This paper presents a hybrid approach called Modified Full Bayesian Classifier (M-FBC) and Artificial Bee Colony (MFBC-ABC) for using it to medical diagnosis support system. The datasets are taken from Iraqi hospitals, these are for the heart diseases and the nervous system diseases. The M-FBC is depended on common structure known as naïve Bayes. The structure for network is represented by D-separated for structure's variables. Each variable has Condition Probability Tables (CPTs) and each table for disease has Probability. The ABC is easy technique for implementation, has fewer control parameters and it could be easier than other swarm optimization algorithms, so that hybrid with other algorithms to reach the optimal structure. In the input stage, the symptoms and the medical history for the patient are processed through the BNs structures to obtain from Modified Full Bayesian Classifier- Artificial Bee Colony (MFBC-ABC). The proposed system inputs all medical dataset and it filters and extracts the dataset. After the evaluation of the structures, the system can select the best optimal structure by determining the accepted accuracy. The accuracy for M-FBC model is approximately (93%) for heart diseases and approximately (98%) for nervous system diseases. While in The MFBC-ABC model, the accuracy is approximately (100%) for heart diseases and is approximately (99%) for nervous model diseases. The experimental results shown that the results for MFBC-ABC is better than on M-FBC.
The aim of the research is to estimate the hidden population. Here، the number of drug users in Baghdad was calculated for the male age group (15-60) years old ، based on the Bayesian models. These models are used to treat some of the bias in the Killworth method Accredited in many countries of the world.
Four models were used: random degree، Barrier effects، Transmission bias، the first model being random، an extension of the Killworth model، adding random effects such as variance and uncertainty Through the size of the personal network، and when expanded by adding the fact that the respondents have different tendencies، the mixture of non-random variables with random to produce
... Show MoreA Bayesian formulation of the ridge regression problem is considerd, which derives from a direct specification of prior informations about parameters of general linear regression model when data suffer from a high degree of multicollinearity.A new approach for deriving the conventional estimator for the ridge parameter proposed by Hoerl and Kennard (1970) as well as Bayesian estimator are presented. A numerical example is studied in order to compare the performance of these estimators.
Background: Accurate measurement of a patient’s height and weight is an essential part of diagnosis and therapy, but there is some controversy as to how to calculate the height and weight of patients with disabilities. Objective: This study aims to use anthropometric measurements (arm span, length of leg, chest circumference, and waist circumference) to find a model (alternatives) that can allow the calculation of the height and the body weight of patients with disabilities. Additionally, a model for the prediction of weight and height measurements of patients with disabilities was established. Method: Four hander patients aged 20-80 years were enrolled in this study and divided into two groups, 210 (52.5%) male and 190 (47.5%) fe
... Show MoreThe largest use of x-ray in medical by dentists, employers or persons that needed by patients with specific conditions, lead to higher exposure of x-ray that may cause many diseases. In the present work radiography films have been used in evaluating the efficiency of using unsaturated polyester polymer reinforced with lead oxide (PbO) as shield material for medical x-ray devices, many parameters studied like concentration and thickness that they are increasing the attenuation of x-ray in them. The results show that the attenuation of X-ray increasing with concentration of reinforced material and with thickness, and the optical density decreases with increasing concentration from 0% to 50%, we chose 30% as suitable concentration to increase
... Show MoreResource estimation is an essential part of reservoir evaluation and development planning which highly affects the decision-making process. The available conventional logs for 30 wells in Nasiriyah oilfield were used in this study to model the petrophysical properties of the reservoir and produce a 3D static geological reservoir model that mimics petrophysical properties distribution to estimate the stock tank oil originally in place (STOOIP) for Mishrif reservoir by volumetric method. Computer processed porosity and water saturation and a structural 2D map were utilized to construct the model which was discretized by 537840 grid blocks. These properties were distributed in 3D Space using sequential Gaussian simulation and the variation in
... Show MoreSpeech is the essential way to interact between humans or between human and machine. However, it is always contaminated with different types of environment noise. Therefore, speech enhancement algorithms (SEA) have appeared as a significant approach in speech processing filed to suppress background noise and return back the original speech signal. In this paper, a new efficient two-stage SEA with low distortion is proposed based on minimum mean square error sense. The estimation of clean signal is performed by taking the advantages of Laplacian speech and noise modeling based on orthogonal transform (Discrete Krawtchouk-Tchebichef transform) coefficients distribution. The Discrete Kra
This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett
... Show MoreAdverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD
... Show MoreA new test system for detecting environment carcinogenes and/or mutagenes and their adversary It has been induced. One hundred and fifty mutants were isolated from the basidiomycete fungus Coprinus cinereus which were resistant to guanine analogue S- az.aguanine .All the spontaneous and induced with UV light origin mutants were isolated from the wild type strains Bc9/6.6 and Hd5.5
.These mutants were te ted on selective medium containing different
concentrations of the analogue and also to their ability to usc purine bases and their degredated &nbs
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