Computer systems and networks are being used in almost every aspect of our daily life; as a result the security threats to computers and networks have also increased significantly. Traditionally, password-based user authentication is widely used to authenticate legitimate user in the current system0T but0T this method has many loop holes such as password sharing, shoulder surfing, brute force attack, dictionary attack, guessing, phishing and many more. The aim of this paper is to enhance the password authentication method by presenting a keystroke dynamics with back propagation neural network as a transparent layer of user authentication. Keystroke Dynamics is one of the famous and inexpensive behavioral biometric technologies, which identi

Foreground object detection is one of the major important tasks in the field of computer vision which attempt to discover important objects in still image or image sequences or locate related targets from the scene. Foreground objects detection is very important for several approaches like object recognition, surveillance, image annotation, and image retrieval, etc. In this work, a proposed method has been presented for detection and separation foreground object from image or video in both of moving and stable targets. Comparisons with general foreground detectors such as background subtraction techniques our approach are able to detect important target for case the target is moving or not and can separate foreground object with high det

Gallium  selective  e lectrodes   based  on  12-crown-4  (12C4)  and benzo-15-crown-5   (B15C5)  and  plasticizers   dioctyl  phenyl phosphonate   (DOPP),   dibutyl  phosphate  (DBP),  dibutyl  phthalate (DBPH)  in  PVC  matrix  membranes  are  constructed.  Specific properties  of  the electrodes  are  studied  including  calibration  curve, slope  detection limit, concentration  

In this paper, an adaptive medical image watermarking technique is proposed based on wavelet transform and properties of human visual system in order to maintain the authentication of medical images. Watermark embedding process is carried out by transforming the medical image into wavelet domain and then adaptive thresholding is computed to determine the suitable locations to hide the watermark in the image coefficients. The watermark data is embedded in the coefficients that are less sensitive into the human visual system in order to achieve the fidelity of medical image. Experimental results show that the degradation by embedding the watermark is too small to be visualized. Also, the proposed adaptive watermarking technique can preserv

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

Implementation of TSFS (Transposition, Substitution, Folding, and Shifting) algorithm as an encryption algorithm in database security had limitations in character set and the number of keys used. The proposed cryptosystem is based on making some enhancements on the phases of TSFS encryption algorithm by computing the determinant of the keys matrices which affects the implementation of the algorithm phases. These changes showed high security to the database against different types of security attacks by achieving both goals of confusion and diffusion.

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

     NS-2 is a tool to simulate networks and events that occur per packet sequentially based on time and are widely used in the research field. NS-2 comes with NAM (Network Animator) that produces a visual representation it also supports several simulation protocols. The network can be tested end-to-end. This test includes data transmission, delay, jitter, packet-loss ratio and throughput. The Performance Analysis simulates a virtual network and tests for transport layer protocols at the same time with variable data and analyzes simulation results based on the network simulator NS-2.

      Recognizing facial expressions and emotions is a basic skill that is learned at an early age and it is important for human social interaction. Facial expressions are one of the most powerful natural and immediate means that humans use to express their feelings and intentions. Therefore, automatic emotion recognition based on facial expressions become an interesting area in research, which had been introduced and applied in many areas such as security, safety health, and human machine interface (HMI). Facial expression recognition transition from controlled environmental conditions and their improvement and succession of recent deep learning approaches from different areas made facial expression representation mostly based on u