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

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

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

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

In this research,  Fuzzy Analytic Hierarchy Process technique is applied (Fuzzy AHP) which is one of  multi-criteria decision making techniques to evaluate the criteria for urban planning projects, the project of developing master plan of Al-Muqdadiyah city to 2035 has been chosen as a case study. The researcher prepared a list of criteria in addition to the authorized departments criteria and previous researches in order to choose optimized master plan according to these criteria. This research aims at employing the foundations of (Fuzzy AHP) technique in evaluating urban planning criteria precisely and flexible. The results of the data analysis to the individuals of the sample who are specialists, in this aspect.  The la