Background: Repeated teenage pregnancy is a major burden on the healthcare system worldwide. Objective: We aimed to compare teenagers with their first and third pregnancies and to evaluate the likelihood of neonatal complications. Materials and Methods: This cross-sectional study was performed on female teenagers (aged ≤ 19 yr) with singleton pregnancies. The subjects (n = 298) were screened over 12 months. Ninety-six women were excluded, based on the exclusion criteria. The remaining subjects (n = 202) were divided into two groups: teenagers with first pregnancy (n = 96) and teenagers with third pregnancy (n = 47). The subjects were observed throughout pregnancy and delivery. The final sample size of the first and thi

The research aims to study the contribution of tax information systems to increase tax revenues, and to identify how efficiently used information systems currently by the tax authority and their effectiveness in the detection of irregularities by the tax payers such as the cleclaration of incorrect statements that do not show real results of their business activities or hide information from sources related to their income subject to tax, which would negatively affect the outcome of tax revenues and thus damage important sourse of the public treasury of the states resources. The data of research was collected by studying and analysing the tax information systems used by the General Commission of taxs and its branches and a number of prac

The study aims to display the scientific benefit offered by modern electronic programs for various scientific research methods, while determining the positive scientific role played by these programs in modernizing the methodologies and logic of scientific thinking, especially with the rapid development of the sciences and their curricula.
These programs link accurately with scientific results. The importance of the study is to provide practical mechanisms to highlight the scientific projection of the electronic programs in various steps of scientific research.
A case study was used for Tropes version 8.4, which analyzes written, audio and visual semantic texts and presents a set of statistical results that facilitate the difficult

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Deep Learning Techniques For Skull Stripping of Brain MR Images

Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of