Special Issue "Machine Learning Algorithms for Big Data"

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

Deadline for manuscript submissions: closed (30 June 2015).

Special Issue Editors

Guest Editor
Prof. Dr. Jeng-Shyang Pan Website E-Mail
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, Kaohsiung 807, Taiwan
Phone: +886-7-381-4526 Ext. 5636
Interests: signal processing; machine learning; image processing
Guest Editor
Dr. Shen Wang E-Mail
Department of Computer Sciences and Technology, Harbin Institute of Technology, Harbin 150080, China
Interests: data processing; machine learning; image processing
Guest Editor
Prof. Dr. Jun-Bao Li E-Mail
Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
Interests: pattern recognition; machine learning; image processing

Special Issue Information

Dear Colleagues,

 

Algorithms will publish a Special Issue on Machine Learning for Big Data in 2015. Big Data refers to, not only the ever-increasing data size, as its name suggests, but also to those attributes associated with efficient and flexible data processing for dealing with various data formats to meet the real-time requirements, constituting its three defining properties, namely, Volume, Velocity, and Variety, which greatly challenge traditional data processing techniques from collecting, storing, transferring, mining, processing and visualizing massive data. Applications of Big Data in various aspects, be it politics, economy and culture, or our daily life, are creating increasingly stronger demands for efficient and accurate data processing techniques to acquire valuable information from pre-existing or dynamic Big Data. Machine Learning provides intelligent and comprehensive analysis to automatically detect properties and patterns buried in the Big Data, which allows the processing systems of Big Data attain better performance. The main focus of this Special Issue is on the recent advancement of machine learning, including the challenges and solutions in designing, developing, and deploying modern machine learning algorithms and systems for the various applications of Big Data. We welcome authors to submit their original research articles, as well as comprehensive reviews. This Special Issue is expected to be an effective platform for researchers to present their state-of-the-art work on machine learning for Big Data, and to present new ideas and directions for future development.

Scope:

  • Big Data management and analysis
  • Cloud computing and cloud data mining
  • Efficient learning algorithms for scalable social media analysis
  • Big Data analysis and social media applications
  • Large-scale social multimedia content analysis and retrieval
  • Social Big Data transport and sharing
  • Text information and knowledge mining on large scale social media
  • Algorithms and systems for Big Data search
  • Link and graph mining for Big Data
  • BigData pre-processing
  • Multimedia and multi-structured data mining
  • Streaming data processing
  • Visualization for Big Data
  • Advanced machine learning methods for Big Data

 

Jeng-Shyang Pan, Shen Wang and Jun-Bao Li
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

  • Big Data
  • Machine Learning
  • Data Processing
  • Cloud Computing

Published Papers (5 papers)

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Research

Open AccessArticle
Code Synchronization Algorithm Based on Segment Correlation in Spread Spectrum Communication
Algorithms 2015, 8(4), 870-894; https://doi.org/10.3390/a8040870 - 09 Oct 2015
Cited by 1
Abstract
Spread Spectrum (SPSP) Communication is the theoretical basis of Direct Sequence Spread Spectrum (DSSS) transceiver technology. Spreading code, modulation, demodulation, carrier synchronization and code synchronization in SPSP communications are the core parts of DSSS transceivers. This paper focuses on the code synchronization problem [...] Read more.
Spread Spectrum (SPSP) Communication is the theoretical basis of Direct Sequence Spread Spectrum (DSSS) transceiver technology. Spreading code, modulation, demodulation, carrier synchronization and code synchronization in SPSP communications are the core parts of DSSS transceivers. This paper focuses on the code synchronization problem in SPSP communications. A novel code synchronization algorithm based on segment correlation is proposed. The proposed algorithm can effectively deal with the informational misjudgment caused by the unreasonable data acquisition times. This misjudgment may lead to an inability of DSSS receivers to restore transmitted signals. Simulation results show the feasibility of a DSSS transceiver design based on the proposed code synchronization algorithm. Finally, the communication functions of the DSSS transceiver based on the proposed code synchronization algorithm are implemented on Field Programmable Gate Array (FPGA). Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data)
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Open AccessArticle
Data Fusion Modeling for an RT3102 and Dewetron System Application in Hybrid Vehicle Stability Testing
Algorithms 2015, 8(3), 632-644; https://doi.org/10.3390/a8030632 - 12 Aug 2015
Cited by 1
Abstract
More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this [...] Read more.
More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data)
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Open AccessArticle
Target Detection Algorithm Based on Two Layers Human Visual System
Algorithms 2015, 8(3), 541-551; https://doi.org/10.3390/a8030541 - 29 Jul 2015
Cited by 6
Abstract
Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false [...] Read more.
Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data)
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Open AccessArticle
A Parallel Search Strategy Based on Sparse Representation for Infrared Target Tracking
Algorithms 2015, 8(3), 529-540; https://doi.org/10.3390/a8030529 - 27 Jul 2015
Cited by 1
Abstract
A parallel search strategy based on sparse representation (PS-L1 tracker) is proposed in the particle filter framework. To obtain the weights of state particles, target templates are represented linearly with the dictionary of target candidates. Sparse constraints on the coefficient guarantee that only [...] Read more.
A parallel search strategy based on sparse representation (PS-L1 tracker) is proposed in the particle filter framework. To obtain the weights of state particles, target templates are represented linearly with the dictionary of target candidates. Sparse constraints on the coefficient guarantee that only true target candidates can be selected, and the nonnegative entries denote the associate weights of efficient target states. Then the optimal target state can be estimated by the linear combination of above weighted states. In this way, efficient target states are selected simultaneously from all the particles, which we call a parallel search strategy. Experimental results demonstrate excellent performance of the proposed method on challenging infrared images. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data)
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Open AccessArticle
Multi-Objective Design Optimization of an Over-Constrained Flexure-Based Amplifier
Algorithms 2015, 8(3), 424-434; https://doi.org/10.3390/a8030424 - 08 Jul 2015
Cited by 2
Abstract
The optimizing design for enhancement of the micro performance of manipulator based on analytical models is investigated in this paper. By utilizing the established uncanonical linear homogeneous equations, the quasi-static analytical model of the micro-manipulator is built, and the theoretical calculation results are [...] Read more.
The optimizing design for enhancement of the micro performance of manipulator based on analytical models is investigated in this paper. By utilizing the established uncanonical linear homogeneous equations, the quasi-static analytical model of the micro-manipulator is built, and the theoretical calculation results are tested by FEA simulations. To provide a theoretical basis for a micro-manipulator being used in high-precision engineering applications, this paper investigates the modal property based on the analytical model. Based on the finite element method, with multipoint constraint equations, the model is built and the results have a good match with the simulation. The following parametric influences studied show that the influences of other objectives on one objective are complicated. Consequently, the multi-objective optimization by the derived analytical models is carried out to find out the optimal solutions of the manipulator. Besides the inner relationships among these design objectives during the optimization process are discussed. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data)
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