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

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

Scheduling considered being one of the most fundamental and essential bases of the project management. Several methods are used for project scheduling such as CPM, PERT and GERT. Since too many uncertainties are involved in methods for estimating the duration and cost of activities, these methods lack the capability of modeling practical projects. Although schedules can be developed for construction projects at early stage, there is always a possibility for unexpected material or technical shortages during construction stage. The objective of this research is to build a fuzzy mathematical model including time cost tradeoff and resource constraints analysis to be applied concurrently. The proposed model has been formulated using fuzzy the

In this study, mean free path and positron elastic-inelastic scattering are modeled for the elements hydrogen (H), carbon (C), nitrogen (N), oxygen (O), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K) and iodine (I). Despite the enormous amounts of data required, the Monte Carlo (MC) method was applied, allowing for a very accurate simulation of positron interaction collisions in live cells. Here, the MC simulation of the interaction of positrons was reported with breast, liver, and thyroid at normal incidence angles, with energies ranging from 45 eV to 0.2 MeV. The model provides a straightforward analytic formula for the random sampling of positron scattering. ICRU44 was used to compile the elemental composition data. In this

Contents IJPAM: Volume 116, No. 3 (2017)

Background: The aim of this study was to evaluate and compare the apical microleakage around retrograde cavities prepared with ultrasonic technique and filled with (Biodentineâ„¢) Materials and methods: 40 extracted single rooted human permanent maxillary teeth with mature apices were selected. The roots were prepared chemo-mechanically using k-files with crown-down technique and then obturated with lateral condensation gutta-percha technique. Teeth were divided into four main groups according to the cavity preparation method either manual or ultrasonic technique: Group A (n=10): A class I retrograde cavity at root end was prepared with traditional handpeice equipped and placement of Biodentine with manual condensation. Group B (n=10):

Background: Diabetes mellitus (DM) could be regarded as a set of chronic metabolic disorders which have a common aspect of hyperglycemia. The resistance in the peripheral actions of insulin or impaired insulin secretion could be the reason  hepcidin which is a peptide hormone derived from liver, in systemic iron homeostasis is an essential regulator, and its lopsided production participates in the pathogenesis of iron disorders in spectrum. Osteoporosis often accompanies many diseases like ß-thalassemia, hemochromatosis, sickle liver diseases, cell disease and hemosiderosis featured by iron overload, evidences suggest that Iron overload and iron deficiency are suggested by evidences that they affect bone in a negative way, acting