In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimensional Convolutional Neural Network Hybrid Model (1D-CNNHM). The MUCT database was considered for training and evaluation. The performance, in terms of classification, of the J48 model reached 96.01% accuracy whereas the DL model that merged LDA with MI and ANOVA reached 100% accuracy. Comparing the proposed models with other works reflects that they are performing very well, with high accuracy and low processing time.
Myriophyllum spicatum distribution in Al-Burgga marsh, Hor Al-Hammar was described in relation to some of the physical-chemical properties for its habitat (water depth, light penetration, water temperature, water salinity, pH, dissolved oxygen, Ca+2, Mg+2, reactive NO2=, reactive NO3-1, and reactive PO4-3) during 2011, seasonally. CANOCO ordination program (CCA) was used to analyse the data. Its vegetation cover percentage was with its peak at summer, its value was 90 %, while the lowest value was 20 % in winter. Statistically, Positive relationships for WT, sal., Ca+2, Mg+2, reactive NO2=, reactive NO3-1, and reactive PO4-3 with the vegetation cover percentage were observed. While, negative relationships for WD, pH, and DO with the ve
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
... Show MoreIn 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
... Show MoreThe relationship between costs of environment and costs of product life – cycle. Boubtlessly when the economical unit exercise their productive works, they lead to pollution in water, air and soil as well as all stages of product life- cycle from Rans Dstage, production stage, packaging stage and finally abandonment stage- Pollution causes environmental costs. Lgnoring or hiding environmental costs and no taking them in consideration with product cost lead to a wrong account of preduot cost.
Therefore, environmental costs should be included and matched for all stages with in product costs to know which activities, processes
... Show MoreBearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us
... Show MoreBackground: Metabolic syndrome (MetS) is a collection of connected cardiovascular risk factors that characterizes the complicated illness. The waist circumference cutoff point fluctuation has so far defined Mets. Objective: This study aimed to determine the cutoff point for WC in healthy Iraqi adults. Methods: This cross-sectional survey establishes the standard value for WC among 300 healthy university students in Wasit city, Iraq. They are aged between 18-25 years. The receiver operator characteristic (ROC) curve was used WC to predict the presence of two or more risk factors for MetS, as defined by IDF. Results: The cutoff level yielding maximum sensitivity and specificity for predicting the presence of multiple risk factors was
... Show MoreConditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.