Models for learning data.

1991 ◽  
pp. 47-68 ◽  
Author(s):  
M. W. Browne ◽  
S. H. C. Du Toit
Keyword(s):  
Learning Data

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

PENINGKATAN KEMANDIRIAN BELAJAR SISWA SMP MELALUI PEMBELAJARAN BERBASIS MASALAH

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Tanti Jumaisyaroh Siregar
Keyword(s):  
Problem Based Learning ◽  
Mathematic Teacher ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Quasi Experimental ◽  
Independent Variable ◽  
Direct Learning ◽  
The Difference ◽  
And Control ◽  
Learning Data

The purposes of this research were to know: the difference of improvement in self-regulated learning of students that given problem-based learning with students that given  direct learning. The type of this research is a quasi-experimental research by taking samples from the existing population. The variable of this research consist of independent variable that is problem based learning model while the dependent variable isself regulated learning of student.The population of this research is all students of SMP Swasta Ar-rahman Percut and the sample of this research is grade eight with taken sample two classes (experiment and control)  with total 60 students. The instrument of this research were: scale of self-regulated learning. Data that have been collected then analyzed and performed hypothesis testing by using T-test. Based of the results analysis, it showed that: improvment  of the students’ self-regulated learning that given problem-based learning was higher than the students’ ability that given direct learning His then, suggested that problem-based learning be used as an alternative for mathematic teacher to improved students’ ability in mathematical critical thinking and self-regulated learning.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

A Manifold Learning Data Enrichment Methodology for Homicide Prediction

2020 ◽  
Author(s):  
Juan S. Moreno Pabon ◽  
Mateo Dulce Rubio ◽  
Yor Castano ◽  
Alvaro J. Riascos ◽  
Paula Rodriguez Diaz
Keyword(s):  
Manifold Learning ◽  
Data Enrichment ◽  
Learning Data

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

scholarly journals STEM Faculty Instructional Data-Use Practices: Informing Teaching Practice and Students’ Reflection on Students’ Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 291
Author(s):  
Cindy Lenhart ◽  
Jana Bouwma-Gearhart
Keyword(s):  
Professional Development ◽  
Instructional Technology ◽  
Teaching Practice ◽  
Data Use ◽  
Faculty Members ◽  
Institutional Policies ◽  
Policies And Procedures ◽  
Stem Faculty ◽  
Standardized Curriculum ◽  
Learning Data

This paper explores the affordances and constraints of STEM faculty members’ instructional data-use practices and how they engage students (or not) in reflection around their own learning data. We found faculty used a wide variety of instructional data-use practices. We also found several constraints that influenced their instructional data-use practices, including perceived lack of time, standardized curriculum and assessments predetermined in scope and sequence, and a perceived lack of confidence and competence in their instructional data-use practices. Novel findings include faculty descriptions of instructional technology that afforded them access to immediate and nuanced instructional data. However, faculty described limited use of instructional data that engaged students in reflecting on their own learning data. We consider implications for faculty’s instructional data-use practices on departmental and institutional policies and procedures, professional development experts, and for faculty themselves.

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