Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN

Abstract

The economic and financial crises in the world economy series led to increased awareness of the importance of the internal control system, because it is one of the main pillars of any economic unit, as it works to verify the application of policies, regulations and laws and verification of asset protection from theft and embezzlement procedures, it is also working on trust accounting information imparted through the validation of accounting information, analyze and detect the misleading.

The existence the internal control system a factor in many of the accounting practices that limit the ability of the administration to produce misleading financial reporting

The

In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels o

A large amount of thermal energy is generated from burning hazardous chemical wastes, and the temperature of the flue gases in hazardous waste incinerators reaches up to (1200 °C). The flue gases are cooled to (40°C) and are treated before emission. This thermal energy can be utilized to produce electrical power by designing a system suitable for dangerous flue gases in the future depending on the results of much research about using a proto-type small steam power plant that uses safe fuel to study and develop the electricity generation process with water tube boiler which is manufactured experimentally with theoretical development for some of its parts which are inefficient in experimental work. The studied system gen

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

Research covers the uses the method of Quality Rating Evaluation to evaluate the
quality of production through which a determination of product quality of its production in
order to determine the amount of sales hence the profits for the company. The most important
function is to satisfy consumer at reasonable prices. Methods were applied to the product
(toothpaste) in the General Company for Vegetable Oil – Almaamoon Factory .
The company's has obtained ISO-certified (ISO 9001-2008). Random samples of
final product intended for sale were collected from the store during months (February, April ,
June , October and December) for the year 2011 to determine the "quality rating " through
the applicat

Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal.  The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in

In this article we derive two reliability mathematical expressions of two kinds of s-out of -k stress-strength model systems; and . Both stress and strength are assumed to have an Inverse Lomax distribution with unknown shape parameters and a common known scale parameter. The increase and decrease in the real values of the two reliabilities are studied according to the increase and decrease in the distribution parameters. Two estimation methods are used to estimate the distribution parameters and the reliabilities, which are Maximum Likelihood and Regression. A comparison is made between the estimators based on a simulation study by the mean squared error criteria, which revealed that the maximum likelihood estimator works the best.

This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord

      MRP is a system intended for the batch manufacturing of discrete parts including assemblies and subassemblies that should be stocked to support future manufacturing needs.  Due to the useful information provided by MRP it has evolved into a Manufacturing Resources Planning, MRP II, a system that ties the basic MRP system to the other functional areas of the company such as marketing, finance, purchasing, etc.  The objective of this research, which was conducted at the State Company for Batteries Manufacturing, is to test the performance of some popular lot-sizing techniques used within MRP II framework. It is hypothesized that the technique which minimizes the total inventory costs does no