scholarly journals A case study on machine learning model for code review expert system in software engineering

2017 ◽  
Author(s):  
Michał Madera ◽  
Rafał Tomoń
Keyword(s):  
Machine Learning ◽  
Expert System ◽  
Software Engineering ◽  
Learning Model ◽  
Code Review ◽  
Machine Learning Model

scholarly journals SELM: Software Engineering of Machine Learning Models

2021 ◽  
Author(s):  
Nafiseh Jafari ◽  
Mohammad Reza Besharati ◽  
Maryam Hourali
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Interdisciplinary Approach ◽  
Interdisciplinary Teams ◽  
Process Efficiency ◽  
Training Dataset ◽  
Learning Models ◽  
Machine Learning Model ◽  
Machine Learning Models

One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams’ proposals for machine learning.

scholarly journals An Approaching Machine Learning Model: Tile Inspection Case Study

2020 ◽  
Vol 4 (01) ◽  
pp. 12-22
Author(s):  
Murman Dwi Prasetio
Keyword(s):  
Machine Learning ◽  
Work Productivity ◽  
Basic Needs ◽  
Learning Model ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Machine Learning Model ◽  
Inspection Process ◽  
Image Test

Clothing, food, and shelter are three basic types of needs in our lives. If one of the basic needs is not met then there can be an imbalance in our lives. One of the basic needs is to build a house. House needs a tile or roof to cover of a building that can protect all weather influences. One company in Majalengka only uses fleeting vision in inspection process. This can result in a decrease in work productivity. This paper proposed an approach machine learning model for classification of defects was carried out in the inspection process. Feature extraction was performed using the Local Binary Pattern (LBP) method to obtain training features. The next stage is training (training) to the characteristics of training that has been obtained. Furthermore, the database obtained from the training results will be used to classify tile image test data using the Support Vector Machine (SVM) method. From the test results, the system is made capable of classifying defects of a maximum accuracy value of 63.21%. The results obtained are the best accuracy value generated is 76.67% with LBP parameters used are 256 × 256 cell size and radius 2. While for SVM parameters use Polynomial kernel type or RBF with OAA multiclass

scholarly journals Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0231300
Author(s):  
Kenneth D. Roe ◽  
Vibhu Jawa ◽  
Xiaohan Zhang ◽  
Christopher G. Chute ◽  
Jeremy A. Epstein ◽  
...  
Keyword(s):  
Machine Learning ◽  
Expert Knowledge ◽  
Learning Model ◽  
Model Complexity ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
And Performance ◽  
Clinical Expert
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