Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

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

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

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

ملخص البحث بالعربي

عنوان البحث : أجـــــوبة الحافظ محمد البرزالي على سؤالات العز ابن الحاجب في الجرح والتعديل

يهدف البحث : إلى جمع أجوبة الحافظ أبي عبدالله محمد البرزالي في الجرح والتعديل، ودراستها.

يتكون البحث من : مقدمة، وتمهيد، نص السؤالات، وخاتمة، وفهارس.

المقدمة : تسمية الموضوع، وسبب اختياره، وأهميته .

والتميهد : ترجمة مختصرة للحافظ أبي عبدالله البرزالي، والتعريف بأجوبة الحافظ الب

  ولد ابو بكر محمد بن زكريا الرازي في الري سنة 250هـ (864م) وتعمق في الطب والكيمياء والفلك والأدب والفلسفة وهو أحد مشاهير الاطباء في زمانه وهذا هو السبب الذي دعاه لأن يتنقل من بلاط الى اَخر , ولم ينعم بالأستقرار في حياته بالنظر الى تقلب الأهواء والاُمراء وأضطراب الأحوال السياسية . قام برحلات كثيرة , وعهدت اليه ادارة بيمارستان ( مصح ومشفى ) الري ثم قام  بهذه المهمة نفسها في بغداد.

لقب بـ( طبي

Efforts of evaluating manuscripts have developed into sciences that take interest into the development of authorship movement. Expanding the rules and fundamentals of this scientific process along with the growing use of modern methods and techniques contributed further to its development.
Such a disciplinedemands comprehensive knowledge in various fields to reach the most valid results that help reveal significant aspects of the cultural heritage since such a process is an ethical responsibility .Therefore, the editor has to be patient and honest in correcting mistakes, choosing the most acceptable narration and pinpointing the additions and differences as well as other requirements of serious editing.
The study was divided into a

This paper presents a hybrid genetic algorithm (hGA) for optimizing the maximum likelihood function ln(L(phi(1),theta(1)))of the mixed model ARMA(1,1). The presented hybrid genetic algorithm (hGA) couples two processes: the canonical genetic algorithm (cGA) composed of three main steps: selection, local recombination and mutation, with the local search algorithm represent by steepest descent algorithm (sDA) which is defined by three basic parameters: frequency, probability, and number of local search iterations. The experimental design is based on simulating the cGA, hGA, and sDA algorithms with different values of model parameters, and sample size(n). The study contains comparison among these algorithms depending on MSE value. One can conc

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s