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This work presented two prediction models for the estimation of student's performance in final examination. The work made use of the popular dataset provided by the University of Minho in Portugal, which relate to the performance in math subject and it consists of 395 data samples. Forecasting the performance of students can be useful in taking early precautions, instant actions, or selecting a student...
This paper presents a salary prediction system using a profile of graduated students as a model. A data mining technique is applied to generate a model to predict a salary for individual students who have similar attributes to the training data. In this work, we also made an experiment to compare five data mining techniques including Decision trees, Naive Bayes, K-Nearest neighbor, Support vector...
Gesture identification plays a vital role in today's human-computer interaction. In this paper, we proposed a sensor based gesture recognition system which makes the teacher to write in Telugu language on digital board from anywhere within the class room. Various classification algorithms k-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Decision tree are individually used for hand gesture...
The goal of this study is the discrimination of seven tree species. As a well known approach the k-nearest neighbor classifier is compared to a support vector machine based decision tree. This classifier uses advanced support vector machines to implement a hierarchical classification scheme by combining it with decision tree induction. At each node of the decision tree a support vector machine is...
This paper proposes a preliminary study regarding the implementation of a pediatric otorhinolaryngology diagnosis support system for the visual diagnosis of the eardrum, based on the automatic evaluation by digital image processing techniques of the otoscopic digital color images of the eardrum. We compare different color descriptors and classification algorithms (k-Nearest Neighbor, Decision Trees,...
Selection of an appropriate classifier for computer-aided diagnosis (CAD) applications has typically been an ad hoc process. It is difficult to know a priori which classifier will yield high accuracies for a specific application, especially when well-annotated data for classifier training is scarce. In this study, we utilize an inverse power-law model of statistical learning to predict classifier...
Content based retrieval and recognition of objects represented in images is a challenging problem making it an active research topic. Shape analysis is one of the main approaches to the problem. In this paper we propose the use of a reduced set of features to describe 2D shapes in images. The design of the proposed technique aims to result in a short and simple to extract shape description. We conducted...
The aim of this paper is to study and compare several machine learning methods for implementing a Thai terrorism event extraction system. The main function of the system is to extract information related to terrorism events found in Thai news articles. The terrorism events can then be classified and presented to intelligence officers who can further analyze and predict terrorism events. This paper...
Protein fold recognition task is important for understanding the biological functions of proteins. The adaptive local hyperplane (ALH) algorithm has been shown to perform better than many other renown classifiers including support vector machines, K-nearest neighbor, linear discriminant analysis, K-local hyperplane distance nearest neighbor algorithms and decision trees on a variety of data sets....
In real world applications, there are great many of DNA expressed microarray data, many supervised classification algorithms such as decision tree, KNN and SVM in the machine learning field have been introduced for microarray data classification. However, in real worlds, the labeled examples, especially gene expression data examples are often very difficult and expensive to obtain. The traditional...
We describe two approaches to reducing human fatigue in interactive evolutionary computation (IEC). A predictor function is used to estimate the human user's score, thus reducing the amount of effort required by the human user during the evolution process. The fuzzy system and four machine learning classifier algorithms are presented. Their performance in a real-world application, the IEC-based design...
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