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In the field of malware analysis, two basic types, which are static analysis and dynamic analysis, are involved in the process of understanding on how particular malware functions. By using dynamic analysis, malware researchers could collect API call sequences that are very valuable sources of information for identifying malware behavior. The proposed malware classification procedures introduced in...
In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection...
Selecting an efficient classifier for medical data is considered as one of the most important part of today's computer aided diagnosis. The performance of single classifiers such as decision tree classifier can be increased by ensemble method. However, this approach relies on the data quality and missing values. In this paper, we propose a new ensemble classifier to overcome overfitting and biasness...
Open educational resources (OER) are important assets for students or teachers, used to help them search for useful resources. However, it is a challenge to improve the user engagement of OER. In this paper, we propose a system, called resource delivery service system (RDSS), in the Taiwan Open Platform for Educational Resources (TOPER) that actively recommends educational resources to users. RDSS...
Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). For solving this high dimensional classification problem, the widely used algorithm remains to be Support Vector Machines (SVM). But due to the high variance of the data, the classification performance of SVM remains...
In Face recognition, a combination of neural network (NN), known as an ensemble of neural network, often outperforms individual ones. This paper is aiming to present a support vector machines (SVM)-ensemble-based efficient face recognition system. The training samples are randomly chosen by means of bootstrap technique to train the different SVM independently. These SVM's are combined together to...
According to the noise and overlapping characteristics of agricultural irrigation water quality monitoring data for the comprehensive evaluation may bring about the boundary fuzzy problem. This paper proposes an improved Genetic Algorithm (GA) to avoid premature convergence, the global optimal solution of the function of the Projection Pursuit (PP) function is used as the comprehensive evaluation...
Web spam is a big problem for search engine users in World Wide Web. They use deceptive techniques to achieve high rankings. Although many researchers have presented the different approach for classification and web spam detection still it is an open issue in computer science. Analyzing and evaluating these websites can be an effective step for discovering and categorizing the features of these websites...
DDoS attacks bring huge threaten to network, how to effectively detect DDoS is a hot topic of information security. Currently, there are some methods designed to detect DDoS attacks, but the detection rate of them is low. Moreover, DDoS detection is easily misled by flash crowd traffic. In this paper, a new method to detect DDoS attacks based on RDF-SVM algorithm is proposed. By considering the importance...
With the progress of the network and technology, the perfect combination of mobile intelligent terminal and internet, people are increasingly dependent on intelligent terminals. So, it was very necessary of a model for assessing the security performance of mobile intelligent terminals, especially to establish the objective model of the security performance of mobile intelligent terminal. In this paper,...
Modern healthcare service records, called Claims, record the medical treatments by a Provider (Doctor/Clinic), medication advised etc., along with the charges, and payments to be made by the patient and the Payer (insurance provider). Denial and rejection of healthcare claims is a significant administrative burden and source of loss to various healthcare providers and payers as well. Automating the...
Machine learning has its tentacles spread over all major areas of science. The current rise in the amount of data being generated as necessitated its adoption in virtually all aspects including chemoinformatics. Several machine learning methods have been applied to the drug discovery process due to the importance of prediction of bioactivity before the release of drug into the market. The need for...
Twitter is a popular microblogging service that allows its users to view and share limited character messages (known as “tweets”) with the public. This paper proposes a tweet sentiment classification framework which pre-processes information from Emoticon and Emoji in such way that their textual representation is included to enrich the tweet. Once the tweets are pre-processed, a hybrid computational...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
Recursive projection twin support vector machine (PTSVM) and Locality preserving projection twin support vector machine (LPPTSVM) are two extensions of traditional support vector machine (SVM). However, they may lead to a weak classifier in the application where some data points may not be fully assigned to one class. In this paper, we introduce the basic idea of fuzzy membership into LPPTSVM and...
The main aim of this work is to compare the performance of different algorithms for human activity recognition by extracting various statistical time domain and frequency domain features from the inertial sensor data. Our results show that Support Vector Machines with quadratic kernel classifier (accuracy: 93.5%) and Ensemble classifier with bagging and boosting (accuracy: 94.6%) outperforms other...
The typical method of entering a password for user authentication is vulnerable to hacking; therefore, various security technologies using bio-signals, such as iris scan, electrocardiography, electromyography (EMG), and fingerprint recognition, are being developed. In this research, an authentication algorithm using an EMG signal is proposed to supplement the weakness of personal certification techniques...
SVM (Support Vector Machine), a state of the art classifier model is implemented on a computational mobile platform and its performances are evaluated against a low complexity classifier such as SFSVC (Super Fast Vector Support Classifier) on the same platform. For a better comparison, similar implementation for the two architectures are considered, such as using the same basic linear algebra library...
The prediction of short term adverse events occurrence in phototherapy treatment is important for the dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique which can detect the potential short term adverse events occurrence in phototherapy treatments is required for clinicians. Based on data mining, this study tends...
An algorithm that can predict the review rating of a potential business with only existing information about the location and business categories would be an invaluable tool in making investment decisions. Utilizing the Yelp business dataset, we built a model, that can do as such, by classifying whether a potential business belongs to a positively-reviewed class (star ratings greater than or equal...
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