Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.

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

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad

The impact of a Schiff base namely 2-((thiophen-2-ylmethylene)amino)benzenethiol  to corrode mild steel in 1 M HCl  resolved was evaluated using different weight loss technique and scanning electron microscopy (SEM).different weight measurements to expand that the 2-((thiophen-2-ylmethylene) amino) benzenethiol  inhibits  the corrosion of mild steel through adsorbing  of  top for mild steel and block the active locality. The inhibitive impacts of 2-((thiophen-2-ylmethylene)amino)benzenethiol  increase with increasing concentration and decrease with increasing temperature. SEM to checking revealed that the alloy surface was quite unaffected and formed protective film on its surface. The investigated

The present work provides theoretical investigation of laser photoacoustic one dimensional imaging to detect a blood vessel or tumor embedded within normal tissue. The key task in photoacoustic imaging is to have acoustic signal that help to determine the size and location of the target object inside normal tissue. The analytical simulation used a spherical wave model representing target object (blood vessel or tumor) inside normal tissue. A computer program in MATLAB environment has been written to realize this simulation. This model generates time resolved acoustic wave signal that include both expansion and contraction parts of the wave. The photoacoustic signal from the target object is simulated for a range of laser pulse duration 1

In this paper, the problem of developing turbulent flow in rectangular duct is investigated by obtaining numerical results of the velocity profiles in duct by using large eddy simulation model in two dimensions with different Reynolds numbers, filter equations and mesh sizes. Reynolds numbers range from (11,000) to (110,000) for velocities (1 m/sec) to (50 m/sec) with (56×56), (76×76) and (96×96) mesh sizes with different filter equations. The numerical results of the large eddy simulation model are compared with k-ε model and analytic velocity distribution and validated with experimental data of other researcher. The large eddy simulation model has a good agreement with experimental data for high Reynolds number with the first, seco

In this paper, the solar surface magnetic flux transport has been simulated by solving the diffusion–advection equation utilizing numerical explicit and implicit methods in 2Dsurface. The simulation was used to study the effect of bipolar tilted angle on the solar flux distribution with time. The results show that the tilted angle controls the magnetic distribution location on the sun’s surface, especially if we know that the sun’s surface velocity distribution is a dependent location. Therefore, the tilted angle parameter has distribution influence.