On Service Community Learning: A Co-clustering Approach

2010 ◽  
Author(s):  
Qi Yu ◽  
Manjeet Rege
Keyword(s):  
Community Learning ◽  
Service Community ◽  
Clustering Approach

scholarly journals The Five Accomplishments: A Framework for Obtaining Customer Feedback in a Health Service Community Learning Disability Team

1998 ◽  
Vol 26 (3) ◽  
pp. 94-99 ◽  
Author(s):  
George C. Murray ◽  
Karen McKenzie ◽  
Gillian R. Kidd ◽  
Shradha LakhaniAssistant Psychologist ◽  
Bridget Sinclair
Keyword(s):  
Health Service ◽  
Learning Disability ◽  
Community Learning ◽  
Service Community ◽  
Customer Feedback

Developing the Character of Learning Community: Islamic Education in Nusantara Perspective

2019 ◽  
Author(s):  
Fariha Azalea
Keyword(s):  
Action Research ◽  
Research And Development ◽  
Learning Community ◽  
Lesson Study ◽  
Scientific Publication ◽  
Islamic Education ◽  
Development Model ◽  
Publication Planning ◽  
Community Learning ◽  
Classroom Action Research

This research aims to promote the development of the character of learning community at Madrasah Tsanawiyah of Bantul Regency Yogyakarta, and two problems posed to be addressed: why the character of community learning pattern does not realize as it has been expected, and how does the development of the character of learning community at MTs of Bantul look like? The research uses the Research and Development model that is consisted of four stages: exploration, development, tests, and dissemination. The data were collected by means of observatiosn, interviews, questionaire, and review of documentation. The results show: (1) the character of learning community at the MTs in Bantul Regency has not been fully established because the teachers were not active in preparing their lesson study, and they did not benefit from it. Also, they were too busy in fulfilling their obligations as teachers; (2) the development of the character of learning community at MTs in Bantul could be implemented through Classroom Action Research-based lesson study plans which are consisted of five stages: consolidation of lesson study concepts, explanation of Classroom Action Research as a form of scientific publication, planning, implementation of action, and reflection.

Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

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