Multicriteria decision making (MCDM) is one of the methods that popularly has been used in solving personnel selection problem. Alternatives, criteria, and weights are some of the fundamental aspects in MCDM that need to be defined clearly in order to achieve a good result. Apart from these aspects, fuzzy data has to take into consideration that it may arise from unobtainable and incomplete information. In this paper, we propose a new approach for personnel selection problem. The proposed approach is based on Hamming distance method with subjective and objective weights (HDMSOW's). In case of vagueness situation, fuzzy set theory is then incorporated onto the HDMSOW's. To determine the objective weight for each attribute, the fuzzy Shannon's entropy is considered. While for the subjective weight, it is aggregated into a comparable scale. A numerical example is presented to illustrate the HDMSOW's.

Detecting steel surface defects is one of the challenging problems for industries worldwide that had been used in manufacturing quality management. Manual inspection of the steel surface detects is a time-consuming process. This work aims at developing deep learning models that can perform steel defect detection and evaluating the potential of transfer learning for this task. In this paper, four types of transfer learning methods: ResNet, VGG, MobileNet, and DenseNet are experimentally evaluated to develop models for steel surface defect detection. The models were developed for binary classification (defect and no-defect) using the SEVERSTAL dataset from that contains 12,568 images of the steel surface. Then, these models were also assessed for multiclass classification using NEU dataset with 1800 images. In this work, image pre-processing is included to improve the result of steel defects detection. The experimental results have shown that the model developed by using the MobileNet method have the highest detection rate with 80.41% for the SEVERSTAL dataset and 96.94% for the NEU dataset compare to ResNet, VGG, and DenseNet transfer learning.

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Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy. In this study, we evaluated the performance of different pre-trained models (VGG-Net, MobileNet, ResNet, and DenseNet) in classifying VF defects and produced a comprehensive comparative analysis to compare the performance of different CNN models before and after hyperparameter tuning and fine-tuning. Using 32 batch sizes, 50 epochs, and ADAM as the optimizer to optimize weight, bias, and learning rate, VGG-16 obtained the highest accuracy of 97.63 percent, according to experimental findings. Subsequently, Bayesian optimization was utilized to execute automated hyperparameter tuning and automated fine-tuning layers of the pre-trained models to determine the optimal hyperparameter and fine-tuning layer for classifying many VF defect with the highest accuracy. We found that the combination of different hyperparameters and fine-tuning of the pre-trained models significantly impact the performance of deep learning models for this classification task. In addition, we also discovered that the automated selection of optimal hyperparameters and fine-tuning by Bayesian has significantly enhanced the performance of the pre-trained models. The results observed the best performance for the DenseNet-121 model with a validation accuracy of 98.46% and a test accuracy of 99.57% for the tested datasets.

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