A decision tree (DTs) is one of the most popular machine learning algorithms that divide data repeatedly to form groups or classes. It is a supervised learning algorithm that can be used on discrete or continuous data for classification or regression. The most traditional classifier in this algorithm is the C4.5 decision tree, which is the point of this research. This classifier has the advantage of building a vast data set and does not stop until it reaches the desired goal. The problem with this classifier is that there are unnecessary nodes and branches leading to overfitting. This overfitting can negatively affect the classification process. In this context, the authors suggest utilizing a genetic algorithm to prune the effect of overfitting. This dataset study consists of four datasets: IRIS, Car Evaluation, GLASS, and WINE collected from UC Irvine (UCI) machine learning repository. The experimental results have confirmed the effectiveness of the genetic algorithm in pruning the effect of overfitting on the four datasets and optimizing confidence factor (CF) of the C4.5 decision tree. The proposed method has reached about 92% accuracy in this work.
Today, the world has heard a lot about artificial intelligence (AI) and its influence in accomplishing responsibilities, and it has become famous through films, series TV and social networking sites. Artificial Intelligence (AI) is a combination of algorithms and techniques developed by developers and programmers to build metal bodies that can work for centuries with individuals. Despite the interest of everyone in this topic and its spread significantly, most people do not have adequate knowledge and understanding of this science. This science is considered as one of the essential topics in computer sciences and engineering. In this article, it has been decided to write an overview on the topic of artificial intelligence and understand how its ideas started and spread universally. In addition, there is a review of Expert Systems, Artificial Neural Networks, Fuzzy Logic, and AI applications in the medical field and power systems, especially in investigating lung images of people with COVID-19. The idea presented in this article is that the future will soon come when humans and machines will merge into cyborgs or cybernetic creatures, and they will work together when completing tasks. This idea is described as transhumanism.
On our planet, chemical waste increases day after day, the emergence of new types of it, as well as the high level of toxic pollution, the difficulty of daily life, the increase in the psychological state of humans, and other factors all have led to the emergence of many diseases that affect humans, including deadly once like COVID-19 disease. Symptoms may appear on a person, and sometimes they may not; some people may know their condition, and others may neglect their health status due to lack of knowledge that may lead to death, or the disease may be chronic for life. In this regard, the author executes machine learning techniques (Support Vector Machine, C5.0 Decision Tree, K-Nearest Neighbours, and Random Forest) due to their influence in medical sciences to identify the best technique that gives the highest level of accuracy in detecting diseases. Thus, this technique will help to recognise symptoms and diagnose them correctly. This article covers a dataset from the UCI machine learning repository, namely the Wisconsin Breast Cancer dataset, Chronic Kidney disease dataset, Immunotherapy dataset, Cryotherapy dataset, Hepatitis dataset and COVID-19 dataset. In the results section, a comparison is made between the execution of each technique to find out which one is the best and which one is the worst in the performance of analysis related to the dataset of each disease.
Artificial intelligence is one of the most popular and influential sciences in many fields. It works continuously to contemporise computer systems to operate with high efficiency and to think like what a human think. In addition, this science seeks to make the work of the machine simulate the work of the human brain in thinking and making decisions, according to the environment in which they live. Therefore, it has become necessary to have artificial intelligence applications in all areas, including education, especially the English language teaching electronically. In this regard, the most influential applications and programs that contribute to the development of teaching English electronically and their effectiveness in developing e-learning will be reviewed. This article concluded that there are applications of artificial intelligence in teaching English electronically, which are of great importance and a great future in the development of language teaching.
The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
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