Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network that uses two convolutional neural networks (CNNs) in short ways. The technique is based on using two similar CNNs with varying input picture quality, integrating their outputs in a single layer, and employing an optimized CNN design on a proposed Sains University Malaysia (FV-USM) finger vein dataset 5904 images. The final pooling CNN, which is composed of the original picture, an image improved using the contrast limited adaptive histogram (CLAHE) approach and the Median filter, And, using Principal Component Analysis (PCA), we retrieved the features and got an acceptable performance from the FV-USM database, with a recognition rate of 98.53 percent. Our proposed strategy outperformed other strategies described in the literature.
In recent decades, the identification of faces with and without masks from visual data, such as video and still images, has become a captivating research subject. This is primarily due to the global spread of the Corona pandemic, which has altered the appearance of the world and necessitated the use of masks as a vital measure for epidemic prevention. Intellectual development based on artificial intelligence and computers plays a decisive role in the issue of epidemic safety, as the topic of facial recognition and identifying individuals who wear masks or not was most prominent in the introduction and in-depth education. This research proposes the creation of an advanced system capable of accurately identifying faces, both with and
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The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. E
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Background: As a multifactorial disorder, temporomandibular joint (TMD) is difficult to diagnose, and multiple factors affect the joint and cause the temporomandibular disorder. Standardization of clinical diagnosis of TMD should be used to reach a definite clinical diagnosis; the condylar bone may degenerate in accordance with these disorders. Aims: Evaluate the correlation between the clinical diagnosis and degenerative condylar change (flattening, sclerosis, erosion, and osteophyte). Materials and Methods: A prospective study with a study group of 97 TMD patients (total of 194 joints) aged 20 to 50. Patients were sent to cone beam computed tomography (CBCT) to assess the degenerative condylar change. Results: No association was found bet
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Polarization is an important property of light, which refers to the direction of electric field oscillations. Polarization modulation plays an essential role for polarization encoding quantum key distribution (QKD). Polarization is used to encode photons in the QKD systems. In this work, visible-range polarizers with optimal dimensions based on resonance grating waveguides have been numerically designed and investigated using the COMSOL Multiphysics Software. Two structures have been designed, namely a singlelayer metasurface grating (SLMG) polarizer and an interlayer metasurface grating (ILMG) polarizer. Both structures have demonstrated high extinction ratios, ~1.8·103 and 8.68·104 , and the bandwidths equal to 45 and 55 nm for th
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Transportation and distribution are the most important elements in the work system for any company, which are of great importance in the success of the chain work. Al-Rabee factory is one of the largest ice cream factories in Iraq and it is considered one of the most productive and diversified factories with products where its products cover most areas of the capital Baghdad, however, it lacks a distribution system based on scientific and mathematical methods to work in the transportation and distribution processes, moreover, these processes need a set of important data that cannot in any way be separated from the reality of fuzziness industrial environment in Iraq, which led to use the fuzzy sets theory to reduce the levels of uncertainty.
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In this research, a low cost, portable, disposable, environment friendly and an easy to use lab-on-paper platform sensor was made. The sensor was constructed using a mixture of Rhodamine-6G and gold nanoparticles also Sodium chloride salt. Drop–casting method was utilized as a technique to make a platform which is a commercial office paper. A substrate was characterized using Field Emission Scanning Electron Microscope, Fourier transform infrared spectroscopy, UV-visible spectrophotometer and Raman Spectrometer. Rh-6G Raman signal was enhanced based on Surface Enhanced Raman Spectroscopy technique utilized gold nanoparticles. High Enhancement factor of Plasmonic commercial office paper reaches up to 0.9 x105 because of local surface pl
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial
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