https://journalofanalytics.com/index.php/ja <p><span style="font-weight: 400;">The Journal of Analytics (JA) is an international journal devoted to original high-quality research in theory, methodology, technology and applications of analytics. The JA seeks to publish a wide range of research and review papers such as in marketing analytics, data science, big data, data mining, knowledge discovery, artificial intelligience, Internet of things, machine learning and deep learning.</span></p> <p><span style="font-weight: 400;">In addition to all analytics related disciplines from business analytics to health analytics and bioinformatics and sports analytics; from marketing analytics and government analytics to data analytics and talent analytics, to music analytics, we also welcome novel applications of analytics in heuristic and analytical decision making methods, operations research, statistics, econometrics, computer science and in all computational management, mathematical and engineering sciences.</span></p> <p><span style="font-weight: 400;">As JA is dedicated to timely and open innovation, we: strive to follow a streamlined peer-review process, publish article quarterly in an electronic format, and make all articles freely available without restriction.</span></p> <p>&nbsp;</p> <p><span style="font-weight: 400;">Founded in 2017. </span></p> <p><span style="font-weight: 400;">The journal is quarterly</span></p> <p><strong>Publisher</strong></p> <p><span style="font-weight: 400;">Journal of Analytics is published by New York Business Global, USA.</span></p> New York Business Global en-US Journal of Analytics 2474-3968 https://journalofanalytics.com/index.php/ja/article/view/6 <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>As the world fights the recent devastating calamity, coronavirus pandemic, humanity experiences the most accelerated vaccine development and vaccination in history. We holistically compare, rate and rank SARS-Cov-2 vaccines as well as vaccine platforms in multi-attributes as the first study in the literature. We use grey relational analysis as a multi-criteria decision-making tool, wider grey systems theory to compare, rate and rank the vaccines. We select 12 leading vaccines and 14 attributes from efficacy rate, safety/reactogenicity and protection against variants to children use, from approvals to prices, to logistics and market share to form a 360-degree comparison. According to equally weighted attributes, Pfizer/BioNTech vaccine is at the top rank. The second rank belongs to Moderna when the third belongs to Sinovac. The top three are followed by Oxford-AstraZeneca, Johnson &amp; Johnson, and others. Pfizer/BioNTech is also at the first rank and followed by Moderna and Novavax with respect to 40% weighted efficacy rate. Our ranking approach, which is unbiased and reproducible employs a Wuhan- originated tool, grey systems theory to rank the vaccines against SARS-CoV-2, another Wuhan- originated agent. Since all vaccines are valuable, our effort is to determine the primus inter pares (= first among equals).</p> </div> </div> </div> Eyüp Çetin Hilal Özen Ömer Özen ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2021-10-16 2021-10-16 1 1 1 19 10.29020/nybg.ja.v1i1.6 https://journalofanalytics.com/index.php/ja/article/view/2 <p>In this paper, in phase-1 we classified hospitals of three categories in the presence of seven performance features, using several MATLAB^® machine learning algorithms that comprised ‘Classification learners’, and compared model validation and classification results. We chose the best trained model validated for prediction of future hospital category, while comparing the models based on accuracy rate and AUC (area under ROC curve). Out of the five classifiers that we short listed for final selection, Ensemble Subspace KNN (k-nearest neighbor) emerged as the best classifier, and it predicted the same original hospitals for treatment in the event of a random surge in Inpatients during a pandemic, indicating that those hospitals can handle the surge with the available resources. Then in phase-2 with the new Inpatients and predicted hospitals, we used ‘Regression learners’ and selected the best learner based on lowest RMSE and highest R² to predict Live Discharges. Out of the four regression learners that were chosen for final selection, Linear Support Vector Machines emerged as the best learner, and it did predict Live Discharges after the surge.</p> Parakramaweera Sunil Dharmapala ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc/4.0 2021-10-24 2021-10-24 1 1 20 41 10.29020/nybg.ja.v1i1.2