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Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation
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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide su the optimal split value.

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Publication Date
Sun Jun 02 2013
Journal Name
Baghdad Science Journal
Comparison of Maximum Likelihood and some Bayes Estimators for Maxwell Distribution based on Non-informative Priors
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In this paper, Bayes estimators of the parameter of Maxwell distribution have been derived along with maximum likelihood estimator. The non-informative priors; Jeffreys and the extension of Jeffreys prior information has been considered under two different loss functions, the squared error loss function and the modified squared error loss function for comparison purpose. A simulation study has been developed in order to gain an insight into the performance on small, moderate and large samples. The performance of these estimators has been explored numerically under different conditions. The efficiency for the estimators was compared according to the mean square error MSE. The results of comparison by MSE show that the efficiency of Bayes est

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Publication Date
Sun Jun 02 2013
Journal Name
Baghdad Science Journal
Comparison of Maximum Likelihood and some Bayes Estimators for Maxwell Distribution based on Non-informative Priors
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In this paper, Bayes estimators of the parameter of Maxwell distribution have been derived along with maximum likelihood estimator. The non-informative priors; Jeffreys and the extension of Jeffreys prior information has been considered under two different loss functions, the squared error loss function and the modified squared error loss function for comparison purpose. A simulation study has been developed in order to gain an insight into the performance on small, moderate and large samples. The performance of these estimators has been explored numerically under different conditions. The efficiency for the estimators was compared according to the mean square error MSE. The results of comparison by MSE show that the efficiency of B

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Publication Date
Fri Apr 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Bayes Estimators With others , for scale parameter and Reliability function of two parameters Frechet distribution
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Oil spill classification based on satellite image using deep learning techniques
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 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

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Publication Date
Fri Sep 30 2016
Journal Name
Australian Journal Of Basic And Applied Sciences
Programming Exam Questions Classification Based On Bloom’s Taxonomy Using Grammatical Rule
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Publication Date
Wed Oct 17 2018
Journal Name
Journal Of Economics And Administrative Sciences
A Comparison of Bayes Estimators for the parameter of Rayleigh Distribution with Simulation
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   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro

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Publication Date
Wed Apr 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
Classification & Evaluation of Evidence of deprivation in Iraq (2009) by using Cluster analysis
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       The study aimed to reach the best rating for the views and variables in the totals characterized by qualities and characteristics common within each group and distinguish them from aggregates other for the purpose of distinguishing between Iraqi provinces which suffer from deprivation, for the purpose of identifying the status of those provinces in the early allowing interested parties and regulators to intervene to take appropriate corrective action in a timely manner. Style has been used cluster analysis Cluster analysis to reach the best rating to those totals from the provinces that suffer from problems, where the provinces were classified, based on the variables (Edu

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
An exploratory study of history-based test case prioritization techniques on different datasets
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In regression testing, Test case prioritization (TCP) is a technique to arrange all the available test cases. TCP techniques can improve fault detection performance which is measured by the average percentage of fault detection (APFD). History-based TCP is one of the TCP techniques that consider the history of past data to prioritize test cases. The issue of equal priority allocation to test cases is a common problem for most TCP techniques. However, this problem has not been explored in history-based TCP techniques. To solve this problem in regression testing, most of the researchers resort to random sorting of test cases. This study aims to investigate equal priority in history-based TCP techniques. The first objective is to implement

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Publication Date
Thu Sep 25 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Postoperative assessment of dental implants by using multi-slice computed tomography
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Background: Implantology is a fast growing area in dentistry. One of the most common issues encountered in dental implantation procedures is the lack of adequate preoperative planning. Conventional radiography may not be able to assess the true regional three-dimensional anatomical presentation. Multi Slice Computed Tomography provides data in 3-dimentional format offering information on craniofacial anatomy for diagnosis; this technology enables the virtual placement of implant in a 3-Dimensional model of the patient jaw (dental planning). Patients, Material and Methods: The sample consisted of (72) Iraqi patients indicated for dental implant (34 male and 38 female), age range between (20-70) years old. They were examined during a time p

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Publication Date
Tue Oct 26 2021
Journal Name
Remote Sensing Technologies And Applications In Urban Environments Vi
DTM Extraction and building detection in DSMs having large holes
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