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Breast cancer is invasive cancer among world's women above 35 years of age. The most common symptoms of breast cancer are lumps, change in shape/skin colour and liquid oozing out from nipple. Breast cancer mostly starts from breast tissues that are either in lobules or in milk ducts. Ductal carcinoma is the common type of breast cancer starts from milk ducts and spread across the. Women between the...
The Zernike moments can achieve high accuracy and strong robustness for the classification and retrieval of images, but involve huge amount of computation caused by its complex definition. It has limited its exploitation in online real-time applications or big data processing. So researches on how to improve the computation speed of Zernike moments are carried out. One of the existing high-accuracy...
Data mining has recently drawn a lot of interests as an effective way of generating a concept map in an adaptive learning system that provides students with the personalized learning guidance. Even with significant progresses witnessed in this field, the data mining-based concept map generation needs further improvement both in accuracy and complexity before it can be employed in actual education...
Integrative complexity is a construct developed in political psychology and clinical psychology to measure an individual's ability to consider different perspectives on a particular issue and reach a justifiable conclusion after consideration of said perspectives. Integrative complexity (IC) is usually determined from text through manual scoring, which is time-consuming, laborious and expensive. Consequently,...
Recently strong AI emerged from artificial intelligence due to need for a thinking machine. In this domain, it is necessary to deal with dynamic incomplete data and understanding of how machines make their decision is also important, especially in information system domain. One type of learning called Covering Algorithms (CA) can be used instead of the difficult statistical machine learning methods...
A statistical analysis of the separability of EEG A-phases, with respect to basal activity, is presented in this study. A-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around...
Clustering is an exploratory data analysis technique, which categorizes the dataset into some groups. These groups are formed in a way so that items which have similar features live in same group and those have dissimilar features remain in other. There are many clustering algorithm available. Different kinds of algorithms are best used for different kinds of data. K-means is most used clustering...
In mining massive datasets, often two of the most important and immediate problems are sampling and feature selection. Proper sampling and feature selection contributes to reducing the size of the dataset while obtaining satisfactory results in model building. Theoretically, therefore, it is interesting to investigate whether a given dataset possesses a critical feature dimension, or the minimum number...
Online network traffic measurements and analysis is critical for detecting and preventing any real-time anomalies in the network. We propose, implement, and evaluate an online, adaptive measurement platform, which utilizes real-time traffic analysis results to refine subsequent traffic measurements. Central to our solution is the concept of Multi-Resolution Tiling (MRT), a heuristic approach that...
The paper presents some experiments investigating the applicability of the Principal Component Analysis method for solving several concept learning tasks defined on images of faces. The results have shown that, in most cases, the applied transformation improves the classification accuracy of used concept learning algorithms. In addition the experiments have confirmed a possible relation between the...
A large number of non-dominated fuzzy rule-based classifiers are often obtained by applying a multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm to a pattern classification problem. The obtained set of non-dominated classifiers can be used to analyze their accuracy-interpretability tradeoff relation. One important issue, which has not been discussed in many studies on MoFGBML,...
In this paper, we exploit a multi-objective evolutionary algorithm (MOEA) to generate fuzzy rule-based classifiers (FRBCs) with different trade-offs between classification accuracy and rule base complexity. In order to learn the rule base we employ a rule and condition selection (RCS) approach which aims to select a reduced number of rules from a heuristically generated rule base and concurrently...
In this paper, a new fast compressive sensing (CS) algorithm for phoneme classification is introduced. In this approach, unlike common CS classification approaches that use CS as a classifier, we use CS as an N-best class selector to limit the secondary classifier input into certain classes. In addition, we use a tree search strategy to select most similar training set for the specific test sample...
Text classification is the process of assigning document to a set of previously fixed categories. It is widely used in many applications, such as web page categorization, email spam filtering, and document indexing, etc. Many popular algorithms for text classification have been proposed, such as Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). However, these classification...
Automatic image annotation is an important but highly challenging problem in semantic-based image retrieval. In this paper, we formulate image annotation as a supervised learning image classification problem under region-based image annotation framework. In region-based image annotation, keywords are usually associated with individual regions in the training data set. This paper applys a novel simple...
This paper analyzes the existing decision tree classification algorithms and finds that these algorithms based on variable precision rough set (VPRS) have better classification accuracies and can tolerate the noise data. But when constructing decision tree based on variable precision rough set, these algorithms have the following shortcomings: the choice of attribute is difficult and the decision...
Among the rule induction algorithms, the classic CN2 is still one of the most popular ones; a great amount of enhancements and improvements to it is to witness this. Despite the growing computing capacities since the algorithm was proposed, one of the main issues is resource demand. The proposed modification, CN2-R, substitutes the star concept of the original algorithm with a technique of randomly...
Naive Bayes classifier has good performance on many datasets, however, the performance is very poor on some datasets which have a strong correlation between attributes due to the conditional independence assumption is not always true in the real world. In the latest Hidden Naive Bayes (HNB) algorithm, each attribute corresponds to a hidden parent which combines the influences of all other attributes...
A new low complexity seizure prediction algorithm is proposed. The algorithm achieves high sensitivity and low false positive rates in 10 out of 18 epileptic patients from the Freiburg database. Its primary achievement is two orders of magnitude computational complexity reduction. The reduced complexity makes an implantable medical device application realizable. In the subset of ten highly predictable...
Automated Essay Scoring is a very significant research subject for the processing of machine scoring. Computer-based College English Test (National English level test) further motivates the research of AES system for Chinese English leaner. In this paper, we introduce an effective AES system based on computer-based CET4. Features belong to three domains: language quality, content and organization,...
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