GPH-International Journal of Applied Science 2024-01-25T09:20:28+00:00 Dr. EKEKE, JOHN NDUBUEZE Open Journal Systems <p><strong>GPH-Int. Journal of Applied Science e-ISSN&nbsp;&nbsp;2805-4364 p-ISSN 2805-4356 is an international, peer-reviewed, open-access journal that welcomes high-quality research articles in all aspects of Applied Science research. Subject areas include, but are not limited to the following fields: Biology, Physics, Chemistry, Pharmacy, Zoology, Health Sciences, Agriculture and Forestry, Environmental Sciences, Business, Mathematics, Statistics, Animal Science, Bio-Technology, Medical Sciences, Geology, Social Sciences, Natural sciences, Political Science, Urban Development, Information Technology, e-Learning, e-Commerce, Architecture, Earth Science, Archaeological Science, A deal with engineering fundamentals.<span style="font-size: medium;"><a title="Journal Impact Factor" href=""><span style="color: #222222;"><span style="font-family: 'Book Antiqua', serif;"><span style="helvetica: Arial, serif;"><span style="color: #000000;"><span style="font-size: 1.5em;"><span style="text-shadow: #FF0000 0px 0px 2px;">Impact Factor: 1.245</span></span></span></span></span></span></a></span></strong></p> Identifying Important Features For Exoplanet Detection: A Machine Learning Approach 2024-01-25T08:43:20+00:00 Abdul Karim Jamal Uddin Md. Mahmudul Hasan Riyad <p><strong>The study and discovery of exoplanets (planets outside the solar system) have been a major focus in astronomy. Many efforts have been made to discover exoplanets using ground based and space based observatory, NASA’s Exoplanet Exploration Program being one of them. It has developed modern satellites like Kepler which are capable of collecting large array of data to help researchers with these objects. With the increasing number of exoplanet candidates, identifying and verifying their existence becomes a challenging task. In this research, we propose a statistical and machine learning approach to identify important features for exoplanet identification. For this purpose, we use the Kepler Cumulative Object of Interest (KCOI) dataset. After pre-processing the data we utilize statistical methods namely ANOVA F-test, Mutual Information Gain (MIG), Recursive Feature Elimination (RFE) to select the most significant features and have trained 10 state-of-the-art classifiers on them recursively to identify the features that leads to best performance. According to the results of our investigation, classifiers trained on features chosen by Recursive Feature Elimination with Random Forest as estimator produces superior results, with CatBoost classifier being the best with an accuracy of 99.61%. Our findings demonstrate the potential of machine learning in helping astronomers to efficiently and accurately verify exoplanet candidates in large astronomical datasets.</strong></p> 2024-01-25T08:40:07+00:00 ##submission.copyrightStatement## A SYSTEMATIC LITERATURE REVIEW ON BUSINESS CYCLES AND MICROECONOMICS 2024-01-25T09:20:28+00:00 Dr. Murillo Dias Dr. Luiz Gustavo Vivanco Elson Teixeira <p><strong>Over the past 150 years, business cycles have been extensively studied. However, there needs to be more research comparing the field's evolution since 1900 with microeconomics, which would provide a broader perspective on both fields. We reviewed more than 7.5 million citations and more than three thousand publications on Google Scholar and Scopus databases through a systematic literature review. The evidence suggests a gradual increase in citations over the decades, with a significant discontinuity in the 2020s due to the coronavirus pandemic. This pandemic has dramatically affected academic production in this research field. Nevertheless, key findings highlighted the global coverage and the top publications in such a field.</strong></p> 2024-01-25T00:00:00+00:00 ##submission.copyrightStatement##