The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group of signatures, numbering 70 images, were used. Image preprocessing steps were performed on them, and their features were extracted using the median filter. After that, the eigenvector and eigenvalue were calculated using the PCA algorithm. Then the backpropagation neural network algorithm was applied for training and testing where the performance reached 6.7995e−07 for 82 epochs and the accuracy was 99.98%.
Pattern matching algorithms are usually used as detecting process in intrusion detection system. The efficiency of these algorithms is affected by the performance of the intrusion detection system which reflects the requirement of a new investigation in this field. Four matching algorithms and a combined of two algorithms, for intrusion detection system based on new DNA encoding, are applied for evaluation of their achievements. These algorithms are Brute-force algorithm, Boyer-Moore algorithm, Horspool algorithm, Knuth-Morris-Pratt algorithm, and the combined of Boyer-Moore algorithm and Knuth–Morris– Pratt algorithm. The performance of the proposed approach is calculated based on the executed time, where these algorithms are applied o
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The efficiency of the Honeywords approach has been proven to be a significant tool for boosting password security. The suggested system utilizes the Meerkat Clan Algorithm (MCA) in conjunction with WordNet to produce honeywords, thereby enhancing the level of password security. The technique of generating honeywords involves data sources from WordNet, which contributes to the improvement of authenticity and diversity in the honeywords. The method encompasses a series of consecutive stages, which include the tokenization of passwords, the formation of alphabet tokens using the Meerkat Clan Algorithm (MCA), the handling of digit tokens, the creation of unique character tokens, and the consolidation of honeywords. The optimization of t
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B3LYP/6-31G, DFT method was applied to hypothetical study the design of six carbon nanotube materials based on [8]circulene, through the use of cyclic polymerization of two and three molecules of [8]circulene. Optimized structures of [8]circulene have saddle-shaped. Design of six carbon nanotubes reactions were done by thermodynamically calculating (Δ S, Δ G and Δ H) and the stability of these hypothetical nanotubes depending on the value of HOMO energy level. Nanotubes obtained have the most efficient gap energy, making them potentially useful for solar cell applications.
Far infrared photoconductive detectors based on multi-wall carbon nanotubes (MWCNTs) were fabricated and their characteristics were tested. MWCNTs films deposited on porous silicon (PSi) nanosurface by dip and drop coating techniques. Two types of deposited methods were used; dip coating sand drop –by-drop methods. As well as two types of detector were fabricated one with aluminum mask and the other without, and their figures of merits were studied. The detectors were illuminated by 2.2 and 2.5 Watt from CO2 of 10.6 m and tested. The surface morphology for the films is studied using AFM and SEM micrographs. The films show homogeneous distributed for CNTs on the PSi layer. The root mean square (r.m.s.) of the films surface roughness in
... Show MorePhotonic Crystal Fiber Interferometers (PCFIs) are greatly used
for sensing applications. This work presents the fabrication and
characterization of a relative humidity sensor based on Mach-
Zehnder Interferometer (MZI), which operates in reflection mode.
The humidity sensor operation based on the adsorption and
desorption of water vapour at the silica-air interface within the PCF.
The fabrication of this sensor is simple, it only includes splicing and
cleaving the PCF with SMF.PCF (LMA-10) with a certain length
spliced to SMF (Corning-28).
The spectrum of PCFI exhibits good sensitivity to humidity
variations. The PCFI response is observed for a range of humidity
values from (27% RH to 85% RH), the positi
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Intrusion detection systems detect attacks inside computers and networks, where the detection of the attacks must be in fast time and high rate. Various methods proposed achieved high detection rate, this was done either by improving the algorithm or hybridizing with another algorithm. However, they are suffering from the time, especially after the improvement of the algorithm and dealing with large traffic data. On the other hand, past researches have been successfully applied to the DNA sequences detection approaches for intrusion detection system; the achieved detection rate results were very low, on other hand, the processing time was fast. Also, feature selection used to reduce the computation and complexity lead to speed up the system
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In this work a chemical sensor was built by using Plane Wave Expansion (PWE) modeling technique by filling the core of 1550 hollow core photonic crystal fiber with chloroform that has different concentrations after being diluted with distilled water. The minimum photonic bandgap width is.0003 and .0005 rad/sec with 19 and 7 cells respectively and a concentration of chloroform that filled these two fibers is 75%.
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