The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree model. Having been in this research compare these methods form a model for additive function to some nonparametric function. It was a trade-off between these process models based on the classification accuracy by misclassification error, and estimation accuracy by the root of the mean squares error: RMSE. It was the application on patients with diabetes data for those aged 15 years and below are taken from the sample size (200) was withdrawn from the Children Hospital in Al-Eskan / Baghdad.
Change detection is a technology ascertaining the changes of
specific features within a certain time Interval. The use of remotely
sensed image to detect changes in land use and land cover is widely
preferred over other conventional survey techniques because this
method is very efficient for assessing the change or degrading trends
of a region. In this research two remotely sensed image of Baghdad
city gathered by landsat -7and landsat -8 ETM+ for two time period
2000 and 2014 have been used to detect the most important changes.
Registration and rectification the two original images are the first
preprocessing steps was applied in this paper. Change detection using
NDVI subtractive has been computed, subtrac
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Data generated from modern applications and the internet in healthcare is extensive and rapidly expanding. Therefore, one of the significant success factors for any application is understanding and extracting meaningful information using digital analytics tools. These tools will positively impact the application's performance and handle the challenges that can be faced to create highly consistent, logical, and information-rich summaries. This paper contains three main objectives: First, it provides several analytics methodologies that help to analyze datasets and extract useful information from them as preprocessing steps in any classification model to determine the dataset characteristics. Also, this paper provides a comparative st
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The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s
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Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
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This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it
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Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas
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Objective:To Evaluate of Estradiol and Prolactin hormones levels for Breast Cancer women in
Baghdad City.
Methodology: The current study was conducted on 60 breast cancer women and 40 apparently
healthy subjects to evaluate the levels of estradiol and prolactin "hormones in the serum" of
({premenopausal & postmenopausal}) breast cancer and healthy controle women. Estradiol and
prolactin hormones estimated for all cases by using the IMMULITE 2000 instrument that performs
chemiluminescent immunoassays results are calculated for each sample.Data were analysed using
SPSS-18.data of two groups was comparison by the student's t-test.
Results: The results showed a non significant""(P>0.05) elevation in the –mean