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

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

Deadline for manuscript submissions: 15 September 2019.

Special Issue Editors

Guest Editor
Prof. Dr. Stefano Cagnoni

Dipartimento di Ingegneria e Architettura
Università degli Studi di Parma
Parco Area delle Scienze 181/a
I-43100 Parma, Italy
Website | E-Mail
Interests: computer vision; evolutionary computation; pattern recognition; 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; pattern recognition; neuro-evolution
Guest Editor
Prof. Dr. Monica Mordonini

Dipartimento di Ingegneria e Archiettura
Università degli Studi di Parma
Parco Area delle Scienze 181/a
I-43100 Parma, Italy
Website | E-Mail
Interests: artificial intelligence; sentiment analysis; soft computing

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 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.

Published Papers (1 paper)

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Research

Open AccessArticle
The Prediction of Intrinsically Disordered Proteins Based on Feature Selection
Algorithms 2019, 12(2), 46; https://doi.org/10.3390/a12020046
Received: 28 December 2018 / Revised: 14 February 2019 / Accepted: 18 February 2019 / Published: 20 February 2019
Cited by 1 | PDF Full-text (908 KB) | HTML Full-text | XML Full-text
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
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