-reported evolutions will also offer companies the opportunity to focus more on the company's activities and business, while also improving employee satisfaction, thanks to greater flexibility to reconcile work and private life. In a context in which the digital revolution will continue to impact the company's organization and markets, transforming them profoundly, collaboration platforms will become an important vehicle to build and spread knowledge among people and to stimulate and govern the circulation of ideas resulting from shared work.…”

Section: Digital Transformation In Italian Smes Supported By Data Miningmentioning

confidence: 99%

Data Mining Applications in SMEs: An Italian Perspective

Topalović

Azzini

2020

Business Systems Research Journal

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BackgroundFrom the last decade, data mining techniques, employed in particular in customer relationship management, have assumed a key role in the profitability and operations of companies. To support small and medium companies (SMEs), several innovative and continuously improving tools have been developed that allow SMEs to utilize the internal and external data sources to increase their competitiveness.ObjectivesIn this paper, an analysis of the impact of digitalization, and in particular data mining techniques, in the context of SMEs development is presented.Methods/ApproachA review of various sources has been conducted, with the focus on open source tools, since in the context of the Italian economy they are used by SMEs the most.ResultsFirst, the analysis presents a brief review of the data mining techniques available and shows how they are practically employed in small companies. Second, an economical review of investments in data mining projects in Italy is presented.ConclusionsThe review indicates that data mining techniques can boost a company in the market. However, the awareness of data mining as a company asset is still not strong in Italian SMEs and most investments in Italy are still carried out by large companies.

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“…This paper is a summarization of previous literature 2010-2016 by use of different data mining techniques, and Inductive Reasoning, Genetic algorithms, Fuzzy logic, Artificial Intelligence methods, Clustering, Visualization methods, Classification methods or Classifiers with Artificial Neural networks [2,6,[8][9][10]. To decrease student's dropout ratio, increase the motivation for students and improve the progress of offered courses with the past experiences of educational systems are the most challenging tasks in [11].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Data Mining Approaches to E-Learning System: A Survey

Ashraf¹,

Khan²

2016

NIJESR

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Now a days, online learning systems increase student's ability to learn on their own. The use of Data Mining in education system has become a major research area, and it is used to collect information efficiently from electronic learning systems. The educational systems are facing various problems such as static delivery of the material; identification of student needs and checking the quality of student interaction level. This paper surveys educational data mining approaches such as pattern mining, clustering, classification, and artificial intelligence. The goal of this paper is to discover efficient knowledge from web-based learning systems. This work provides particular web-based courses, wellknown adaptive environment, and intelligent learning systems. The comparison of electronic learning systems and detailed analysis enable students to improve the learning experience. This paper presents the previously performed research related studies, techniques that can be used to improve the student knowledge and academic progress in an E-Learning system.

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A web-based intelligent report e-learning system using data mining techniques

Cited by 37 publications

References 5 publications

Artificial neural network model for air pollution prediction: case study of Moravica District, Serbia

Artificial neural network model for air pollution prediction: case study of Moravica District, Serbia

Data Mining Applications in SMEs: An Italian Perspective

Effectiveness of Data Mining Approaches to E-Learning System: A Survey

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