Abstract

The research stems from the problem that focuses on a number of questions. They are as follows:   What is the extent of interest in the topic of efficiency by the banks and their role in raising the efficiency of the banking business and its development?  Is the banking efficiency used in Iraqi banks clear and specific for the Iraqi banking sector? How the banking sector efficiency is measured and what are the approaches adopted in determining the banking inputs and outputs? What is the level of efficiency in the research sample of the banks and what are the causes of its decline or rise in private banks individually and in the Iraqi banking sector in general?

 The re

Hypertrophic scars are fibroproliferative illnesses caused by improper wound healing, during that, excessive inflammation, angiogenesis, and differentiated human dermal fibroblast (HDF ) function contribute to scarring, whereas hyperpigmentation negatively affects scar quality. Over 100 million patients heal with a scar every year. To investigate the role of the beta 2 adrenergic receptor (β2AR); Ritodrine, in wound scarring, the ability of beta 2 adrenergic receptor agonist (β2ARag) to alter HDF differentiation and function, wound inflammation, angiogenesis, and wound scarring was explored in HDFs, zebrafish, chick chorioallantoic membrane assay (CAM), and a porcine skin wound model, respectively. A study identify a β2AR-mediated m

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

Deep Learning Techniques For Skull Stripping of Brain MR Images