Preferred Language
Articles
/
_hdZaI8BVTCNdQwCm3ZY
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
...Show More Authors

The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying time led to an increase in carbohydrates, sweetness, and CIE-L*a*b levels, while it led to a decrease in the moisture content in dried banana slices. Therefore, there is a direct relationship between CIE-L*a*b levels and sweetness. On the other hand, the RF and CART algorithms gave the highest prediction accuracy of 86% and 0.8 on the Kappa measure. While the other algorithms (SVM, LDA, KNN) gave a prediction accuracy of 80% and 0.7 on the Kappa measure. In terms of testing statistical significance, the null hypothesis (H0) was accepted because there is no relationship between the metric distributions of the algorithms used.

Scopus Clarivate Crossref
Preview PDF
Quick Preview PDF
Publication Date
Sat Mar 30 2002
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Effects of Drying Conditions on the Properties of Dried Particles of Powdered steep Liquor
...Show More Authors

View Publication Preview PDF
Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
New algorithms to Enhanced Fused Images from Auto-Focus Images
...Show More Authors

Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contras

... Show More
View Publication Preview PDF
Scopus (9)
Crossref (3)
Scopus Clarivate Crossref
Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
A New Hybrid Meta-Heuristics Algorithms to Solve APP Problems
...Show More Authors
Abstract<p>In this paper, a new hybrid algorithm for linear programming model based on Aggregate production planning problems is proposed. The new hybrid algorithm of a simulated annealing (SA) and particle swarm optimization (PSO) algorithms. PSO algorithm employed for a good balance between exploration and exploitation in SA in order to be effective and efficient (speed and quality) for solving linear programming model. Finding results show that the proposed approach is achieving within a reasonable computational time comparing with PSO and SA algorithms.</p>
View Publication
Scopus (11)
Crossref (9)
Scopus Crossref
Publication Date
Tue Dec 01 2020
Journal Name
Baghdad Science Journal
A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset
...Show More Authors

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

... Show More
View Publication Preview PDF
Scopus (13)
Crossref (7)
Scopus Clarivate Crossref
Publication Date
Sat Dec 14 2019
Journal Name
International Journal On Emerging Technologies
Utilizing an Artificial Neural Network Model to Predict Bearing Capacity of Stone Columns
...Show More Authors

ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous rel

... Show More
View Publication
Scopus (4)
Scopus
Publication Date
Wed May 01 2013
Journal Name
2013 Fourth International Conference On E-learning "best Practices In Management, Design And Development Of E-courses: Standards Of Excellence And Creativity"
Students' Perspectives in Adopting Mobile Learning at University of Bahrain
...Show More Authors

View Publication
Scopus (10)
Crossref (7)
Scopus Clarivate Crossref
Publication Date
Sun Jan 13 2019
Journal Name
Iraqi Journal Of Physics
Design and manufacturing of supercritical drying autoclave for aerogel production
...Show More Authors

This article will address autoclave design considerations and
manufacturing working with high pressure low temperature
supercritical drying technique to produce silica aerogel. The design
elects carbon dioxide as a supercritical fluid (31.7 oC and 72.3 bar).
Both temperature and pressure have independently controlling
facility through present design. The autoclave was light weight (4.5
kg) and factory-made from stainless steel. It contains a high pressure
window for monitoring both transfer carbon dioxide gas to liquid
carbon dioxide and watching supercritical drying via aerogel
preparation process. In this work aerogel samples were prepared and
the true apparent densities, total pore volume and pore size

... Show More
View Publication Preview PDF
Crossref (2)
Crossref
Publication Date
Mon Mar 31 2025
Journal Name
The Iraqi Geological Journal
Evaluation of Machine Learning Techniques for Missing Well Log Data in Buzurgan Oil Field: A Case Study
...Show More Authors

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

... Show More
View Publication Preview PDF
Scopus Crossref
Publication Date
Thu Oct 29 2020
Journal Name
Complexity
Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting
...Show More Authors

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

... Show More
View Publication
Scopus (61)
Crossref (33)
Scopus Clarivate Crossref
Publication Date
Thu Oct 17 2024
Journal Name
مجلة ميسان لعلوم التربية البدنية
A historical study of the origins of the sport of polo and its development from the royal court to the stadiums of modern times
...Show More Authors

The sport of polo, the game of kings and sultans, is one of the traditional sports that has defied time throughout its long history. It has preserved its historical roots and been able to adapt to contemporary requirements, societal transformations, and temporal and spatial variables. It has moved from an aristocratic sport practiced as a form of entertainment to a professional competitive sport. It attracts many players and millions of viewers, so it is a living model of how sports develop and transform from a local tradition into global sports with organized rules managed by international institutions and with many championships worldwide. Through this, the research objectives were formulated, including exploring sports' origins. Polo in

... Show More
View Publication Preview PDF
Crossref