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Machine learning classifiers help physicians to make near-perfect diagnoses, minimizing costs and time. Since medical data usually contains a high degree of uncertainty and ambiguity, proper ordering and classification require a proper comparative performance analysis of machine learning classifiers. Machine learning classifiers are applied on the Ovarian Cancer Dataset. Ovarian cancer is the fifth...
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...
The Robot based landmine detection problem is a multiphase problem in which one element is the classification of landmines and clutter. To design an efficient and effective classification model requires considering factors such as the failure to detect a landmine, detection time and the high amount of false alarms that occur due of improper classification. In the absence of an extensive analysis on...
With the large volume of network traffic flow, it is necessary to preprocess raw data before classification to gain the accurate results speedily. Feature selection is an essential approach in preprocessing phase. The Principal Component Analysis (PCA) is recognized as an effective and efficient method. In this paper, we classify network traffic by using the PCA technique together with six machine...
Dengue virus infection or dengue fever is caused by the dengue virus (DENV). It is transmitted to humans by mosquitoes. There are four serotypes classified together based on their surface antigens. Each serotype can provide specific immunity and short-term cross-immunity in human. Several studies have examined the classification of dengue molecules into four major classes including methods such as...
A challenge is indexing the facial beauty by a machine as same evaluated by human beings. A question arises: Can beauty be learnt by machines? Every individual have different concept of facial beauty. Somebody can be attracted by someone but might not be by another person. In recent past, many psychologists, neurologists and other scientists have done tremendous work in this area. This work presents...
In this paper a universal, coarse-grained reconfigurable architecture for hardware acceleration of decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs) is proposed. Using proposed architecture, two versions of DTs (Functional DT and Axis-Parallel DT), two versions of SVMs (with polynomial and radial kernels) and two versions of ANNs (Multi Layer Perceptron and...
Image classification is one of the most multifaceted disciplines in image processing. There are quite a few approaches to categorize images and they offer good classification outcome but they not be up to snuff to provide acceptable classification upshots when the image comprises blurry content. The two chief techniques for image classification are supervised and unsupervised classification. Mutually...
Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’...
the objective to develop clinical decision support system (CDSS) tools is to help physicians making faster and more reliable clinical decisions. The first step in their development is choose a machine learning classifier as the system core. Previous works reported implementation of artificial neural networks, support vector machines, genetic algorithms, etc. as core classifiers for CDSS; however,...
In this paper an ensemble model is proposed for the recognition of Odia handwritten character. The ensemble model is constructed from four base classifiers: Support Vector Machine (SVM), Artificial Neural Network (ANN), C5.0 Decision Tree and Discriminant Analysis (DA). Gradient and curvature based features are extracted from the numerals and a combination of gradient and curvature based features...
Extreme learning machine (ELM) is an efficient learning algorithm which can be easily used with least human intervene. But when ELM is applied as multiclass classifier, the results of some classes are not satisfactory and it's hard to adjust the parameters for these classes without affecting other classes. To overcome these limitations, a novel method is proposed. In proposed approach, binary ELM...
Common stream mining tasks include classification, clustering and frequent pattern mining among them, data stream classification has drawn particular attention due to its vast real-time application. Through these applications, the main goal is to efficiently build classification models from data streams for accurate prediction. The development of such model has shown the need for machine learning...
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,...
Malware family identification is a complex process involving extraction of distinctive characteristics from a set of malware samples. Malware authors employ various techniques to prevent the identification of unique characteristics of their programs, such as, encryption and obfuscation. In this paper, we present n-gram based sequential features extracted from content of the files. N-grams are extracted...
Support vector machine (SVM) has been widely used for its outstanding performance. But, it still has flaws. One of them is that SVM is unit sensitive. In this paper, we analyze how will the different units effect the SVM. Then, we propose a preprocess method not only to conquer this flaw, but also improve the generalization precision of SVM. The preprocess method is base on decision tree(DT). The...
Support vector machines (SVMs) proved to be highly efficient in various classification tasks. However, the knowledge learned by the SVM is encoded in a long list of parameter values and it is not easy to comprehend what the SVM is actually computing. We show that certain types of SVMs are mathematically equivalent to a specific fuzzy — rule base, the fuzzy all — permutations rule base (FARB). This...
Land cover change assessment is one of the main applications of remote sensed data. Change in forest cover have widespread effects on the provision of ecosystem services, and provide important feedbacks to climate change and biodiversity. Moreover, it will be extremely critical if the accuracy of image interpretation can be improved for better understanding the change of forest. Parametric methods...
An Intrusion detection system is designed to classify the system activities into normal and abnormal. We use a combination of machine learning approaches as to detect the system attacks. The experimental results of the study show that increasing the number of classifiers has a threshold limit and the system accuracy will remain constant if the number of classifiers goes beyond this limit. The determination...
MicroRNAs are one type of noncoding RNA that regulate their target mRNAs before mRNAs are translated into proteins. Although it has been demonstrated that the regulation is through partial binding of the seed region of a miRNA and its targets, the mechanism of this process is not fully discovered. Some biological experiments have shown that even perfect base pairing in the seed region does not always...
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