An efficient active learning method for multi-task learning

Segmentation of Brain Tissues from MRI Images Using Multitask Fuzzy Clustering Algorithm

In recent years, brain magnetic resonance imaging (MRI) image segmentation has drawn considerable attention. MRI image segmentation result provides a basis for medical diagnosis. The segmentation result influences the clinical treatment directly. Nevertheless, MRI images have shortcomings such as noise and the inhomogeneity of grayscale. The performance of traditional segmentation algorithms still needs further improvement. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. First, we introduce multitask learning strategy into FCM to extract public information among different segmentation tasks. It combines the advantages of the two algorithms. The algorithm enables to utilize both public information among different tasks and individual information within tasks. Then, we design an adaptive task weight learning mechanism, and a weighted multitask fuzzy C-means (WMT-FCM) clustering algorithm is proposed. Under the adaptive task weight learning mechanism, each task obtains the optimal weight and achieves better clustering performance. Simulated MRI images from McConnell BrainWeb have been used to evaluate the proposed algorithm. Experimental results demonstrate that the proposed method provides more accurate and stable segmentation results than its competitors on the MRI images with various noise and intensity inhomogeneity.

Interest and Awareness as Factors Mediating the Content of Online Search Queries Made by Schoolchildren When They Are Doing Simple and Problem-Oriented Training Assignments

Online search for additional information is an essential element of the educational activities of modern schoolchildren. However, the factors determining its effectiveness remain understudied. The article presents the results of a study of the contribution of awareness in the subject field of online search and interest in the subject of a training assignment to the content characteristics of online search queries made by schoolchildren when they are doing simple and problem-oriented training assignments. The study involved 206 schoolchildren studying in grades 5-9 of secondary schools, including 115 girls and 91 boys. The average age of the study participants was 13.24 ±1.56 years. The study was conducted using the experimental method and the questionnaire method. The study participants were asked to sequentially find information on the Internet and do first a simple training assignment, then a problem-oriented one, and assess their interest and awareness in the subject field of the search query. Mathematical processing was carried out using the Fischer φ* criterion and Mann - Whitney U test, ANOVA, and regression analysis. As a result, it was revealed that interest in the topic of the search query is not related to its qualitative and quantitative characteristics. The schoolchildren’s awareness is related to the qualitative characteristics of clarifying search queries rather than to their number. It was found that simple training assignments are more conducive to searching the Internet for ready-made solutions for their implementation. Problem-oriented training assignments are more effective for stimulating schoolchildren’s independent information search on the Internet than simple training assignments. The results of the study can be used for developing educational materials in the general educational practice.

Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.