Hyperglycemia is a complication of diabetes (high blood sugar). This condition causes biochemical alterations in the cells of the body, which may lead to structural and functional problems throughout the body, including the eye. Diabetes retinopathy (DR) is a type of retinal degeneration induced by long-term diabetes that may lead to blindness. propose our deep learning method for the early detection of retinopathy using an efficient net B1 model and using the APTOS 2019 dataset. we used the Gaussian filter as one of the most significant image-processing algorithms. It recognizes edges in the dataset and reduces superfluous noise. We will enlarge the retina picture to 224×224 (the Efficient Net B1 standard) and utilize data aug

Intrusion detection system is an imperative role in increasing security and decreasing the harm of the computer security system and information system when using of network. It observes different events in a network or system to decide occurring an intrusion or not and it is used to make strategic decision, security purposes and analyzing directions. This paper describes host based intrusion detection system architecture for DDoS attack, which intelligently detects the intrusion periodically and dynamically by evaluating the intruder group respective to the present node with its neighbors. We analyze a dependable dataset named CICIDS 2017 that contains benign and DDoS attack network flows, which meets certifiable criteria and is ope

Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness o

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

    Semantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding. With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. Most of the algorithms gain the required accuracy while compromising on their storage and computational requirements. The work showcases the implementation of Convolutional Neural Network (CNN) using Discrete Cosine Transform (DCT), where DCT exhibit exceptional energy compaction properties. The proposed Adaptive Weight Wiener Filter (AWWF) rearranges the DCT coefficients by truncating the high frequency coefficients. AWWF-DCT model reinstate the convolutional l

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

In recent years, with the growing size and the importance of computer networks, it is very necessary to provide adequate protection for users data from snooping through the use of one of the protection techniques: encryption, firewall and intrusion detection systems etc. Intrusion detection systems is considered one of the most important components in the computer networks that deal with Network security problems. In this research, we suggested the intrusion detection and classification system through merging Fuzzy logic and Artificial Bee Colony Algorithm. Fuzzy logic has been used to build a classifier which has the ability to distinguish between the behavior of the normal user and behavior of the intruder. The artificial bee colony al

Regarding the security of computer systems, the intrusion detection systems (IDSs) are essential components for the detection of attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in real time. A major drawback of the IDS is their inability to provide adequate sensitivity and accuracy, coupled with their failure in processing enormous data. The issue of classification time is greatly reduced with the IDS through feature selection. In this paper, a new feature selection algorithm based on Firefly Algorithm (FA) is proposed. In addition, the naïve bayesian classifier is used to discriminate attack behaviour from normal behaviour in the network tra

     Sentiment Analysis is a research field that studies human opinion, sentiment, evaluation, and emotions towards entities such as products, services, organizations, events, topics, and their attributes. It is also a task of natural language processing. However, sentiment analysis research has mainly been carried out for the English language. Although the Arabic language is one of the most used languages on the Internet, only a few studies have focused on Arabic language sentiment analysis.

     In this paper, a review of the most important research works in the field of Arabic text sentiment analysis using deep learning algorithms is presented. This review illustrates the main steps used in these studies, which include