In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.

Objective: To determine the effectiveness of the educational program on nursing staffs' knowledge about uses of steroids and their side effects.
Methodology: A pre-experimental study design (one group design: pre-test and post-test) was used. This study was conducted in Al-Diwaniya Teaching Hospital for the period from ( 28th May to 10 th June, 2020) on a non-probability (purposive) sample consisting of (30 nurses) working in Oncology unit. A questionnaire was built as a data collection tool and consisted of two parts:
First part: The demographic characteristics of the nursing staff (gender, age, level of education, years of experience in hospital, participation in training courses related to nursing care for a patients undergoing

A Factorial Study for separation anxiety in students, of Baghdad City

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

The road network serves as a hub for opportunities in production and consumption, resource extraction, and social cohabitation. In turn, this promotes a higher standard of living and the expansion of cities. This research explores the road network's spatial connectedness and its effects on travel and urban form in the Al-Kadhimiya and Al-Adhamiya municipalities. Satellite images and paper maps have been employed to extract information on the existing road network, including their kinds, conditions, density, and lengths. The spatial structure of the road network was then generated using the ArcGIS software environment. The road pattern connectivity was evaluated using graph theory indices. The study demands the abstraction and examin