Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades

Kumar, P. Suresh; Behera, Himansu Sekhar; K, Anisha Kumari; Nayak, Janmenjoy; Naik, Bighnaraj

doi:10.1016/j.cosrev.2020.100288

Cited by 72 publications

(23 citation statements)

References 41 publications

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“…Graphs of the value of the learning and classifying errors for a multilayer perceptron are presented in Figure 4 . To prevent the retraining process of the neural network [ 18 ], the set of metallographic images is divided into 2 subsets, namely learning and control ones. Based on the error graphs, the optimal number of learning epochs for the multilayer perceptron with structure 550-150-10 was calculated.…”

Section: Algorithm For Automated Metallographic Analysis Of Metals Ba...mentioning

confidence: 99%

Application of artificial intelligence technologies in metallographic analysis for quality assessment in the shipbuilding industry

Emelianov

Zhilenkov

Chernyi

et al. 2022

Heliyon

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Section: Algorithm For Automated Metallographic Analysis Of Metals Ba...mentioning

confidence: 99%

Application of artificial intelligence technologies in metallographic analysis for quality assessment in the shipbuilding industry

Emelianov

Zhilenkov

Chernyi

et al. 2022

Heliyon

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“…As shown in Figure 4b, the error is increased when steps are more than 800. This fact is explained by the retraining process [18]. To prevent the retraining process [18], the set of microstructure images is divided into two sub-sets, namely The neural network was learnt on the basis of reference images of metal microstructures described in the standards.…”

Section: Algorithm For Automating Metallographic Quality Control Of Metalsmentioning

confidence: 99%

“…This fact is explained by the retraining process [18]. To prevent the retraining process [18], the set of microstructure images is divided into two sub-sets, namely The neural network was learnt on the basis of reference images of metal microstructures described in the standards. The training sample consisted of 950 images of microstructures, of which 475 images belonged the "correct" class, and 475 images to the "incorrect" class.…”

Section: Algorithm For Automating Metallographic Quality Control Of Metalsmentioning

confidence: 99%

“…This fact is explained by the retraining process [18]. To prevent the retraining process [18], the set of microstructure images is divided into two sub-sets, namely learning and control ones. Based on the graphs of learning and classification errors, the optimal number of learning epochs was calculated to be 800 epochs for this neural network structure.…”

Section: Algorithm For Automating Metallographic Quality Control Of Metalsmentioning

confidence: 99%

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Application of Artificial Intelligence Technologies to Assess the Quality of Structures

2021

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The timeliness of the complex automated diagnostics of the metal condition for all characteristics has been substantiated. An algorithm for the automation of metallographic quality control of metals is proposed and described. It is based on the use of neural networks for recognizing images of metal microstructures and a precedent method for determining the metal grade. An approach to preliminarily process the images of metal microstructures is described. The structure of a neural network has been developed to determine the quantitative characteristics of metals. The results of the functioning of neural networks for determining the quantitative characteristics of metals are presented. The high accuracy of determining the characteristics of metals using neural networks is shown. Software has been developed for the automated recognition of images of metal microstructures, and for the determination of the metal grade. Comparative results of carrying out metallographic analysis with the developed tools are demonstrated. As a result, there is a significant reduction in the time required for analyzing metallographic images, as well as an increase in the accuracy of determining the quantitative characteristics of metals. The study of this problem is important not only in the metallurgical industry, but also in production, the maritime industry, and other engineering fields.

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“…The authors used two types of neural networks: a multilayer perceptron and a RBF network. A sigmoidal activation function was used for a multilayer perceptron [ 18 ].…”

Section: Expert Subsystem For the Metallographic Analysismentioning

confidence: 99%

Application of Information Technologies and Programming Methods of Embedded Systems for Complex Intellectual Analysis

Emelianov

Emelianova

Zhilenkov

et al. 2021

Entropy

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An information model is outlined, which represents an intelligent system of metallographic analysis in the form of a set of subsystems, the interaction of which ensures the performance of metallographic analysis functions. The structure of the information storage subsystem for metallographic analysis is presented. The deployment model of an intelligent metallographic analysis system is proposed and described. The paper outlines the approach to the presentation of an expert subsystem for metallographic quality control of metals based on a neural network. The process of finding a close precedent in metallographic analysis with reference to a multilayer neural network is described. An intelligent metallographic analysis system is described, which based on proposed information model. A specialized software of an intelligent metallographic analysis system is presented. The functioning results of the developed system for processing images of steel microstructures to determine the steel quantitative parameters is presented.

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Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades

Cited by 72 publications

References 41 publications

Application of artificial intelligence technologies in metallographic analysis for quality assessment in the shipbuilding industry

Application of artificial intelligence technologies in metallographic analysis for quality assessment in the shipbuilding industry

Application of Artificial Intelligence Technologies to Assess the Quality of Structures

Application of Information Technologies and Programming Methods of Embedded Systems for Complex Intellectual Analysis

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