There is a growing need to use technology when it comes to consumers. Automatic Number Plate Recognition (ANPR) is a system that allows real time recognition of a vehicle license number plate. For a smart service auto, ANPR is helping promoting development, personalizing classic application and increasing productivity for clients and workers. The main role of ANPR, in the application, is to extract the characters of a vehicle license number plate from an image. A smart car service brings in addition to other services, an application through which the customer can see the repairs of the vehicle using only the license plate number extracted from a loaded image. It has been noticed that the technological development is growing, so it is considered that there is a need for development in this field too, and a smart car service is the best option for car services. By using ANPR to develop an application, it can ease the work of many employees as well as clients of car services.

Machine Learning method is increasingly used for data analysis, where large databases has to be analyzed. One of the areas where solutions include the use of machine learning is the field of medical prediction, used to observe the probability of a person may suffer from a disease in the future. One of the domains of medicine prediction in which machine learning solutions are used is predictions in the case of diabetes. Diabetes is a disease that is increasingly present in today’s society. This paper presents a comparison among the results experimentally obtained, using three machine learning algorithms in the prediction of diabetes. The three considered algorithms are support vector machine, Naive Bayes, and random forest. The aim of this paper is to analyze the performance of the algorithms considering different metrics in order to compare different techniques to obtain better accuracy. We found that support vector machine and random forest obtained an accuracy of over 80%.

Nowadays, the consequence of quick access to information has lead to the spread of fake news, which has a strong damaging impact on democracy, justice, and public trust. Thus, it is crucial to analyze and evaluate detection methods for fake news. This paper focuses on the detection of Romanian fake news. In this study, we made a comparative analysis of machine learning algorithms and Transformer-based models on Romanian fake news detection using three datasets—FakeRom, NEW, and both FakeRom + NEW. The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses the following machine learning models for detection: Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). We also used two Transformer-based models—BERT-based-multilingual-cased and RoBERTa-large. The performance of the models was evaluated using various metrics: accuracy, precision, recall, and F1 score. The results revealed that the BERT model trained on the NEW dataset consistently achieved the highest performance metrics across all test sets, with 96.5%. Also, Support Vector Machine trained on NEW was another top performer, reaching a very good accuracy of 94.6% on the combined test set.

Nowadays, the consequence of quick access to information has lead to the spread of fake news, which has a strong damaging impact on democracy, justice, and public trust. Thus, it is crucial to analyze and evaluate detection methods of fake news. This paper focuses on the detection of Romanian fake news. In this study, we made a comparative analysis of machine learning algorithms and transformer-based models on Romanian fake news detection using three datasets – FakeRom, New, and both FakeRom + New. The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses for detection machine learning models: Native Bayes (NB,) Logistic regression (LR) and Support Vector Machine (SVM). We also used two transformer based models – BERT based multilingual based and RoBERTaLarge. The performance of the models was evaluated using various metrics: accuracy, precision, recall and F1-score. The results revealed that BERT model trained on NEW dataset consistently achieved the highest performance metrics across all test sets. Also, Support Vector Machine trained on NEW was another top performer, reaching a very good accuracy on the combined test set.