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 identifies a user based on the analysis of his/her typing rhythm. This paper utilizes keystroke features including dwell time (DT), flight time (FT), up-up time (UUT), and a mixture of theses features as keystroke representation. The back propagation neural network is trained with the mean of keystroke timing information for each character of password. These times are used to discriminate between the authentic users and impostors. Results of the experiments demonstrate that the backpropagation network with UUT features comparable to combination of DT and FT. Also, the results of backpropagation with combination of DT, FT and UUT provide low False Alarm Rate (FAR) and False Reject Rate (FRR) and high accuracy.
This research supports the UN’s 2030 Agenda and its goals of ending poverty and hunger. Fruit and vegetable (FandV) lose their freshness and weight when stored at unsuitable temperatures and relative humidity. This study was conducted in Baghdad governorate, located at latitude 33.3128057 and longitude 44.3614875, in the Karrada region from February 27 to April 17, 2024. It compared the effectiveness of different storage technologies, including evaporative cooling, various air velocities, and diverse packaging methods, against sustainable and nonpackaging approaches. The study employed an air cooler with a volume of 2000 ft3/s and insulated packaging. Temperature and relative humidity were recorded in the storage environment
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This research aims to distinguish the reef environment from the non-reef environment. The Oligocene-Miocene-succussion in western Iraq was selected as a case study, represented by the reefal limestone facies of the Anah Formation (Late Oligocene) deposited in reef-back reef environments, dolomitic limestone of the Euphrates Formation (Early Miocene) deposited in open sea environments, and gypsiferous marly limestone of the Fatha Formation (Middle Miocene) deposited in a lagoonal environment. The content of the rare earth elements (REEs) (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Er, Ho, Tm, Yb, Lu, and Y) in reef facies appear to be much lower than of those in the non-reef facies. The open sea facies have a low content of REEs due to bein
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Most of the known cases of strong gravitational lensing involve multiple imaging of an active galactic nucleus. The properties of lensed active galactic nuclei make them promising systems for astrophysical applications of gravitational lensing. So we present a simple model for strong lensing in the gravitational lensed systems to calculate the age of four lensed galaxies, in the present work we take the freedman models with (k curvature index =0) Euclidian case, and the result show a good agreement with the other models.
The Adaptive Optics technique has been developed to obtain the correction of atmospheric seeing. The purpose of this study is to use the MATLAB program to investigate the performance of an AO system with the most recent AO simulation tools, Objected-Oriented Matlab Adaptive Optics (OOMAO). This was achieved by studying the variables that impact image quality correction, such as observation wavelength bands, atmospheric parameters, telescope parameters, deformable mirror parameters, wavefront sensor parameters, and noise parameters. The results presented a detailed analysis of the factors that influence the image correction process as well as the impact of the AO components on that process
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Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from answer sheets by using analysis phase. This proposal will be able to detect no selection or select more than one choice, in addition, using M
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In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor
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