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

This article discusses the change of values in urban family, because of various communication media and modern technologies as one of the most important factors affecting the changing family values in urban areas, this means becoming part of urban life. And focuses on the family in urban areas, for the privacy of the social, economic, demographic and cultural help to this effect, and because cities are the most friction and interaction with modern technologies.

Because of the experience of the mixture problem of high correlation and the existence of linear MultiCollinearity between the explanatory variables, because of the constraint of the unit and the interactions between them in the model, which increases the existence of links between the explanatory variables and this is illustrated by the variance inflation vector (VIF), L-Pseudo component to reduce the bond between the components of the mixture.

    To estimate the parameters of the mixture model, we used in our research the use of methods that increase bias and reduce variance, such as the Ridge Regression Method and the Least Absolute Shrinkage and Selection Operator (LASSO) method a

Due to the great evolution in digital commercial cameras, several studies have addressed the using of such cameras in different civil and close-range applications such as 3D models generation. However, previous studies have not discussed a precise relationship between a camera resolution and the accuracy of the models generated based on images of this camera. Therefore the current study aims to evaluate the accuracy of the derived 3D buildings models captured by different resolution cameras. The digital photogrammetric methods were devoted to derive 3D models using the data of various resolution cameras and analyze their accuracies. This investigation involves selecting three different resolution cameras (low, medium and

Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE),  Border

This study investigates the impact of spatial resolution enhancement on supervised classification accuracy using Landsat 9 satellite imagery, achieved through pan-sharpening techniques leveraging Sentinel-2 data. Various methods were employed to synthesize a panchromatic (PAN) band from Sentinel-2 data, including dimension reduction algorithms and weighted averages based on correlation coefficients and standard deviation. Three pan-sharpening algorithms (Gram-Schmidt, Principal Components Analysis, Nearest Neighbour Diffusion) were employed, and their efficacy was assessed using seven fidelity criteria. Classification tasks were performed utilizing Support Vector Machine and Maximum Likelihood algorithms. Results reveal that specifi

The choice of gate dielectric materials is fundamental for organic field effect transistors (OFET), integrated circuits, and several electronic applications. The operation of the OFET depends on two essential parameters: the insulation between the semiconductor layer and the gate electrode and the capacitance of the insulator. In this work, the electrical behavior of a pentacene-based OFET with a top contact / bottom gate was studied. Organic polyvinyl alcohol (PVA) and inorganic hafnium oxide (HfO₂) were chosen as gate dielectric materials to lower the operation voltage to achieve the next generation of electronic applications. In this study, the performance of the OFET was studied using monolayer and bilayer gate insulators.

The choice of gate dielectric materials is fundamental for organic field effect transistors (OFET), integrated circuits, and several electronic applications. The operation of the OFET depends on two essential parameters: the insulation between the semiconductor layer and the gate electrode and the capacitance of the insulator. In this work, the electrical behavior of a pentacene-based OFET with a top contact / bottom gate was studied. Organic polyvinyl alcohol (PVA) and inorganic hafnium oxide (HfO2) were chosen as gate dielectric materials to lower the operation voltage to achieve the next generation of electronic applications. In this study, the performance of the OFET was studied using monolayer and bilayer gate insulators. To mo