The fingerprints are the more utilized biometric feature for person identification and verification. The fingerprint is easy to understand compare to another existing biometric type such as voice, face. It is capable to create a very high recognition rate for human recognition. In this paper the geometric rotation transform is applied on fingerprint image to obtain a new level of features to represent the finger characteristics and to use for personal identification; the local features are used for their ability to reflect the statistical behavior of fingerprint variation at fingerprint image. The proposed fingerprint system contains three main stages, they are: (i) preprocessing, (ii) feature extraction, and (iii) matching. The preprocessi

In this study, a different design of passive air Solar Chimney(SC)was tested by installing it in the south wall of insulated test room in Baghdad city. The SC was designed from vertical and inclined parts connected serially together, the vertical SC (first part) has a single pass and Thermal Energy Storage Box Collector (TESB (refined paraffin wax as Phase Change Material(PCM)-Copper Foam Matrix(CFM))), while the inclined SC was designed in single pass, double passes and double pass with TESB (semi refined paraffin wax with copper foam matrix) with selective working angle ((30^o, 45^o and 60^o). A computational model was employed and solved by Finite Volume Method (FVM) to simulate the air i

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

The reaction of 2, 4, 6-trihydroxyactophenonemonohydrate with hydrazine monohydrate was realized under reflux in methanol and a few drops of glacial acetic acid were added to give the (intermediate) 2-(1-hydrazono-ethyl)-benzene-1, 3, 5-triol, which reacted with salicylaldehyde in methanol to give a new type (NO) ligand [HL][(2-1-[(2-hydroxy-benzyliidene)-hydrazono]-ethyl) benzene-1, 3, 5-triol. The ligand was reacted with Mcl.(where M-Co, Ni, Cu, and Zn) under reflux in methanol with (l: 1) ratio to give complexes of the general formula [M (HL)]. All compounds have been characterized by spectroscopic methods I" H NMR, IR. UV-Vis, HPLC, atomic absorption] microanalysis along with conductivity measurement. From the above data the proposed mo

The high temperature superconductor’s compounds are one of the hot spot field of science, due to their applications in industries. Hg0.8Sb0.2Ba2Ca2Cu3O8+δ and Hg0.8Sb0.2Ba2Ca1Cu2O6+δ, were manufactured using a doable-step of solid state reaction method. The samples were sintered at 800 ° C. The transition temperatures Tc are found from electrically resistively by using four probe techniques. The resistivity become zero when the transition temperature Tc(offset) have 131 and 119 K, and the onset temperature Tc(onset) have 139 K for Hg0.8Sb0.2Ba2Ca2Cu3O8+δ and 132 K for Hg0.8Sb0.2Ba2Ca1Cu2O6+δ. Analysis of X-ray diffraction showed a tetragonal structure with lattice parameters changes for all samples.

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