Special Issue "The Second Symposium on Machine Intelligence and Data Analytics"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 May 2019).

Special Issue Editors

Guest Editor
Dr. Wanquan Liu Website E-Mail
Faculty of Science and Engineering, Curtin University, Kent Street, Bentley, Perth, Western Australia 6102, Australia
Interests: machine learning; image processing; deep learning
Guest Editor
Prof. Dr. Xiaochun Cao Website E-Mail
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
Interests: multimedia and security; computer vision; big data and security
Guest Editor
Dr. Jianhou Gan E-Mail
Yunnan Normal University
Interests: pattern recognition, ethnical minority characterisation, big data analysis for education

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the increasing interest in the algorithm design for machine intelligence and data analytics. The Symposium on Machine Intelligence and Data Analytics (MIDA 2018) is an international forum that brings together researchers and practitioners working in different areas of machine intelligence. In the context of the workshop, machine intelligence encompasses works on concurrent leading techniques in machine learning, especially deep learning and sparse sensing techniques and their applications. Concerning applications, big data analysis in different areas including computer vision, health, cyber-security, and educational systems will be another focus. Given the rise of deep learning and big data analysis in different research disciplines, the second MIDA is particularly interested in research works that address theoretical results on deep learning, new techniques on the training of deep learning networks, and novel tools that attempt to improve big data analytics and work towards improved intelligent systems. Topics relevant to this Special Issue cover the scope of the MIDA 2018 (http://www.midaforum.com).

  • Deep learning techniques and applications
  • Big data analysis
  • Novel algorithms for signal processing
  • Robust face recognition
  • Data-driven control
  • Cybersecurity
  • Educational data analysis
  • Randomised algorithms
  • Object tracking
  • Data mining and knowledge discovery
  • Computer vision and image understanding

Extended versions of papers presented at MIDA 2018 are considered first, but this call for papers is fully open to all those who wish to contribute by submitting a relevant research manuscript.

Dr Wanquan Liu
Prof. Dr. Xiaochun Cao
Dr. Jianhou Gan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • algorithm design
  • machine intelligence
  • data analytics
  • deep learning
  • intelligent systems

Published Papers (4 papers)

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Research

Open AccessArticle
A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP
Algorithms 2019, 12(8), 174; https://doi.org/10.3390/a12080174 - 16 Aug 2019
Abstract
Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional [...] Read more.
Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM). Full article
(This article belongs to the Special Issue The Second Symposium on Machine Intelligence and Data Analytics)
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Open AccessArticle
A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
Algorithms 2019, 12(8), 155; https://doi.org/10.3390/a12080155 - 31 Jul 2019
Abstract
In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative [...] Read more.
In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments. Full article
(This article belongs to the Special Issue The Second Symposium on Machine Intelligence and Data Analytics)
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Open AccessArticle
A Study on Sensitive Bands of EEG Data under Different Mental Workloads
Algorithms 2019, 12(7), 145; https://doi.org/10.3390/a12070145 - 22 Jul 2019
Abstract
Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. [...] Read more.
Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads. Full article
(This article belongs to the Special Issue The Second Symposium on Machine Intelligence and Data Analytics)
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Open AccessArticle
Drum Water Level Control Based on Improved ADRC
Algorithms 2019, 12(7), 132; https://doi.org/10.3390/a12070132 - 28 Jun 2019
Abstract
Drum water level systems show strong disturbance, big inertia, large time delay, and non-linearity characteristics. In order to improve the antidisturbance performance and robustness of the traditional active disturbance rejection controller (ADRC), an improved linear active disturbance rejection controller (ILADRC) for drum water [...] Read more.
Drum water level systems show strong disturbance, big inertia, large time delay, and non-linearity characteristics. In order to improve the antidisturbance performance and robustness of the traditional active disturbance rejection controller (ADRC), an improved linear active disturbance rejection controller (ILADRC) for drum water level is designed. On the basis of the linear active disturbance rejection controller (LADRC) structure, an identical linear extended state observer (ESO) is added with the same parameters as that of the original one. The estimation error value of the total disturbance is introduced, and the estimation error of the total disturbance is compensated, which can improve the control system’s ability to suppress unknown disturbances, so as to improve the antidisturbance performance and robustness. The antijamming performance and robustness of LADRC and ILADRC for drum water level are simulated and analyzed under the influence of external disturbance and model parameter variation. Results show that the proposed control system ILADRC has shorter settling time, smaller overshot, and strong anti-interference ability and robustness. It has better performance than the LADRC and has certain application value in engineering. Full article
(This article belongs to the Special Issue The Second Symposium on Machine Intelligence and Data Analytics)
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