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Algorithms
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Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and...
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Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition II

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 February 2020) | Viewed by 8825

Special Issue Editors

Dr. Stefano Cagnoni

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Guest Editor
Dipartimento di Ingegneria e Architettura, Università degli Studi di Parma, Parco Area delle Scienze 181/a, I-43100 Parma, Italy
Interests: computer vision; evolutionary computation; pattern recognition; neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Mauro Castelli

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Guest Editor
NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal
Interests: machine learning; evolutionary computation; deep learning; pattern recognition; neuro-evolution
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Monica Mordonini

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Guest Editor
Department of Computer Engineering, University of Parma
Interests: social media analysis; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) and nature-inspired computation (NIC) are mature branches of artificial intelligence. The main feature common to artificial intelligence techniques is that they mimic a natural system or process to construct solutions that are 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 are 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 the following:

  • State-of-the-art methods having general applicability
  • Domain-specific solutions
  • Hybrid algorithms that integrate CI and NIC with traditional numerical and mathematical methods

Potential application domains include the following:

* 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

* Opinion mining 

Prof. Dr. Stefano Cagnoni
Prof. Dr. Mauro Castelli
Prof. Dr. Monica Mordonini
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 submissions that pass pre-check are 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 1600 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 (2 papers)

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31 pages, 3475 KiB  
Article
An Effective and Efficient Genetic-Fuzzy Algorithm for Supporting Advanced Human-Machine Interfaces in Big Data Settings
by Alfredo Cuzzocrea, Enzo Mumolo and Giorgio Mario Grasso
Algorithms 2020, 13(1), 13; https://doi.org/10.3390/a13010013 - 31 Dec 2019
Cited by 3 | Viewed by 4617
Abstract
In this paper we describe a novel algorithm, inspired by the mirror neuron discovery, to support automatic learning oriented to advanced man-machine interfaces. The algorithm introduces several points of innovation, based on complex metrics of similarity that involve different characteristics of the entire [...] Read more.
In this paper we describe a novel algorithm, inspired by the mirror neuron discovery, to support automatic learning oriented to advanced man-machine interfaces. The algorithm introduces several points of innovation, based on complex metrics of similarity that involve different characteristics of the entire learning process. In more detail, the proposed approach deals with an humanoid robot algorithm suited for automatic vocalization acquisition from a human tutor. The learned vocalization can be used to multi-modal reproduction of speech, as the articulatory and acoustic parameters that compose the vocalization database can be used to synthesize unrestricted speech utterances and reproduce the articulatory and facial movements of the humanoid talking face automatically synchronized. The algorithm uses fuzzy articulatory rules, which describe transitions between phonemes derived from the International Phonetic Alphabet (IPA), to allow simpler adaptation to different languages, and genetic optimization of the membership degrees. Large experimental evaluation and analysis of the proposed algorithm on synthetic and real data sets confirms the benefits of our proposal. Indeed, experimental results show that the vocalization acquired respects the basic phonetic rules of Italian languages and that subjective results show the effectiveness of multi-modal speech production with automatic synchronization between facial movements and speech emissions. The algorithm has been applied to a virtual speaking face but it may also be used in mechanical vocalization systems as well. Full article
(This article belongs to the Special Issue Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition II)
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10 pages, 908 KiB  
Article
The Prediction of Intrinsically Disordered Proteins Based on Feature Selection
by Hao He, Jiaxiang Zhao and Guiling Sun
Algorithms 2019, 12(2), 46; https://doi.org/10.3390/a12020046 - 20 Feb 2019
Cited by 7 | Viewed by 3690
Abstract
Intrinsically disordered proteins perform a variety of important biological functions, which makes their accurate prediction useful for a wide range of applications. We develop a scheme for predicting intrinsically disordered proteins by employing 35 features including eight structural properties, seven physicochemical properties and [...] Read more.
Intrinsically disordered proteins perform a variety of important biological functions, which makes their accurate prediction useful for a wide range of applications. We develop a scheme for predicting intrinsically disordered proteins by employing 35 features including eight structural properties, seven physicochemical properties and 20 pieces of evolutionary information. In particular, the scheme includes a preprocessing procedure which greatly reduces the input features. Using two different windows, the preprocessed data containing not only the properties of the surroundings of the target residue but also the properties related to the specific target residue are fed into a multi-layer perceptron neural network as its inputs. The Adam algorithm for the back propagation together with the dropout algorithm to avoid overfitting are introduced during the training process. The training as well as testing our procedure is performed on the dataset DIS803 from a DisProt database. The simulation results show that the performance of our scheme is competitive in comparison with ESpritz and IsUnstruct. Full article
(This article belongs to the Special Issue Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition II)
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