The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Co-crystals are new solid forms of drugs that could resolve more than one problem associated with drugs formulations like solubility, stability, bioavailability, mechanical and tableting properties. A preliminary theoretical study for estimating the possible bonding between the co-crystal components (paracetamol and naproxen) was performed using the ChemOffice program. The results revealed a high possibility for bonding between paracetamol and naproxen and indicated the ability of molecular mechanics study to predict the co-crystal design.

       In this work, four different methods were used for the preparation of three different ratios 1:1, 2:1, and 1:2 of paracetamol:naproxen co-crystals. The four

Estimation of elements: Pb, Zn, Mn, Cd, and Cu, which were conducted seasonally from October-2021 till March-2022 in residential areas of Baghdad City using Geoaccumulation index (Igeo), enrichment factor ratios (EF), the factor of contamination (CF), contamination degree (Cd), index of pollution load (PLI) and index of potential ecological risk (Eif). The overall contamination factor in the research area is limited from low contamination with Cu, Mn, and Zn, moderately contaminated to very high contamination with Pb and Cd, while the assessment according to the I-geo index shows categories that vary from a slightly polluted to unpolluted by those examined heavy metals. The pollution load index indicates that the soils in some resi

Diabetes mellitus type 2 (T2DM) is a chronic and progressive condition, which affects people all around the world. The risk of complications increases with age if the disease is not managed properly. Diabetic neuropathy is caused by excessive blood glucose and lipid levels, resulting in nerve damage. Apelin is a peptide hormone that is found in different human organs, including the central nervous system and adipose tissue. The aim of this study is to estimate Apelin levels in diabetes type 2 and Diabetic peripheral Neuropathy (DPN) Iraqi patients and show the extent of peripheral nerve damage. The current study included 120 participants: 40 patients with Diabetes Mellitus, 40 patients with Diabetic peripheral Neuropathy, and 40 healthy

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to