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bsj-1471
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 estimators of the shape parameter of the Maxwell distribution decreases with the increase of Jeffreys prior constants. The results also show that values of Bayes estimators are almost close to the maximum likelihood estimator when the Jeffreys prior constants are small, yet they are identical in some certain cases. Comparison with respect to loss functions show that Bayes estimators under the modified squared error loss function has greater MSE than the squared error loss function especially with the increase of r.

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Publication Date
Mon Dec 05 2022
Journal Name
Baghdad Science Journal
Proposed Framework for Official Document Sharing and Verification in E-government Environment Based on Blockchain Technology
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Progression in Computer networks and emerging of new technologies in this field helps to find out new protocols and frameworks that provides new computer network-based services. E-government services, a modernized version of conventional government, are created through the steady evolution of technology in addition to the growing need of societies for numerous services. Government services are deeply related to citizens’ daily lives; therefore, it is important to evolve with technological developments—it is necessary to move from the traditional methods of managing government work to cutting-edge technical approaches that improve the effectiveness of government systems for providing services to citizens. Blockchain technology is amon

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
Ecological and Geographical distribution of species and genera of Primulaceae in Iraq
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The present research deal with ecological and geographical distribution of species and genera of Primulaceae in Iraq. The results were revealed that species distributed in the north , north-east and west of Iraq. Anagallis arvensis L. is the most prevalent species tolerant to different environmental conditions, while the species of Primula L. characterized as less widespread and limited in one District. In addition, the districts Rawanduz (MRO) and Sulaymaniyah (MSU) have ranked first in distribution of the species on geographical districts with (75%), while the districts southern desert (DSD) and Basra (LBA) in last place with (16.7%). Maps for geographical distribution for all species were illustrated.

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Publication Date
Tue Dec 06 2022
Journal Name
Iraqi National Journal Of Nursing Specialties
Assessment of Reasons for Partial Compliance and Non-compliance to the Routine Childhood Vaccination Schedule in Al-Karkh District
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Abstract

      Objective (s): To evaluate reasons for partial compliance and non-compliance to the

                              routine childhood vaccination schedule in Al-Karkh district 

Methodology: Descriptive study , using the evaluation approach, is carried throughout the present study to determine the reasons for the Routine Childhood Vaccination at  health care sectors and primary health care centers at  Al-Karkh District in Baghdad City, Convenient, non-probability, sample of (90) mother who are recruited from health care sectors at Al-Karkh District in Baghdad City. All mothers, who ha

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Publication Date
Tue Feb 28 2023
Journal Name
Applied System Innovation
Earthquake Hazard Mitigation for Uncertain Building Systems Based on Adaptive Synergetic Control
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This study presents an adaptive control scheme based on synergetic control theory for suppressing the vibration of building structures due to earthquake. The control key for the proposed controller is based on a magneto-rheological (MR) damper, which supports the building. According to Lyapunov-based stability analysis, an adaptive synergetic control (ASC) strategy was established under variation of the stiffness and viscosity coefficients in the vibrated building. The control and adaptive laws of the ASC were developed to ensure the stability of the controlled structure. The proposed controller addresses the suppression problem of a single-degree-of-freedom (SDOF) building model, and an earthquake control scenario was conducted and simulat

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Publication Date
Sat Oct 03 2026
Journal Name
Journal Of Advanced Research Design
Sustainable Leaf Plant Disease Based on Salp Swarm Algorithm for Feature Selection
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Sustainable plant protection and the economy of plant crops worldwide depend heavily on the health of agriculture. In the modern world, one of the main factors influencing economic growth is the quality of agricultural produce. The need for future crop protection and production is growing as disease-affected plants have caused considerable agricultural losses in several crop categories. The crop yield must be increased while preserving food quality and security and having the most negligible negative environmental impact. To overcome these obstacles, early discovery of satisfactory plants is critical. The use of Advances in Intelligent Systems and information computer science effectively helps find more efficient and low-cost solutions. Thi

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Publication Date
Sat Dec 01 2018
Journal Name
2018 Third Scientific Conference Of Electrical Engineering (scee)
An Intelligent Cognitive System Design for Mobile Robot based on Optimization Algorithm
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Publication Date
Wed Apr 15 2020
Journal Name
Al-mustansiriyah Journal Of Science
Adaptation Proposed Methods for Handling Imbalanced Datasets based on Over-Sampling Technique
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Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE),  Border

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Publication Date
Sat Jan 01 2022
Journal Name
Computers, Materials & Continua
An Optimal Method for Supply Chain Logistics Management Based on Neural Network
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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network
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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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Scopus (9)
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Publication Date
Thu Nov 17 2022
Journal Name
Journal Of Information And Optimization Sciences
Hybrid deep learning model for Arabic text classification based on mutual information
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