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


Tensor Deep Learning Model for Heterogeneous Data Fusion in Internet of Things
Authors: Wei Wang and Min Zhang
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 4, Issue 1 – February 2020
Pages: 32-41

Abstract: With the rapid evolvement of the Internet and data acquisition technology as well as the continuous advancement of science and technology, the amount of data in many fields has reached the level of terabyte or petabyte and most data collection comes from the Internet of Things (IoT). The rapid advancement of IoT big data has provided valuable opportunities for the development of people in all areas of society. At the same time, it has also brought severe challenges to various types of current information processing systems. Effectively using the big data technology, discovering the hidden laws in big data, tapping the potential value of big data, and predicting the development trend of things to allocate resources more reasonably will promote the overall development of society. However, most of the IoT big data are presented as heterogeneous data, with high dimensions, different forms of expression, and a lot of redundant information. The current machine learning model works in vector space, which makes it impossible to gain big data features because vectors cannot simulate the highly nonlinear distribution of IoT big data. This paper presents a deep learning calculation model called tensor deep learning (TDL), which further improves big data feature learning and high-level feature fusion. It uses tensors to model the complexity of multisource heterogeneous data and extends the vector space data to the tensor space, when feature extraction in the tensor space is included. To fully understand the underlying data distribution, the tensor distance is adopted as the average square sum error term of the output layer reconstruction error. Based on the conventional back-propagation algorithm, this study proposes a high-order back-propagation algorithm to extend the data from the linear space to multiple linear space and train the parameters of the proposed model. Then, to evaluate its performance, the proposed TDL model is compared with the stacked auto encoder and the multimodal deep learning model. Furthermore, experiments are performed on two representative datasets, namely CUAVE and STL-10. Experimental results show that the proposed model not only excels in heterogeneous data fusion but also provides a higher recognition accuracy than the conventional deep learning model or the multimodal learning model for big data..

Index Terms: Big data, Heterogeneous data fusion, Tensor feature extraction, Tensor deep learning model.
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8522024


Available in Open Access from 1 April 2020 to 30 June 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

 

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

Index Terms: Data-driven decision-making, Decision support systems, Computational intelligence
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8732997

 

Well-M3N: A Maximum-Margin Approach to Unsupervised Structured Prediction
Authors: Shaukat Abidi, Massimo Piccardi, Ivor W. Tsang and Mary-Anne Williams
Publication: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Issue: Volume 3, Issue 6 – December 2019
Pages: 427-439

Index Terms: Structured prediction, Unsupervised training, Convex relaxation, Maximum-margin Markov networks, Well-SVM
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8515243