Automatic topic Detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic Detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-Detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic Detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

We propose a real-time Yolov5 based deep convolutional neural network for detecting ships in the video. We begin with two famous publicly available SeaShip datasets each having around 9,000 images. We then supplement that with our self-collected dataset containing another thirteen thousand images. These images were labeled in six different classes, including passenger ships, military ships, cargo ships, container ships, fishing boats, and crane ships. The results confirm that Yolov5s can classify the ship's position in real-time from 135 frames per second videos with 99 % precision.

The current study is focused on determining the mRNA expression levels of tetranectin, to detect oral squamous cell carcinoma (OSCC) and thus aiding in its classification at an early stage. RNA was isolated and cDNA synthesis was performed from the saliva samples of the patients and healthy individuals. A semiquantitative PCR based analysis was performed prior to quantitative and expression based analysis using Real time PCR. The study showed that the mRNA levels are much lesser in patients suffering from dysplastic and metastatic tumors as compared to healthy individuals (P≤ 0. 05). This study can be a breakthrough in medical and dentistry studies. One of the most common malignant carcinomas, OSCC is a type of cancer of the mouth. Though surgical methods have been quite effective in delaying the metastasis, the Detection methods using histology parameters are not very efficient and the disease is diagnosed generally in the last stages of the cancer. Tetranectin is a protein biomarker which has been used for Detection of several cancers including oral cancer where the protein quantity is calculated.

mullerian duct anomalies (MDAs) are relatively common disorders that are particularly responsible for obstetrical complications and have a prevalence of about 3% - 4% in the general population.Uterine malformations may result from arrested development of the mullerian ducts, failure of fusion of the mullerian ducts or failure of resorption of the median septum…

Recent developments in the theory of nonlinear dynamics have paved the way for analyzing signals generated from nonlinear biological systems. The main purpose of the present work is based on the analysis of the ECG signal, initially extracting the features of ECG, which are used for the Detection and/or classification of ECGs. For this work, Correlation Dimension (D2), Largest Lyapunov Exponent (LLE), Ap-proximate Entropy (ApEn), Sample Entropy (SampEn) and Poincare plot methods were used from nonlinear time series analysis to characterize human ECG signals obtained from 24 hour-Holter recording. Four groups of ECG signals have been investigated. D2 and LLE are increasingly used to classify ECG signals. ECG time series were classified according to the results obtained from computation of above chaotic features. Our results, obtained from clinical data, improved the previous studies, which allow one to distinguish between healthy group and patients groups with more confidence than the standard methods for heart rate time series and gain more significant understanding of heart dynamics using Entropy features and Poincare plot along with D2 and LLE.

A 35 year-old woman referred to Royan Institute (Reproductive Biomedicine Research Center) for infertility treatment.  She had an eleven-year history of primary infertility with a normal abdominal ultrasound. Hysterosalpingography (HSG) was obtained one month prior to referral in another center The HSG finding of an apparent unicorn uterus followed by a normal vaginal ultrasound led us to perform a three-dimensional vaginal ultrasound before resorting to hysteroscopy. Results of the three-dimensional vaginal ultrasound revealed a normal uterus.Accurate characterization of congenital Mullerian anomalies (MDAs) such as an arcuate, unicornuate, didelphys, bicornuate or septate uterus is challenging. While HSG has been the standard test in the diagnosis of MDAs, some limitations may favor the use of three-dimensional ultrasound. The most difficult part of HSG is interpreting the two-dimensional radiographic image into a complex, three-dimensional living organ. A variety of technical problems may occur while performing HSG. In this case, only an oblique view could lead to a correct interpretation. It is advisable for the interpreter to perform the procedure rather than to inspect only the finished radiographic images.One of the most useful scan planes obtained on three-dimensional ultrasound is the coronal view of the uterus. This view is known to be a valuable problem-solving tool that assists in differentiating between various types of MDAs due to the high level of agreement between three-dimensional ultrasound and HSG.Recently, three-dimensional ultrasound has become the sole mandatory step in the initial investigation of MDAs due to its superiority to other techniques that have been used for the same purpose.