Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Increased attention to corporate governance with the increasing need for investors and other parties in the Iraqi market for securities of the information credible and confidence and greater transparency in the disclosure as well as the systems of governance lead to raise the value of the company and that by reducing the cost of capital and reduce the cost of financing, as well as that there are indications modern measurement can be adopted by the Iraqi market for securities for the purpose of evaluating the performance of listed companies and then raise their value.

   The research problem is that there is no framework or structure of the legal and local rules for the application of corporate governance in Iraq obliges

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Lymphoma is a cancer arising from B or T lymphocytes that are central immune system ‎components. It is one of the three most common cancers encountered in the canine; ‎lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, ‎such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of ‎canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still ‎crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic ‎conditions and to improve decision-making around treating and what treatment type to use. ‎This study aimed to evaluate a potential novel biomarker related to iron metabolism,

Background: Change in palatal vault shape and Reinforcement of high impact acrylic denture base resin may in turn affect the dimensional accuracy of acrylic resin and affecting the fitness of the denture.This study evaluated tostudy the effect of fiber reinforcement for high-impact acrylic resin denture base with different palatal vault shapes on adaptation or gap space between the denture base and the stone cast and compare with non-fiber reinforcement and effect of palatal vault shapes on adaptation of non-reinforced and fiber reinforced high impact denture base acrylic resin Material and method: Three different palatal vault shapes were prepared on standard casts using CNC (computer numerical control) machine. 60 samples of heat polymeri

Background: Change in palatal vault shape and Reinforcement of high impact acrylic denture base resin may in turn affect the dimensional accuracy of acrylic resin and affecting the fitness of the denture. The aim of study is to evaluate the effect of fiber reinforcement for high-impact acrylic resin denture base with different palatal vault shapes on linear dimensional change and effect of palatal vault shapes on linear dimensional changes of non-reinforced and fiber reinforced high impact denture base acrylic resin Material and method: Three different palatal vault shapes were prepared on standard casts using CNC (computer numerical control) machine. 60 samples of heat polymerized high impact acrylic resin maxillary denture base were fabri

Background: Debonding and fracture of artificial teeth from denture bases are common clinical problem, bonding of artificial teeth to heat cure acrylic and high impact heat cure acrylic denture base materials with autoclave processing method is not well known. The aim of this study was to evaluate the effect of autoclave processing method on shear bond of artificial teeth to heat cure denture base material and high impact heat cure denture base material. Materials and methods: Heat polymerized (Vertex) and high impact acrylic (Vertex) acrylic resins were used. Teeth were processed to each of the denture base materials after the application of different surface treatments. The sample (which consist of artificial tooth attached to the dentur