Engineering and Technology Journal

Regardless of the data source and type (text, digital, photo group, etc.), they are usually unclean data. The term (unclean) means that data contains some bugs and paradoxes that can strongly impact machine learning processes. The nature of the input data of the dataset is the most important reason for the success of the learning algorithm. More than one factor influences machine learning results in a specific task. The characteristics and the nature of the data are the main reasons for the algorithm's success. This paper generally examines data processing entered into an algorithm to learn machines. The paper explains the operations of each stage of prior treatment data for the best achievement of its data set. In this paper, four models for teaching machines (SVM, Multiple Bayes - NB, and Bernoulli - NB) will be used. Best accuracy (Bernoulli - NB) model 89%. The pre-processing algorithm applied to the data set (dirty data) will be developed and compared to previous results before development. The Bernoulli-NB model reaches 91% accuracy and improves the value of the rest of the models used in this process.

The religious endowments are one of the important sources, which acquire historical, cultural, and economic importance in all countries of the world. In particular, a religious endowment in Iraq includes several distributed real estates and lands that usually require efficient management systems. One of the most important factors affecting the management of real estates that belong to religious endowments is the rental amount of each real estate. In general, the estimation of the rental of real estates can support the future development of religious endowments. Governmental agencies are faced with some challenges in the management of religious endowments in terms of rental pricing due to numerous economic and geographic factors. The rapid development of artificial intelligence systems and Geomatic techniques can present a framework for rental amount estimation based on spatial and non-spatial factors. In this study, a machine learning algorithm (Support Vector Regression) will be combined with Geographic Information System (GIS) to predict and evaluate the rental amount of real estates that belong to a religious institution in Iraq (Shiite endowment in Hillah city). The final results indicated that the proposed method achieved an overall accuracy of 71%, a root mean square error of 0.2257 million Iraq, Dinar (IQD), and a correlation coefficient of 0.9272. This study can be used as an effective tool for the decision-makers to plan and manage the religious endowments in developing countries.  

E-commerce is one of the new virtual technologies that make life simpler for both traders, marketers, and customers. However, the main problem for the seller was how to know customer's intentions when they enter the website. This paper proposed to predict whether customers make a purchase or not from their previous behaviors.  Therefore, this paper aims to predict the intentions of users that using online -shopping. the main aim of this study is to highlight customer behaviors to predict purchases and make a compression between the works that are related to the subject of this paper to conclude and suggest the best method to predict purchasing in e-commerce treading that depends on customer behaviors.

A learning classifier system is one of the methods for applying a genetic-based
approach to machine learning applications. An enhanced version of the system that
employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating
rules is implemented and assigned the task of learning rules for classifying simple
objects. The task is to classify an object that has one or more of the following features:
wing, 2-legs/wheels, 3-legs/wheels, 4-legs/wheels, big, flies into one of the following:
bird, vehicle. the main goal is to exploit the ability of the algorithm to perform well in a
noisy environment and its ability to make little or no assumption about its problem
domain. Results are presented which show that the system was able to learn rules for
the task using only a few training examples and starting with classifiers that were
randomly generated. It is argued that a classifier based learning method requires little
training examples and that by its use of genetic algorithms to search for new plausible
rules, the method should be able to cope with changing conditions. Results show also
The parallel implementation of the algorithm would speed up the training process.