For any electric power system, it is crucial to guarantee a reliable performance of its High Voltage Circuit Breaker (HCVB). Determining when the HCVB needs maintenance is an important and non-trivial problem, since these devices are used over extensive periods of time. In this paper, we propose the use of data mining techniques in order to predict the need of maintenance. In the corresponding ...

Early warning and forecasting of tornadoes began to combine artificial intelligence (AI) machine learning (ML) algorithms improve identification efficiency in the past few years. Applying detect usually encounters class imbalance problems because are rare events weather processes. The ADASYN-LOF algorithm (ALA) was proposed solve problem tornado sample sets based on radar data. adaptive synthet...

In this report, I presented my results to the tasks of 2008 UC San Diego Data Mining Contest. This contest consists of two classification tasks based on data from scientific experiment. The first task is a binary classification task which is to maximize accuracy of classification on an evenly-distributed test data set, given a fully labeled imbalanced training data set. The second task is also ...

The low and high arrhythmic risk of myocardial infarction is classified based on size, location, and textural information of scarred myocardium. These features are extracted from late gadolinium (LG) enhanced cardiac magnetic resonance images (MRI) of post-MI patients. The risk level caused by features are evaluated by using various classifiers including k-nearest neighbor (k-NN), support vecto...

We developed a multiscale object-based classification method for detecting diseased trees (Japanese Oak Wilt and Japanese Pine Wilt) in high-resolution multispectral satellite imagery. The proposed method involved (1) a hybrid Intensity-Hue-Saturation (IHS)/Smoothing Filter-based Intensity Modulation (SFIM) pansharpening approach 10 (IHS-SFIM) to obtain more spatially and spectrally accurate im...

Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.) in recent decades. However, there is little discussion regarding the problem of between-class imbalance in stroke datasets, which leads to prediction bias and decreased performance. In this paper, we demonstrate the use of the Synthetic Minority Over-sampling Technique to overcome su...

The problem of imbalanced data, i.e., when the class labels are unequally distributed, is encountered in many real-life application, e.g., credit scoring, medical diagnostics. Various approaches aimed at dealing with the imbalanced data have been proposed. One of the most well known data pre-processing method is the Synthetic Minority Oversampling Technique (SMOTE). However, SMOTE may generate ...

Data in the real world, there are many conditions (situations) where number of instances one class is much less than other classes. This situation a problem unbalanced datasets (imbalance class). As result, performance classification will decrease some data systems. In this study, it was identified that apple leaf disease dataset used had large enough imbalance comparison between 1:5, so an ove...

Convolutional neural networks (CNNs) are the specific architecture of feed-forward artificial networks. It is de-facto standard for various operations in machine learning and computer vision. To transform this performance towards task network anomaly detection cyber-security, study proposes a model using one-dimensional CNN architecture. The authors' approach divides traffic data into transmiss...