The fact that the signature is widely used as a means of personal verification
emphasizes the need for an automatic verification system. Verification can be
performed either Offline or Online based on the application. Offline systems work on
the scanned image of a signature. In this paper an Offline Verification of handwritten
signatures which use set of simple shape based geometric features. The features used
are Mean, Occupancy Ratio, Normalized Area, Center of Gravity, Pixel density,
Standard Deviation and the Density Ratio. Before extracting the features,
preprocessing of a scanned image is necessary to isolate the signature part and to
remove any spurious noise present. Features Extracted for whole signature first, then
extracted for every part after dividing the signature into four sections. For verification,
statistical verification techniques are used (Euclidean Distance, Hellinger Distance
and Square Chord Distance). The system is trained on three datasets of signatures.
The first and the second datasets have English signatures while the third one is
collected from people; it contains Arabic and English signatures. The system has been
tested on every dataset. The experimental results show that the Euclidean Distance
has the average accuracy of 94.42, the Hellinger Distance has the average accuracy of
95.27 and the Square Chord Distance has the average accuracy of 93.14. That result
for whole the image and the following average accuracy for image using grid the
Euclidean Distance has the average accuracy of 93.54, the Hellinger Distance has the
average accuracy of 95.87, and the Square Chord Distance has the average accuracy
of 95.93.
A number of aqueous samples were collected from river Tigris in Baghdad city, enriched ~1000 times using solid phase extraction (SPE), then extracted the trace concentrations of some polychlorinated biphenyls (PCB) using an aqueous two-phase system (ATPS) composed of 1Methylpyridinium chloride [MePy]Cl and KH2PO4 salt. High performance liquid chromatography technique coupled with ultraviolet (HPLC-UV) is used for the quantification. Extraction under the optimized conditions of pH, solvent composition, duration and temperature has given with a yield of PCB about 91%. The limit of detection (LOD) and limit of quantification (LOQ) for analyses are 0.11-0.62 µg.L−1 and 2.67–3.43 µg.L−1 respectively with relative stan
... Show MoreThis paper proposes a new methodology for improving network security by introducing an optimised hybrid intrusion detection system (IDS) framework solution as a middle layer between the end devices. It considers the difficulty of updating databases to uncover new threats that plague firewalls and detection systems, in addition to big data challenges. The proposed framework introduces a supervised network IDS based on a deep learning technique of convolutional neural networks (CNN) using the UNSW-NB15 dataset. It implements recursive feature elimination (RFE) with extreme gradient boosting (XGB) to reduce resource and time consumption. Additionally, it reduces bias toward
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