Visual inspection of defective tires post-production is vital for human safety, as faulty tires can lead to explosions, accidents, and loss of life. With the advancement of technology, transfer learning (TL) plays an influential role in many computer vision applications, including the tire defect detection problem. However, automatic tire defect detection is difficult for two reasons. The first is the presence of complex anisotropic multi-textured rubber layers. Second, there is no standard tire X-ray image dataset to use for defect detection. In this study, a TL-based tire defect detection model is proposed using a new dataset from a global tire company. First, we collected and labeled the dataset consisting of 3366 X-ray images of faulty tires and 20,000 images of qualified tires. Although the dataset covers 15 types of defects arising from different design patterns, our primary focus is on binary classification to detect the presence or absence of defects. This challenging dataset was split into 70, 15, and 15% for training, validation, and testing, respectively. Then, nine common pre-trained models were fine-tuned, trained, and tested on the proposed dataset. These models are Xception, InceptionV3, VGG16, VGG19, ResNet50, ResNet152V2, DenseNet121, InceptionResNetV2, and MobileNetV2. The results show that the fine-tuned VGG19, DenseNet21 and InceptionNet models achieve compatible results with the literature. Moreover, the Xception model outperformed the compared TL models and literature methods in terms of recall, precision, accuracy, and F1 score. Moreover, it achieved on the testing dataset 73.7, 88, 80.2, and 94.75% of recall, precision, F1 score, and accuracy, respectively, and on the validation dataset 73.3, 90.24, 80.9, and 95% of recall, precision, F1 score, and accuracy, respectively.

Developments in the economic environment in the 2000s have become increasingly dynamic and complex. Rapid developments in this kind of economic environment threaten and restrain the sustainability of enterprises. Enterprises need to respond quickly to these burdens and threats to survive and sustain their operations efficaciously in a competitive market in the long run. In order to reduce possible uncertainties in the future and to anticipate economic crises, early risk warning systems should be developed. However, it is seen that management accounting researches are very limited or insufficient on the demand of enterprises for coping with such crises. The aim of this study is to diminish the deficiency in the strategic cost management and prediction of economic crises. Sustainable Balanced Scorecard (SBSC), which was developed as a strategic cost management tool, is constructed in a dynamic way by integrating the early warning system developed for enterprises with an innovative approach into SBSC. Additionally, early warning system model is developed in a manner that successfully predicts economic crises with long short time memory (LSTM) networks using economic macro variables in micro field. As a result of the integration of risk early warning system with SBSC, economic crises will be predicted and necessary strategies will be developed to cope with problems of the crises. Furthermore, predicting economic crises will be turned into opportunities or cause enterprises to make measures with minimum losses. In this model, crisis periods are successfully predicted two crises of 2002 and 2008 with 95.41% accuracy with macroeconomic data between 1998 and 2011.