Evaluation of the trends in jobs and skill-sets using data analytics: a case study

Autor: Armin Alibasic, Himanshu Upadhyay, Mecit Can Emre Simsekler, Thomas Kurfess, Wei Lee Woon, Mohammed Atif Omar

Publikováno v: Journal of Big Data, Vol 9, Iss 1, Pp 1-28 (2022)

Abstract Introduction Fast-emerging technologies are making the job market dynamic, causing desirable skills to evolve continuously. It is therefore important to understand the transitions in the job market to proactively identify skill sets required

Externí odkaz: https://doaj.org/article/96d9e54fc7d34156b2f24ef299546762

A Multistage Passive Islanding Detection Method for Synchronous-Based Distributed Generation

Autor: Abdullah Sawas, Ravikumar Pandi, Hatem H. Zeineldin, Mostafa F. Shaaban, Wei Lee Woon

Publikováno v: IEEE Transactions on Industrial Informatics. 18:2078-2088

A multistage approach to passive islanding detection is proposed that utilizes a decision-tree-like classification algorithm. The novelty of the proposed method is centered on the way in which features are passed to subsequent stages of the decision

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8c86a5ed00eabd992e1ac5d9e3008e12
https://doi.org/10.1109/tii.2021.3065015

Measuring the predictability of life outcomes with a scientific mass collaboration

Autor: Kristin E. Porter, Malte Möser, Flora Wang, Bingyu Zhao, Wei Lee Woon, Yoshihiko Suhara, Adaner Usmani, Erik H. Wang, Kun Jin, Samantha Weissman, William Eggert, Hamidreza Omidvar, Andrew Or, Lisa M Hummel, Gregory Faletto, Ben Sender, Qiankun Niu, Viola Mocz, Antje Kirchner, Catherine Wu, Karen Ouyang, Ian Lundberg, Allison C. Morgan, Abdulla Alhajri, Arvind Narayanan, Khaled AlGhoneim, Louis Raes, Ilana M. Horwitz, Barbara E. Engelhardt, Ben Leizman, Crystal Qian, Drew Altschul, Guanhua He, Jeanne Brooks-Gunn, Ridhi Kashyap, Eaman Jahani, Ryan James Compton, Anna Filippova, Sara McLanahan, Tejomay Gadgil, Claudia V. Roberts, Muna Adem, Julia Wang, Jeremy Freese, Alexander T. Kindel, Daniel E Rigobon, Naijia Liu, Lisa P. Argyle, Mayank Mahajan, Jonathan D Tang, Moritz Hardt, Ethan Porter, Diana Mercado-Garcia, Andrew Halpern-Manners, Anahit Sargsyan, Duncan J. Watts, Alex Pentland, Sonia P Hashim, Dean Knox, Onur Varol, Ryan Amos, James M. Wu, Thomas Davidson, Emma Tsurkov, Bernie Hogan, Areg Karapetyan, William Nowak, Jingwen Yin, Livia Baer-Bositis, Landon Schnabel, Chenyun Zhu, Noah Mandell, Ahmed Musse, Yue Gao, Josh Gagné, Stephen McKay, Jennie E. Brand, Abdullah Almaatouq, Katy M. Pinto, Andrew E Mack, Austin van Loon, Bedoor K. AlShebli, Helge Marahrens, Xiafei Wang, Bryan Schonfeld, Sonia Hausen, Kengran Yang, Maria Wolters, Brandon M. Stewart, Naman Jain, Moritz Büchi, Nicole Bohme Carnegie, Redwane Amin, Caitlin Ahearn, Kirstie Whitaker, Bo-Ryehn Chung, Diana Stanescu, Thomas Schaffner, Patrick Kaminski, David Jurgens, Kivan Polimis, Kimberly Higuera, Zhilin Fan, Matthew J. Salganik, Debanjan Datta, Connor Gilroy, E H Kim, Katariina Mueller-Gastell, Karen Levy, Brian J. Goode, Zhi Wang, Tamkinat Rauf

Publikováno v: PNAS
Proceedings of the National Academy of Sciences of the United States of America (PNAS), 117(15), 8398-8403. NATL ACAD SCIENCES
Proc Natl Acad Sci U S A
Salganik, M J, Lundberg, I, Kindel, A T, Ahearn, C E, Al-ghoneim, K, Almaatouq, A, Altschul, D M, Brand, J E, Carnegie, N B, Compton, R J, Datta, D, Davidson, T, Filippova, A, Gilroy, C, Goode, B J, Jahani, E, Kashyap, R, Kirchner, A, Mckay, S, Morgan, A C, Pentland, A, Polimis, K, Raes, L, Rigobon, D E, Roberts, C V, Stanescu, D M, Suhara, Y, Usmani, A, Wang, E H, Adem, M, Alhajri, A, Alshebli, B, Amin, R, Amos, R B, Argyle, L P, Baer-bositis, L, Büchi, M, Chung, B, Eggert, W, Faletto, G, Fan, Z, Freese, J, Gadgil, T, Gagné, J, Gao, Y, Halpern-manners, A, Hashim, S P, Hausen, S, He, G, Higuera, K, Hogan, B, Horwitz, I M, Hummel, L M, Jain, N, Jin, K, Jurgens, D, Kaminski, P, Karapetyan, A, Kim, E H, Leizman, B, Liu, N, Möser, M, Mack, A E, Mahajan, M, Mandell, N, Marahrens, H, Mercado-garcia, D, Mocz, V, Mueller-gastell, K, Musse, A, Niu, Q, Nowak, W, Omidvar, H, Or, A, Ouyang, K, Pinto, K M, Porter, E, Porter, K E, Qian, C, Rauf, T, Sargsyan, A, Schaffner, T, Schnabel, L, Schonfeld, B, Sender, B, Tang, J D, Tsurkov, E, Van Loon, A, Varol, O, Wang, X, Wang, Z, Wang, J, Wang, F, Weissman, S, Whitaker, K, Wolters, M K, Woon, W L, Wu, J, Wu, C, Yang, K, Yin, J, Zhao, B, Zhu, C, Brooks-gunn, J, Engelhardt, B E, Hardt, M, Knox, D, Levy, K, Narayanan, A, Stewart, B M, Watts, D J & Mclanahan, S 2020, ' Measuring the predictability of life outcomes with a scientific mass collaboration ', Proceedings of the National Academy of Sciences, vol. 117, no. 15, pp. 8398-8403 . https://doi.org/10.1073/pnas.1915006117

© This open access article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND). How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the co

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e79e5a0edeb2b5d23585b10ea4b178d
https://doi.org/10.1073/pnas.1915006117

Explainable AI as a Social Microscope: A Case Study on Academic Performance

Autor: Aamena Alshamsi, Anahit Sargsyan, Areg Karapetyan, Wei Lee Woon

Publikováno v: Machine Learning, Optimization, and Data Science ISBN: 9783030645823
LOD (1)

Academic performance is perceived as a product of complex interactions between students’ overall experience, personal characteristics and upbringing. Data science techniques, most commonly involving regression analysis and related approaches, serve

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1b6eb20e575553b6ac415739ecb9d398
https://doi.org/10.1007/978-3-030-64583-0_24

Evaluation of tree-based ensemble learning algorithms for building energy performance estimation

Autor: Elie Azar, Sokratis Papadopoulos, Constantine E. Kontokosta, Wei Lee Woon

Publikováno v: Journal of Building Performance Simulation. 11:322-332

Tree-based ensemble learning has received significant interest as one of the most reliable and broadly applicable classes of machine learning techniques. However, thus far, it has rarely been used to model and evaluate the drivers of energy consumpti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9231382bfe95bd3b808f1d90f2d57816
https://doi.org/10.1080/19401493.2017.1354919