Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

International audienceBlood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and Sparse Coding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details

Implementation Equal-Width Interval Discretization in Naive Bayes Method for Increasing Accuracy of Students' Majors Prediction

The Selection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the field of interest. In previous research, Naive Bayes method has been tested to classify the student’s department based on the criteria that support the case study on Private Madrasah Aliyah PAB 6 Helvetia students and the accuracy of the test from 100 student data is 90%. in this study, the researcher developed a previously used method by applying an equal-width interval discretization that would transform numerical or continuous criteria into a categorical criteria with a predetermined k value, different k values ??would be tested to find the best accuracy value. from the 120-student data that have been tested, it is proved that the result of the classification of the application of equal-width interval discretization on the Naive Bayes method with the value of k = 8 is better and increased the accuracy value 91.7% to 93.3%

An improved bees algorithm local search mechanism for numerical dataset

Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any optimum solutions for the problem mainly because of lack of accuracy. To solve this issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Background: Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. Methods: This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. Results and conclusion: The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed

Optimization of Coastal Cruise Lines in China

The paper analyzes the current state of the Chinese cruise market and presents the idea of building a business model of coastal cruising. The cruise demand of middle-income families, which includes the desired travel days, ports of call, is surveyed. The data of the previous non-cruise travels and the data of future cruises of middle-income families are used to develop a model designed to identify the maximum passenger volume with minimum operating costs while taking cruise itineraries and schedules into account. A matrix coding genetic algorithm was designed to solve the model. The case study found that a voyage of 4.79 days results in equilibrium, that the annual demand is 200,840 passengers, and that the daily voyage cost is 0.843 million Yuan

Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection

Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effective features, using an extended wrapper method, ensemble classification is performed. The extended feature selection approach includes a prior feature filtering and a wrapper approach using C4.5 decision tree. Ensemble classification, using cost sensitive decision trees is performed in a decision forest framework. A locally gathered fraud detection dataset is used to estimate the proposed method. The proposed method is assessed using accuracy, recall, and F-measure as evaluation metrics and compared with basic classification algorithms including ID3, J48, Naïve Bayes, Bayesian Network and NB tree. Experiments show that considering the F-measure as evaluation metric, the proposed approach yields 1.8 to 2.4 percent performance improvement compared to other classifiers