Preferred Language
Articles
/
ruassZ0BmraWrQ4dEVol
Evaluation of the Accuracy of Machine Learning Classifiers and Spectral Indices in Land Cover Classification
...Show More Authors

Population growth and economic and industrial development coupled have significantly accelerated the rate of Land Use and Land Cover (LULC) changes, particularly in developing countries, so finding optimum ways to observe these change has become a pressing issue. Quantification evaluation of these changes is crucial to comprehend and oversee land management conversion, therefore, it is necessary to evaluate the accuracy of various algorithms for LULC classification to determine the most effective classifier for Earth observation applications. The performance of Maximum Likelihood (ML), Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) was examined in this study, based on Sentinel 2A satellite images. The accuracy of those classifiers was evaluated using the Kappa Coefficient and normalized difference index-based verification. The findings indicate that all classifiers exhibit high accuracy levels with variations. The RF algorithm had the highest Kappa coefficient of 0.90, while the KNN algorithm the lowest of 0.76. The accuracy values for RF, SVM, ML, and KNN were 93.1%, 91.2%, 86.2%, and 82.5%, respectively. Results from this study using index-based LULC show that the RF classifier outperforms the others. The results of this study can be used in monitoring LULC change tasks.

Scopus Crossref
View Publication
Publication Date
Thu Sep 01 2016
Journal Name
2016 8th Computer Science And Electronic Engineering (ceec)
Class-specific pre-trained sparse autoencoders for learning effective features for document classification
...Show More Authors

View Publication
Scopus (6)
Crossref (2)
Scopus Crossref
Publication Date
Tue Oct 01 2024
Journal Name
E3s Web Of Conferences
The land transport efficiency in Wasit Governorate: Urban and rural settlements
...Show More Authors

The study of human factors and the extent of their impact on the construction of land transport roads is of great importance because they are variable factors, which are on the contrary natural factors, and the impact of human factors on road construction operations is evident through the study of population distribution and size, and economic activities (agricultural, industrial, tourism). And its direct mutual relationship with the construction and extension of land transport routes and the development of each of them, and then directing them to serve urban and rural human settlements, which leads to their development and improvement of their economic, living and social conditions on the one hand, and contributes to the emergence

... Show More
View Publication
Scopus Crossref
Publication Date
Fri Jul 30 2021
Journal Name
Iraqi Journal For Electrical And Electronic Engineering
EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm
...Show More Authors

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communi

... Show More
View Publication
Scopus (5)
Crossref (4)
Scopus Crossref
Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Simplified Novel Approach for Accurate Employee Churn Categorization using MCDM, De-Pareto Principle Approach, and Machine Learning
...Show More Authors

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

... Show More
View Publication Preview PDF
Scopus (5)
Crossref (5)
Scopus Crossref
Publication Date
Mon Feb 18 2019
Journal Name
Iraqi Journal Of Physics
STVI calculating the changes in vegetable cover for a vegetated environment in west Iraq
...Show More Authors

Remote sensing provide the best means to monitoring change in vegetation over a wide range of temporal scales over large areas. In this study, the vegetation index which has been applied known as the Stress Related Vegetation Index (STVI) on in the area around the Euphrates River and part of Al-Habbaniyah lake which located at western side of the river in Ramadi city, Al-Anbar province at Iraq to study the vegetation cover changes and detect the areas of changes, using two satellite sensors multispectral images such as TM and ALI, after geometric correction procedure to rectifying these images. The STVI-4 index result was the best than other vegetation indices (STVI-1 and STVI-3) to discriminate the vegetable cover distribution. The diff

... Show More
View Publication Preview PDF
Crossref
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
...Show More Authors

View Publication
Scopus (77)
Crossref (66)
Scopus Clarivate Crossref
Publication Date
Mon Oct 22 2018
Journal Name
Journal Of Economics And Administrative Sciences
Measuring and analyzing the relationship between the volatility of US stock market indices and the volatility of oil prices
...Show More Authors

The volatility of the financial markets and the oil market plays a major role in influencing macroeconomic activity, as well as the high interaction between the both markets and the remarkable sensitivity to their each other fluctuations which cause the undesirable impact on other economic sectors as an expected result due the mentioned interaction.

The study aimed to analyze the relationship between the volatility of the major US market indices represented by the DJIA index, S & P500, due to their comprehensiveness of the financial market, as they summarize the performance of the entire US market which is the largest economy in the world, as well as the difference in the calculation mechanism, and oi

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sun Jun 08 2025
Journal Name
J Nat Sc Biol Med
The Value of White Blood Cells and Platelets Indices in Prediction of Tubal Ectopic Pregnancy Rupture
...Show More Authors

Preview PDF
Scopus
Publication Date
Sun Jun 08 2025
Journal Name
Journal Of Natural Science, Biology And Medicine
The Value of White Blood Cells and Platelets Indices in Prediction of Tubal Ectopic Pregnancy Rupture
...Show More Authors

View Publication
Publication Date
Mon Jan 01 2018
Journal Name
Lecture Notes Of The Institute For Computer Sciences, Social Informatics And Telecommunications Engineering
Sensor Data Classification for the Indication of Lameness in Sheep
...Show More Authors

View Publication Preview PDF
Scopus (6)
Crossref (1)
Scopus Clarivate Crossref