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Compare to the conditional logistic regression models with fixed and mixed effects for longitudinal data
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Mixed-effects conditional logistic regression is evidently more effective in the study of qualitative differences in longitudinal pollution data as well as their implications on heterogeneous subgroups. This study seeks that conditional logistic regression is a robust evaluation method for environmental studies, thru the analysis of environment pollution as a function of oil production and environmental factors. Consequently, it has been established theoretically that the primary objective of model selection in this research is to identify the candidate model that is optimal for the conditional design. The candidate model should achieve generalizability, goodness-of-fit, parsimony and establish equilibrium between bias and variability. In the practical sphere it is however more realistic to capture the most significant parameters of the research design through the best fitted candidate model for this research. Simulation studies demonstrate that the mixed-effects conditional logistic regression is more accurate for pollution studies, with fixed-effects conditional logistic regression models potentially generating flawed conclusions.  This is because mixed-effects conditional logistic regression provides detailed insights on clusters that were largely overlooked by fixed-effects conditional logistic regression.

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Publication Date
Wed Sep 01 2021
Journal Name
Baghdad Science Journal
Some Common Fixed Points Theorems of Four Weakly Compatible Mappings in Metric Spaces
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                 In this paper, we proved coincidence points theorems for two pairs mappings which are defined on nonempty subset   in metric spaces by using condition (1.1). As application, we established a unique common fixed points theorems for these mappings by using the concept weakly compatible (R-weakly commuting) between these mappings.

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Publication Date
Thu Mar 19 2015
Journal Name
Al-academy
The Significance of the Folk Symbols and Their Influence on The National Identity (Chosen samples from the Contemporary Iraqi Pottery): زينب كاظم صالح
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The contemporary art culture is considered a resultant of preceding human civilizations from the early days. At the same time, it got closer to the local privacy, because the contemporary Iraqi potter worked hard to create new art, refusing to base his art on the early works, creating art pieces that contains properties to inherited art and their form significance, in order to create a new identity of his own, bringing an empowerment to his deepened civilization, with the goal to create national identity ideas from one hand, and entity and locality from another. Europeans also followed the direction of our civilization, such as (Picasso, Henry Moore, Barbra Hurth, Brankouzi, Hans Arp,..etc). From this point of view comes the importance o

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Publication Date
Mon Jun 30 2025
Journal Name
Alrafidain Of Law
The Effects of the research and technology development contract -A comparative study-
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Researching the effects of the research and technological development contract, determining its extent and demarcating the boundaries of the obligations imposed in it, is the cornerstone of economic growth and development, because defining these obligations removes the ambiguity and conflict between interests, by stating the rights owed to each party and even trying to reconcile them, or impose protection by specifying guarantees that are compatible with the essence of the R&D contract, For the purpose of studying the subject thoroughly, we will divide this research into two sections. The first is devoted to identifying the parties to the research and technological development contract. As for the other topic, we will explain the obligation

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Publication Date
Fri Aug 13 2021
Journal Name
Neural Computing And Applications
Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction
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Publication Date
Mon Jun 07 2021
Journal Name
Jurnal Teknologi
MODELS, DETECTION METHODS, AND CHALLENGES IN DC ARC FAULT: A REVIEW
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The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is significantly growing in the present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identification, diagnosis, and interruption tools. Meanwhile, the faults in unidentified arc lead to serious fire hazards to commercial, residential, and utility-scale PV systems. To ensure secure and dependable distribution of electricity, the detection of such ha

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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
A Modified 2D-Checksum Error Detecting Method for Data Transmission in Noisy Media
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In data transmission a change in single bit in the received data may lead to miss understanding or a disaster. Each bit in the sent information has high priority especially with information such as the address of the receiver. The importance of error detection with each single change is a key issue in data transmission field.
The ordinary single parity detection method can detect odd number of errors efficiently, but fails with even number of errors. Other detection methods such as two-dimensional and checksum showed better results and failed to cope with the increasing number of errors.
Two novel methods were suggested to detect the binary bit change errors when transmitting data in a noisy media.Those methods were: 2D-Checksum me

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Geological Journal
Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data
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Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

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Publication Date
Sat Aug 01 2015
Journal Name
Journal Of Engineering
Analytical Approach for Load Capacity of Large Diameter Bored Piles Using Field Data
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An analytical approach based on field data was used to determine the strength capacity of large diameter bored type piles. Also the deformations and settlements were evaluated for both vertical and lateral loadings. The analytical predictions are compared to field data obtained from a proto-type test pile used at Tharthar –Tigris canal Bridge. They were found to be with acceptable agreement of 12% deviation.

               Following ASTM standards D1143M-07e1,2010, a test schedule of five loading cycles were proposed for vertical loads and series of cyclic loads to simulate horizontal loading .The load test results and analytical data of 1.95

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Publication Date
Thu Jun 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

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Publication Date
Sun Mar 01 2015
Journal Name
Journal Of Engineering
Multi-Sites Multi-Variables Forecasting Model for Hydrological Data using Genetic Algorithm Modeling
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A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was

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