Tạp chí tin học và điều khiển học

This paper presents an automatic Heart Disease (HD) prediction method based on feature selection and data mining techniques using provided symptoms and clinical information in the patient’s dataset. Data mining which allows the extraction of hidden knowledges from the data and explores the relationship between attributes, is the promising technique for HD prediction. HD symptoms can be effectively learned by the computer to classify HD into different classes. However, the informationprovided may include redundant and interrelated symptoms. The use of such information may degrade the classification performance. Feature selection is an effective way to remove such noisy informationmeanwhile improving the learning accuracy and facilitating a better understanding for learning model. In our method, HD attributes are re-selected based on their rank and weights assigned by Infinite LatentFeature Selection (ILFS) method. Support Vector Machine (SVM) algorithm is applied to classify a subset of the selected attributes into different HD classes. SMOTE (Synthetic Minority Over-sampling Technique) data over-sampling technique is adopted to generate more amounts and varieties of data. The experiment is performed on the UCI Machine Learning Repository Heart Disease public dataset. Experimental results demonstrated that by only using a subset of selected 24 attributes over a total of 46 attributes, our method achieved an accuracy of 97.87% for distinguishing ‘no presence’ HD with ‘presence’ HD and an accuracy of 93.92% for distinguishing 5 different classes of HD.

Approximation of a picture fuzzy set on a crisp approximation space gives a rough picture fuzzy set. In this paper, the concept of a rough picture set is introduced, besides, we also investigate some topological structures of a rough picture fuzzy set are investigated, such are lower and upper rough picture fuzzy approximation operators.

Recently, picture fuzzy clustering (FC-PFS) has been introduced as a new computational intelligence tool for various problems in knowledge discovery and pattern recognition. However, an important question that was lacked in the related researches is examination of mathematical properties behind the picture fuzzy clustering algorithm such as the convergence, the boundary or the convergence rate, etc. In this paper, we will prove that FC-PFS converges to at least one local minimum. The similarities and differences between this algorithm and other clustering methods are compared. Analysis on the loss function is also considered.

The dissimilarity measures between fuzzy sets/intuitionistic fuzzy sets/picture fuzzy sets are studied and applied in various matters. In this paper, we propose some new dissimilarity measures on picture fuzzy sets. This new dissimilarity measures overcome the restrictions of all existing dissimilarity measures on picture fuzzy sets. After that, we apply these new measures to the pattern recognition problems. Finally, we introduce a multi-criteria decision making (MCDM) method that used the new dissimilarity measures and apply them in the supplier selection problems.

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Picture fuzzy set (2013) is a generalization of the Zadeh‟ fuzzy set (1965) and the Antanassov‟intuitionistic fuzzy set. The new concept could be useful for many computational intelligentproblems. Basic operators of the picture fuzzy logic were studied by Cuong, Ngan [10,11 ].Newconcept –Pythagorean picture fuzzy set ( PPFS) is a combination of Picture fuzzy set with theYager‟s Pythagorean fuzzy set [12-14].First, in the Part 1 of this paper, we consider basic notionson PPFS as set operators of PPFS‟s , Pythagorean picture relation, Pythagorean picture fuzzy softset. Next, the Part 2 of the paper is devoted to main operators in fuzzy logic on PPFS: picturenegation operator, picture t-norm, picture t-conorm, picture implication operators on PPFS.As aresult we will have a new branch of the picture fuzzy set theory.

Trong những thập niên gần đây, một số mở rộng của khái niệm tập mờ được đề xuất. Tập mờ loại hai và tập mờ trực cảm là hai khái niệm mới đã thu hút được rất nhiều sựquan tâm của các nhà nghiên cứu vì sự phong phú của các ứng dụng. Bài báo này giới thiệu một khái niệm mới – tập mờ trực cảm loại hai và chứng minh một số tính chất của các phép toán trên đó.

Along with the explosion of user reviews on the Internet, sentiment analysis has becomeone of the trending research topics in the field of natural language processing. In the last five years,many shared tasks were organized to keep track of the progress of sentiment analysis for various lan-guages. In the Fifth International Workshop on Vietnamese Language and Speech Processing (VLSP2018), the Sentiment Analysis shared task was the first evaluation campaign for the Vietnamese lan-guage. In this paper, we describe our system for this shared task. We employ a supervised learningmethod based on the Support Vector Machine classifiers combined with a variety of features. Weobtained the F1-score of 61% for both domains, which was ranked highest in the shared task. For theaspect detection subtask, our method achieved 77% and 69% in F1-score for the restaurant domainand the hotel domain respectively.

In this paper we construct two families of nonstandard finite difference (NSFD) schemes preserving the essential properties of a computer virus propagation model, such as positivity, boundedness and stability. The first family of NSFD schemes is constructed based on the nonlocal discretization and has first order of accuracy, while the second one is based on the combination of a classical Runge-Kutta method and selection of a nonstandard denominator function and it is of fourth order of accuracy. The theoretical study of these families of NSFD schemes is performed with support of numerical simulations. The numerical simulations confirm the accuracy and the efficiency of the fourth order NSFD schemes. They hint that the disease-free equilibrium point is not only locally stable but also globally stable, and then this fact is proved theoretically. The experimental results also show that the global stability of the continuous model is preserved.

In data mining, anomaly detection aims at identifying the observations which do not conform to an expected behavior. To date, a large number of techniques for anomaly detection have been proposed and developed. These techniques have been successfully applied to many real world applications such as fraud detection for credit cards and intrusion detection in network security. However, there are very little research relating to the method for evaluating the goodness of unsupervised anomaly detection techniques. In this paper, the authors introduce a method for evaluating the performance of unsupervised anomaly detection techniques. The method is based on the application of internal validation metrics in clustering algorithms to anomaly detection. The experiments were conducted on a number of benchmarking datasets. The results are compared with the result of a recent proposed approach that shows that some proposed metrics are very consistent when being used to evaluate the performance of unsupervised anomaly detection algorithms.