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.
Many numerical approaches have been suggested to solve nonlinear problems. In this paper, we suggest a new two-step iterative method for solving nonlinear equations. This iterative method has cubic convergence. Several numerical examples to illustrate the efficiency of this method by Comparison with other similar methods is given.
Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt
... Show MoreThe presence of White Blood Cells (WBCs) in the body of human has a great role in the protection of the body against many pathogens. The recognition of the WBC is the first important step to diagnose some particular diseases. The pathologists usually use an optical microscope to recognize WBCs, but, this process is a quite tedious, time-consuming, error prone, very slow, and expensive. In addition, it needs experts with long practice in this field. For these reasons, a computer assisted diagnostic system that helps pathologists in the process of diagnosis can be effective, easy and safe. This research is devoted to develop a system based on digital image processing methods to localize WBCs nuclei. The proposed system involved a collectio
... Show MoreThe present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be
... Show MoreIn this paper, first and second order sliding mode controllers are designed for a single link robotic arm actuated by two Pneumatic Artificial Muscles (PAMs). A new mathematical model for the arm has been developed based on the model of large scale pneumatic muscle actuator model. Uncertainty in parameters has been presented and tested for the two controllers. The simulation results of the second-order sliding mode controller proves to have a low tracking error and chattering effect as compared to the first order one. The verification has been done by using MATLAB and Simulink software.
Wastewater projects are one of the most important infrastructure projects, which require developing strategic plans to manage these projects. Most of the wastewater projects in Iraq don’t have a maintenance plan. This research aims to prepare the maintenance management plan (MMP) for wastewater projects. The objective of the research is to predict the cost and time of maintenance projects by building a model using ANN. The research sample included (15) completed projects in Wasit Governorate, where the researcher was able to obtain the data of these projects through the historical information of the Wasit Sewage Directorate. In this research artificial neural networks (ANN) technique was used to build two models (cost
... Show MoreObjectives: Recently, there have been important advances in the clinical application of targeted hybrid near-infrared (NIR) fluorescent-radioactive tracers. ICG-99mTc-nanocolloid, for example, is already being used by some centres for sentinel lymph node biopsy in head and neck cancer. The radioactive component allows imaging at depths which would not be possible with NIR alone and, once exposed, the NIR fluorescence reporter can be imaged at very high resolution. Gamma detection is currently carried out with a separate hand-held gamma camera or with a non-imaging probe. Visualisation of NIR fluorescence during surgery requires a dedicated NIR camera, several of which are available commercially. We describe a novel hand-held hybrid NIR-gamm
... Show MoreIn this paper, a new hybrid algorithm for linear programming model based on Aggregate production planning problems is proposed. The new hybrid algorithm of a simulated annealing (SA) and particle swarm optimization (PSO) algorithms. PSO algorithm employed for a good balance between exploration and exploitation in SA in order to be effective and efficient (speed and quality) for solving linear programming model. Finding results show that the proposed approach is achieving within a reasonable computational time comparing with PSO and SA algorithms.
The goal beyond this Research is to review methods that used to estimate Logistic distribution parameters. An exact estimators method which is the Moment method, compared with other approximate estimators obtained essentially from White approach such as: OLS, Ridge, and Adjusted Ridge as a suggested one to be applied with this distribution. The Results of all those methods are based on Simulation experiment, with different models and variety of sample sizes. The comparison had been made with respect to two criteria: Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE).