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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
Fri Jul 19 2019
Journal Name
Iraqi Journal Of Science
Efficient Iterative Method for Solving Korteweg-de Vries Equations
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The Korteweg-de Vries equation plays an important role in fluid physics and applied mathematics. This equation is a fundamental within study of shallow water waves. Since these equations arise in many applications and physical phenomena, it is officially showed that this equation has solitary waves as solutions, The Korteweg-de Vries equation is utilized to characterize a long waves travelling in channels. The goal of this paper is to construct the new effective frequent relation to resolve these problems where the semi analytic iterative technique presents new enforcement to solve Korteweg-de Vries equations. The distinctive feature of this method is, it can be utilized to get approximate solutions for travelling waves of 

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Publication Date
Sat Oct 01 2016
Journal Name
International Journal Of Pure And Apllied Mathematics
A SEMI ANALYTICAL ITERATIVE TECHNIQUE FOR SOLVING DUFFING EQUATIONS
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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Modern Mathematical Sciences
Coupled Laplace-Decomposition Method for Solving Klein- Gordon Equation
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In this paper, we consider a new approach to solve type of partial differential equation by using coupled Laplace transformation with decomposition method to find the exact solution for non–linear non–homogenous equation with initial conditions. The reliability for suggested approach illustrated by solving model equations such as second order linear and nonlinear Klein–Gordon equation. The application results show the efficiency and ability for suggested approach.

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Publication Date
Fri May 01 2020
Journal Name
Journal Of Physics: Conference Series
New Approach for Solving (1+1)-Dimensional Differential Equation
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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Efficient Approach for Solving (2+1) D- Differential Equations
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     In this article, a new efficient approach is presented to solve a type of partial differential equations, such (2+1)-dimensional differential equations non-linear, and nonhomogeneous. The procedure of the new approach is suggested to solve important types of differential equations and get accurate analytic solutions i.e., exact solutions. The effectiveness of the suggested approach based on its properties compared with other approaches has been used to solve this type of differential equations such as the Adomain decomposition method, homotopy perturbation method, homotopy analysis method, and variation iteration method. The advantage of the present method has been illustrated by some examples.

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Publication Date
Thu Oct 01 2015
Journal Name
American Research Thoughts
Investigation of some Oral Strategies and their Development to the Iraqi EFL Learners` Performance.
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The present study deals with the effect of teaching oral Communication Strategies on English language learners. The use of oral communication strategies not only solves learners’ communication problems, but also enhances the learner’s interaction in target language, which in turn, improves their oral proficiency .The sample of this study is EFL students in the first stage at the Department of English Language, morning studies, College of Education /Ibn-Rushd .The sample of the study consists of (60) students distributed on experimental group(A) includes (30) students, and (30) students as control group(B). In order to achieve the aim of the study, and to gain a closer idea about the impact of teaching oral Communication Strategies on th

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Publication Date
Sat Mar 01 2025
Journal Name
Iraqi Journal Of Physics
Design an Efficient Neural Network to Determine the Rate of Contamination in the Tigris River in Baghdad City
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This article proposes a new technique for determining the rate of contamination. First, a generative adversarial neural network (ANN) parallel processing technique is constructed and trained using real and secret images. Then, after the model is stabilized, the real image is passed to the generator. Finally, the generator creates an image that is visually similar to the secret image, thus achieving the same effect as the secret image transmission. Experimental results show that this technique has a good effect on the security of secret information transmission and increases the capacity of information hiding. The metric signal of noise, a structural similarity index measure, was used to determine the success of colour image-hiding t

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Publication Date
Wed Oct 01 2014
Journal Name
Engineering And Technology Journal
Investigating Forward kinematic Analysis of a 5-axes Robotic Manipulator using Denavit-Hartenberg Method and Artificial Neural Network
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Publication Date
Fri Nov 20 2020
Journal Name
Journal Of Physical Organic Chemistry
Spectroscopic characterization for new model from Schiff base and its complexes
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Abstract<p>New complexes have been prepared from the new ligand [2,2′‐(5,5‐dimethylcyclohexane‐1,3‐diylidene)bis(azan‐1‐yl‐1‐ylidene)dibenzoic acid] derived from 5,5‐dimethylcyclohexane‐1,3‐dione and 2‐aminobenzoic acid. Accordingly, its mono and binuclear Mn(II), Co(II), Cu(II), Zn(II), and Cd(II) complexes were prepared. The prepared components have been characterized by various spectroscopic techniques and elemental analysis. The thermal stability of the ligand and its complexes were performed by TGA. It was found that all the complexes have excellent thermal stability and do not contain water molecules within their structure, but the ligand has little stability. Additionally, theor</p> ... Show More
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Publication Date
Wed Jul 01 2020
Journal Name
Proceedings Of The Institution Of Mechanical Engineers, Part H: Journal Of Engineering In Medicine
Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury
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Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental

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