TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model

2007 ◽  
Author(s):  
G. Howells ◽  
B. Howlett ◽  
K. McDonald-Maier
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Condition Monitoring ◽  
Network Model ◽  
Neural Network Model ◽  
Complex Data

scholarly journals Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

2020 ◽  
Vol 12 (8) ◽  
pp. 137
Author(s):  
Bo Jiang ◽  
Yanbai He ◽  
Rui Chen ◽  
Chuanyan Hao ◽  
Sijiang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Teaching And Learning ◽  
Large Scale ◽  
Small Scale ◽  
Teaching Improvement ◽  
Data Feedback ◽  
Learning Data

Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.

Neural network model for condition monitoring of wear and film thickness in a gearbox

2013 ◽  
Vol 24 (7-8) ◽  
pp. 1943-1952 ◽  
Author(s):  
C. Sreepradha ◽  
A. Krishna Kumari ◽  
A. Elaya Perumal ◽  
Rames C. Panda ◽  
K. Harshabardhan ◽  
...  
Keyword(s):  
Neural Network ◽  
Film Thickness ◽  
Condition Monitoring ◽  
Network Model ◽  
Neural Network Model

Artificial Neural Network Model as a Data Analysis Tool in Precision Farming

2006 ◽  
Vol 49 (6) ◽  
pp. 2027-2037 ◽  
Author(s):  
A. Irmak ◽  
J. W. Jones ◽  
W. D. Batchelor ◽  
S. Irmak ◽  
K. J. Boote ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Precision Farming ◽  
Analysis Tool ◽  
Data Analysis Tool ◽  
Artificial Neural

scholarly journals A SURVEY ON DATA ANALYSIS APPROACH FOR EFFECTIVE CONTAINMENT TOWARDS PANDEMIC INFECTIONS USING NEURAL NETWORKS

2021 ◽  
pp. 559-563
Author(s):  
Anix Mary Javitha. A ◽  
Dr, Mary Livinsa. Z
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Human Life ◽  
Maximum Level ◽  
Research Area ◽  
Analysis Approach ◽  
Near Future

This Covid-19 is a new pandemic infectious era for our human life, in order to deal with it in proper manner, the necessity and importance of data analysis research reports, models, approaches are of the basic requirements for our sustainable, patience and stress less future. Neural network model organization and development with proper incorporation of pandemic condition parameters with maximum level of accuracy and efficiency are the main issues related with data analysis in pandemic infection containment. The implementation of neural networks techniques associated with data analysis approaches will be the best combination in this research area in near future. KEYWORDS— Neural networks, Covid-19, Pandemic ,Data analysis, Containment.

Predicting the determinants of online learning adoption during the COVID-19 outbreak: a two-staged hybrid SEM-neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nattaporn Thongsri ◽  
Chalothon Chootong ◽  
Orawan Tripak ◽  
Piyaporn Piyawanitsatian ◽  
Rungtip Saengae
Keyword(s):  
Neural Network ◽  
Higher Education ◽  
Online Learning ◽  
Data Analysis ◽  
Empirical Study ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Context ◽  
Content Type ◽  
Study Results

Purpose This study aims to study the adoption of online learning in higher education through the perspective of the readiness of the following factors: self-directed learning (SDL), motivation for learning (ML), online communication self-efficacy (OCE) and learner control (LC). This was an empirical study in the context of developing countries, specifically Thailand. Design/methodology/approach This research applied a quantitative study method by collecting data from 605 higher education students in autonomous government institutions. The data analysis applied a structural equation model (SEM) to identify the significant determinants that affected the adoption of online learning. Moreover, this study applied a neural network model to examine the findings from the SEM. Findings From the data analysis using the SEM and neural network model, the results matched each other. The results of the empirical study were firm and supported that the readiness factors of students had statistical significance in the following order: SDL, OCE, LC and ML. Practical implications The study results showed an operational perspective to be prepared for online teaching, both for the related department of the Ministry of Education to support the infrastructure for online learning and for universities and instructors to create learning conditions and design teaching processes consistently with the online learning context. Originality/value Since the learning management in the 21st century is focused on student-centred learning, the empirical results obtained from this study presented the view of learners’ readiness that would influence the acceptance of online learning. In addition, this research presented the challenges and opportunities of online instruction during the COVID-19 pandemic.

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