There has been a sudden increase in the usage of Learning Management Systems applications to support learner's learning process in higher education. Many studies in learning management system evaluation are implemented under complete information, while the real environment has uncertainty aspects. As these systems were described by development organizations with uncertainty terms such as vague, imprecise, ambiguity and inconsistent, that is why traditional evaluation methods may not be effective. This paper suggests neutrosophic logic as a better option to simulate human thinking than fuzzy logic because unlike fuzzy logic, it is able to handle indeterminacy of information which expresses the percentage of unknown parameters. As previous studies suggested neutrosophic decision maker and neutrosophic expert systems as future work in ecommerce and e‐learning applications, this paper presents neutrosphic expert system for learning management systems evaluation. Information for building and validating the neutrosophic expert system is collected from five experts using surveys, and then analysis is done by using Fuzzytech 5.54d software. Finally, the comparison between fuzzy expert system and neutrosophic expert system results show that the neutrosophic logic is capable of representing uncertainty in human thinking for evaluating Learning Management Systems.
According to the growing evolution in complex systems and their integrations, Internet of things, communication, massive information flows and big data, a new type of systems has been raised to software engineers known as Ultra Large Scale (ULS) Systems. Hence, it requires dramatic change in all aspects of "Software Engineering" practices and their artifacts due to its unique characteristics.Attendance of all software development members is impossible to meet in regular way and face-to-face, especially stakeholders from different national and organizational cultures. In addition, huge amount of data stored, number of integrations among software components and number of hardware elements. Those obstacles constrict design, development, testing, evolution, assessment and implementation phases of current software development methodsIn this respect, ULS system that's considered as a system of systems, has gained considerable reflections on system development activities, as the scale is incomparable to the traditional systems since there are thousands of different stakeholders are involved in developing software, were each of them has different interests, complex and changing needs beside there are already new services are being integrated simultaneously to the current running ULS systems.The scale of ULS systems makes a lot of challenges for Requirements Engineers (RE). As a result, the requirements engineering experts are working on some automatic tools to support requirement engineering activities to overcome many challenges.This paper points to the limitations of the current RE practices for the challenges forced by ULS nature, and focus on the contributions of several approaches to overcome these difficulties in order to tackle unsolved areas of these solutions.As a result, the current approaches for ULS miss some RE essential practices related to find vital dependent requirements, and are not capable to measure the changes impact on ULS systems or other integrated legacy systems, in addition the requirements validation are somehow depended on the user ratings without solid approval from the stakeholders.
There is a need to a small set of words-known as a query-to searching for information. Despite the existence gap between a user's information need and the way in which such need is represented. Information retrieval system should be able to analyze a given query and present the appropriate web resources that best meet the user's needs. In order to improve the quality of web search results, while increasing the user's satisfaction, this paper presents the current work to identify user's intent sources and how to understand the user behavior and how to discover the users' intentions during the web search. This paper also discusses the social network analysis and the web queries analysis. The objective of this paper is to present the challenges and new research trends in understanding the user behavior and discovering the user intent to improve the quality of search engine results and to search the web quickly and thoroughly.
Due to the huge numbers of genes that produced from microarray technology versus genes that actually discriminate disease classes, gene selection methods for microarray data analysis are vital to identify the significant genes that distinguish disease classes and to use these selected genes as diagnostic biomarkers in clinical treatment decisions. In this study, we describe how to achieve reduction of microarray data dimensionality by two attribute selection methods (AS), namely information gain method (IG) and support vector machine method (SVM) which can greatly reduce the number of attributes used to discriminate microarray data. We employ both methods, to pre-process gene expression profiles achieved from DNA microarray experiments in three steps: (i) Ranking genes according to the highest dataset separation between diseased and normal classes, (ii) Choosing the smallest subset of ranked genes that assures the highest classification accuracy, (iii) Constructing the classification models to classify diseased versus normal samples using multiple algorithms based on the extracted subset in (ii). Evaluation of this approach was conducted by using ten different classification algorithms, with eight variant cancerous microarray dataset. Based on the obtained results, this pre-processing approach improved classification accuracy compared to using the whole original dataset. All the evaluated algorithms which used in our approach provided classification accuracy exceeds over (94%) with majority of datasets. By using a few numbers of top ranking genes, we obtained higher classification accuracy instead of using original dataset, the average values of enhancement were (1.31%, 3.01%, 4.06%, 3.54% and 3.59%) using (2, 5, 10, 20, 50) subset of ranking genes by information gain attribute selection respectively, and (0.19%, 4.33%, 5.05%, 5.54% and 5.63%) using (2, 5, 10, 20, 50) subset of ranking genes by SVM attribute selection. Experimental results shows that using SVM attributes selections method yields better results than using information gain attribute selection method as preprocessing stage of the classification task. Also, it can be shown that Artificial Neural Network (ANN) outperforms all classifiers when SVM attribute selection method used while Bayes Net outperforms all classifiers when information gain attribute selection is applied.
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