Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.

Diseases which affect the respiratory system are considered some of the most dangerous, since defects in the breathing process may lead to death. Currently, the coronavirus is one of the most complex strains of the corona family of diseases for which, as yet, there is no successful vaccine. Therefore, detecting the virus is of the utmost importance to global health. The use of deep learning techniques is considered to be a successful method by which to diagnose such diseases. However, there is a lack of data which models deep learning techniques required for training. In this paper, we suggest a method for data augmentation based on the CGAN model. To synthesize realistic chest X-ray images, we proposed to set edge information of the image to the generator, which is used as supporting information to increase the reality of the generated images. A MobileNet CNN model was used for diagnosing COVID-19. When this suggestion was applied to chest data, the diagnostic results were very satisfactory which the accuracy exceeds 99% in some cases.

Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.

Every individual has a specific language with which he/she communicates. Each language has special letters and features distinguishing it from other languages. Ideas, cultures, and sciences are exchanged through some notions of languages, including retrieval, translation, and classification of texts from journals, books, journals, research, and the internet. It is accomplished through database availability. Unfortunately, due to some reasons, Kurdish language databases may be rare or non-existent. In the present study, a Comprehensive Kurdish-Sorani Database (CKSD) is generated, which contains datasets of dates, letters, and common words in the Kurdish language, as well as the documents employed for the extraction of these datasets. Elements of these collections were extracted from the written documents in 27 different fonts. It bestows a comprehensiveness feature to the CKSD database that can be utilized by researchers. In order to determine the extent to which classifiers can categorize such data, these data were utilized in this study. Indeed, this study demonstrated the reliability of this data and its suitability for use in the fields of machine learning and other artificial intelligence applications.