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The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

This study aims to determine whether or not fashion retail brands can maintain their essence by providing personalized care through conventional face-to-face interactions or the use of e-services.

An exploratory investigation is being conducted to attain this goal. According to the findings of this research, Chatbots have an impact on consumer loyalty. The quality of a Chatbot’s system, service and information are all critical to providing a positive consumer experience.

The study concluded that Chatbot e-services might potentially enable dynamic and fascinating interactions between firms and their consumers. To personalize a Chatbot, firms might change the tone of the language used. Customers are more likely to use a Chatbot if it resembles a real person, which increases their pleasure and confidence in the product.

More precisely, the emphasis of the inquiry was on Chatbot, a relatively new digital tool that offers user-friendly, personalized and one-of-a-kind support to customers. Using information supplied by consumers, the authors examine a five-dimensional model that gauges how customers feel about Chatbots in terms of their ability to communicate with users, offer amusement, be trendy, personalize interactions and solve problems.

Current studies show that the Lean Six Sigma (LSS) methodology is used all over the world, by different types of companies in different countries. However, this is not yet the case for certain developing countries such as Morocco, where this methodology is still being discovered and applied and where also the relationship between the adoption of this methodology and sustainable performance is not yet clear. Thus, the aim of this study is to investigate the impact of LSS tools, used by industrial companies in Morocco, on the three dimensions of performance: economic, social and environmental.

This study used partial least squares-based structural equation modeling (PLS-SEM) to conduct an empirical examination of the impact of LSS tools used by Moroccan manufacturing industries on their sustainable performance. Data were collected using a semistructured questionnaire, with a total of 121 valid responses collected for this study.

The results showed that the adoption of LSS tools has a positive effect on the sustainable performance of these industries. The analysis of the collected data also revealed that this effect is most significant when it comes to social performance, followed by environmental and finally economic performance. It was also found that Lean Management tools have a greater impact than Six Sigma tools.

The results of this study may encourage Moroccan industries that are new to LSS to adopt it, as it proves to have positive results not only on the economic aspects of the firm but also on the improvement of employee well-being and the protection of our planet’s environment. In addition, this study gives them an idea of the tools that are most widely used in their Moroccan context, thus facilitating the choice of tools with which they can begin to embed a continuous improvement mindset.

Although several studies have already analyzed the relationship between the LSS approach and sustainable performance, these studies have generally focused on developed countries that are well advanced in the use of these tools. Hence, the originality of this study is its relevance to the Moroccan context, which still needs more studies in the fields of continuous improvement and sustainability.