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

Assessing the accuracy of classification algorithms is paramount as it provides insights into reliability and effectiveness in solving real-world problems. Accuracy examination is essential in any remote sensing-based classification practice, given that classification maps consistently include misclassified pixels and classification misconceptions. In this study, two imaginary satellites for Duhok province, Iraq, were captured at regular intervals, and the photos were analyzed using spatial analysis tools to provide supervised classifications. Some processes were conducted to enhance the categorization, like smoothing. The classification results indicate that Duhok province is divided into four classes: vegetation cover, buildings,

This study is an objective literary study of the poems of Abu Dhu'ayb Al-Hudhali and Su'da Bint Al-Shamrdal. It aims to shed light on the philosophy of death in the poems, and the way this philosophy is treated by both poets. Thus, the main prominent poetic images have been addressed and the outstanding ones have been singled out. The study further investigates the intertextuality with other poetic verses and Quranic verses and reviews the deviation in their semantic, syntactic, and phonetic types. The study adopts the historical approach to examine the occasion of the poems by referring to several historical sources that indicate the seniority. It also adopts the and descriptive-analytical approach represented by the use of content anal

نتيجة للتطورات الأخيرة في أبحاث الطرق السريعة بالإضافة إلى زيادة استخدام المركبات، كان هناك اهتمام كبير بنظام النقل الذكي الأكثر حداثة وفعالية ودقة (ITS) في مجال رؤية الكمبيوتر أو معالجة الصور الرقمية، يلعب تحديد كائنات معينة في صورة دورًا مهمًا في إنشاء صورة شاملة. هناك تحدٍ مرتبط بالتعرف على لوحة ترخيص السيارة (VLPR) بسبب الاختلاف في وجهة النظر، والتنسيقات المتعددة، وظروف الإضاءة غير الموحدة في وقت الحصول

Cytokines are signaling molecules between inflammatory cells that play a significant role in the pathogenesis of a disease. Among these cytokines are interleukins (ILs) 17A and 33, and accordingly, the current case-control study sought to investigate the role of each of the two cytokines in the risk of developing multiple sclerosis (MS). Sixty-eight relapsing-remitting MS (RRMS) Iraqi patients and twenty healthy individuals (control group) were enrolled. Enzyme linked immunosorbent assay (ELISA) kits were used to determine serum levels of IL-17A and IL-33. Results revealed that IL-17A and IL-33 levels were significantly higher in MS patients than in controls (14.1 ± 4.5 vs. 7.5 ± 3.8 pg/mL; p < 0.001 and 65.3 ± 16

An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

Diverting river flow during construction of a main dam involves the construction of cofferdams, and tunnels, channels or other temporary passages. Diversion channels are commonly used in wide valleys where the high flow makes tunnels or culverts uneconomic. The diversion works must form part of the overall project design since it will have a major impact on its cost, as well as on the design, construction program and overall cost of the permanent works. Construction costs contain of excavation, lining of the channel, and construction of upstream and downstream cofferdams. The optimization model was applied to obtain optimalchannel cross section, height of upstream cofferdam, and height of downstream cofferdamwith minimum construction cost

Abstract

 This research deals will the declared production planning operation in the general company of planting oils, which have  great role in production operations management who had built mathematical model for correct non-linear programming according to discounting operation during raw materials or half-made materials purchasing operation which concentration of six main products by company but discount included just three products of raw materials, and there were six months taken from the 1^st half of 2014 as a planning period has been chosen . Simulated annealing algorithm  application on non-linear model which been more difficulty than possible solution when imposed restric

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c