Special Issue "Applied Artificial Intelligence (AI)"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Prof. Dr. Akemi Galvez Tomida
Website
Guest Editor
Department of Applied Mathematics and Computational Sciences, University of Cantabria, Santander, C.P. 39005, Spain
Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, 274-8510, Funabashi, Japan
Interests: Artificial Intelligence; Soft Computing for Optimization; Evolutionary computation; Computational Intelligence
Special Issues and Collections in MDPI journals
Prof. Dr. Andres Iglesias Prieto
Website
Guest Editor
Department of Applied Mathematics and Computational Sciences, University of Cantabria, Santander, C.P. 39005, Spain
Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, 274-8510, Funabashi, Japan
Interests: Swarm Intelligence; Meta-heuristic techniques; Bio-inspired optimization; Swarm Robotics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Modern life is immersed in a highly interconnected technological world. Many of the applications designed for this digital ecosystem make use of sophisticated artificial intelligence techniques to solve all kinds of problems, from optimized searching engines to advanced facial recognition features on the web, from shape recognition algorithms for image processing to pattern recognition methods for social networks and economic studies, and from complex behavioral engines for synthetic characters in computer movies and video games to advanced routines for robotics, unmanned autonomous vehicles, natural language processing, business intelligence, etc. Artificial intelligence is poised to change the world in the coming decades, from the way we do business, to domestic applications at home. It has been anticipated that AI’s contribution to the global economy will exceed that of China and India combined. It is also believed that within the next 10 years, almost any successful industry or company will use some kind of AI to ensure their business runs smoothly and efficiently.

This Special Issue aims to disseminate the most recent research results and developments in artificial intelligence, with a special focus on their practical applications to science, engineering, industry, medicine, robotics, manufacturing, entertainment, optimization, business, and other fields. We kindly invite researchers and practitioners to contribute their high-quality original research or review articles on these topics to this Special Issue.

Prof. Dr. Akemi Galvez Tomida
Prof. Dr. Andres Iglesias Prieto
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.

Keywords

  • Artificial intelligence
  • Evolutionary computation
  • Nature-inspired metaheuristic techniques
  • Genetic algorithms
  • Swarm intelligence
  • Hybrid methods
  • Swarm robotics
  • Cognitive sciences
  • Neural processing
  • AI-based optimization
  • AI-based medical imaging
  • AI-based image processing
  • AI-based shape/pattern recognition

Published Papers (6 papers)

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Research

Open AccessArticle
Interferometric Wavefront Sensing System Based on Deep Learning
Appl. Sci. 2020, 10(23), 8460; https://doi.org/10.3390/app10238460 - 27 Nov 2020
Abstract
At present, most wavefront sensing methods analyze the wavefront aberration from light intensity images taken in dark environments. However, in general conditions, these methods are limited due to the interference of various external light sources. In recent years, deep learning has achieved great [...] Read more.
At present, most wavefront sensing methods analyze the wavefront aberration from light intensity images taken in dark environments. However, in general conditions, these methods are limited due to the interference of various external light sources. In recent years, deep learning has achieved great success in the field of computer vision, and it has been widely used in the research of image classification and data fitting. Here, we apply deep learning algorithms to the interferometric system to detect wavefront under general conditions. This method can accurately extract the wavefront phase distribution and analyze aberrations, and it is verified by experiments that this method not only has higher measurement accuracy and faster calculation speed but also has good performance in the noisy environments. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Open AccessArticle
Framework to Diagnose the Metabolic Syndrome Types without Using a Blood Test Based on Machine Learning
Appl. Sci. 2020, 10(23), 8404; https://doi.org/10.3390/app10238404 - 26 Nov 2020
Abstract
Metabolic Syndrome (MetS) is a set of risk factors that increase the probability of heart disease or even diabetes mellitus. The diagnosis of the pathology implies compliance with at least three of five risk factors. Doctors obtain two of those factors in a [...] Read more.
Metabolic Syndrome (MetS) is a set of risk factors that increase the probability of heart disease or even diabetes mellitus. The diagnosis of the pathology implies compliance with at least three of five risk factors. Doctors obtain two of those factors in a medical consultation: waist circumference and blood pressure. The other three factors are biochemical variables that require a blood test to determine triglyceride, high-density lipoprotein cholesterol, and fasting plasma glucose. Consequently, scientists are developing technology for non-invasive diagnostics, but medical personnel also need the risk factors involved in MetS to start a treatment. This paper describes the segmentation of MetS into ten types based on harmonized Metabolic Syndrome criteria. It proposes a framework to diagnose the types of MetS based on Artificial Neural Networks and Random undersampling Boosted tree using non-biochemical variables such as anthropometric and clinical information. The framework works over imbalanced and balanced datasets using the Synthetic Minority Oversampling Technique and for validation uses random subsampling to get performance evaluation indicators between the classifiers. The results showed an excellent framework for diagnosing the 10 MetS types that have Area under Receiver Operating Characteristic (AROC) curves with a range of 71% to 93% compared with AROC 82.86% from traditional MetS. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Open AccessArticle
Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
Appl. Sci. 2020, 10(21), 7905; https://doi.org/10.3390/app10217905 - 07 Nov 2020
Abstract
The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The [...] Read more.
The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Open AccessArticle
Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
Appl. Sci. 2020, 10(21), 7541; https://doi.org/10.3390/app10217541 - 26 Oct 2020
Abstract
Social media is a popular platform for information sharing. Any piece of information can be spread rapidly across the globe at lightning speed. The biggest challenge for social media platforms like Twitter is how to trust news shared on them when there is [...] Read more.
Social media is a popular platform for information sharing. Any piece of information can be spread rapidly across the globe at lightning speed. The biggest challenge for social media platforms like Twitter is how to trust news shared on them when there is no systematic news verification process, which is the case for traditional media. Detecting false information, for example, detection of rumors is a non-trivial task, given the fast-paced social media environment. In this work, we proposed an ensemble model, which performs majority-voting scheme on a collection of predictions of neural networks using time-series vector representation of Twitter data for fast detection of rumors. Experimental results show that proposed neural network models outperformed classical machine learning models in terms of micro F1 score. When compared to our previous works the improvements are 12.5% and 7.9%, respectively. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Open AccessArticle
Combining Machine Learning and Logical Reasoning to Improve Requirements Traceability Recovery
Appl. Sci. 2020, 10(20), 7253; https://doi.org/10.3390/app10207253 - 16 Oct 2020
Abstract
Maintaining traceability links of software systems is a crucial task for software management and development. Unfortunately, dealing with traceability links are typically taken as afterthought due to time pressure. Some studies attempt to use information retrieval-based methods to automate this task, but they [...] Read more.
Maintaining traceability links of software systems is a crucial task for software management and development. Unfortunately, dealing with traceability links are typically taken as afterthought due to time pressure. Some studies attempt to use information retrieval-based methods to automate this task, but they only concentrate on calculating the textual similarity between various software artifacts and do not take into account the properties of such artifacts. In this paper, we propose a novel traceability link recovery approach, which comprehensively measures the similarity between use cases and source code by exploring their particular properties. To this end, we leverage and combine machine learning and logical reasoning techniques. On the one hand, our method extracts features by considering the semantics of the use cases and source code, and uses a classification algorithm to train the classifier. On the other hand, we utilize the relationships between artifacts and define a series of rules to recover traceability links. In particular, we not only leverage source code’s structural information, but also take into account the interrelationships between use cases. We have conducted a series of experiments on multiple datasets to evaluate our approach against existing approaches, the results of which show that our approach is substantially better than other methods. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Open AccessArticle
Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion
Appl. Sci. 2020, 10(18), 6417; https://doi.org/10.3390/app10186417 - 15 Sep 2020
Abstract
Most road accidents occur due to human fatigue, inattention, or drowsiness. Recently, machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors starting from in-vehicle sensors signals such as vehicle and engine speed, throttle position, and engine load. [...] Read more.
Most road accidents occur due to human fatigue, inattention, or drowsiness. Recently, machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors starting from in-vehicle sensors signals such as vehicle and engine speed, throttle position, and engine load. In this work, we investigated the fusion of different external sensors, such as a gyroscope and a magnetometer, with in-vehicle sensors, to increase machine learning identification of unsafe driver behavior. Starting from those signals, we computed a set of features capable to accurately describe the behavior of the driver. A support vector machine and an artificial neural network were then trained and tested using several features calculated over more than 200 km of travel. The ground truth used to evaluate classification performances was obtained by means of an objective methodology based on the relationship between speed, and lateral and longitudinal acceleration of the vehicle. The classification results showed an average accuracy of about 88% using the SVM classifier and of about 90% using the neural network demonstrating the potential capability of the proposed methodology to identify unsafe driver behaviors. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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