Preferred Language
Articles
/
ZxbdCocBVTCNdQwCXDKQ
Developing A Mathematical Model for Planning Repetitive Construction Projects By Using Support Vector Machine Technique
...Show More Authors
Abstract<p>Each project management system aims to complete the project within its identified objectives: budget, time, and quality. It is achieving the project within the defined deadline that required careful scheduling, that be attained early. Due to the nature of unique repetitive construction projects, time contingency and project uncertainty are necessary for accurate scheduling. It should be integrated and flexible to accommodate the changes without adversely affecting the construction project’s total completion time. Repetitive planning and scheduling methods are more effective and essential. However, they need continuous development because of the evolution of execution methods, essentially based on the repetitive construction projects’ composition of identical production units. This study develops a mathematical model to forecast repetitive construction projects using the Support Vector Machine (SVM) technique. The software (WEKA 3.9.1©2016) has been used in the process of developing the mathematical model. The number of factors affecting the planning and scheduling of the repetitive projects has been identified through a questionnaire that analyzed its results using SPSS V22 software. Three accuracy measurements, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), were used to check the mathematical model and to compare the actual values with predicted values. The results showed that the SVM technique was more precise than those calculated by the conventional methods and was found the best generalization with R 97 %, MAE 3.6 %, and RMSE 7 %.</p>
Clarivate Crossref
View Publication
Publication Date
Sat Jan 01 2022
Journal Name
Proceedings Of International Conference On Computing And Communication Networks
Automatic Health Speech Prediction System Using Support Vector Machine
...Show More Authors

View Publication
Scopus (3)
Scopus Clarivate Crossref
Publication Date
Thu Sep 15 2022
Journal Name
Knowledge And Information Systems
Multiresolution hierarchical support vector machine for classification of large datasets
...Show More Authors

Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

... Show More
View Publication
Scopus (5)
Crossref (2)
Scopus Clarivate Crossref
Publication Date
Fri Apr 22 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Developing models to predicting the effect of crises on construction projects using MLR technique
...Show More Authors

View Publication
Scopus (1)
Crossref (1)
Scopus Crossref
Publication Date
Mon Sep 25 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Mathematical Model for One Year Planning of a Manufactory
...Show More Authors

This paper is an attempt to help the manager of a manufactory to

plan for the next year by a scientific approach, to maximize the profit and Ø¢ provide optimal Ø¢ monthly quantities of Ø¢ production, Ø¢ inventory,

work-force, prices and sales. The computer programming helps us to execute that huge number of calculations.

View Publication Preview PDF
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 (10)
Crossref (6)
Scopus Clarivate Crossref
Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Engineering
Developing Crisis Management System for Construction Projects in Iraq
...Show More Authors

Construction is a complicated process that takes place in an almost uncontrollable environment. Although projects can be carefully planned in advance in principle, there is a chance that unforeseen events and crises can disrupt these plans, affecting project development. Because the initial investment expenditures in construction projects are so large, they may be quickly influenced by crises, resulting in significant financial losses. The 2014 financial crisis was one of the most prominent crises that Iraq faced, which significantly impacted various activities in general and the construction industry in particular. Despite the importance of crisis management systems, the researchers found a great lack of local studies looking at cr

... Show More
View Publication
Crossref (6)
Crossref
Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Engineering
Developing Crisis Management System for Construction Projects in Iraq
...Show More Authors

Construction is a complicated process that takes place in an almost uncontrollable environment. Although projects can be carefully planned in advance in principle, there is a chance that unforeseen events and crises can disrupt these plans, affecting project development. Because the initial investment expenditures in construction projects are so large, they may be quickly influenced by crises, resulting in significant financial losses. The 2014 financial crisis was one of the most prominent crises that Iraq faced, which significantly impacted various activities in general and the construction industry in particular. Despite the importance of crisis management systems, the researchers found a great lack of local studies l

... Show More
View Publication Preview PDF
Crossref (6)
Crossref
Publication Date
Tue Dec 07 2021
Journal Name
2021 14th International Conference On Developments In Esystems Engineering (dese)
Content Based Image Retrieval Based on Feature Fusion and Support Vector Machine
...Show More Authors

View Publication
Scopus (5)
Crossref (5)
Scopus Clarivate Crossref
Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine
...Show More Authors

View Publication
Scopus (15)
Crossref (13)
Scopus Clarivate Crossref
Publication Date
Sat Jan 20 2024
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset
...Show More Authors

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a

... Show More
View Publication Preview PDF
Crossref