Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

Objective(s): The present study aims at studying the relationship between immunoglobulin IgG, IgA,
IgM , as well as to C-3 and C-4 in brain tumours patients immunity (meningioms and gliomas).
Methodology: Forty sera of brain tumour patients were included 20 glioma and 20 meningioma was
tested to determine the levels of IgM, IgG IgA, C-3 and C-4 by using single radial immune-diffusion
technique and compared with 20 apparently healthy blood donors.
Results: The study revealed a significant decreasing in IgG levels in glioma as compare to meningioma
and control. The concentration of two other serum immunoglobulins and complement in both
meningioma and glioma show no significant differences with those in control group.

The aim of the current study is to in evaluate the role of SOD activity in the previously reported oxidative stress in our laboratory(1), in the patients with different brain tumors. SOD activity was assayed according to riboflavin/NBT method and its specific activity was calculated in patients with benign and malignant brain tumors and control. Moreover the specific activity was compared in these samples according to gender and the occurrence of disease.Non significant elevation (P > 0.05) in SOD specific activity was observed in tissue of malignant tumors in comparison to that of in benign brain tumors. While a highly significant decrease (P < 0.001) of the specific activity was found in sera of malignant patients group in comparison to t

This investigation presents an experimental and analytical study on the behavior of reinforced concrete deep beams before and after repair. The original beams were first loaded under two points load up to failure, then, repaired by epoxy resin and tested again. Three of the test beams contains shear reinforcement and the other two beams have no shear reinforcement. The main variable in these beams was the percentage of longitudinal steel reinforcement (0, 0.707, 1.061, and 1.414%). The main objective of this research is to investigate the possibility of restoring the full load carrying capacity of the reinforced concrete deep beam with and without shear reinforcement by using epoxy resin as the material of repair. All be

Background: Complete denture wearers show lower levels of bite force than dentate subjects. This has a significant influence on their chewing efficiency. In this study an attempt was made to investigate the effect of the impression technique on the maximum bite force in complete denture wearers. Materials and methods: The patients selected for this research were 12 edentulous patients. Three different techniques for registering the final impression were made; the mucostatic, mucofunctional, and the selective pressure impression technique. Two sets of upper and lower denture bases and one set of upper and lower dentures were constructed for each subject. Intraoral and extraoral instruments and devices, as well as a computer program were used

Steganography art is a technique for hiding information where the unsuspicious cover signal carrying the secret information. Good steganography technique must be includes the important criterions robustness, security, imperceptibility and capacity. The improving each one of these criterions is affects on the others, because of these criterions are overlapped each other.  In this work, a good high capacity audio steganography safely method has been proposed based on LSB random replacing of encrypted cover with encrypted message bits at random positions. The research also included a capacity studying for the audio file, speech or music, by safely manner to carrying secret images, so it is difficult for unauthorized persons to suspect

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class