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Traditional vehicle detectors always utilize singletemplate model to represent the vehicle which can not encircle vehicles with different aspect ratios. In this paper, we propose a fast and accurate approach for detecting vehicles which joints classification and aspect ratio regression. The key idea is extending the boosting decision trees method to estimate vehicle's aspect ratio during vehicle detection,...
One of the major causes of death in the world is Heart Failure. This disease affects directly the heart's pumping job. Because of this perturbation, nutriments and oxygen are not well circulated and distributed. The New York Heart Association has classified this disease into four different classes based on patient symptoms. In this paper, we are using a data mining technique, more precisely a sequential...
The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrixand generates two dimensional coordinates. By measuring thedistance between categories and the assigned points, ranking of key wordswill...
This paper aims to identify lead users from an online user innovation community. Based on three dimensions of user characteristics — user activeness, community influence, and user relations, a Random Forest classification model for lead user identification is proposed. Using the data from the MIUI forum of Xiaomi community, this model is tested. The result shows that Random Forest classification based...
In the hyperspectral remote sensing community, decision forests combine the predictions of multiple decision trees (DTs) to achieve better prediction performance. Two well-known and powerful decision forests are Random Forest (RF) and Rotation Forest (RoF). In this work, a novel decision forest, called Partial Least Square Forest (PLSF), is proposed. In the PLSF, we adapt PLS to obtain the components...
A decision tree is an important classification technique in data mining classification. Decision trees have proved to be valuable tools for the classification, description, and generalization of data. J48 is a decision tree algorithm which is used to create classification model. J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. In this paper, we present...
Bagging is a popular method used to increase the accuracy of classification, by training a set of classifiers on slightly different datasets and aggregating their output by voting. Usually, the majority voting is used for this purpose, or the plurality voting, when the problem has multiple class values. In this study, we analyze the influence of several voting methods on the performance of two classification...
Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to...
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 paper proposes a hybrid approach, integrating Decision Trees (DT) and Artificial Neural Networks (ANN) for energy price classification in deregulated electricity market. The proposed model does not aim to predict future values of energy prices, but classify and explain the negative Locational Marginal Price (LMP) that are observed in the grid. The negative LMPs are grouped by the K-means technique...
Everyday huge amount of information are transferred from one network to another, the information may be exposed to attacks. The information and information system should be protected from unauthorized users. To provide and maintain the Confidentiality and Integrity of the information is a very tedious job so Intrusion Detection plays a very important role. Although various methods are used to protect...
The Islamic State of Iraq and Syria (ISIS) is a extremist militant group in the Middle East known to employ social media for propaganda and recruiting purposes. In particular, the social media website Twitter is well known to be exploited by ISIS supporters. To this end, we devise an effective and scalable classification scheme to filter out ISIS propaganda accounts from the rest of the Twitter accounts...
Classification is the process of developing a model which assigns records in a collection to predefined categories or classes. Various algorithms like K Nearest Neighbour, Naive Bayes, Support Vector Machine, and C4.5 have been implemented to develop a classification model. The ultimate goal of a classifier is to accurately predict the target class for a given set of input data. The process of classification...
Currently, the air quality monitoring becomes important things for knowing the value of air pollution especially in the cities. In our previous research, we built the air quality monitoring system using wireless sensor network (WSN). Each sensor nodes will transmit all of the air quality data to the base station controller (BSC). This data consists of weather condition, temperature, humidity, carbon...
In this study, we address the problem of infrared (IR) object classification that divides the object appearance space hierarchically with a binary decision tree structure. Binary decisions are made by using the special features of the object appearances. These features are extracted using a fully connected deep neural network learnt by training samples. At each node of the tree, we train individual...
An optimization classification algorithm for MRI images of premature brain injury is introduced. Based on the shortcomings of the classical ID3 algorithm in dealing with the continuous attributes of medical image, the new algorithm selects the testing feature by comparing the information gain ratio and adds the handling methods for filling null values. Then it discrete the continuous attributes by...
In this paper, we introduce a new land cover mapping technique by taking advantages of a weighted random forest [1] and the level set method [2] to remove the weaknesses of each other. The weighted random forest can accurately estimate the likelihood that a pixel belonging to each classes while the level set method can capture the dependency among neighboring pixels. As a result, by combining their...
Fast and efficient network intrusion detection is a very challenging issue as the size of network traffic has become increasingly big and complex. A real time intrusion detection system should be able to process large size of network traffic data as quickly as possible in order to prevent intrusion in the communication system as early as possible. In this paper, we have employed five machine learning...
Education is one of the primary requirements for leading a good life. In India, still a large section of population is not educated, which makes them lag behind everyone. For overall development of our country, the citizens have to be educated and consequently employed. This paper analyses the most important factor that will result in the improved education level of our country, using data mining...
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