This paper presents a hybrid approach for solving null values problem; it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estimation through working with rough set theory. The results obtained from most code sets show that Bees algorithm better than ID3 in decreasing the number of extracted rules without affecting the accuracy and increasing the accuracy ratio of null values estimation, especially when the number of null values is increasing
In this work, the finite element analysis of moving coordinates has been used to study the thermal behavior of the tissue subjected to both continuous wave and pulsed CO2 laser. The results are compared with previously published data, and a good agreement has been found, which verifies the implemented theory. Some conclusions are obtained; As pulse width decreases, or repetition rate increases, or fluence increases then the char depth is decreased which can be explained by an increase in induced energy or its rate, which increases the ablation rate, leading to a decrease in char depth. Thus: An increase in the fluence or decreasing pulse width or increasing repetition rate will increase ablation rate, which will increase the depth of cut
... Show MoreIn this paper, we study the convergence theorems of the Modified Ishikawa iterative sequence with mixed errors for the uniformly continuous mappings and solving nonlinear uniformly continuous mappings equation in arbitrary real Banach space.
The corrosion behavior and the influence of the concentration of adenine (AD) on the corrosion of 316L stainless steel in 0.6 mol.dm-3 sodium chloride acid solutions were studied. The research was performed in two pH values (pH=2 and pH=4) over the temperature range (293- 308)K. The investigation involved electrochemical polarization method using potentiostatic technique. Tafel polarization study revealed that (AD) acted as a mixed inhibitor. The inhibition efficiency increased with an increase in the concentration of adenine, but decreased with increase in temperature. (efficiency= 87% at 0.01 M AD & at T= 293K). The adsorption of (AD) has been found to occur on the surface of 316L stainless steel according to the Langmuir isotherm.
... Show MoreThe corrosion behavior of Titanium in a simulated saliva solution was improved by Nanotubular Oxide via electrochemical anodizing treatment using three electrodes cell potentiostat at 37°C. The anodization treatment was achieved in a non-aqueous electrolyte with the following composition: 200mL ethylene glycol containing 0.6g NH4F and 10 ml of deionized water and using different applied directed voltage at 10°C and constant time of anodizing (15 min.). The anodized titanium layer was examined using SEM, and AFM technique.
The results showed that increasing applied voltage resulted in formation titanium oxide nanotubes with higher corrosion resistance
Addressed this research the impact of intelligence emotional dimensions of the main(self awareness, and self-management, and social awareness, and relationship management) in the performance excellence the university(performance optimization, and strategic development) this is by middling the styles decision making which are (rational and intuitive, and dependent, and spontaneous, and avoidant), and Go search of an intellectual dilemma raise fundamental questions revolve around the search was to answer those questions through a theoretical framework for search variables first and test models of the relationship and second through the impact six hypotheses President.The objective of the research to make sure the contr
... Show MorePurpose: The research aims to estimate models representing phenomena that follow the logic of circular (angular) data, accounting for the 24-hour periodicity in measurement. Theoretical framework: The regression model is developed to account for the periodic nature of the circular scale, considering the periodicity in the dependent variable y, the explanatory variables x, or both. Design/methodology/approach: Two estimation methods were applied: a parametric model, represented by the Simple Circular Regression (SCR) model, and a nonparametric model, represented by the Nadaraya-Watson Circular Regression (NW) model. The analysis used real data from 50 patients at Al-Kindi Teaching Hospital in Baghdad. Findings: The Mean Circular Erro
... Show MoreThe region-based association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease. Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls. To tackle the problem of the sparse distribution, a two-stage approach was proposed in literature: In the first stage, haplotypes are computationally inferred from genotypes, followed by a haplotype coclassification. In the second stage, the association analysis is performed on the inferred haplotype groups. If a haplotype is unevenly distributed between the case and control samples, this haplotype is labeled
... Show MoreThis paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe
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غالبا ما يكون تعامل المنظمات المالية والمصرفية مع الزبائن بشكل أساسي مما يتطلب منها جمع كميات هائلة من البيانات عن هؤلاء الزبائن هذا بالإضافة الى ما يرد اليها يوميا من بيانات يجعلها أمام أكداس كبيرة من البيانات تحتاج الى جهود جبارة تحسن التعامل معها والاستفادة منها بما يخدم المنظمة.
ان التعامل اليدوي مع مثل هذه البيانات دون استخدام تقنيات حديثة يبعد المنظمة عن التط
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