Productivity and Cycle Time Prediction Using Artificial Neural Network

Gelmereanu, Cristian; Morar, Liviu; Bogdan, Stefan

doi:10.1016/s2212-5671(14)00626-1

Cited by 11 publications

(3 citation statements)

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“…GRNN is a memory-based network that enables the evaluation of increasing variables that converge to underlying nonlinear or linear regression facet. The GRNN is a single pass learning paradigm with a high parallel structure or a profoundly parallel structure that is taking from the input side to the output side [208]. GRNN functions well on disorderly representation than back-generation.…”

Section: U a General Regression Neural Networkmentioning

confidence: 99%

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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

et al. 2019

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The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the abovehighlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.INDEX TERMS Artificial neural networks, application to pattern recognition, feedforward neural networks, feedback neural networks, hybrid models.

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Section: U a General Regression Neural Networkmentioning

confidence: 99%

“…18. Input, hidden, or output variables represent nodes, graph edges equivalent to adaptive parameters [208]. FFNN analytic function could be written in mathematical term to show the output of jth hidden node in linear combination (LC) weight of ''n'' with input values ''x i '' as follow;…”

Section: A Feedforward Neural Networkmentioning

confidence: 99%

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

et al. 2019

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“…The scientific purpose of AI is to create computer programs that can make symbolic inferences or display logical behaviors to understand intelligence (Singh, Mishra & Sagar, 2013). AI is used in the fields of transportation (Bösch et al, 2018), health (Sitterdinget al, 2019, agriculture (Elahi et al, 2019), production (Gelmereanu et al, 2014), and banking (Tavana et al, 2018). It has also started to come into play in education with teaching robots, and intelligent tutoring and adaptive learning systems (Chassignol et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Determining middle school students' perceptions of the concept of artificial intelligence: A metaphor analysis

Demir

Güraksın

2022

Participatory Educational Research

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Apart from the fact that human-like robots are still one of the most interesting topics in science fiction, artificial intelligence (AI) continues to develop rapidly as a popular phenomenon for all sectors. Although the idea that this rapid rise of AI means the rise of humanity has been voiced by many, the point of how AI will affect humanity continues to raise doubts in certain parts of the society. In this study, it is aimed to determine the perceptions of middle school students, which are a part of the future of humanity, towards the concept of AI, on which many discussions have been made, through metaphors. The sample consisted of 339 seventh and eighth grade students of four secondary schools in the central districts of Afyonkarahisar and Izmir in the 2019-2020 academic year. This study used a qualitative approach utilizing metaphor analysis as a research tool to investigate phenomena. Participants were asked the complete the sentence “Artificial intelligence is like.................., because ..................” Data were analyzed using content analysis. Participants’ metaphors for AI were grouped under 11 categories: smart, brain, nature, security, humanistic, the dilemma of good and evil, service, object, technology, life, and time. The data obtained showed that the participants generally used positive metaphors to describe AI, that is, they had positive perceptions about AI. However, in this study, which focused on the AI perceptions of middle school students, all of the metaphors collected under 11 conceptual categories containing positive and negative perceptions were examined and interpreted separately. It is thought that educational computer systems can be designed to shape students' perceptions of AI. Teachers can consider students' perceptions of AI by using AI-assisted teaching and designing content to enhance students' learning skills.

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A Proposal of Training Data Selection for Remaining Job Cycle Time Prediction in Industrial Monitoring: A Real Case Study

García-Celis,

Villalón-Falcón,

Piñero-Aguilar

et al. 2023

Studies in Systems, Decision and Control

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Productivity and Cycle Time Prediction Using Artificial Neural Network

Cited by 11 publications

References 10 publications

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Determining middle school students' perceptions of the concept of artificial intelligence: A metaphor analysis

A Proposal of Training Data Selection for Remaining Job Cycle Time Prediction in Industrial Monitoring: A Real Case Study

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