The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

       This research aims to modify the components of stainless steel alloy by the method of surface engineering through the single diffusion coating technique in order to obtain new alloys with high efficiency in resisting harsh environmental conditions. Steam a mixture of sodium chloride ( ) and sodium sulfate ( ) at a temperature of 900 and then compare it with the base alloy. The results showed that the alloys produced in this way are very efficient. The results showed that the aluminum coating showed high efficiency in resisting oxidation and provided better protection for a longer time compared to the uncoated alloy due to the  oxide crust layer formed with high adhesion as well as the aluminum-rich phases, whether the phase

Background: Poly propylene fibers with and without silane treatment have been used to reinforce heat cure denture base acrylic but, some mechanical properties like transverse strength, impact strength, tensile strength, hardness, wear resistance and wettability. Which are related to the clinical use of the prosthesis are not evaluated yet. The aim of the study is to identify the influence of incorporation of treated and untreated fibers on these properties Materials and methods: Eighty four heat cure acrylic specimens were constructed by conventional flasking technique. They were divided into six groups according to the tests and each group was subdivided into two subgroups control and experimental groups (seven samples for each subgroup

Abstract

The study aims to examine the relationships between cognitive absorption and E-Learning readiness in the preparatory stage. The study sample consisted of (190) students who were chosen randomly. The Researcher has developed the cognitive absorption and E-Learning readiness scales. A correlational descriptive approach was adopted. The research revealed that there is a positive statistical relationship between cognitive absorption and eLearning readiness.

This work is divided into two parts first part study electronic structure and vibration properties of the Iobenguane material that is used in CT scan imaging. Iobenguane, or MIBG, is an aralkylguanidine analog of the adrenergic neurotransmitter norepinephrine and a radiopharmaceutical. It acts as a blocking agent for adrenergic neurons. When radiolabeled, it can be used in nuclear medicinal diagnostic techniques as well as in neuroendocrine antineoplastic treatments. The aim of this work is to provide general information about Iobenguane that can be used to obtain results to diagnose the diseases. The second part study image processing techniques, the CT scan image is transformed to frequency domain using the LWT. Two methods of contrast

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Background: Gallstone disease (GSD) is a significant global health burden with variable prevalence influenced by metabolic, genetic, and infectious factors. Increasing evidence suggests that Gram-positive bacteria, particularly Staphylococcus aureus and Enterococcus species, contribute to gallstone pathogenesis through enzymatic activity and biofilm formation. Objectives: To characterize Gram-positive bacteria within gallstones from Iraqi patients, evaluate their biofilm-forming capacity, and analyze the relationship between bacterial colonization, gallstone type, and cholesterol levels. Methods: A total of 100 gallstones were obtained from patients undergoing elective cholecystectomy between October 2024 and March 2025. Stones were

Background: The internally displaced children are vulnerable groups have less access to dental services, worse oral health, and bear a disproportionate burden of oral diseases. Aim of the study: This study was conducted on group of internally displaced children living in Baghdad governorate camps to measure their dental caries prevalence and experience and find out the association between dental caries and the duration of displacement in camps. Subjects and methods: A sample of 1393 children were selected, 567 internally displaced children from camps in Baghdad governorate and 826 school children as control matching in age and gender. The age of children ranged from 5-12 years old. Oral examination was performed using WHO 2013 criteria, to