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Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing
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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: closed (31 December 2020) | Viewed by 53591

Special Issue Editors

Prof. Dr. Hiram Ponce

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Panamericana, Mexico City 03920, Mexico
Interests: machine learning; nature-inspired computing; robotics; ambient assisted living; sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Lourdes Martínez-Villaseñor

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Panamericana, Mexico City 03920, Mexico
Interests: ubiquitous user modeling interoperability; wearable sensors; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel González-Mendoza

E-Mail Website
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

E-Mail 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 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. 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 2400 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.

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Editorial

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4 pages, 206 KiB  
Editorial
Special Issue on Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing
by Hiram Ponce, Lourdes Martínez-Villaseñor, Miguel González-Mendoza and Pablo A. Fonseca
Appl. Sci. 2022, 12(14), 7279; https://0-doi-org.brum.beds.ac.uk/10.3390/app12147279 - 20 Jul 2022
Viewed by 907
Abstract
Inspiration in nature has been widely explored, from the macro to micro-scale [...] Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)

Research

Jump to: Editorial

18 pages, 7879 KiB  
Article
Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
by Rogelio Bautista-Sánchez, Liliana Ibeth Barbosa-Santillan and Juan Jaime Sánchez-Escobar
Appl. Sci. 2021, 11(5), 2429; https://0-doi-org.brum.beds.ac.uk/10.3390/app11052429 - 09 Mar 2021
Cited by 6 | Viewed by 2537
Abstract
The prediction of vessel maritime navigation has become an exciting topic in the last years, especially considering economics, commercial exchange, and security. In addition, vessel monitoring requires better systems and techniques that help enterprises and governments to protect their interests. Specifically, the prediction [...] Read more.
The prediction of vessel maritime navigation has become an exciting topic in the last years, especially considering economics, commercial exchange, and security. In addition, vessel monitoring requires better systems and techniques that help enterprises and governments to protect their interests. Specifically, the prediction of vessel movements is essential for safety and tracking. However, the applications of prediction techniques have a high cost related to computational efficiency and low resource saving. This article presents a sample method to select historical data on vessel-specific routes to optimize the computational performance of the prediction of vessel positions and route estimation in real-time. These historical navigation data can help to estimate a complete path and perform vessel position predictions through time. This Select Best AIS Data in Prediction Vessel Movements and Route Estimation (PreMovEst) method works in a Vessel Traffic Service database to save computational resources when predictions or route estimations are executed. This article discusses AIS data and the artificial neural network. This work aims to present a prediction model that correctly predicts the physical movement in the route. It supports path planning for the Vessel Traffic Service. After testing the method, the results obtained for route estimation have a precision of 76.15%, and those for vessel position predictions through time have an accuracy of 81.043%. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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18 pages, 2150 KiB  
Article
Integration of Ordinal Optimization with Ant Lion Optimization for Solving the Computationally Expensive Simulation Optimization Problems
by Shih-Cheng Horng and Chin-Tan Lee
Appl. Sci. 2021, 11(1), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010136 - 25 Dec 2020
Cited by 9 | Viewed by 1612
Abstract
The optimization of several practical large-scale engineering systems is computationally expensive. The computationally expensive simulation optimization problems (CESOP) are concerned about the limited budget being effectively allocated to meet a stochastic objective function which required running computationally expensive simulation. Although computing devices continue [...] Read more.
The optimization of several practical large-scale engineering systems is computationally expensive. The computationally expensive simulation optimization problems (CESOP) are concerned about the limited budget being effectively allocated to meet a stochastic objective function which required running computationally expensive simulation. Although computing devices continue to increase in power, the complexity of evaluating a solution continues to keep pace. Ordinal optimization (OO) is developed as an efficient framework for solving CESOP. In this work, a heuristic algorithm integrating ordinal optimization with ant lion optimization (OALO) is proposed to solve the CESOP within a short period of time. The OALO algorithm comprises three parts: approximation model, global exploration, and local exploitation. Firstly, the multivariate adaptive regression splines (MARS) is adopted as a fitness estimation of a design. Next, a reformed ant lion optimization (RALO) is proposed to find N exceptional designs from the solution space. Finally, a ranking and selection procedure is used to decide a quasi-optimal design from the N exceptional designs. The OALO algorithm is applied to optimal queuing design in a communication system, which is formulated as a CESOP. The OALO algorithm is compared with three competing approaches. Test results reveal that the OALO algorithm identifies solutions with better solution quality and better computing efficiency than three competing algorithms. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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25 pages, 33591 KiB  
Article
DM: Dehghani Method for Modifying Optimization Algorithms
by Mohammad Dehghani, Zeinab Montazeri, Ali Dehghani, Haidar Samet, Carlos Sotelo, David Sotelo, Ali Ehsanifar, Om Parkash Malik, Josep M. Guerrero, Gaurav Dhiman and Ricardo A. Ramirez-Mendoza
Appl. Sci. 2020, 10(21), 7683; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217683 - 30 Oct 2020
Cited by 27 | Viewed by 2989
Abstract
In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules of individual and group games, the behaviors [...] Read more.
In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules of individual and group games, the behaviors of animals, plants and other living things. Implementation of optimization algorithms on some objective functions has been successful and in others has led to failure. Improving the optimization process and adding modification phases to the optimization algorithms can lead to more acceptable and appropriate solution. In this paper, a new method called Dehghani method (DM) is introduced to improve optimization algorithms. DM effects on the location of the best member of the population using information of population location. In fact, DM shows that all members of a population, even the worst one, can contribute to the development of the population. DM has been mathematically modeled and its effect has been investigated on several optimization algorithms including: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching-learning-based optimization (TLBO), and grey wolf optimizer (GWO). In order to evaluate the ability of the proposed method to improve the performance of optimization algorithms, the mentioned algorithms have been implemented in both version of original and improved by DM on a set of twenty-three standard objective functions. The simulation results show that the modified optimization algorithms with DM provide more acceptable and competitive performance than the original versions in solving optimization problems. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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19 pages, 446 KiB  
Article
GRASP and Iterated Local Search-Based Cellular Processing algorithm for Precedence-Constraint Task List Scheduling on Heterogeneous Systems
by Alejandro Santiago, J. David Terán-Villanueva, Salvador Ibarra Martínez, José Antonio Castán Rocha, Julio Laria Menchaca, Mayra Guadalupe Treviño Berrones and Mirna Ponce-Flores
Appl. Sci. 2020, 10(21), 7500; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217500 - 25 Oct 2020
Cited by 8 | Viewed by 2509
Abstract
High-Performance Computing systems rely on the software’s capability to be highly parallelized in individual computing tasks. However, even with a high parallelization level, poor scheduling can lead to long runtimes; this scheduling is in itself an NP-hard problem. Therefore, it is our interest [...] Read more.
High-Performance Computing systems rely on the software’s capability to be highly parallelized in individual computing tasks. However, even with a high parallelization level, poor scheduling can lead to long runtimes; this scheduling is in itself an NP-hard problem. Therefore, it is our interest to use a heuristic approach, particularly Cellular Processing Algorithms (CPA), which is a novel metaheuristic framework for optimization. This framework has its foundation in exploring the search space by multiple Processing Cells that communicate to exploit the search and in the individual stagnation detection mechanism in the Processing Cells. In this paper, we proposed using a Greedy Randomized Adaptive Search Procedure (GRASP) to look for promising task execution orders; later, a CPA formed with Iterated Local Search (ILS) Processing Cells is used for the optimization. We assess our approach with a high-performance ILS state-of-the-art approach. Experimental results show that the CPA outperforms the previous ILS in real applications and synthetic instances. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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21 pages, 401 KiB  
Article
Scheduling in Heterogeneous Distributed Computing Systems Based on Internal Structure of Parallel Tasks Graphs with Meta-Heuristics
by Apolinar Velarde Martinez
Appl. Sci. 2020, 10(18), 6611; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186611 - 22 Sep 2020
Cited by 6 | Viewed by 2675
Abstract
The problem of scheduling parallel tasks graphs (PTGs) represented by directed acyclic graphs (DAGs) in heterogeneous distributed computing systems (HDCSs) is considered an nondeterministic polynomial time (NP) problem due to the diversity of characteristics and parameters, generally opposed, intended to be optimized. The [...] Read more.
The problem of scheduling parallel tasks graphs (PTGs) represented by directed acyclic graphs (DAGs) in heterogeneous distributed computing systems (HDCSs) is considered an nondeterministic polynomial time (NP) problem due to the diversity of characteristics and parameters, generally opposed, intended to be optimized. The PTGs are scheduled by a scheduler that determines the best location for the sub-tasks that constitute the PTGs and is responsible for allocating the resources of the HDCS to the sub-tasks of the PTGs. To optimize scheduling and allocations, the scheduler extracts characteristics from the internal structure of the PTGs. The prevailing characteristic in existing research is the critical path (CP), which is limited to providing execution paths of PTGs; considering this limitation, we extend the array method proposed in Velarde, which extracts two additional characteristics to the CP: the layering and the density of the graph for scheduling. These characteristics are represented as integer values of the PTGs to be scheduled; the values obtained from the characteristics are stored in arrays representing populations that are evaluated with the heuristic univariate marginal distribution algorithm (UMDA) and in terms of comparison with the genetic algorithm. With the best allocations produced by the algorithms, two performance parameters are evaluated: makespan and waiting time. The results indicate that when more PTGs characteristics are considered, resource allocations are optimized, and scheduling times are reduced. The results obtained with the heuristic algorithms show that UMDA provides shorter scheduling and allocation times compared with the genetic algorithm; UMDA widely distributes the sub-tasks in the clusters, whereas the genetic algorithm compacts the assignments of the PTGs in the clusters with a longer convergence time that translates into longer scheduling and allocation times. Extensive explanations of these conclusions are provided in this work, based on the conducted experiments. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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13 pages, 6031 KiB  
Article
Written Documents Analyzed as Nature-Inspired Processes: Persistence, Anti-Persistence, and Random Walks—We Remember, as Along Came Writing—T. Holopainen
by Omar López-Ortega, Obed Pérez-Cortés, Heydy Castillejos-Fernández, Félix-Agustín Castro-Espinoza and Miguel González-Mendoza
Appl. Sci. 2020, 10(18), 6354; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186354 - 12 Sep 2020
Cited by 2 | Viewed by 1593
Abstract
Written communication is pivotal for societies to develop. However, lexicon and depth of information vary greatly among texts according to their purpose. Scientific texts, diffusion of science reports, general and area-specific news are all written differently. Thus, we explore the characterization of different [...] Read more.
Written communication is pivotal for societies to develop. However, lexicon and depth of information vary greatly among texts according to their purpose. Scientific texts, diffusion of science reports, general and area-specific news are all written differently. Thus, we explore the characterization of different text categories through a nature-inspired feature known as the Hurst parameter. We contend that the Hurst exponent is useful to unveil the rhetorical structure within written documents. We collected and processed texts in five categories: scientific articles, diffusion of science reports, business news, entertainment news, and random texts. Each category contains 350 documents. We found that the median for scientific texts has the highest value of the Hurst parameter (0.575), followed by business news (0.54); the median for randomly-generated texts is 0.48, which lies in the region associated with random walks. The median value for diffusion texts is 0.49, and for entertainment texts is 0.53. However, these two categories present high dispersion. We conclude that the Hurst parameter is a measure that quantifies the structure of communication in the selected categories of texts. Application of our finding in the field of e-research is discussed. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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24 pages, 995 KiB  
Article
A Spring Search Algorithm Applied to Engineering Optimization Problems
by Mohammad Dehghani, Zeinab Montazeri, Gaurav Dhiman, O. P. Malik, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza, Ali Dehghani, Josep M. Guerrero and Lizeth Parra-Arroyo
Appl. Sci. 2020, 10(18), 6173; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186173 - 04 Sep 2020
Cited by 106 | Viewed by 4424
Abstract
At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s [...] Read more.
At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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22 pages, 728 KiB  
Article
A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem
by Mohammad Dehghani, Mohammad Mardaneh, Josep M. Guerrero, Om Parkash Malik, Ricardo A. Ramirez-Mendoza, José Matas, Juan C. Vasquez and Lizeth Parra-Arroyo
Appl. Sci. 2020, 10(17), 5791; https://0-doi-org.brum.beds.ac.uk/10.3390/app10175791 - 21 Aug 2020
Cited by 43 | Viewed by 2678
Abstract
Regular assessments of events taking place around the globe can be a conduit for the development of new ideas, contributing to the research world. In this study, the authors present a new optimization algorithm named doctor and patient optimization (DPO). DPO is designed [...] Read more.
Regular assessments of events taking place around the globe can be a conduit for the development of new ideas, contributing to the research world. In this study, the authors present a new optimization algorithm named doctor and patient optimization (DPO). DPO is designed by simulating the process of treating patients by a physician. The treatment process has three phases, including vaccination, drug administration, and surgery. The efficiency of the proposed algorithm in solving optimization problems compared to eight other optimization algorithms on a benchmark standard test function with 23 objective functions is been evaluated. The results obtained from this comparison indicate the superiority and quality of DPO in solving optimization problems in various sciences. The proposed algorithm is successfully applied to solve the energy commitment problem for a power system supplied by a multiple energy carriers system. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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18 pages, 6731 KiB  
Article
Robust Parking Block Segmentation from a Surveillance Camera Perspective
by Nisim Hurst-Tarrab, Leonardo Chang, Miguel Gonzalez-Mendoza and Neil Hernandez-Gress
Appl. Sci. 2020, 10(15), 5364; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155364 - 03 Aug 2020
Cited by 5 | Viewed by 2969
Abstract
Parking block regions host dangerous behaviors that can be detected from a surveillance camera perspective. However, these regions are often occluded, subject to ground bumpiness or steep slopes, and thus they are hard to segment. Firstly, the paper proposes a pyramidal solution that [...] Read more.
Parking block regions host dangerous behaviors that can be detected from a surveillance camera perspective. However, these regions are often occluded, subject to ground bumpiness or steep slopes, and thus they are hard to segment. Firstly, the paper proposes a pyramidal solution that takes advantage of satellite views of the same scene, based on a deep Convolutional Neural Network (CNN). Training a CNN from the surveillance camera perspective is rather impossible due to the combinatory explosion generated by multiple point-of-views. However, CNNs showed great promise on previous works over satellite images. Secondly, even though there are many datasets for occupancy detection in parking lots, none of them were designed to tackle the parking block segmentation problem directly. Given the lack of a suitable dataset, we also propose APKLOT, a dataset of roughly 7000 polygons for segmenting parking blocks from the satellite perspective and from the camera perspective. Moreover, our method achieves more than 50% intersection over union (IoU) in all the testing sets, that is, at both the satellite view and the camera view. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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27 pages, 3441 KiB  
Article
Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel
by Jorge Arturo Flores-López, Leticia Flores-Pulido, Lidia Patricia Jaramillo-Quintero and Carolina Rocío Sánchez-Pérez
Appl. Sci. 2020, 10(15), 5059; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155059 - 23 Jul 2020
Cited by 1 | Viewed by 2436
Abstract
Electrophoresis allows us to identify the types of proteins present in food, DNA, tissues and more. With the help of the molecular marker their weight is known, these markers are applied within the one-dimensional gel, and their protein value is known by means [...] Read more.
Electrophoresis allows us to identify the types of proteins present in food, DNA, tissues and more. With the help of the molecular marker their weight is known, these markers are applied within the one-dimensional gel, and their protein value is known by means of marks. In this research, the molecular marker is obtained and the wavelet transform (WT) is obtained, generating approximation coefficients, which were taken to determine a molecular weight using three classification paradigms. The first paradigm is an approach in content-based image retrieval (CBIR) which makes a detection of the molecular weight in electrophoresis samples. The second approach is a neural network, thus two models are employed: self-organization maps (SOM) and back propagation in a supervised and unsupervised way, respectively. The third approach is based in a J48 decision tree. A comparison is made between the three paradigms for molecular weight computation. Neural networks obtained an improvement in the precision compared versus the CBIR-WT. Five parametric statistics were generated from the wavelet approximation coefficients. The CBIR-WT, SOM, back propagation and J48 were decisive for the classification and calculation of the molecular weight of each protein stain in the one-dimensional electrophoresis gel. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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25 pages, 10561 KiB  
Article
Novel Design Methodology for DC-DC Converters Applying Metaheuristic Optimization for Inductance Selection
by Efrain Mendez, Israel Macias, Alexandro Ortiz, Pedro Ponce, Adriana Vargas-Martinez, Jorge de Jesús Lozoya-Santos, Ricardo A. Ramirez-Mendoza, Ruben Morales-Menendez and Arturo Molina
Appl. Sci. 2020, 10(12), 4377; https://0-doi-org.brum.beds.ac.uk/10.3390/app10124377 - 25 Jun 2020
Cited by 12 | Viewed by 2964
Abstract
Nowadays in modern industrial applications, where the power supply efficiency is more important than the output noise performance, DC-DC converters are widely used in order to fulfill the requirements. Yet, component selection and precise estimation of parameters can improve the converter’s performance, leading [...] Read more.
Nowadays in modern industrial applications, where the power supply efficiency is more important than the output noise performance, DC-DC converters are widely used in order to fulfill the requirements. Yet, component selection and precise estimation of parameters can improve the converter’s performance, leading to smaller and more efficient designs. Hence, metaheuristic optimization algorithms can be applied using the mathematical model of DC-DC converters, in order to optimize their performance through an optimal inductance selection. Therefore, this work presents a novel design methodology for DC-DC converters, where the inductance selection is optimized, in order to achieve an optimal relation between the inductance size and the required energy. Moreover, a multi-objective metaheuristic optimization is presented through the Earthquake Algorithm, for parameter estimation and component selection, using the inductance of a buck DC-DC converter as a case study. The experimental results validate the design methodology, showing ripple improvement and operating power range extension, which are key features to have an efficient performance in DC-DC converters. Results also confirm the Small-Signal Model of the circuit, as a correct objective function for the parameter optimization, achieving more than 90% of accuracy on the presented behavior. Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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16 pages, 3425 KiB  
Article
An Asphalt Damage Dataset and Detection System Based on RetinaNet for Road Conditions Assessment
by Gilberto Ochoa-Ruiz, Andrés Alonso Angulo-Murillo, Alberto Ochoa-Zezzatti, Lina María Aguilar-Lobo, Juan Antonio Vega-Fernández and Shailendra Natraj
Appl. Sci. 2020, 10(11), 3974; https://0-doi-org.brum.beds.ac.uk/10.3390/app10113974 - 08 Jun 2020
Cited by 24 | Viewed by 5741
Abstract
The analysis and follow up of asphalt infrastructure using image processing techniques has received increased attention recently. However, the vast majority of developments have focused only on determining the presence or absence of road damages, forgoing other more pressing concerns. Nonetheless, in order [...] Read more.
The analysis and follow up of asphalt infrastructure using image processing techniques has received increased attention recently. However, the vast majority of developments have focused only on determining the presence or absence of road damages, forgoing other more pressing concerns. Nonetheless, in order to be useful to road managers and governmental agencies, the information gathered during an inspection procedure must provide actionable insights that go beyond punctual and isolated measurements: the characteristics, type, and extent of the road damages must be effectively and automatically extracted and digitally stored, preferably using inexpensive mobile equipment. In recent years, computer vision acquisition systems have emerged as a promising solution for road damage automated inspection systems when integrated into georeferenced mobile computing devices such as smartphones. However, the artificial intelligence algorithms that power these computer vision acquisition systems have been rather limited owing to the scarcity of large and homogenized road damage datasets. In this work, we aim to contribute in bridging this gap using two strategies. First, we introduce a new and very large asphalt dataset, which incorporates a set of damages not present in previous studies, making it more robust and representative of certain damages such as potholes. This dataset is composed of 18,345 road damage images captured by a mobile phone mounted on a car, with 45,435 instances of road surface damages (linear, lateral, and alligator cracks; potholes; and various types of painting blurs). In order to generate this dataset, we obtained images from several public datasets and augmented it with crowdsourced images, which where manually annotated for further processing. The images were captured under a variety of weather and illumination conditions and a quality-aware data augmentation strategy was employed to filter out samples of poor quality, which helped in improving the performance metrics over the baseline. Second, we trained different object detection models amenable for mobile implementation with an acceptable performance for many applications. We performed an ablation study to assess the effectiveness of the quality-aware data augmentation strategy and compared our results with other recent works, achieving better accuracies (mAP) for all classes and lower inference times (3× faster). Full article
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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16 pages, 2418 KiB  
Article
Empirical Modeling of Liquefied Nitrogen Cooling Impact during Machining Inconel 718
by Matija Hribersek, Lucijano Berus, Franci Pusavec and Simon Klancnik
Appl. Sci. 2020, 10(10), 3603; https://0-doi-org.brum.beds.ac.uk/10.3390/app10103603 - 22 May 2020
Cited by 4 | Viewed by 1744
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
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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21 pages, 5906 KiB  
Article
Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle
by Jesús Jaime Moreno Escobar, Oswaldo Morales Matamoros, Ricardo Tejeida Padilla, Ixchel Lina Reyes, Liliana Chanona Hernández and Ana Gabriela Ramírez Gutiérrez
Appl. Sci. 2020, 10(10), 3532; https://0-doi-org.brum.beds.ac.uk/10.3390/app10103532 - 20 May 2020
Cited by 5 | Viewed by 3139
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
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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10 pages, 3484 KiB  
Article
Neuronless Knowledge Processing in Forests
by Aviv Segev, Dorothy Curtis, Christine Balili and Sukhwan Jung
Appl. Sci. 2020, 10(7), 2509; https://0-doi-org.brum.beds.ac.uk/10.3390/app10072509 - 05 Apr 2020
Cited by 2 | Viewed by 2639
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
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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27 pages, 8590 KiB  
Article
Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
by Mahdi Naghshvarianjahromi, Shiva Kumar and M. Jamal Deen
Appl. Sci. 2020, 10(3), 1150; https://0-doi-org.brum.beds.ac.uk/10.3390/app10031150 - 08 Feb 2020
Cited by 7 | Viewed by 2051
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
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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17 pages, 764 KiB  
Article
Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
by Luwei Xiao, Xiaohui Hu, Yinong Chen, Yun Xue, Donghong Gu, Bingliang Chen and Tao Zhang
Appl. Sci. 2020, 10(3), 957; https://0-doi-org.brum.beds.ac.uk/10.3390/app10030957 - 02 Feb 2020
Cited by 32 | Viewed by 3535
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
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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30 pages, 2669 KiB  
Article
Fuzzy Rules to Help Predict Rains and Temperatures in a Brazilian Capital State Based on Data Collected from Satellites
by Paulo Vitor de Campos Souza, Lucas Batista de Oliveira and Luiz Antônio Ferreira do Nascimento, Jr.
Appl. Sci. 2019, 9(24), 5476; https://0-doi-org.brum.beds.ac.uk/10.3390/app9245476 - 13 Dec 2019
Cited by 3 | Viewed by 2566
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
(This article belongs to the Special Issue Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing)
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