The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

Background: Cigarette smoking is an important risk factor that has a clear strong association with the prevalence and severity of chronic periodontitis (CP). Salivary biochemical parameters may be affected by both smoking and CP together. Materials and methods: Eighty systematically healthy male patients were included in this study. They were grouped based on their periodontal and smoking status. Unstimulated whole saliva (UWS) was collected from all subject. Salivary flow rate (FR) was measured during sample collection. Parameters such as salivary pH, total protein (TP), albumin (Alb), total fucose (TF), protein bound fucose (PBF) and C-reactive protein (CRP) were estimated. Results: Salivary flow rate was not altered regarding to smoking

Background : Hyperglycosylated hCG a newly discovered variant of hCG which can be used as a predictor of invasion of trophoblastic cells in patient with gestational trophoblastic disease. Objectives : To measure hyperglycosylated human chorionic gonadotrophin and to assess how far it can be used as predictor of invasion in invasive mole and choriocarcinoma. Study design control study. Setting: : Case Gynecological department in Baghdad Teaching Hospital from January 2016 to January 2017. Patient and Methods : 60 women were enrolled in this study 30 of them were with gestational trophoblastic disease (no.= 30 ) the remainder were normal pregnancy (no. =30) , hCG –H level was measured in both groups. Results : Mean serum hCG-H le

KE Sharquie, SA Al-Meshhadani, AA Al-Nuaimy, Saudi medical journal, 2007 - Cited by 9

ABSTRACT: Protein isolate was achieved from local peeled non soaked pumpkins seeds by using petroleum ether with protein percentage of 53.15%. Protein isolate was used in manufacturing meat burger with two substitution10 and 20%. The shrinkage percentage for burger diameter was decreased from 25.5 to 16.6%, the sample with 10% substitution was distinguished in water holding capacity (WHC) which was 54.52%. Sensitive evaluation for these samples showed that the burger with 10% substitution was similar to the control.

This work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obta

Let R be a ring with 1 and W is a left Module over R. A Submodule D of an R-Module W is small in W(D ≪ W) if whenever a Submodule V of W s.t W = D + V then V = W. A proper Submodule Y of an R-Module W is semismall in W(Y ≪_S W) if Y = 0 or Y/F ≪ W/F ∀ nonzero Submodules F of Y. A Submodule U of an R-Module E is essentially semismall(U ≪es E), if for every non zero semismall Submodule V of E, V∩U ≠ 0. An R-Module E is essentially semismall quasi-Dedekind(ESSQD) if Hom(E/W, E) = 0 ∀ W ≪es E. A ring R is ESSQD if R is an ESSQD R-Module. An R-Module E is a scalar R-Module if, ∀ , ∃ s.t V(e) = ze ∀ . In this paper, we study the relationship between ESSQD Modules with scalar and multiplication Modules. We show that