The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

      Today, the world is living in a time of epidemic diseases that spread unnaturally and infect and kill millions of people worldwide. The COVID-19 virus, which is one of the most well-known epidemic diseases currently spreading, has killed more than six million people as of May 2022. The World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) after an outbreak of SARS-CoV-2 infection. COVID-19 is a severe and potentially fatal respiratory disease caused by the SARS-CoV-2 virus, which was first noticed at the end of 2019 in Wuhan city. Artificial intelligence plays a meaningful role in analyzing medical images and giving accurate results that serve healthcare workers, especially X-ray images, which are co

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

A preventing shield for neutrons and gamma rays was designed using alternate layers of water and iron with pre-fixed dimensions in order to study the possibility of attenuating both neutrons and gamma-rays. ANISN CODE was prepared and adapted for the shield calculation using radiation doses calculation: Two groups of cross-section were used for each of neutrons and gamma-rays that rely on the one – dimensional transport equation using discrete ordinate's method, and through transforming cross-section values to values that are independent on the number of groups. The memory size required for the applied code was reduced and the results obtained were in agreement with those of standard acceptable document samples of cross –section, this a

conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. 

Network security is defined as a set of policies and actions taken by a network administrator in order to prevent unauthorized access, penetrated the defenses and infiltrated the network from unnecessary intervention. The network security also involves granting access to data using a pre-defined policy. A network firewall, on the other hand, is a network appliance that controls incoming and outgoing traffic by examining the traffic flowing through the network. This security measure establishes a secure wall [firewall] between a trusted internal network and the outside world were a security threat in shape of a hacker or a virus might have existed

This paper presents a proposed neural network algorithm to solve the shortest path problem (SPP) for communication routing. The solution extends the traditional recurrent Hopfield architecture introducing the optimal routing for any request by choosing single and multi link path node-to-node traffic to minimize the loss. This suggested neural network algorithm implemented by using 20-nodes network example. The result shows that a clear convergence can be achieved by 95% valid convergence (about 361 optimal routes from 380-pairs). Additionally computation performance is also mentioned at the expense of slightly worse results.

This paper presents a hierarchical two-stage outdoor scene classification method using multi-classes of Support Vector Machine (SVM). In this proposed method, the gist feature of all the images in the database is extracted first to obtain the feature vectors. The image of database is classified into eight outdoor scenes classes, four manmade scenes and four natural scenes. Second, a hierarchical classification is applied, where the first stage classifies all manmade scene classes against all natural scene classes, while the second stage of a hierarchical classification classifies the outputs of first stage into either one of the four manmade scene classes or natural scene classes. Binary SVM and multi-classes SVMs are employed in the fir