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Educational data mining is a widely interesting issue in data mining research field. One of the topics is feature selection method to reduce a feature set. The main purpose of this study is to compare feature selection methods for the efficiency of student performance prediction improvement. In this research, we proposed 4 feature selection methods: greedy algorithm, Information gain ratio, chi-square,...
Traditionally, only experts who are equipped with professional knowledge and rich experience are able to recognize different species of wood. Applying image processing techniques for wood species recognition can not only reduce the expense to train qualified identifiers, but also increase the recognition accuracy. In this paper, a wood species recognition technique base on Scale Invariant Feature...
In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
In this paper, artificial neural network (ANN) and improved binary gravitational search algorithm (IBGSA) are utilized to detect objects in images. Watershed algorithm is used to segment images and extract the objects. Color, texture and geometric features are extracted from each object. IBGSA is used as a feature selection method to find the best subset of features for classifying the desired objects...
Machine based systems can't keep up with the task of organizing the data in an up-to-date manner unless and until the data acquired is being planned or scheduled and managed in an appropriate manner. Today's datasets start as small chunk of information and grow exponentially over a period of time. Once the size is extremely large it becomes difficult to make decisions and to predict consistently and...
Extracting meaningful pattern from data can be challenging. Irrelevant, redundant, noisy and unreliable data, misinterpretation of results and incompatibility of a technique to extract unknown patterns from data may lead analyst to develop an erroneous classifier. This research is encouraged by ‘No Free Lunch’ theorem that can be simplified as no classification technique that works best for every...
RS (Remote Sensing) image classification based on ANN (Artificial Neural Network) is carried out with high spatial resolution images of the wetland, which is the most important ecological environment element within the land components. Wetland dynamic change monitoring is often built upon its classification result concerned here. The typical high spatial resolution image of the wetland in Nanjing...
A novel polarimetric synthetic aperture radar (PolSAR) image classification method based on Deep Belief Networks (DBNs) is proposed in this paper. First, the coherency matrix data are converted to a 9-dimentional data. Second, many patches are randomly selected from each dimension in the 9-dimentional data, and many filters can be obtained from a Restricted Boltzmann Machine (RBM) trained by using...
This paper reports an empirical investigation on the use of functionally expanded input data for the constructive learning of neural networks; a functional expansion can be helpful when approximating nonlinear functions. The investigation was conducted considering six constructive neural network algorithms (Tower, Pyramid, Tiling, PTI, Perceptron Cascade and Shift), six data domains (four real data...
Droughts continue to be the number one hydro- and meteorological natural disaster inflicting Africa. In the last decade alone, the Continent contributed 55% of the world's droughts and 64% of deaths emanating from these disasters. With over 24% of her population affected, Africa accounted for 47% of people affected by droughts in the same period. Given that rain-fed agriculture is a major source of...
Credit-risk evaluation is a challenging and important task in the domain of financial analysis for which many classification methods have been suggested. In this paper, we present the results for eight real-life credit-risk two-class mixed datasets (i.e., discrete and continuous attributes) analyzed by the Three-MLP Ensemble Re-RX algorithm (shortened to “Three-MLP Ensemble”). Clarifying the neural...
According to the pattern recognition theory, a very important stage into a classification chain is represented by the feature selection. Although in literature a lot of feature selection techniques are indicated, one of the most important methods as application area is focused on Sammon projection algorithm use. Consequently, in this paper an improved neural approach of Sammon mapping is described...
With the developing technology, Hyperspectral images can be obtained with the satellites, aircraft and even unmanned aerial vehicles. Therefore, the classification applications made on the HSI are becoming increasingly important. In particular, fast and reliable classification algorithms are needed. The basic principle in classification algorithms is using characteristics of the data to find classification...
The goal of this research is to focus and adopt a fast, accurate and reliable fault detection technique and classification method for the high voltage transmission line. The proposed method reduces the outage time and hence this eliminates any possible damage to the other parts of the system. First, detection of the fault is carried out using a new proposed technique that combines three type of relays...
Anomalies in computer networks has increased in the last decades and raised concern to create techniques to identify these unusual traffic patterns. This research aims to use data mining techniques in order to correctly identify these anomalies, particularly in spam detection, for it was applied an collection of machine learning algorithms for data mining tasks and an dataset called SPAMBASE to identify...
This paper proposes a short-term energy price classification model using decision tree. The proposed model does not predict the exact value of future electricity price, but the class to which it belongs, established with respect to pre-specified threshold. This strategy is proposed since for some applications, the exact value of future prices is not required for the decision-making process. A feature...
This article proposed a new smart diagnosis algorithm of the open-circuit fault in a PV generator. For the faults conventional diagnosis, it used the analysis of the actual operation parameters of the PV generator. For the faults smart diagnosis, it based on the optimization of SVM technique by the neural network for the classification of observations located on its margin. The resulting algorithm...
In the paper the method of the transformation of the learning samples into their representatives is presented. The proposed algorithm combines the features of the neural nets approach, i.e. the representatives lie near the boundaries separating the classes, and cluster seeking approach — each representative corresponds to the group of elements lying close to each other. By using the consistent subset...
In previous works of ours [1-3], we proposed a neural network-based face detection and facial expression analysis system, which was able to classify three expressions in frontal view face images. In the present work, we examine the possibility of classifying these expressions in side view face images. Specifically, we evaluate the extracted facial feature discrimination power of three image acquisition...
Augmented and Virtual Reality has become a highly researched subfield in the Computer Vision domain. Among various researched topics, object detection and tracking is a major field of interest, with various algorithms being developed for it. Various algorithms exist that employs different approaches to solving the problem of object detection. The approach that we take is a mixture of investigating...
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