COGNITIVE DIALOG GAMES AS COGNITIVE ASSISTANTS: TRACKING AND ADAPTING KNOWLEDGE AND INTERACTIONS IN STUDENT’S DIALOGS

This study introduces a system design in a form of cognitive dialog game (DiaCog) to support pedagogical factors and student learning model ideas. The purpose of the study is to describe how such a design achieves tracking and adapting students’ knowledge and mastery learning levels as a cognitive assistant. Also, this study shows alternative ways for supporting intelligent personal learning, tutoring systems, and MOOCS. This paper explains method called DiaCog that uses structure for students` thinking in an online dialog by tracking student`s level of learning/knowledge status. The methodology of computing is the semantic that match between students` interactions in a dialog. By this way it informs DiaCog’s learner model to inform the pedagogical model. Semantic fingerprint matching method of DiaCog allows making comparisons with expert knowledge to detect students` mastery levels in learning. The paper concludes with the DiaCog tool and methodologies that used for intelligent cognitive assistant design to implement pedagogical and learner model to track and adapt students’ learning. Finally, this paper discusses future improvements and planned experimental set up to advance the techniques introduced in DiaCog design

A data mining approach for desire and intention to participate in virtual communities

The purpose of this study is to investigate performances of some of the data mining approaches while understanding desire and intention to participate in virtual communities and its antecedents. A research model has been developed following the literature review and the model was tested afterwards. In research part of the study, some of the data mining approaches as JRip, Part, OneR Method, Multilayer Perceptron (Neural Networks), Bayesian Networks have been used. Based on the analysis conducted it has been found out that Multilayer Neural Network had the best correct classification rate and lowest RMSE

Using Data Mining to Identify COSMIC Function Point Measurement Competence

Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need

Benchmarking data mining approaches for traveler segmentation

The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers

Predicting existence of Mycobacterium tuberculosis on patients using data mining approaches

AbstractA correct diagnosis of tuberculosis (TB) can be only stated by applying a medical test to patient’s phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining(DM) solution which makes diagnosis of tuberculosis as accurate as possible and helps deciding if it is reasonable to start tuberculosis treatment on suspected patients without waiting the exact medical test results or not.In this research, we proposed the use of Sugeno-type “adaptive-network-based fuzzy inference system” (ANFIS) to predict the existence of mycobacterium tuberculosis. 667 different patient records which are obtained from a clinic are used in the entire process of this research. Each of the patient records consist of 30 separate input parameters. ANFIS model is generated by using 500 of those records. We also implemented a multilayer perceptron and PART model using the same data set.The ANFIS model classifies the instances with an RMSE of 18% whereas Multilayer Perceptron does the same classification with an RMSE of % 19 and PART algorithm with an RMSE of % 20.ANFIS is an accurate and reliable method when compared with Multilayer Perceptron and PART algorithms for classification of tuberculosis patients. This study has contribution on forecasting patients before the medical tests

Classification of confidential documents by using adaptive neurofuzzy inference systems

AbstractDetecting the security level of a confidential document is a vital task for organizations to protect the confidential information encapsulated in. Diverse classification rules and techniques are being applied by human experts. Increasing number of confidential information in organizations are making difficult to classify all the documents carefully with human effort. A hybrid approach involving support vector classifier and adaptive neuro-fuzzy classifier is proposed in this study. Also states preprocessing tasks required for document classification with natural language processing. To represent term-document relations a recommended metric TF-IDF was chosen to construct a weight matrix. Agglutinative nature of Turkish documents is handled by Turkish stemming algorithms. At the end of the article some experimental results and success metrics are projected with accuracy rates