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ORCIDInternational Journal of Data Science and Analytics, Volume 11
Volume 11, Number 1, January 2021
- Sung Yang Ho
, Sophia Tan, Chun Chau Sze, Limsoon Wong, Wilson Wen Bin Goh:
What can Venn diagrams teach us about doing data science better? 1-10 - Ruth M. Pfeiffer, Daniel B. Kapla, Efstathia Bura:
Least squares and maximum likelihood estimation of sufficient reductions in regressions with matrix-valued predictors. 11-26 - Tao Yang, Rujing Yao, Qing Yin, Qiang Tian, Ou Wu:
Mitigating sentimental bias via a polar attention mechanism. 27-36 - Rajendra Kumar Roul, Jajati Keshari Sahoo:
A novel approach for ranking web documents based on query-optimized personalized pagerank. 37-55 - Julia D. Warnke-Sommer, Franklin E. Damann:
An improved machine learning application for the integration of record systems for missing US service members. 57-68 - Jinee Goyal, Padmavati Khandnor, Trilok Chand Aseri:
A Comparative Analysis of Machine Learning classifiers for Dysphonia-based classification of Parkinson's Disease. 69-83
Volume 11, Number 2, March 2021
- François Torregrossa, Robin Allesiardo, Vincent Claveau, Nihel Kooli, Guillaume Gravier:
A survey on training and evaluation of word embeddings. 85-103 - Eva García-Martín, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio, Veselka Boeva:
Energy-aware very fast decision tree. 105-126 - Jiuyong Li, Weijia Zhang, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu:
A general framework for causal classification. 127-139 - Manoj Apte, Sushodhan Vaishampayan, Girish Keshav Palshikar:
Detection of causally anomalous time-series. 141-153 - Trisha Lawrence, Patrick Hosein, Isaac Dialsingh:
An influence model for influence maximization-revenue optimization. 155-168 - Atikur R. Khan, Khandaker Tabin Hasan, Towhidul Islam, Saleheen Khan:
Forecasting respiratory tract infection episodes from prescription data for healthcare service planning. 169-180
Volume 11, Number 3, April 2021
- Xi Qiu, Shen Liang, Lingxiao Meng, Yanchun Zhang, Fan Liu:
Exploiting feature fusion and long-term context dependencies for simultaneous ECG heartbeat segmentation and classification. 181-193 - Zurab Khasidashvili, Adam J. Norman:
Feature range analysis. 195-219 - Wojtek Buczynski, Fabio Cuzzolin, Barbara J. Sahakian:
A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life. 221-242 - Luca Rendsburg, Damien Garreau:
Comparison-based centrality measures. 243-259
Volume 11, Number 4, May 2021
- Luca Pappalardo, Valerio Grossi, Dino Pedreschi:
Introduction to the special issue on social mining and big data ecosystem for open, responsible data science. 261-262 - Valerio Grossi, Fosca Giannotti, Dino Pedreschi, Paolo Manghi, Pasquale Pagano, Massimiliano Assante:
Data science: a game changer for science and innovation. 263-278 - Vasiliki Voukelatou, Lorenzo Gabrielli, Ioanna Miliou, Stefano Cresci, Rajesh Sharma, Maurizio Tesconi, Luca Pappalardo:
Measuring objective and subjective well-being: dimensions and data sources. 279-309 - Gennady L. Andrienko, Natalia V. Andrienko, Chiara Boldrini, Guido Caldarelli, Paolo Cintia, Stefano Cresci, Angelo Facchini, Fosca Giannotti, Aristides Gionis, Riccardo Guidotti, Michael Mathioudakis, Cristina Ioana Muntean, Luca Pappalardo, Dino Pedreschi, Evangelos Pournaras, Francesca Pratesi, Maurizio Tesconi, Roberto Trasarti:
(So) Big Data and the transformation of the city. 311-340 - Alina Sîrbu, Gennady L. Andrienko, Natalia V. Andrienko, Chiara Boldrini, Marco Conti, Fosca Giannotti, Riccardo Guidotti, Simone Bertoli, Jisu Kim, Cristina Ioana Muntean, Luca Pappalardo, Andrea Passarella, Dino Pedreschi, Laura Pollacci, Francesca Pratesi, Rajesh Sharma:
Human migration: the big data perspective. 341-360 - Leonardo Candela, Valerio Grossi, Paolo Manghi, Roberto Trasarti:
A workflow language for research e-infrastructures. 361-376 - Nikolaus Forgó, Stefanie Hänold, Jeroen van den Hoven, Tina Krügel, Iryna V. Lishchuk, René Mahieu, Anna Monreale, Dino Pedreschi, Francesca Pratesi, David van Putten:
An ethico-legal framework for social data science. 377-390
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