Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare

This study focused on determining the markers of Macrophage migration inhibitor (MIF), as well as the N-telopeptides of type I bone collagen (NTX), and some other parameters (alkaline phosphatase (ALP), vitamin D (Vit D), calcium (Ca), phosphorus (P), and magnesium (Mg), and their correlation with other parameters in osteoporosis. One hundred ten subjects were involved in the current study. There were two groups of patients: group I (30) women with severe osteoporosis and group II (30) women with mild osteoporosis. For comparison, 50 apparently healthy individuals were included as a control. Serum levels of MIF, and NTX were significantly higher in groups I and II as compared to the control group, which indicate that these two parameters

The present research was conducted to investigate the effectiveness of a training program to improve some aspects of sensory integration disorder and its effect on self-direction among a sample of children with intellectual disabilities. The study sample consists of (10 subjects as an experimental group) were exposed to the training program، and the control group consists of (10 subjects as a control group) were not exposed to the training program. The study included the following tools: A scale of self-direction for intellectual disability (prepared by the researcher). Training program (prepared by the researcher). The Results of the study showed the following: There are no statistically significant differences between the means ranks

Objective: to identify the effect of the Instruction program on the knowledge of pregnant women who suffering anemia.

Methodology: A quasi-experimental design was carried out with the application of pre- post test for the study and the control group. Purposive sample, consists of (60) pregnant women diagnosed with anemia attending four health care centers in Baquba city.

Result: The findings indicate that the level of hemoglobin is increasing post instructional program among women in the study group, in which (46.7%) of women are reveal a level of (8.1-9) g/dl that is less than normal pre instructional program and the level is increased to normal level post instructional

One of the most important problems in tablet process is to control the flow of the catalyst through the hopper; Controlling the flow can be done either by changing the size of particles or added the different lubricant (stearic acid, starch, graphite) or blending of different lubricants. The study showed that we can control (increase or decrease) on the flow of the catalyst through the hopper by blending different lubricants for the constant percentage. The flow increasing when particles size (0.6 mm) and then decrease with or without lubricants, no effect on flow when particles size lower than (0.2 mm) with use that lubricants, and good flow on (0.4 mm) when use stearic acid and starch.

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

     This paper introduces a relation between resultant and the Jacobian determinant
by generalizing Sakkalis theorem from two polynomials in two variables to the case of (n) polynomials in (n) variables. This leads us to study the results of the type:  ,            and use this relation to attack the Jacobian problem. The last section shows our contribution to proving the conjecture.