Computer systems and networks are being used in almost every aspect of our daily life, the security threats to computers and networks have increased significantly. Usually, password-based user authentication is used to authenticate the legitimate user. However, this method has many gaps such as password sharing, brute force attack, dictionary attack and guessing. Keystroke dynamics is one of the famous and inexpensive behavioral biometric technologies, which authenticate a user based on the analysis of his/her typing rhythm. In this way, intrusion becomes more difficult because the password as well as the typing speed must match with the correct keystroke patterns. This thesis considers static keystroke dynamics as a transparent layer of t

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

<span>As a result of numerous applications and low installation costs, wireless sensor networks (WSNs) have expanded excessively. The main concern in the WSN environment is to lower energy consumption amidst nodes while preserving an acceptable level of service quality. Using multi-mobile sinks to reduce the nodes' energy consumption have been considered as an efficient strategy. In such networks, the dynamic network topology created by the sinks mobility makes it a challenging task to deliver the data to the sinks. Thus, in order to provide efficient data dissemination, the sensor nodes will have to readjust the routes to the current position of the mobile sinks. The route re-adjustment process could result in a significant m

In this work, a simple and new method is proposed to simultaneously improve the physical layer security and the transmission performance of the optical orthogonal frequency division multiplexing system, by combining orthogonal frequency division multiplexing technique with chaotic theory principles. In the system, a 2-D chaotic map is employed. The introduced system replaces complex operations such as matrix multiplication with simple operations such as multiplexing and inverting. The system performance in terms of bit error rate (BER) and peak to average ratio (PAPR) is enhanced. The system is simulated using Optisystem15 with a MATLAB2016 and for different constellations. The simulation results showed that the  BE

<abstract><p>Many variations of the algebraic Riccati equation (ARE) have been used to study nonlinear system stability in the control domain in great detail. Taking the quaternion nonsymmetric ARE (QNARE) as a generalized version of ARE, the time-varying QNARE (TQNARE) is introduced. This brings us to the main objective of this work: finding the TQNARE solution. The zeroing neural network (ZNN) technique, which has demonstrated a high degree of effectiveness in handling time-varying problems, is used to do this. Specifically, the TQNARE can be solved using the high order ZNN (HZNN) design, which is a member of the family of ZNN models that correlate to hyperpower iterative techniques. As a result, a novel

The increasing discharge of dye-containing wastewater has become a serious environmental problem, requiring efficient and sustainable treatment technologies. In this study, poultry litter–derived biochar (BC) was prepared via pyrolysis at 550 °C and subsequently modified with chitosan to produce a composite adsorbent (BC/CS) for the removal of Congo Red (CR) from aqueous solutions. The materials were characterized using SEM, BET, XRD, FTIR, and XPS analyses. BET results showed that chitosan modification slightly decreased the specific surface area from 8.28 to 8.18 m²/g and pore volume from 0.035 to 0.030 cm³ /g, while introducing abundant amine and hydroxyl functional groups on the surface. Maximum adsorption occurred at pH 3, w

Herein, a biocomposite of crosslinked chitosan polyethylene glycol diglycidyl ether (CS-PEDGE), montmorillonite (MMT), and foodgrade algae (FGA) was successfully prepared by a hydrothermal technique. The resulting absorbent (CS-PEDGE/FGA/MMT) was assessed for its adsorption property with methyl violet 2B (MV 2B) a toxic cationic dye. The physicochemical properties of CS-EDGE/ FGA/MMT were assessed via various analytical techniques, including BET, Elemental analysis, pHpzc, and spectroscopy (FTIR, XRD, SEM-EDX). The influence of three adsorption variables, namely adsorbent dose (A: 0.02–0.1 g/100 mL), solution pH (B: 4–10), and contact time (C: 10–420 min) on the rate of MV 2B dye removal was examined using the Box-Behnken design (RSM-