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Entropy in Bioinspired Intelligence

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (30 November 2014) | Viewed by 34152

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Institute for Technological Development and Innovation in Communications, Signals and Communications Department, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, s/n, Pabellón B - Despacho 102, E-35017, Las Palmas de Gran Canaria, Spain
Interests: bayesian inference; discriminative information; prediction systems; biomathemathics; data mining; clustering
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Special Issue Information

Dear Colleagues,

Many specifics of bioinspired intelligence are not well addressed by conventional models currently used in the field of artificial intelligence. The purpose of this special issue is, therefore, to present and discuss novel ideas, work and results related to alternative techniques for bioinspired approaches, which depart from mainstream procedures.

Nowadays, studies based on complex systems are opening new doors in research, particularly in improving the quality and results of diverse applications. Bioinspired intelligence achieves this easily in areas such as biodiversity conservation, biomedicine, security applications, etc.

This special issue focuses on research concerning bioinspired systems in science and engineering. Manuscripts are encouraged that discuss bioinspired computation and its entropy on applications of pattern recognition, artificial intelligence techniques, bioinformatics, clustering, data mining, biomathematics and biostatistics. We welcome submissions addressing novel issues as well as those addressing more specific topics that illustrate the broad impact of bioinspired entropy-based techniques on image coding, processing and analysis, signal processing and analysis, natural sounds and video analysis.

Dr. Carlos M. Travieso-González
Dr. Jesús B. Alonso-Hernández
Guest Editor

Keywords

  • applications of pattern recognition
  • artificial intelligence techniques
  • bioinformatics
  • clustering
  • data mining
  • fuzzy and hybrid techniques
  • image coding, processing and analysis
  • neural networks
  • signal processing and analysis
  • video analysis
  • natural sounds and speech recognition
  • sensor networks
  • biomathematics

Published Papers (5 papers)

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1286 KiB  
Article
A Fuzzy Logic-Based Approach for Estimation of Dwelling Times of Panama Metro Stations
by Aranzazu Berbey Alvarez, Fernando Merchan, Francisco Javier Calvo Poyo and Rony Javier Caballero George
Entropy 2015, 17(5), 2688-2705; https://doi.org/10.3390/e17052688 - 27 Apr 2015
Cited by 17 | Viewed by 5801
Abstract
Passenger flow modeling and station dwelling time estimation are significant elements for railway mass transit planning, but system operators usually have limited information to model the passenger flow. In this paper, an artificial-intelligence technique known as fuzzy logic is applied for the estimation [...] Read more.
Passenger flow modeling and station dwelling time estimation are significant elements for railway mass transit planning, but system operators usually have limited information to model the passenger flow. In this paper, an artificial-intelligence technique known as fuzzy logic is applied for the estimation of the elements of the origin-destination matrix and the dwelling time of stations in a railway transport system. The fuzzy inference engine used in the algorithm is based in the principle of maximum entropy. The approach considers passengers’ preferences to assign a level of congestion in each car of the train in function of the properties of the station platforms. This approach is implemented to estimate the passenger flow and dwelling times of the recently opened Line 1 of the Panama Metro. The dwelling times obtained from the simulation are compared to real measurements to validate the approach. Full article
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
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369 KiB  
Article
Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction
by Ricardo E. Monge and Juan L. Crespo
Entropy 2015, 17(4), 1673-1689; https://doi.org/10.3390/e17041673 - 27 Mar 2015
Cited by 3 | Viewed by 6887
Abstract
This study delves further into the analysis of genomic data by computing a variety of complexity measures. We analyze the effect of window size and evaluate the precision and recall of the prediction of gene zones, aided with a much larger dataset (full [...] Read more.
This study delves further into the analysis of genomic data by computing a variety of complexity measures. We analyze the effect of window size and evaluate the precision and recall of the prediction of gene zones, aided with a much larger dataset (full chromosomes). A technique based on the separation of two cases (gene-containing and non-gene-containing) has been developed as a basic gene predictor for automated DNA analysis. This predictor was tested on various sequences of human DNA obtained from public databases, in a set of three experiments. The first one covers window size and other parameters; the second one corresponds to an analysis of a full human chromosome (198 million nucleic acids); and the last one tests subject variability (with five different individual subjects). All three experiments have high-quality results, in terms of recall and precision, thus indicating the effectiveness of the predictor. Full article
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
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375 KiB  
Article
Application of the Permutation Entropy over the Heart Rate Variability for the Improvement of Electrocardiogram-based Sleep Breathing Pause Detection
by Antonio G. Ravelo-García, Juan L. Navarro-Mesa, Ubay Casanova-Blancas, Sofia Martin-Gonzalez, Pedro Quintana-Morales, Iván Guerra-Moreno, José M. Canino-Rodríguez and Eduardo Hernández-Pérez
Entropy 2015, 17(3), 914-927; https://doi.org/10.3390/e17030914 - 20 Feb 2015
Cited by 45 | Viewed by 7662
Abstract
In this paper the permutation entropy (PE) obtained from heart rate variability (HRV) is analyzed in a statistical model. In this model we also integrate other feature extraction techniques, the cepstrum coefficients derived from the same HRV and a set of band powers [...] Read more.
In this paper the permutation entropy (PE) obtained from heart rate variability (HRV) is analyzed in a statistical model. In this model we also integrate other feature extraction techniques, the cepstrum coefficients derived from the same HRV and a set of band powers obtained from the electrocardiogram derived respiratory (EDR) signal. The aim of the model is detecting obstructive sleep apnea (OSA) events. For this purpose, we apply two statistical classification methods: Logistic Regression (LR) and Quadratic Discriminant Analysis (QDA). For testing the models we use seventy ECG recordings from the Physionet database which are divided into equal-size learning and testing sets. Both sets consist of 35 recordings, each containing a single ECG signal. In our experiments we have found that the features extracted from the EDR signal present a sensitivity of 65.6% and specificity of 87.7% (auc = 85) in the LR classifier, and sensitivity of 59.4% and specificity of 90.3% (auc = 83.9) in the QDA classifier. The HRV-based cepstrum coefficients present a sensitivity of 63.8% and specificity of 89.2% (auc = 86) in the LR classifier, and sensitivity of 67.2% and specificity of 86.8% (auc = 86.9) in the QDA. Subsequent tests show that the contribution of the permutation entropy increases the performance of the classifiers, implying that the complexity of RR interval time series play an important role in the breathing pauses detection. Particularly, when all features are jointly used, the quantification task reaches a sensitivity of 71.9% and specificity of 92.1% (auc = 90.3) for LR. Similarly, for QDA the sensitivity is 75.1% and the specificity is 90.5% (auc = 91.7). Full article
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
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266 KiB  
Article
A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem
by Zhaolu Guo, Xuezhi Yue, Kejun Zhang, Shenwen Wang and Zhijian Wu
Entropy 2014, 16(12), 6263-6285; https://doi.org/10.3390/e16126263 - 28 Nov 2014
Cited by 13 | Viewed by 6046
Abstract
Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the [...] Read more.
Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the drawbacks of the selection operator in the traditional differential evolution (DE), we present a novel discrete differential evolution (TDDE) for solving 0-1 knapsack problem. In TDDE, an enhanced selection operator inspired by the principle of the minimal free energy in thermodynamics is employed, trying to balance the conflict between the selective pressure and the diversity of population to some degree. An experimental study is conducted on twenty 0-1 knapsack test instances. The comparison results show that TDDE can gain competitive performance on the majority of the test instances. Full article
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
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3076 KiB  
Article
Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture
by Harkaitz Eguiraun, Karmele López-de-Ipiña and Iciar Martinez
Entropy 2014, 16(11), 6133-6151; https://doi.org/10.3390/e16116133 - 19 Nov 2014
Cited by 38 | Viewed by 7216
Abstract
The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to [...] Read more.
The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni’s FD algorithms respectively. The methodology was tested on three case groups of European sea bass (Dicentrarchus labrax), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry. Full article
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
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