Open Access selected Papers

IEEE Open Access

IEEE Transactions on Emerging Topics in Computational Intelligence now offers publication of its highlighted papers in Open Access for the duration of 3 months in order to assist authors gain maximum exposure for their groundbreaking research and application-oriented papers to all reader communities.

The fifth highlighted paper offered to make Open Access in TETCI is now available for the duration of 3 months starting from 1 January 2020.


Data-Driven Decision-Making (D3M): Framework, Methodology, and Directions

Authors: Jie Lu, Zheng Yan, Jialin Han and Guangquan Zhang

Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)

Issue: Volume 3, Issue 4 – August 2019

Pages: 286-296

Abstract: A decision problem, according to traditional principles, is approached by finding an optimal solution to an analytical programming decision model, which is known as model-driven decision-making. The fidelity of the model determines the quality and reliability of the decision-making; however, the intrinsic complexity of many real-world decision problems leads to significant model mismatch or infeasibility in deriving a model using the first principle. To overcome the challenges that are present in the big data era, both researchers and practitioners emphasize the importance of making decisions that are backed up by data related to decision tasks, a process called data-driven decision-making (D3M). By building on data science, not only can decision models be predicted in the presence of uncertainty or unknown dynamics, but also inherent rules or knowledge can be extracted from data and directly utilized to generate decision solutions. This position paper systematically discusses the basic concepts and prevailing techniques in data-driven decision-making and clusters-related developments in technique into two main categories: programmable data-driven decision-making (P-D3M) and nonprogrammable data-driven decision-making (NP-D3M). This paper establishes a D3M technical framework, main methodologies, and approaches for both categories of D3M, as well as identifies potential methods and procedures for using data to support decision-making. It also provides examples of how D3M is implemented in practice and identifies five further research directions in the D3M area. We believe that this paper will directly support researchers and professionals in their understanding of the fundamentals of D3M and of the developments in technical methods.

Index Terms: Data-driven decision-making, Decision support systems, Computational intelligence

IEEE Xplore Link: https://ieeexplore.ieee.org/document/8732997


Available in Open Access from 1 January 2020 to 31 March 2020 in IEEE Xplore Digital Library.


Previous Open Access selected Papers

Light Gated Recurrent Units for Speech Recognition

Authors: Mirco Ravanelli, Philemon Brakel, Maurizio Omologo and Yoshua Bengio

Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)

Issue: Volume 2, Issue 2 – April 2018

Pages: 92-102

Index Terms: Speech recognition, Deep learning, Recurrent neural networks, LSTM, GRU

IEEE Xplore Link: https://ieeexplore.ieee.org/document/8323308

End-to-End Learning for Physics-Based Acoustic Modeling

Authors: Leonardo Gabrielli, Stefano Tomassetti, Carlo Zinato and Francesco Piazza

Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)

Issue: Volume 2, Issue 2 – April 2018

Pages: 160-170

Index Terms: Physics-based acoustic modeling, End-to-end learning, Convolutional neural networks

IEEE Xplore Link: https://ieeexplore.ieee.org/document/8323323

An All-Memristor Deep Spiking Neural Computing System: A Step Toward Realizing the Low-Power Stochastic Brain

Authors: Parami Wijesinghe, Aayush Ankit, Abhronil Sengupta and Kaushik Roy

Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)

Issue: Volume 2, Issue 5 – October 2018

Pages: 345-358

Index Terms: Memristor, Stochasticity, Deep stochastic spiking neural networks

IEEE Xplore Link: https://ieeexplore.ieee.org/document/8471280

New Shades of the Vehicle Routing Problem: Emerging Problem Formulations and Computational Intelligence Solution Methods

Authors: Jacek Mańdziuk

Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)

Issue: Volume 3, Issue 3 – June 2019

Pages: 230-244

Index Terms: Computational intelligence, Vehicle routing, Combinatorial optimization, Metaheuristics

IEEE Xplore Link: https://ieeexplore.ieee.org/document/8591961