GPH-International Journal of Applied Science https://gphjournal.org/index.php/as <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="http://www.gphjournal.org/index.php/index/jif"><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> en-US <p>Author(s) and co-author(s)&nbsp;jointly&nbsp;and severally represent and warrant that the Article is original with the author(s) and does not infringe any&nbsp;copyright or violate any other right of any third parties, and that the Article has not been published&nbsp;elsewhere.&nbsp;Author(s) agree to the terms that the <strong>GPH Journal</strong> will have the full right to remove the published article on any misconduct found in the published article.</p> drekekejohn@gmail.com (Dr. EKEKE, JOHN NDUBUEZE) idress.hamad@omu.edu.ly (Dr. Idress Hamad Attitalla) Thu, 25 Jan 2024 00:00:00 +0000 OJS 3.1.1.2 http://blogs.law.harvard.edu/tech/rss 60 Identifying Important Features For Exoplanet Detection: A Machine Learning Approach https://gphjournal.org/index.php/as/article/view/1201 <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> Abdul Karim, Jamal Uddin, Md. Mahmudul Hasan Riyad ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc-nd/4.0 https://gphjournal.org/index.php/as/article/view/1201 Thu, 25 Jan 2024 08:40:07 +0000 A SYSTEMATIC LITERATURE REVIEW ON BUSINESS CYCLES AND MICROECONOMICS https://gphjournal.org/index.php/as/article/view/1200 <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> Dr. Murillo Dias, Dr. Luiz Gustavo Vivanco, Elson Teixeira ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc-nd/4.0 https://gphjournal.org/index.php/as/article/view/1200 Thu, 25 Jan 2024 00:00:00 +0000