The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.

Authentication is the process of determining whether someone or something is, in fact, who or what it is declared to be. As the dependence upon computers and computer networks grows, the need for user authentication has increased. User’s claimed identity can be verified by one of several methods. One of the most popular of these methods is represented by (something user know), such as password or Personal Identification Number (PIN). Biometrics is the science and technology of authentication by identifying the living individual’s physiological or behavioral attributes. Keystroke authentication is a new behavioral access control system to identify legitimate users via their typing behavior. The objective of this paper is to provide user

Peak ground acceleration (PGA) is one of the critical factors that affect the determination of earthquake intensity. PGA is generally utilized to describe ground motion in a particular zone and is able to efficiently predict the parameters of site ground motion for the design of engineering structures. Therefore, novel models are developed to forecast PGA in the case of the Iraqi database, which utilizes the particle swarm optimization (PSO) approach. A data set of 187 historical ground-motion recordings in Iraq’s tectonic regions was used to build the explicit proposed models. The proposed PGA models relate to different seismic parameters, including the magnitude of the earthquake (Mw), average shear-wave velocity (VS30), focal depth (FD

This paper shows an approach for Electromyography (ECG) signal processing based on linear and nonlinear adaptive filtering using Recursive Least Square (RLS) algorithm to remove two kinds of noise that affected the ECG signal. These are the High Frequency Noise (HFN) and Low Frequency Noise (LFN). Simulation is performed in Matlab. The ECG, HFN and LFN signals used in this study were downloaded from ftp://ftp.ieee.org/uploads/press/rangayyan/, and then the filtering process was obtained by using adaptive finite impulse response (FIR) that illustrated better results than infinite impulse response (IIR) filters did.

A simple, low cost and rapid flow injection turbidimetric method was developed and validated for mebeverine hydrochloride (MBH) determination in pharmaceutical preparations. The developed method is based on forming of a white, turbid ion-pair product as a result of a reaction between the MBH and sodium persulfate in a closed flow injection system where the sodium persulfate is used as precipitation reagent. The turbidity of the formed complex was measured at the detection angle of 180° (attenuated detection) using NAG dual&Solo (0-180°) detector which contained dual detections zones (i.e., measuring cells 1 & 2). The increase in the turbidity of the complex was directly proportional to the increase of the MBH concentration

Abstract: Word sense disambiguation (WSD) is a significant field in computational linguistics as it is indispensable for many language understanding applications. Automatic processing of documents is made difficult because of the fact that many of the terms it contain ambiguous. Word Sense Disambiguation (WSD) systems try to solve these ambiguities and find the correct meaning. Genetic algorithms can be active to resolve this problem since they have been effectively applied for many optimization problems. In this paper, genetic algorithms proposed to solve the word sense disambiguation problem that can automatically select the intended meaning of a word in context without any additional resource. The proposed algorithm is evaluated on a col

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respectively. For