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Wind speed prediction has been used in various fields such as Satellite launch, Air traffic control, Weather forecasting etc. Wind speed can be calculated by various atmospheric variables such as temperature, humidity, pressure, wind direction, etc. A number of methods have been proposed by various researchers to predict the wind speed. During the last few years a lot of research has been carried...
The electroencephalography (EEG) data records vast amounts of human cerebral activity yet is still reviewed primarily by human readers. Most of the times, the data is contaminated with non-cerebral originated signals, called artifacts, which could be very difficult to visually detect and, undiscovered, could damage the neural information analysis. The purpose of our work is to detect the artifacts...
We explored supervised machine learning (ML) techniques to understand and predict the adequacy and fluency of English-Spanish machine translation. Five experiments were conducted using three classifiers in Weka, an open-source ML tool. We found that the highest performance was achieved by applying a dimensionality reduction approach to the classification task, which included collapsing a numeric scale...
The biggest concern of Network is security. Intro find the tricks and tools of the Attackers. Data Mining techniques automatically learn the pattern of the tuples and Intelligent decision are made. Supervised learning methods finds the attack based on previous knowledge and unknown attacks are detected by using Unsupervised learning. Dos, Probe and Normal data are correctly detected by maximum Data...
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...
A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale...
Over past few decades, frog species have been experiencing dramatic decline around the world. The reason for this decline includes habitat loss, invasive species, climate change and so on. To better know the status of frog species, classifying frogs has become increasingly important. In this study, acoustic features are investigated for multi-level classification of Australian frogs: family, genus...
In the area of recommender systems, user-based collaborative filtering algorithm has been extensively studied and discussed. In the traditional approach of this method, a target user's preference for an item is predicted by the integrated preference of the user's neighbors for the item, ignoring the structure of these neighbors. That is, these neighbors form two distinct groups: some neighbors may...
Work on sentiment analysis has thus far been limited in the news article domain. This has mainly been caused by 1) news articles lacking a clearly defined target, 2) the difficulty in separating good and bad news from positive and negative sentiment, and 3) the seeming necessity of, and complexity in, relying on domain-specific interpretations and background knowledge. In this paper we propose, define,...
In several concept attainment systems, ranging from recommendation systems to information filtering, a sliding window of learning instances has been used in the learning process to allow the learner to follow concepts that change over time. However, no analytic study has been performed on the relation between the size of the sliding window and the performance of a learning system. In this work, we...
It is worthwhile to point out the fact that nature of given data plays considerable role in classifying the data accurately. To select an appropriate classifier for certain type of data, we are required to understand the behavior of classifiers on different data characteristics. The varying dimensions, number of instances, class labels, data correlation, and data distribution on different data classes,...
Feature selection and ensemble classification increase system efficiency and accuracy in machine learning, data mining and biomedical informatics. This research presents an analysis of the effect of removing irrelevant and redundant features with ensemble classifiers using two datasets from UCI machine learning repository. Accuracy and computational time were evaluated by four base classifiers; NaiveBayes,...
Traditional SVM (support vector machine) multi-class classification methods are mainly based on one-to-one and one-to-multi, which both have disadvantages in applications: slow computational speed and low classification precision. This paper introduces a new method based on error correcting code to reduce the training time and improve the classification precision. In view of the relations among the...
Combining different machine learning models (decision fusion) has been shown to be an effective method for estimating the underlying physical mechanism by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. To be effective, decision fusion requires that the different models provide some degree of complementary information...
In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system intelligence modeling was presented. In the processor, wavelet-fuzzy technique and neural network technique are combined. Uses the fuzzy wavelet extraction image feature, and wavelet function is used as fuzzy membership function...
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