Preferred Language
Articles
/
ixaF44sBVTCNdQwCV-Nn
Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem
...Show More Authors

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.

Scopus Clarivate Crossref
View Publication
Publication Date
Mon Sep 07 2020
Journal Name
Environmental Science And Pollution Research
The biosorption of reactive red dye onto orange peel waste: a study on the isotherm and kinetic processes and sensitivity analysis using the artificial neural network approach
...Show More Authors

View Publication
Scopus (36)
Crossref (29)
Scopus Clarivate Crossref
Publication Date
Fri Jun 29 2018
Journal Name
Journal Of The College Of Education For Women
THE PROBLEM RELATED TO WORKING IN KINDERGARTEN FROM THE POINT OF VIEW OF TEACHERS
...Show More Authors

           kindergarten teacher is one of the fundamental pillars upon which the kindergarten environment so that exposure to a number of problems that could affect its functioning in addition to any deficiencies in this environment leads to deprive a child of some activities and acquisition of concepts so the researcher studying the problems of working in kindergartens from the perspective of the parameters, so the researcher based measuring instrument for labour problems of (30) search sample was paragraph (50) parameter that was chosen at random and have been extracted Sincerity and strength tool researcher used statistical methods and discriminatory (Pearson correlation coefficient, t

... Show More
View Publication Preview PDF
Publication Date
Wed Apr 01 2015
Journal Name
Mathematical Methods In The Applied Sciences
An inverse problem of finding the time-dependent diffusion coefficient from an integral condition
...Show More Authors

View Publication
Scopus (34)
Crossref (25)
Scopus Clarivate Crossref
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
...Show More Authors

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

... Show More
View Publication
Scopus (10)
Crossref (2)
Scopus Crossref
Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
A Novel Technique for Secure Data Cryptosystem Based on Chaotic Key Image Generation
...Show More Authors

The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

... Show More
View Publication Preview PDF
Scopus (14)
Crossref (3)
Scopus Clarivate Crossref
Publication Date
Sat Jan 01 2005
Journal Name
Iraqi Journal Of Science
Comparison between Zernike moment and central moments for matching problem
...Show More Authors

Moment invariants have wide applications in image recognition since they were proposed.

Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
A Parallel Adaptive Genetic Algorithm for Job Shop Scheduling Problem
...Show More Authors

Publication Date
Sat Jun 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Using Some Robust Methods For Handling the Problem of Multicollinearity
...Show More Authors

The multiple linear regression model is an important regression model that has attracted many researchers in different fields including applied mathematics, business, medicine, and social sciences , Linear regression models involving a large number of independent variables are poorly performing due to large variation and lead to inaccurate conclusions , One of the most important problems in the regression analysis is the multicollinearity Problem, which is considered one of the most important problems that has become known to many researchers  , As well as their effects on the multiple linear regression model, In addition to multicollinearity, the problem of outliers in data is one of the difficulties in constructing the reg

... Show More
View Publication Preview PDF
Crossref
Publication Date
Wed Oct 17 2018
Journal Name
Journal Of Economics And Administrative Sciences
Study About The Robustness Of The Bayesian Criterion
...Show More Authors

In this research work an attempt has been made to investigate about the Robustness of the Bayesian Information criterion to estimate the order of the autoregressive process when the error of this model,  Submits to a specific distributions and different cases of the time series on various size of samples by using the simulation,  This criterion has been studied by depending on ten distributions, they are (Normal, log-Normal, continues uniform, Gamma , Exponential, Gamble, Cauchy, Poisson, Binomial, Discrete uniform) distributions, and then it has been reached to many collection and recommendations related to this object , when the series residual variable is subject to each  ( Poisson , Binomial , Exponential , Dis

... Show More
View Publication Preview PDF
Crossref
Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
On Training Of Feed Forward Neural Networks
...Show More Authors

In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

View Publication Preview PDF