Several deep learning techniques have been intensively reviewed for captioning tasks, enabling the possibility of textual understanding, and description of both simple and complex images. In advancing this knowledge, this paper proposes a multimodal end-to-end siamese difference captioning model (SDCM) to automatically generate a natural language description of differences in an image pair. The proposed supervised learning model combines several deep learning techniques in exploring the practicability of capturing, aligning, and computing the disparities between two image features, for the purpose of creating corresponding language model probability distribution. First, a deep siamese convolutional neural network is used to extract the feature vector discrepancies of an image pair, and then an attention mechanism enables the detection of salient regions of the feature vector which effectively allows a bidirectional long short-term memory decoder to generate a matching and semantically associated textual sequence. The evaluation of the model is tested on the spot-the-diff baseline dataset which consists of pairs of images and their equivalent captions. The results indicate that our proposed model demonstrates a highly competitive performance in comparison to the state of the art. INDEX TERMSDeep convolutional neural network, Siamese network, recurrent neural network, image captioning, deep learning.
Background: The interest in using fractional calculus operators has grown in the field of image processing.Image enhancement is one of image processing tools that aims to improve the details of an image. The enhancement of medical images is a challenging task due to the unforeseeable variation in the quality of the captured images. Methods: In this study, we present a mathematical model based on the class of fractional partial differential equations (FPDEs). The class is formulated by the proportional-Caputo hybrid operator (PCHO). Moreover, some properties of the geometric functions in the unit disk are applied to determine the upper bound solutions for this class of FPDEs. The upper bound solution is indicated in the relations of the general hypergeometric functions. The main advantage of FPDE lies in its capability to enhance the low contrast intensities through the proposed fractional enhanced operator.Results: The proposed image enhancement algorithm is tested against brain and lungs computed tomography (CT) scans datasets of different qualities to show that it is robust and can withstand dramatic variations in quality. The quantitative results of Brisque, Piqe, SSEQ, and SAMGVG were 40.93%, 41.13%, 66.09%, and 31.04%, respectively for brain magnetic resonance imaging (MRI) images and 39. 07, 41.33, 30.97, and 159.24 respectively for the CT lungs images. The comparative results show that the proposed image enhancement model achieves the best image quality assessments.Conclusions: Overall, this model significantly improves the details of the given datasets, and could potentially help the medical staff during the diagnosis process.
Social media is increasingly used to share thoughts and feelings. By mining the social media posts, a picture of users' behavior may be obtained, to predict mental illnesses, like depression. Depression is seen as a taboo in the Arab world and cannot be discussed publicly out of fear of criticism. This led many to express their feelings over social media as an escape. Research has been conducted on the ability to use machine learning to make an early-stage diagnosis of depression. However, there is not enough research on this in the Arab world. This research was conducted on Arab women's tweets during the COVID-19 pandemic to predict whether they have depression symptoms using machine learning. The proposed contribution is to create a Recurrent Neural Network (RNN) model to predict depression from tweets. The model is evaluated using 10000 tweets extracted from 200 users and the obtained results shows its effectiveness.
Obesity is one of the world’s most serious health issues. Therefore, therapists have looked for methods to fight obesity. Currently, technology-based intervention options in medical settings are very common. One such technology is virtual reality (VR) which has been used in the treatment of obesity since the late 1990s. The main objective of this study is to review the literature on the use of VR in the treatment of obesity and overweight to better understand the role of VR-based interventions in this field. To this end, four databases (PubMed, Medline, Scopus, and Web of Science) were searched for related publications from 2000 to 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 645 articles identified, 24 were selected. The main strength of this study is that it is the first systematic review to focus completely on the use of VR in the treatment of obesity. It includes most research in which VR was utilized to carry out the intervention. Although several limitations were detected in the reviewed studies, the findings of this review suggest that employing VR for self-monitoring of diet, physical activity, and/or weight is effective in supporting weight loss as well as improving satisfaction of body image and promoting health self-efficacy in overweight or obese persons.
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