A Web-based Multilingual Intelligent Tutor System based on Jackson's Learning Styles Profiler and Expert Systems

Nowadays, Intelligent Tutoring Systems (ITSs) are so regarded in order to improve education quality via new technologies in this area. One of the problems is that the language of ITSs is different from the learner's. It forces the learners to learn the system language. This paper tries to remove this necessity by using an Automatic Translator Component in system structure like Google Translate API. This system carry out a pre-test and post-test by using Expert System and Jackson Model before and after of training a concept. It constantly updates learner model to save all changes in learning process. So this paper offers an E-Learning system which is web-based, intelligent, adaptive, multilingual and remotely accessible where tutors and learners can have non-identical language. It is also applicable Every Time and Every Where (ETEW). Furthermore, it trains the concepts in the best method with any language and low cost.Comment: 12 pages, 2 figures, IAENG Transactions on Electrical Engineering Volume 1 - Special Issue of the International MultiConference of Engineers and Computer Scientists 2012. arXiv admin note: substantial text overlap with arXiv:1304.404

A Novel Sep-Unet Architecture of Convolutional Neural Networks to Improve Dermoscopic Image Segmentation by Training Parameters Reduction

Nowadays, we use dermoscopic images as one of the imaging methods in diagnosis of skin lesions such as skin cancer. But due to the noise and other problems, including hair artifacts around the lesion, this issue requires automatic and reliable segmentation methods. The diversity in the color and structure of the skin lesions is a challenging reason for automatic skin lesion segmentation. In this study, we used convolutional neural networks (CNN) as an efficient method for dermoscopic image segmentation. The main goal of this research is to recommend a novel architecture of deep neural networks for the injured lesion in dermoscopic images which has been improved by the convolutional layers based on the separable layers. By convolutional layers and the specific operations on the kernel of them, the velocity of the algorithm increases and the training parameters decrease. Additionally, we used a suitable preprocessing method to enter the images into the neural network. Suitable structure of the convolutional layers, separable convolutional layers and transposed convolution in the down sampling and up sampling parts, have made the structure of the mentioned neural network. This algorithm is named Sep-unet and could segment the images with 98% dice coefficient

A New Kernel-Based Fuzzy Level Set Method for Automated Segmentation of Medical Images in the Presence of Intensity Inhomogeneity

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation