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 the user for user authentication. Back Propagation Neural Network (BPNN) and the Probabilistic Neural Network (PNN) are used as a classifier to discriminate between the authentic and impostor users. Furthermore, four keystroke dynamics features namely: Dwell Time (DT), Flight Time (FT), Up-Up Time (UUT), and a mixture of (DT) and (FT) are extracted to verify whether the users could be properly authenticated. Two datasets (keystroke-1) and (keystroke-2) are used to show the applicability of the proposed Keystroke dynamics user authentication system. The best results obtained with lowest false rates and highest accuracy when using UUT compared with DT and FT features and comparable to combination of DT and FT, because of UUT as one direct feature that implicitly contained the two other features DT, and FT; that lead to build a new feature from the previous two features making the last feature having more capability to discriminate the authentic users from the impostors. In addition, authentication with UUT alone instead of the combination of DT and FT reduce the complexity and computational time of the neural network when compared with combination of DT and FT features.
The Present Work includes the study of the population dynamics of Armadillidium vulgare in AL- Jadiriya region in Baghdad. Monthly samples were collected using a quadrat 0.0625 m2 from November 2007 to November 2008.. The population density of A.vulgare, ranged from 880 ind/m2 in May to251 ind/m2 in January respectively. This species showed high aggregation dispersion in the study area. The sex ratio showed that the number of females were more than that of males and significantly differd (P < 0.05) during the reproductive months. Furthermore, it was found that the juveniles of species were present at most time of the year, But the large sized groups have been observed during summer and spring. And showed a positive linear correlations betwe
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In this research we built a mathematical model of the transportation problem for data of General Company for Grain Under the environment of variable demand ,and situations of incapableness to determining the supply required quantities as a result of economic and commercial reasons, also restrict flow of grain amounts was specified to a known level by the decision makers to ensure that the stock of reserves for emergency situations that face the company from decrease, or non-arrival of the amount of grain to silos , also it took the capabilities of the tanker into consideration and the grain have been restricted to avoid shortages and lack of processing capability, Function has been adopted
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In this research, the dynamics process of charge transfer from the sensitized D35CPDT dye to tin(iv) oxide( ) or titanium dioxide ( ) semiconductors are carried out by using a quantum model for charge transfer. Different chemical solvents Pyridine, 2-Methoxyethanol. Ethanol, Acetonitrile, and Methanol have been used with both systems as polar media surrounded the systems. The rate for charge transfer from photo-excitation D35CPDTdye and injection into the conduction band of or semiconductors vary from a to for system and from a to for the system, depending on the charge transfer parameters strength coupling, free energy, potential of donor and acceptor in the system. The charge transfer rate in D35CPDT / the system is
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In this research, the dynamics process of charge transfer from the sensitized D35CPDT dye to tin(iv) oxide( ) or titanium dioxide ( ) semiconductors are carried out by using a quantum model for charge transfer. Different chemical solvents Pyridine, 2-Methoxyethanol. Ethanol, Acetonitrile, and Methanol have been used with both systems as polar media surrounded the systems. The rate for charge transfer from photo-excitation D35CPDTdye and injection into the conduction band of or semiconductors vary from a to for system and from a to for the system, depending on the charge transfer parameters strength coupling, free energy, potential of donor and acceptor in the system. The charge transfer rate in D35CPDT / the syst
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In this paper, first and second order sliding mode controllers are designed for a single link robotic arm actuated by two Pneumatic Artificial Muscles (PAMs). A new mathematical model for the arm has been developed based on the model of large scale pneumatic muscle actuator model. Uncertainty in parameters has been presented and tested for the two controllers. The simulation results of the second-order sliding mode controller proves to have a low tracking error and chattering effect as compared to the first order one. The verification has been done by using MATLAB and Simulink software.
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Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le
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In our world, technological development has become inherent in all walks of life and is characterized by its speed in performance and uses. This development required the emergence of new technologies that represent a future revolution for a fourth industrial revolution in various fields, which contributed to finding many alternatives and innovative technical solutions that shortened time and space in terms of making Machines are smarter, more accurate, and faster in accomplishing the tasks intended for them, and we find the emergence of what is called artificial intelligence (artificial intelligence), which is the technology of the future, which is one of the most important outputs of the fourth industrial revolution, and artificial inte
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Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le
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