Special Issue "Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 26 June 2020.

Special Issue Editors

Dr. Hiram Ponce
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Guest Editor
Facultad de Ingeniería, Universidad Panamericana, 03920 Mexico City, Mexico
Interests: machine learning; nature-inspired computing; robotics; ambient assisted living; sensors.
Special Issues and Collections in MDPI journals
Dr. Lourdes Martínez-Villaseñor
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Guest Editor
Facultad de Ingeniería, Universidad Panamericana, 03920 Mexico City, Mexico
Interests: ubiquitous user modeling interoperability; wearable sensors; machine learning
Special Issues and Collections in MDPI journals
Dr. Miguel González-Mendoza
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Guest Editor
Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, 52926 State of Mexico, Mexico
Interests: machine learning and soft computing; web ontologies and designing of mobile applications; business intelligence framework; ambient intelligence framework
Dr. Pablo Fonseca
Website
Guest Editor
University of Montreal / MILA, Quebec H2S 3H1, Canada
Interests: deep learning; machine learning; probabilistic graphical models

Special Issue Information

Dear Colleagues,

Inspiration in nature has been widely explored, from macro to micro-scale. From a scientific perspective, these methods inspired by nature have proven to be efficient tools for tackling real-world problems, because most of the latter are highly complex or the resources are limited to analyze them. This inspiration is justified by the fact that natural phenomena mainly consider adaptability, optimization, robustness, and organization, among other properties, to deal with complexity. In that sense, three methodologies are commonly considered: human-designed problem-solving techniques inspired by nature, the synthesis of natural phenomena to develop algorithms, and the use of nature-inspired materials to perform computations. Applications of nature-inspired computing include data mining, machine learning, optimization, robotics, engineering control systems, human–machine interaction, healthcare, Internet-of-Things, cloud computing, smart cities, and many others.

In this regard, this Special Issue aims to cover original research works with emphasis on the methodologies and applications of nature-inspired computing to handle the above-mentioned complex systems. This Special Issue invites submissions on topics related to—but not limited to—the following:

  • Biologically inspired methods (e.g., evolutionary algorithms, artificial immune systems, swarm intelligence, artificial neural networks).
  • Chemically inspired methods (e.g., molecular computing, artificial organic networks, DNA computing, chemical reaction optimization).
  • Physically inspired methods (e.g., simulated annealing, quantum computing).
  • Fuzzy systems and hybrid methods for learning, reasoning, and optimization.
  • Machine learning and data mining based on nature-inspired computing.
  • Applications of nature-inspired computing in robotics, bio-robotics, and control systems.
  • Applications of nature-inspired computing in healthcare.
  • Applications of nature-inspired computing in big data and data science.
  • Applications of nature-inspired computing for Internet-of-Things.
  • Applications of nature-inspired computing for smart cities, smart grids, and sensors.
  • Applications of nature-inspired computing for engineering in general.

Dr. Hiram Ponce
Dr. Lourdes Martínez-Villaseñor
Dr. Miguel González-Mendoza
Dr. Pablo Fonseca
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1800 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 (6 papers)

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Research

Open AccessArticle
Empirical Modeling of Liquefied Nitrogen Cooling Impact during Machining Inconel 718
Appl. Sci. 2020, 10(10), 3603; https://doi.org/10.3390/app10103603 (registering DOI) - 22 May 2020
Abstract
This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental [...] Read more.
This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error ( t e s t   R M S E ) = 0.2620 , and t e s t   R 2 = 0.8585 , proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts. Full article
Open AccessArticle
Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle
Appl. Sci. 2020, 10(10), 3532; https://doi.org/10.3390/app10103532 (registering DOI) - 20 May 2020
Abstract
This work presents the HSS-Cognitive project, which is a Healthcare Smart System that can be applied in measuring the efficiency of any therapy where neuronal interaction gives a trace whether the therapy is efficient or not, using mathematical tools. The artificial intelligence of [...] Read more.
This work presents the HSS-Cognitive project, which is a Healthcare Smart System that can be applied in measuring the efficiency of any therapy where neuronal interaction gives a trace whether the therapy is efficient or not, using mathematical tools. The artificial intelligence of the project underlies in the understanding of brain signals or Electroencephalogram (EEG) by means of the determination of the Power Spectral Density (PSD) over all the EEG bands in order to estimate how efficient was a therapy. Our project HSS-Cognitive was applied, recording the EEG signals from two patients treated for 8 min in a dolphin tank, measuring their activity in five experiments and for 6 min measuring their activity in a pool without dolphin in four experiments. After applying our TEA (Therapeutic Efficiency Assessment) metric for patient 1, we found that this patient had gone from having relaxation states regardless of the dolphin to attention states when the dolphin was presented. For patient 2, we found that he had maintained attention states regardless of the dolphin, that is, the DAT (Dolphin Assisted Therapy) did not have a significant effect in this patient, perhaps because he had a surgery last year in order to remove a tumor, having impact on the DAT effectiveness. However, patient 2 presented the best efficiency when doing physical therapy led by a therapist in a pool without dolphins around him. According to our findings, we concluded that our Brain-Inspired Healthcare Smart System can be considered a reliable tool for measuring the efficiency of a dolphin-assisted therapy and not only for therapist or medical doctors but also for researchers in neurosciences. Full article
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Open AccessArticle
Neuronless Knowledge Processing in Forests
Appl. Sci. 2020, 10(7), 2509; https://doi.org/10.3390/app10072509 - 05 Apr 2020
Abstract
Neurons are viewed as the basic cells that process and transmit information. Trees and neurons share a similar structure and neurotransmitter-like substances. No evidence for structures such as neurons, synapses, or a brain has been found inside plants. Consequently, the ability of a [...] Read more.
Neurons are viewed as the basic cells that process and transmit information. Trees and neurons share a similar structure and neurotransmitter-like substances. No evidence for structures such as neurons, synapses, or a brain has been found inside plants. Consequently, the ability of a network of trees to process information in a method similar to that of a neural network and to make decisions regarding the usage of resources is unperceived. We show that the network between trees is used for knowledge processing to implement decisions that prioritize the forest over a single tree regarding forest use and optimization of resources, similar to the processes of a biological neural network. We found that when there is resection of a network of trees in a forest, namely a trail, each network part will try optimizing its overall access to light resources, represented by canopy tree coverage, independently. This was analyzed in 323 forests in different locations across the US where forest resection is performed by trails. Our results demonstrate that neuron-like relations can occur in a forest knowledge processing system. We anticipate that other systems exist in nature where the basic knowledge processing for resource usage is performed by components other than neurons. Full article
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Open AccessArticle
Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
Appl. Sci. 2020, 10(3), 1150; https://doi.org/10.3390/app10031150 - 08 Feb 2020
Abstract
The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in [...] Read more.
The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of ~7 dB and an enhancement in data rate efficiency ~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications. Full article
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Open AccessArticle
Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
Appl. Sci. 2020, 10(3), 957; https://doi.org/10.3390/app10030957 - 02 Feb 2020
Abstract
Targeted sentiment classification aims to predict the emotional trend of a specific goal. Currently, most methods (e.g., recurrent neural networks and convolutional neural networks combined with an attention mechanism) are not able to fully capture the semantic information of the context and they [...] Read more.
Targeted sentiment classification aims to predict the emotional trend of a specific goal. Currently, most methods (e.g., recurrent neural networks and convolutional neural networks combined with an attention mechanism) are not able to fully capture the semantic information of the context and they also lack a mechanism to explain the relevant syntactical constraints and long-range word dependencies. Therefore, syntactically irrelevant context words may mistakenly be recognized as clues to predict the target sentiment. To tackle these problems, this paper considers that the semantic information, syntactic information, and their interaction information are very crucial to targeted sentiment analysis, and propose an attentional-encoding-based graph convolutional network (AEGCN) model. Our proposed model is mainly composed of multi-head attention and an improved graph convolutional network built over the dependency tree of a sentence. Pre-trained BERT is applied to this task, and new state-of-art performance is achieved. Experiments on five datasets show the effectiveness of the model proposed in this paper compared with a series of the latest models. Full article
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Open AccessArticle
Fuzzy Rules to Help Predict Rains and Temperatures in a Brazilian Capital State Based on Data Collected from Satellites
Appl. Sci. 2019, 9(24), 5476; https://doi.org/10.3390/app9245476 - 13 Dec 2019
Cited by 1
Abstract
The forecast for rainfall and temperatures in underdevelope countries can help in the definition of public and private investment strategies in preventive and corrective nature. Water is an essential element for the economy and living things. This study had a main objective to [...] Read more.
The forecast for rainfall and temperatures in underdevelope countries can help in the definition of public and private investment strategies in preventive and corrective nature. Water is an essential element for the economy and living things. This study had a main objective to use an intelligent hybrid model capable of extracting fuzzy rules from a historical series of temperatures and rainfall indices of the state of Minas Gerais in Brazil, more specifically in the capital. Because this is state has several rivers fundamental to the Brazilian economy, this study intended to find knowledge in the data of the problem to help public managers and private investors to act dynamically in the prediction of future temperatures and how they can interfere in the decisions related to the population of the state. The results confirm that the intelligent hybrid model can act with efficiency in the generation of predictions about the temperatures and average rainfall indices, being an efficient tool to predict the water situation in the future of this critical state for Brazil. Full article
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