The Experts below are selected from a list of 865662 Experts worldwide ranked by ideXlab platform
C Philip L Chen - One of the best experts on this subject based on the ideXlab platform.
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optic disk and cup segmentation through fuzzy broad Learning System for glaucoma screening
IEEE Transactions on Industrial Informatics, 2021Co-Authors: Riaz Ali, Bin Sheng, Yan Chen, Po Yang, Younhyun Jung, Jinman Kim, C Philip L ChenAbstract:Glaucoma is an ocular disease that causes permanent blindness if not cured at an early stage. Cup-to-disk ratio (CDR), obtained by dividing the height of optic cup (OC) with the height of optic disk (OD), is a widely adopted metric used for glaucoma screening. Therefore, accurately segmenting OD and OC is crucial for calculating a CDR. Most methods have employed deep Learning methods for the segmentation of OD and OC. However, these methods are very time consuming. In this article, we present a new fuzzy broad Learning System-based technique for OD and OC segmentation with glaucoma screening. We comprehensively integrated extracting a region of interest from RGB images, data augmentation, extracting red and green channel images, and inputting them to the two separate fuzzy broad Learning System-based neural networks for segmenting the OD and OC, respectively, and then calculated CDR. Experiments show that our fuzzy broad Learning System-based technique outperforms many state-of-the-art methods.
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broad Learning System an effective and efficient incremental Learning System without the need for deep architecture
IEEE Transactions on Neural Networks, 2018Co-Authors: C Philip L Chen, Zhulin LiuAbstract:Broad Learning System (BLS) that aims to offer an alternative way of Learning in deep structure is proposed in this paper. Deep structure and Learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the System. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as “mapped features” in feature nodes and the structure is expanded in wide sense in the “enhancement nodes.” The incremental Learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental Learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental Learning is developed for a System that has been modeled encounters a new incoming input. Specifically, the System can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.
Yuehmin Huang - One of the best experts on this subject based on the ideXlab platform.
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the Learning style based adaptive Learning System architecture
International Journal of Online Pedagogy and Course Design archive, 2015Co-Authors: Chyunchyi Chen, Posheng Chiu, Yuehmin HuangAbstract:In the current study of Learning process that show learners will take a different way and use different types of Learning resources in order to Learning better. Any many researchers also agree that Learning materials must be able to meet the various Learning styles of learners. Therefore, let learners can effective to improve their Learning, for different Learning styles of learners should be given different types of Learning materials. In this paper the authors propose a learner's Learning style-based adaptive Learning System architecture that is designed to help learners advance their on-line Learning along an adaptive Learning path. The investigation emphasizes the relationship of Learning content to the Learning style of each participant in adaptive Learning. An adaptive Learning rule was developed to identify how learners of different Learning styles may associate those contents which have the higher probability of being useful to form an optimal Learning path. In this adaptive Learning System architecture, it will according to different Learning styles given different types of Learning materials and will according to learner's profile to adjust learner's Learning style for providing suitable Learning materials.
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a context aware mobile Learning System for supporting cognitive apprenticeships in nursing skills training
Educational Technology & Society, 2012Co-Authors: Gwojen Hwang, Yuehmin HuangAbstract:The aim of nursing education is to foster in students the competence of applying integrated knowledge with clinical skills to the application domains. In the traditional approach, in-class knowledge Learning and clinical skills training are usually conducted separately, such that the students might not be able to integrate the knowledge and the skills in performing standard nursing procedures. Therefore, it is important to develop an integrated curriculum for teaching standard operating procedures in physical assessment courses. In this study, a context-aware mobile Learning System is developed for nursing training courses. During the Learning activities, each student is equipped with a mobile device; moreover, sensing devices are used to detect whether the student has conducted the operations on the correct location of the dummy patient’s body for assessing the physical status of the specified disease. The Learning System not only guides individual students to perform each operation of the physical assessment procedure on dummy patients, but also provides instant feedback and supplementary materials to them if the operations or the operating sequence is incorrect. The experimental results show that the students’ Learning outcomes are notably improved by utilizing the mobile Learning System for nursing training.
Qi Luo - One of the best experts on this subject based on the ideXlab platform.
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research on mobile english assistant Learning System based on wireless communication
International Conference on Pervasive Computing, 2007Co-Authors: Guoliang Zhang, Feng Xiong, Qi LuoAbstract:In order to improve Learning flexibility and efficiency, and break the constraints of time and space, an English mobile Learning System based on Bluetooth as proposed and realized in the paper. English mobile Learning System used Bluetooth technology as the wireless communication media. To accommodate with the characteristic of portable devices, the System is designed for low power consumption and low Learning cost. With the help of the System, a teacher could receive instant feedback from the students' mean while a student can receive course information at any time. After the test of System in net or school, they found that students could actively participate in the English class. The System architecture, security issues, and techniques are also explained in the paper.
Gwojen Hwang - One of the best experts on this subject based on the ideXlab platform.
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development of an adaptive Learning System with multiple perspectives based on students Learning styles and cognitive styles
Educational Technology & Society, 2013Co-Authors: Tzuchi Yang, Gwojen Hwang, Stephen J H YangAbstract:In this study, an adaptive Learning System is developed by taking multiple dimensions of personalized features into account. A personalized presentation module is proposed for developing adaptive Learning Systems based on the field dependent/independent cognitive style model and the eight dimensions of Felder-Silverman's Learning style. An experiment has been conducted to evaluate the performance of the proposed approach in a computer science course. Fifty-four participants were randomly assigned to an experimental group which learned with an adaptive Learning System developed based on the personalized presentation module, and a control group which learned with the conventional Learning System without personalized presentation. The experimental results showed that the experimental group students revealed significantly better Learning achievements than the control group students, implying that the proposed approach is able to assist the students in improving their Learning performance.
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a context aware mobile Learning System for supporting cognitive apprenticeships in nursing skills training
Educational Technology & Society, 2012Co-Authors: Gwojen Hwang, Yuehmin HuangAbstract:The aim of nursing education is to foster in students the competence of applying integrated knowledge with clinical skills to the application domains. In the traditional approach, in-class knowledge Learning and clinical skills training are usually conducted separately, such that the students might not be able to integrate the knowledge and the skills in performing standard nursing procedures. Therefore, it is important to develop an integrated curriculum for teaching standard operating procedures in physical assessment courses. In this study, a context-aware mobile Learning System is developed for nursing training courses. During the Learning activities, each student is equipped with a mobile device; moreover, sensing devices are used to detect whether the student has conducted the operations on the correct location of the dummy patient’s body for assessing the physical status of the specified disease. The Learning System not only guides individual students to perform each operation of the physical assessment procedure on dummy patients, but also provides instant feedback and supplementary materials to them if the operations or the operating sequence is incorrect. The experimental results show that the students’ Learning outcomes are notably improved by utilizing the mobile Learning System for nursing training.
Richi Nayak - One of the best experts on this subject based on the ideXlab platform.
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a user driven data mining process model and Learning System
Database Systems for Advanced Applications, 2008Co-Authors: Richi NayakAbstract:This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A Learning System framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the System. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS System and the QBFS algorithm is successfully demonstrated with a real-world application.
