Flying Ad hoc Networks (FANETs) has developed as an innovative technology for access places without permanent infrastructure. This emerging form of networking is construct of flying nodes known as unmanned aerial vehicles (UAVs) that fly at a fast rate of speed, causing frequent changes in the network topology and connection failures. As a result, there is no dedicated FANET routing protocol that enables effective communication between these devices. The purpose of this paper is to evaluate the performance of the category of topology-based routing protocols in the FANET. In a surveillance system involving video traffic, four routing protocols with varying routing mechanisms were examined. Additionally, simulation experiments conduct

Electrospun nanofiber membranes are employed in a variety of applications due to its unique features. the nanofibers' characterizations are effected by the polymer solution. The used solvent for dissolving the polymer powder is critical in preparing the precursor solution. In this paper, the Polyacrylonitrile (PAN)-based nanofibers were prepared in a concentration of 10 wt.% using various solvents (NMP, DMF, and DMSO). The surface morphology, porosity, and the mechanical strength of the three prepared 10 wt.% PAN-based nanofibers membranes (PAN/NMP, PAN/DMF, and PAN/DMSO) were characterized using the Scanning Electron Microscopy (SEM), Dry-wet Weights method, and Dynamic Mechanical Analyzer (DMA). Using DMF as a solvent resulted in a lon

At the level of both individuals and companies, Wireless Sensor Networks (WSNs) get a wide range of applications and uses. Sensors are used in a wide range of industries, including agriculture, transportation, health, and many more. Many technologies, such as wireless communication protocols, the Internet of Things, cloud computing, mobile computing, and other emerging technologies, are connected to the usage of sensors. In many circumstances, this contact necessitates the transmission of crucial data, necessitating the need to protect that data from potential threats. However, as the WSN components often have constrained computation and power capabilities, protecting the communication in WSNs comes at a significant performance pena

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

Attacking a transferred data over a network is frequently happened millions time a day. To address this problem, a secure scheme is proposed which is securing a transferred data over a network. The proposed scheme uses two techniques to guarantee a secure transferring for a message. The message is encrypted as a first step, and then it is hided in a video cover.  The proposed encrypting technique is RC4 stream cipher algorithm in order to increase the message's confidentiality, as well as improving the least significant bit embedding algorithm (LSB) by adding an additional layer of security. The improvement of the LSB method comes by replacing the adopted sequential selection by a random selection manner of the frames and the pixels wit

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the