Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels wit

The subject matter of the article Prediction of COVID-19 disease and infection rate based on a pre-trained model that supports deep learning. The goal is to build a system to diagnose people as infected or not with covid disease with the percentage of infection and the affected site and to present it with interactive interfaces to facilitate the use of the system for anyone not specialized in the software field. The task is to detect or predict the Coronavirus that affects the airways, lungs, and breathing. It is the cause of many deaths and is still in the process of transformation and development, but with less media exposure. From this standpoint, a medical system was proposed to detect the presence of the Coronavirus in the lung based o

Diabetic retinopathy is an eye disease, because of pressure in eye nerve fiber. It is a major cause of blindness in middle as well as older age groups; therefore it is essential to diagnose it earlier. Some of the challenges are in the diagnosis of the disease is detection edges of the image, may be some important edges are missed outcome the noise around the corners.
Wherefore, in order to reduce these effects in this paper, we proposed a new technique for edge detection using traditional operators in combination with fuzzy logic based on fuzzy inference system. The results show that the proposed fuzzy edge detection technique better than of traditional techniques, where vascular are markedly detected over the original.

This paper proposes a new methodology for improving network security by introducing an optimised hybrid intrusion detection system (IDS) framework solution as a middle layer between the end devices. It considers the difficulty of updating databases to uncover new threats that plague firewalls and detection systems, in addition to big data challenges. The proposed framework introduces a supervised network IDS based on a deep learning technique of convolutional neural networks (CNN) using the UNSW-NB15 dataset. It implements recursive feature elimination (RFE) with extreme gradient boosting (XGB) to reduce resource and time consumption. Additionally, it reduces bias toward

in this paper we adopted ways for detecting edges locally classical prewitt operators and modification it are adopted to perform the edge detection and comparing then with sobel opreators the study shows that using a prewitt opreators

    In this paper, we devoted to use circular shape sliding block, in image edge determination. The circular blocks have symmetrical properties in all directions for the mask points around the central mask point. Therefore, the introduced method is efficient to be use in detecting image edges, in all directions curved edges, and lines. The results exhibit a very good performance in detecting image edges, comparing with other edge detectors results.

      The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than othe

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