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The advent of Social Medias, Email services and other internet facilities are found helpful for a wide range of users. But some of them are interested in finding loop holes in such web based services to hinder the normal activities of common users. In this, spam Emails are one of the most disturbing activity in social network. In this context there is a need for efficient spam filters and most of...
Email spam is an increasing problem because it disrupting and time consuming for user, since the easy and cheap of sending email. Email Spam filtering can be done with a binary classification with machine learning as classifier. To date, email spam detection still challenging since the email spam still happens a lot and the detection still need improvement. Decision Tree (DT) is one of famous classifier...
A novel algorithm named Missing-Rate-Oriented Selective (MROS) algorithm - including: Most-Similar (M-S) algorithm and Attribute-Selective Imputation (ASI) approach-is proposed to achieve effective Mean Identification Rate (MIR) with minimal imputation effort for multi-classification systems in a complex and High Missing Rate (HMR) dataset. This dataset was developed from real server power supply...
The ν-nonparallel support vector machine (ν-SNPSVM) for classification has the advantage of using a parameter ν on controlling the number of support vectors. However, it ignores the prior structural information in data. In this paper, we propose a novel nonparallel classifier, named ν-Structural Nonparallel Support Vector Machine (ν-SNPSVM), for binary classification. Each model of ν-SNPSVM considers...
We present an object matching method that employs matches of local graphs of keypoints, called keygraphs, instead of simple keypoint matches. For a keygraph match to be valid, vertex (keypoint) descriptors must be similar and both keygraphs must satisfy structural properties concerning keypoints orientation, scale, relative position and cyclic ordering; as a result, the large majority of initial incorrect...
In this paper, we present a Conditional Random Field (CRF) model to deal with the problem of segmenting handwritten historical document images into different regions. We consider page segmentation as a pixel-labeling problem, i.e., each pixel is assigned to one of a set of labels. Features are learned from pixel intensity values with stacked convolutional autoencoders in an unsupervised manner. The...
Many classification problems involve nodes that have a natural connection between them, such as links between people, pages, or social network accounts. Recent work has demonstrated how to learn relational dependencies from these links, then leverage them as predictive features. However, while this can often improve accuracy, the use of linked information can also lead to cascading prediction errors,...
Computers cause an impact in almost every single aspect of our lives, however, unfortunately, schools have not been able to keep up with this irreversible evolution. The simple use of technological apparatuses in the classroom does not guarantee the improvement of the learning process, however it can be the medium through which the students find the alternatives for the solution of complex problems...
We propose several new concepts for providing enhanced explanations of classifier decisions in linguistic (human readable) form. These are intended to help operators to better understand the decision process and support them during sample annotation to improve their certainty and consistency in successive labeling cycles. This is expected to lead to better, more consistent data sets (streams) for...
Over the decades, tourism is a rapidly growing industry and has become one of the largest economic sectors in the world which leads to the intensive competition among tourism businesses. Most businesses seek for effective strategies to increase a scale of customer base from both local and international markets by focusing on customer satisfaction as it is a key indicator of customer repurchase intention...
The comparison of education systems, training courses electrical engineering and electronics for example, some universities in Europe, USA, universities in Russia and other countries is made. The requirements for obtaining the degrees in electrical engineering and electronics are also provided.
The analysis of quantitative and quality indicators of ratings of technical and electrotechnical universities of the world through international, national and private criteria is considered and made. A prospective student undertaking a similar analysis will have the opportunity to select a profession taking into account many of the same factors.
Individuals have distinctive ways of speaking and writing, and there exists a long history of linguistic and stylistic investigation into authorship attribution. Most authorship identification approaches are exclusively based on lexical measures such as vocabulary richness and lexico-syntactic features, or substantially generate relevant features for different machine learning approaches. These techniques...
The purpose of the present case series was to investigate whether three lower limb rehabilitation training approaches have any effects on trunk stability of persons with motor complete SCI during a 10-minute assisted walk. These trainings included electrical stimulation (ES), standing retraining (SRT), and a novel multi-modality approach that combined ES with SRT. We observed that multi-muscle ES...
In this article, we focus on the problem that the training performance of the training algorithm, which adjusts both timing and the number of spikes, depends on the initial network structure, especially the number of hidden units. To dissolve the problem, we propose a training algorithm to adjust the number of hidden units during training. The method is based on a growing strategy, which increases...
The objective of this paper is to discuss different scenarios for Principal Component Analysis classifier implemented for email filtering process (Ham vs. spam emails). The study highlights on the variation of the accuracy of these classifiers with respect to the variation in feature preprocessing. Four scenarios were considered: Scenario 1: Ham and Spam classes are represented with different features...
This paper deals with the design of a weighted ensemble of classifiers to classify imbalance data having heterogeneous features. For this purpose, a meta ensemble model is created and instead of class labels, the output of each base classifier used in the ensemble model is transformed into a [class label, weight] pair to deal with the problem. The performances of the proposed method on various datasets...
This work aims to improve the accuracy of the SVDD-based Intrusion Detection Systems. In this study we are interested by approaches using only one-class classification, namely the class of normal user sessions. Sessions are modeled by vectors of points in a finite features space. The goal of using the SVDD in anomaly detection is to find the hypersphere with a minimal volume that encloses the entire...
The major challenges current network anomaly detection methods are facing is how to handle large amounts of data and how to model normal and anomalous behaviour in continuously changing environments. To address these issues, this paper investigates the applicability of natural laws as a baseline for network anomaly detection. Natural laws have the advantage of being computationally efficient without...
MOOCs are developing rapidly with low completion rates which is questioned widely. But with its massive participation, there still are many MOOCs completers, who are what the course designers and investors want to attract and develop. What are the differences between the learners who complete the MOOCs and those who do not? How was the professional training MOOC designed to improve the completion...
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