This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

Abstract

Knowing the amount of residual stresses and find technological solutions to minimize and control them during the production operation are an important task because great levels of deformation which occurs in single point incremental forming (SPIF), this induce highly non-uniform residual stresses. In this papera propose of a method for multilayer single point incremental forming with change in thickness of the top plate (0.5, 0.7, 0.9) mm and lubrication or material between two plates(polymer, grease, grease with graphite, mos₂) to knowing an effect of this method  and parameters on residual stresses for the bottom plates. Also compare these results for the

Polyaromatic hydrocarbons (PAHs) are a group of aromatic compounds that contain at least two rings. These compounds are found naturally in petroleum products and are considered the most prevalent pollutants in the environment. The lack of microorganism capable of degrading some PAHs led to their accumulation in the environment which usually causes major health problems as many of these compounds are known carcinogens. Xanthene is one of the small PAHs which has three rings. Many xanthene derivatives are useful dyes that are used for dyeing wood and cosmetic articles. However, several studies have illustrated that these compounds have toxic and carcinogenic effects. The first step of the bacterial degradation of xanthene is conducted by d

Abstraet

Students dropout from the Education has a negative phenomena on individual and society and even on different aspects of life especially on the economic aspect , Thus our research tries studying and analyzing the relation between the size of dropout and human development level in Iraq and as (research sample) the first decade of this century as a studying period, the study includes the dropout in Secondary schools and depending the formal records as a main source to evaluate the size of this problem in Iraq , which shows an increase in the size of dropout in this period in comparison with the last decades of the twentieth century, this produces a negative effect on human developme

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod