Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

    Periodontal diseases such as gingivitis and periodontitis are considered common diseases. This study is aimed to predicate these diseases using non-harmful specimens such as saliva throughout the detection of some parameters. In the beginning, a random 51 patients' saliva was collected from outpatients who suffered from different degrees of periodontal diseases. Concurrently, another 40 people who appeared to be healthy were collected, and both patients and healthy people were subjected to a questionnaire form and diagnosed by a specialist dentist. The results of this study revealed that there is no significant difference between males and females in infection with periodontal diseases. As well, smoking is not acting as the m