Subcutaneous vascularization has become a new solution for identification management over the past few years. Systems based on dorsal hand veins are particularly promising for high-security settings. The dorsal hand vein recognition system comprises the following steps: acquiring images from the database and preprocessing them, locating the region of interest, and extracting and recognizing information from the dorsal hand vein pattern. This paper reviewed several techniques for obtaining the dorsal hand vein area and identifying a person. Therefore, this study just provides a comprehensive review of existing previous theories. This model aims to offer the improvement in the accuracy rate of the system that was shown in previous studies and

The sensitive and important data are increased in the last decades rapidly, since the tremendous updating of networking infrastructure and communications. to secure this data becomes necessary with increasing volume of it, to satisfy securing for data, using different cipher techniques and methods to ensure goals of security that are integrity, confidentiality, and availability. This paper presented a proposed hybrid text cryptography method to encrypt a sensitive data by using different encryption algorithms such as: Caesar, Vigenère, Affine, and multiplicative. Using this hybrid text cryptography method aims to make the encryption process more secure and effective. The hybrid text cryptography method depends on circular queue. Using circ

Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft

Spent hydrodesulfurization (Co-Mo/γ-Al2O3) catalyst generally contains valuable metals like molybdenum (Mo), cobalt (Co), aluminium (Al) on a supporting material, such as γ-Al2O3. In the present study, a two stages alkali/acid leaching process was conducted to study leaching of cobalt, molybdenum and aluminium from Co-Mo/γ-Al2O3 catalyst. The acid leaching of spent catalyst, previously treated by alkali solution to remove molybdenum, yielded a solution rich in cobalt and aluminium.

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

Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essentia

KE Sharquie, AF Hameed, AA Noaimi, Indian Journal of Pathology and Microbiology, 2016 - Cited by 12

Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classif