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

Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage

Objectives: The purpose of the study is to ascertain the relationship between the training program and the socio-demographic features of patients with peptic ulcers in order to assess the efficiency of the program on patients' nutritional habits.

Methodology: Between January 17 and October 30 of 2022, The Center of Gastrointestinal Medicine and Surgery at Al-Diwanyiah Teaching Hospital conducted "a quasi-experimental study". A non-probability sample of 30 patients for the case group and 30 patients for the control group was selected based on the study's criteria. The study instrument was divided into 4 sections: the first portion contained 7 questions about demographic information, the second sect

The study aimed to compare the expression of miR-126-3p and miR-423-5p in patients and normal subjects, and correlate their expression with response to induction therapy. Circulating miR-126-3p and miR-423-5p were measured in the plasma of 43 adult AML patients and 35 age- and sex-matched controls by real time PCR. The foldchange in differential expression for each gene was calculated using the comparative cycle threshold (CT) method (also known as the 2−CT method). For statistical purposes, the fold change was calculated using DDCT (or 2–∆∆Ct) method to find the relative expression of miRNAs. The expression fold change of miR-126-3p was 1.73-fold increase in patients than controls (p= 0.010). The expression fold change of miR-423-5

Abstract Background: Acute myeloid leukemia (AML) results from sequential genetic alterations in a normal hematopoietic stem cell or its progenitors giving rise to an autonomous clone that dominates the bone marrow leading to marrow failure. MicroRNAs are short non-coding nucleic acid sequences that regulate post-transcriptional gene expression by base-pairing with their target mRNAs. MiRNAs can be secreted into extracellular fluids and carried to target cells by vesicles or bound to proteins. Intracellular and circulating miRNAs are believed to be useful markers in the diagnosis, prognosis, and treatment of various cancers. Practically, circulating miRNAs are more stable at room temperatures and extreme conditions. Purpose: This study aim