Special Issue "Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition"

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

Deadline for manuscript submissions: closed (30 September 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Stefano Cagnoni

Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Parma, Parco Area delle Scienze 181/a, I-43100 Parma, Italy
Website | E-Mail
Interests: computer vision; robotics; evolutionary computation; neural networks
Guest Editor
Dr. Mauro Castelli

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal
Website | E-Mail
Interests: Machine learning; evolutionary computation; deep learning

Special Issue Information

Dear Colleagues,

Computational Intelligence (CI) and Nature-Inspired Computation (NIC) are mature branches of Artificial Intelligence. The main feature common to the techniques they deal with is that, given a problem, they mimic a natural system or process to construct a solution that is optimal for both quality and robustness. The analogies and abstractions developed in these fields have been able to provide valuable insights for successful algorithmic design and improvement, in many cases outperforming traditional search and heuristics. Relevant examples include fuzzy systems, evolutionary algorithms, and neural networks.

CI and NIC have demonstrated to be able to produce human-competitive results, as has happened with neural models that have led to the development of Deep Learning, or with the study of artificial evolution and the development of Genetic Algorithms and Genetic Programming. These techniques have been particularly successful in the fields of Pattern Recognition and Data Analytics.

The aim of this Special Issue is to gather and present recent work where CI and NIC algorithms are specifically designed for, or applied to, solving complex real-world problems in Data Analytics and Pattern Recognition, by means of:

     - state-of-the-art methods having general applicability,
     - domain-specific solutions, or
     - hybrid algorithms that integrate CI and NIC with traditional numerical and mathematical methods.

Potential application domains include:

  •  Biomedical applications
  •  Big Data problems in industry
  •  Intelligent manufacturing and industrial processes optimization
  •  Computer vision and image processing
  •  Automatic modeling and programming
  •  Efficient implementations using parallel and distributed computing

Dr. Stefano Cagnoni
Dr. Mauro Castelli
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 850 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.


 

Published Papers (7 papers)

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Editorial

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Open AccessEditorial Special Issue on Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition
Algorithms 2018, 11(3), 25; https://doi.org/10.3390/a11030025
Received: 22 February 2018 / Revised: 22 February 2018 / Accepted: 24 February 2018 / Published: 28 February 2018
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Abstract
This special issue of Algorithms is devoted to the study of Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition. The special issue considered both theoretical contributions able to advance the state-of-the-art in this field and practical applications that describe
[...] Read more.
This special issue of Algorithms is devoted to the study of Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition. The special issue considered both theoretical contributions able to advance the state-of-the-art in this field and practical applications that describe novel approaches for solving real-world problems. Full article

Research

Jump to: Editorial

Open AccessArticle Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
Algorithms 2017, 10(4), 129; https://doi.org/10.3390/a10040129
Received: 30 September 2017 / Revised: 12 November 2017 / Accepted: 17 November 2017 / Published: 22 November 2017
Cited by 1 | PDF Full-text (5388 KB) | HTML Full-text | XML Full-text
Abstract
We investigate the feasibility of classifying (inferring) the emergency braking situations in road vehicles from the motion pattern of the accelerator pedal. We trained and compared several classifiers and employed genetic algorithms to tune their associated hyperparameters. Using offline time series data of
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We investigate the feasibility of classifying (inferring) the emergency braking situations in road vehicles from the motion pattern of the accelerator pedal. We trained and compared several classifiers and employed genetic algorithms to tune their associated hyperparameters. Using offline time series data of the dynamics of the accelerator pedal as the test set, the experimental results suggest that the evolved classifiers detect the emergency braking situation with at least 93% accuracy. The best performing classifier could be integrated into the agent that perceives the dynamics of the accelerator pedal in real time and—if emergency braking is detected—acts by applying full brakes well before the driver would have been able to apply them. Full article
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Open AccessArticle Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network
Algorithms 2017, 10(4), 117; https://doi.org/10.3390/a10040117
Received: 28 June 2017 / Revised: 23 July 2017 / Accepted: 26 July 2017 / Published: 13 October 2017
Cited by 2 | PDF Full-text (1677 KB) | HTML Full-text | XML Full-text
Abstract
Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification
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Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation. Full article
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Open AccessArticle Variable Selection in Time Series Forecasting Using Random Forests
Algorithms 2017, 10(4), 114; https://doi.org/10.3390/a10040114
Received: 29 July 2017 / Revised: 25 September 2017 / Accepted: 1 October 2017 / Published: 4 October 2017
Cited by 5 | PDF Full-text (5509 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in
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Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy. Full article
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Open AccessArticle Game Theory-Inspired Evolutionary Algorithm for Global Optimization
Algorithms 2017, 10(4), 111; https://doi.org/10.3390/a10040111
Received: 4 August 2017 / Revised: 20 September 2017 / Accepted: 25 September 2017 / Published: 30 September 2017
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Abstract
Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields. This paper introduces a game theory-inspired evolutionary algorithm for global optimization (GameEA). A formulation to estimate payoff
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Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields. This paper introduces a game theory-inspired evolutionary algorithm for global optimization (GameEA). A formulation to estimate payoff expectations is provided, which is a mechanism to make a player become a rational decision-maker. GameEA has one population (i.e., set of players) and generates new offspring only through an imitation operator and a belief-learning operator. An imitation operator adopts learning strategies and actions from other players to improve its competitiveness and applies these strategies to future games where one player updates its chromosome by strategically copying segments of gene sequences from a competitor. Belief learning refers to models in which a player adjusts his/her strategies, behavior or chromosomes by analyzing the current history information to improve solution quality. Experimental results on various classes of problems show that GameEA outperforms the other four algorithms on stability, robustness, and accuracy. Full article
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Open AccessArticle Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
Algorithms 2017, 10(3), 87; https://doi.org/10.3390/a10030087
Received: 1 July 2017 / Revised: 25 July 2017 / Accepted: 25 July 2017 / Published: 27 July 2017
Cited by 1 | PDF Full-text (44006 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a robust technique based on a genetic algorithm is proposed for estimating two-view epipolar-geometry of uncalibrated perspective stereo images from putative correspondences containing a high percentage of outliers. The advantages of this technique are three-fold: (i) replacing random search with
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In this paper, a robust technique based on a genetic algorithm is proposed for estimating two-view epipolar-geometry of uncalibrated perspective stereo images from putative correspondences containing a high percentage of outliers. The advantages of this technique are three-fold: (i) replacing random search with evolutionary search applying new strategies of encoding and guided sampling; (ii) robust and fast estimation of the epipolar geometry via detecting a more-than-enough set of inliers without making any assumptions about the probability distribution of the residuals; (iii) determining the inlier-outlier threshold based on the uncertainty of the estimated model. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC (random sample consensus), MSAC, MLESAC, Cov-RANSAC, LO-RANSAC, StaRSAC, Multi-GS RANSAC and least median of squares (LMedS). Experimental results showed that the proposed approach performed better than other methods regarding the accuracy of inlier detection and epipolar-geometry estimation, as well as the computational efficiency for datasets majorly contaminated by outliers and noise. Full article
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Open AccessArticle An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
Algorithms 2017, 10(2), 70; https://doi.org/10.3390/a10020070
Received: 14 March 2017 / Revised: 5 June 2017 / Accepted: 9 June 2017 / Published: 14 June 2017
Cited by 4 | PDF Full-text (2840 KB) | HTML Full-text | XML Full-text
Abstract
Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications.
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Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm. Full article
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