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Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem
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Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.

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Publication Date
Thu Feb 07 2019
Journal Name
Journal Of The College Of Education For Women
Double Exile: Jean Rhys's Wide Sargasso Sea
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The problems of cultural displacement and a shaky sense of one's own identity
have been the main concern of the twentieth century Caribbean writer, Jean Rhys. As
a white Creole writer living in England, Rhys attempts to capture the ambivalence of
what it means to be caught between two cultures and never able to identify fully with
any one .Born to a Welsh father and a Creole(white West Indian) mother on the island
of Dominica in the West Indies, Jean Rhys was white but not English, West Indian but
not black. Her sense of belonging to the West Indies was necessarily charged with
awareness of being part of another culture. Thus, the ambiguity of being an
insider/outsider in both the metropolis, England, and the colo

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Publication Date
Sun Feb 03 2019
Journal Name
Journal Of The College Of Education For Women
The U.S.A strategy in the red sea
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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Robotics And Control (jrc)
Automated Stand-alone Surgical Safety Evaluation for Laparoscopic Cholecystectomy (LC) using Convolutional Neural Network and Constrained Local Models (CNN-CLM)
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In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

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Publication Date
Thu Feb 29 2024
Journal Name
International Journal Of Design & Nature And Ecodynamics
Artificial Neural Network Assessment of Groundwater Quality for Agricultural Use in Babylon City: An Evaluation of Salinity and Ionic Composition
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Publication Date
Thu Jun 19 2025
Journal Name
Al–bahith Al–a'alami
Selective Exposure Patterns to the Iraqi Daily Newspapers and Its Motives for Iraqi University Students (College of Mass Communication - University of Baghdad as a Model)
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This study examines patterns of exposure of Iraqi university students to selective daily Iraqi newspapers and the motives of this exposure, as well as its associated factors that affect the average exposure. It tries to answer several questions, including those related to the levels of exposure of Iraqi university students to daily Iraqi newspapers and classification of patterns of selective exposure to daily Iraqi newspapers and the most prominent Iraqi daily newspapers that are selectively exposed by Iraqi university students. It also examines the motives of this selective exposure and factors that increase the degree of exposure to the daily Iraqi newspapers, and the most prominent stages in which Iraqi university students find their

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Publication Date
Wed Feb 05 2020
Journal Name
Journal Of Physical Education
The Effect of Group Investigation Model on Learning overhead and underarm Pass in Volleyball
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Volleyball is one of the sports that require physical and skill abilities thus many teaching models appeared to teach these abilities like group investigation model. The research aimed at identifying the effect of group investigation model on learning underarm and overhead passing in volleyball. The researchers hypothesized statistical differences between pre and posttests in learning underarm and overhead passing in volleyball as well as differences in posttests of controlling and experimental groups in learning underarm and overhead passing in volleyball. The researcher used the experimental method on (30) second year female students of physical education and sport sciences college/ university of Baghdad. Group investigation model was app

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Publication Date
Tue Dec 22 2020
Journal Name
Collaboration And Integration In Construction, Engineering, Management And Technology
A Hybrid Conceptual Model for BIM Adoption in Facilities Management: A Descriptive Analysis for the Collected Data
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Publication Date
Tue Oct 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Robust M Estimate With Cubic Smoothing Splines For Time-Varying Coefficient Model For Balance Longitudinal Data
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In this research، a comparison has been made between the robust estimators of (M) for the Cubic Smoothing Splines technique، to avoid the problem of abnormality in data or contamination of error، and the traditional estimation method of Cubic Smoothing Splines technique by using two criteria of differentiation which are (MADE، WASE) for different sample sizes and disparity levels to estimate the chronologically different coefficients functions for the balanced longitudinal data which are characterized by observations obtained through (n) from the independent subjects، each one of them is measured repeatedly by group of  specific time points (m)،since the frequent measurements within the subjects are almost connected an

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Publication Date
Wed Apr 20 2022
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Solving Nonlinear COVID-19 Mathematical Model Using a Reliable Numerical Method
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This research aims to numerically solve a nonlinear initial value problem presented as a system of ordinary differential equations. Our focus is on epidemiological systems in particular. The accurate numerical method that is the Runge-Kutta method of order four has been used to solve this problem that is represented in the epidemic model. The COVID-19 mathematical epidemic model in Iraq from 2020 to the next years is the application under study. Finally, the results obtained for the COVID-19 model have been discussed tabular and graphically. The spread of the COVID-19 pandemic can be observed via the behavior of the different stages of the model that approximates the behavior of actual the COVID-19 epidemic in Iraq. In our study, the COV

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Publication Date
Wed Jun 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Compared with Genetic Algorithm Fast – MCD – Nested Extension and Neural Network Multilayer Back propagation
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The study using Nonparametric methods for roubust to estimate a location and scatter it is depending  minimum covariance determinant of multivariate regression model , due to the presence of outliear values and increase the sample size and presence of more than after the model regression multivariate therefore be difficult to find a median location .       

It has been the use of genetic algorithm Fast – MCD – Nested Extension and compared with neural Network Back Propagation of multilayer in terms of accuracy of the results and speed in finding median location ,while the best sample to be determined by relying on less distance (Mahalanobis distance)has the stu

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