An application of Artificial Neural Network solution in the apparel industry for Job distribution to subcontractors

Mumcu, Yelda Karatepe; Kayar, Mahmut; Bulur, Öykü Ceren

doi:10.1016/j.advengsoft.2022.103325

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…To assess our framework performance, the accuracy, sensitivity (Se) or recall, precision (Pr), F1-score, area under the receiver-operating characteristic (ROC) curve (AUC), and computational time are introduced as shown in the following equations ( 4) to (7). 41 Accuracy Equation (1) explains the evaluation metrics for our proposed algorithm, the symbols are not duplicated but each symbol represents a definition in the equation of the used metrics, where TP: true positive, TN: true negative, FP: false positive, and, finally, FN: false negative.…”

Section: Resultsmentioning

confidence: 99%

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Efficiency marker evaluation based on optimized deep learning supported by Bayesian optimization technique

Salama,

Aly,

Abed

et al. 2023

Textile Research Journal

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In this article the efficiency of marker patterns is calculated based on a new optimized automated computer-aided design system interested in nesting the irregular objects using deep learning techniques to reduce the unused area of garment layout. The automatic hyper-parameter is adjusted based on Bayesian optimization technique. Our proposed work is based on four phases: segmentation using the Grab-Cut technique which is utilized to increase our framework performance and decrease the cost. Moreover, different optimization algorithms, including k-nearest neighbor, naive Bayes, decision trees, and support vector machine are performed for classifying the pattern size from the fabric layer. Furthermore, the data augmentation technique is applied to overcome the lack of datasets and improve our framework performance by increasing the number of datasets. The optimized framework achieves 98.99% area under the receiver-operating characteristic curve, an accuracy of 98.97%, a sensitivity ([Formula: see text]) of 98.98%, a precision ([Formula: see text]) of 98.89%, an [Formula: see text]-score of 98.99%, a mean square error of 0.03% to 4.91%, an efficiency of 99.66%, a p-value of 0.0207, a t-statistics of 1.336, and a computational time of 3.21 s.

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Section: Resultsmentioning

confidence: 99%

“…To assess our framework performance, the accuracy, sensitivity ( Se) or recall, precision ( Pr), F1-score, area under the receiver-operating characteristic (ROC) curve ( italicAUC), and computational time are introduced as shown in the following equations (4) to (7). 41 …”

Section: Resultsmentioning

confidence: 99%

Efficiency marker evaluation based on optimized deep learning supported by Bayesian optimization technique

Salama,

Aly,

Abed

et al. 2023

Textile Research Journal

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“…As seen in many multidisciplinary studies that have done by the researchers [ [46] , [47] , [48] , [49] , [54] , [53] , [52] , [51] , [50] ], artificial neural networks have shown that they can be applied to any problem as a method that can solve complex problems inspired by biological nervous system easily, quickly and with scientific accuracy. The artificial neural network was trained with the data set of the study, the time of the operations of the production of emergency fixtures with 29 operations, the machine types, the priority order of the operations, the weight distributions of the operations and the efficiency values of each station.…”

Section: Materials and Methodologymentioning

confidence: 99%

Solution approach using heuristic and artificial neural networks methods in assembly line balancing problems: A case study in the lighting industry

Karatepe Mumcu

2024

Heliyon

Self Cite

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“…Artificial neural network (ANN) is a machine learning algorithm that imitates the nervous system of human brain [18]. The ANN is a system that consists of many interconnected processing elements or neurons.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Forecasting of Thailand Electricity Load Consumption Using Machine Learning Algorithms

Kantanantha

2023

Advances in Transdisciplinary Engineering

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Electricity is important in our modern life and also essential to the development of the country. Since the electricity load consumption in Thailand increases almost every year, the power systems capacity expansion is unavoidable. The purpose of this paper was to apply the machine learning algorithms to forecast the long-term electricity load consumption in Thailand. Three algorithms employed were artificial neural network (ANN), support vector regression (SVR), and k-nearest neighbors (KNN). The performances were evaluated in terms of mean absolute percentage error (MAPE). The results showed that the ANN outperformed other algorithms with a MAPE of 2.586%.

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An application of Artificial Neural Network solution in the apparel industry for Job distribution to subcontractors

Cited by 4 publications

References 29 publications

Efficiency marker evaluation based on optimized deep learning supported by Bayesian optimization technique

Efficiency marker evaluation based on optimized deep learning supported by Bayesian optimization technique

Solution approach using heuristic and artificial neural networks methods in assembly line balancing problems: A case study in the lighting industry

Forecasting of Thailand Electricity Load Consumption Using Machine Learning Algorithms

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