A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m

This paper including a gravitational lens time delays study for a general family of lensing potentials, the popular singular isothermal elliptical potential (SIEP), and singular isothermal elliptical density distribution (SIED) but allows general angular structure. At first section there is an introduction for the selected observations from the gravitationally lensed systems. Then section two shows that the time delays for singular isothermal elliptical potential (SIEP) and singular isothermal elliptical density distributions (SIED) have a remarkably simple and elegant form, and that the result for Hubble constant estimations actually holds for a general family of potentials by combining the analytic results with data for the time dela

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

Background: Nowadays there is an increasing of the emphasis on aesthetic, dentist have been concerned about providing aesthetics and functional removable partial dentures to their patients and this was make the mission more difficult because of the goal now is achieving optimal aesthetic of the denture - while maintaining retentive, stable, and conservative to the health of supporting tooth and supporting tissue. The traditional use of metal clasp like cobalt-chromium, gold, stainless-steel and titanium hampers esthetics because of its obvious display conflicts with patient’s prosthetic confidentiality. Acetal resin (poly oxy methylene) may be used as alternative denture clasp material. This material was promoted primarily on the basis of

Multilevel models are among the most important models widely used in the application and analysis of data that are characterized by the fact that observations take a hierarchical form, In our research we examined the multilevel logistic regression model (intercept random and slope random model) , here the importance of the research highlights that the usual regression models calculate the total variance of the model and its inability to read variance and variations between levels ,however in the case of multi-level regression models, the calculation of  the total variance is inaccurate and therefore these models calculate the variations for each level of the model, Where the research aims to estimate the parameters of this m

This study conducts a systematic comparative critical discourse analysis of news reports from prominent American (CNN) and Russian (RT) media sources covering the Russia-Ukraine conflict. Utilizing the theoretical frameworks of Norman Fairclough's multidimensional model and Teun van Dijk's socio-cognitive approach, the research examines the underlying ideological assumptions and discursive strategies employed by the two contrasting news channels. Quantitative analysis of discursive techniques and linguistic features provides insights into how each channel selectively utilizes language to convey distinct ideological positions. The findings demonstrate how media discourse constructs and normalizes particular ideological representations of pol

Twitter data analysis is an emerging field of research that utilizes data collected from Twitter to address many issues such as disaster response, sentiment analysis, and demographic studies. The success of data analysis relies on collecting accurate and representative data of the studied group or phenomena to get the best results. Various twitter analysis applications rely on collecting the locations of the users sending the tweets, but this information is not always available. There are several attempts at estimating location based aspects of a tweet. However, there is a lack of attempts on investigating the data collection methods that are focused on location. In this paper, we investigate the two methods for obtaining location-based dat

This research sought to present a concept of cross-sectional data models,  A crucial double data to take the impact of the change in time and obtained from the measured phenomenon of repeated observations in different time periods, Where the models of the panel  data were defined by different types of fixed , random and mixed, and Comparing them by studying and analyzing the mathematical relationship between the influence of time with a set of basic variables Which are the main axes on which the research is based and is represented by the monthly revenue of the working individual and the profits it generates, which represents the variable response And its relationship to a set of explanatory variables represented by the

Abstract:

 This research aims to compare Bayesian Method and Full Maximum Likelihood to estimate hierarchical Poisson regression model.

The comparison was done by  simulation  using different sample sizes (n = 30, 60, 120) and different Frequencies (r = 1000, 5000) for the experiments as was the adoption of the  Mean Square Error to compare the preference estimation methods and then choose the best way to appreciate model and concluded that hierarchical Poisson regression model that has been appreciated Full Maximum Likelihood Full Maximum Likelihood  with sample size  (n = 30) is the best to represent the maternal mortality data after it has been reliance value param

Abstract Depending on their protective properties against different cases of Colorectal Cancer (CRC), vitamins C, D, and E are the main focus of this research. CRC is one of the global public health concerns. 30 healthy individuals provided serum samples, whereas the group of CRC patients was divided into three, totaling 90 individuals. Group I consisted of 30 newly diagnosed cases of CRC. Group II 30 consisted of consisted of 30 CRC patients who were administered three cycles of chemotherapy. Group III consisted of 30 diagnosed CRC patients who also have non-alcoholic fatty liver disease (NAFLD). The concentrations and groups of vitamins C, D, and E were evaluated using ELISA. The levels of Vitamin C were significantly lower (p &l