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: closed (30 November 2021).

Special Issue Editors

Prof. Dr. Akemi Galvez Tomida
E-Mail Website
Guest Editor
1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, C.P. 39005 Santander, Spain
2. Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, Funabashi 274-8510, Japan
Interests: Artificial Intelligence; soft computing for optimization; evolutionary computation; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andres Iglesias Prieto
E-Mail Website
Guest Editor
1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, C.P. 39005 Santander, Spain
2. Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, 274-8510 Funabashi, Japan
Interests: swarm intelligence and swarm robotics; bio-inspired optimisation; computer graphics; geometric modelling
Special Issues, Collections and Topics 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

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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 2000 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 (14 papers)

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Research

Article
A Proposal for Clothing Size Recommendation System Using Chinese Online Shopping Malls: The New Era of Data
Appl. Sci. 2021, 11(23), 11215; https://doi.org/10.3390/app112311215 - 25 Nov 2021
Viewed by 172
Abstract
Research was conducted in this study to design data-based size recommendation and size coding systems specifically for online shopping malls, expecting to lighten the burden of holding excessive inventories often caused by the high return rate in these online malls. The recommendation system [...] Read more.
Research was conducted in this study to design data-based size recommendation and size coding systems specifically for online shopping malls, expecting to lighten the burden of holding excessive inventories often caused by the high return rate in these online malls. The recommendation system has been implemented focusing mainly on size extraction and recommendation functions along with a UI (user interface). For the former function, data are necessary to extract customers’ sizes and, for instance, the system to be used in China adopts their Chinese standard body size GB/T (Chinese national standard) considering that there are a variety of body types in their substantial population. The system shows the most similar size dataset among the body size GB/T dataset to the customer once he/she inputs his/her height and weight. Each GB/T data was entered after categorizing it according to the proportion between height and weight. For the latter function, size recommendation, size coding was performed first for all the clothes by the shop owner by entering individual size data. The clothes providing the most suitable fit for the customer are recommended by the selection of that which has the smallest deviation between coded clothes size and the customer body data after performing a series of comparative calculations. To validate the effectiveness of the extraction, a method that checks whether the difference between extracted size and the body size that has been measured remains within the error range of 4cm was used. The result showed there to be an approximate 88% matching rate for women and a slightly lower accuracy of 80% for men. Moreover, the error rate was relatively smaller for the upper half clothing such as shirts, jackets, and blouses or one-piece dresses. Such a result may have been generated since the GB/T data were actually the average data entered 10 years prior without categorizing nationalities, ages, and body types in detail. This research emphasized the necessity of a database containing a more segmented human body size data, which can be effective for extracting and recommending sizes more accurately as the latest ones continue to accumulate. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Article
An Optimization Technique for Linear Manifold Learning-Based Dimensionality Reduction: Evaluations on Hyperspectral Images
Appl. Sci. 2021, 11(19), 9063; https://doi.org/10.3390/app11199063 - 28 Sep 2021
Viewed by 574
Abstract
Manifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical especially for classification purposes. Locality preserving projection (LPP) [...] Read more.
Manifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical especially for classification purposes. Locality preserving projection (LPP) and orthogonal locality preserving projection (OLPP) are two known linear manifold learning algorithms. In this study, scatter information of a distance matrix is used to construct a weight matrix with a supervised approach for the LPP and OLPP algorithms to improve classification accuracy rates. Low-dimensional data are classified with SVM and the results of the proposed method are compared with some other important existing linear manifold learning methods. Class-based enhancements and coefficients proposed for the formulization are reported visually. Furthermore, the change on weight matrices, band information, and correlation matrices with p-values are extracted and visualized to understand the effect of the proposed method. Experiments are conducted on hyperspectral imaging (HSI) with two different datasets. According to the experimental results, application of the proposed method with the LPP or OLPP algorithms outperformed traditional LPP, OLPP, neighborhood preserving embedding (NPE) and orthogonal neighborhood preserving embedding (ONPE) algorithms. Furthermore, the analytical findings on visualizations show consistency with obtained classification accuracy enhancements. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Article
Business Intelligence in Airline Passenger Satisfaction Study—A Fuzzy-Genetic Approach with Optimized Interpretability-Accuracy Trade-Off
Appl. Sci. 2021, 11(11), 5098; https://doi.org/10.3390/app11115098 - 31 May 2021
Cited by 1 | Viewed by 652
Abstract
The main objective and contribution of this paper is the application of our knowledge-discovery business-intelligence technique (fuzzy rule-based classification systems) characterized by genetically optimized interpretability-accuracy trade-off (using multi-objective evolutionary optimization algorithms) to decision support related to airline passenger satisfaction problems. Recently published and [...] Read more.
The main objective and contribution of this paper is the application of our knowledge-discovery business-intelligence technique (fuzzy rule-based classification systems) characterized by genetically optimized interpretability-accuracy trade-off (using multi-objective evolutionary optimization algorithms) to decision support related to airline passenger satisfaction problems. Recently published and accessible at Kaggle’s repository airline passengers satisfaction data set containing 259,760 records is used in our experiments. A comparison of our approach with an alternative method (using SAS-system’s accuracy-oriented prediction tools to determine the attribute importance hierarchy) is also performed showing the advantages of our method in terms of: (i) discovering the actual hierarchy of attribute significance for passenger satisfaction and (ii) knowledge-discovery system’s interpretability-accuracy trade-off optimization. The main results and findings of our work include: (i) an introduction of the modern fuzzy-genetic business-intelligence solution characterized both by high interpretability and high accuracy to the airline passenger satisfaction decision support, (ii) an analysis of the effect of possible "overlapping" of some input attributes over the other ones in order to discover the real hierarchy of influence of particular input attributes upon the airline passengers satisfaction, and (iii) an extended cross-validation experiment confirming high effectiveness of our approach for different learning-test splits of the data set considered. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Article
Identification of Synonyms Using Definition Similarities in Japanese Medical Device Adverse Event Terminology
Appl. Sci. 2021, 11(8), 3659; https://doi.org/10.3390/app11083659 - 19 Apr 2021
Viewed by 421
Abstract
Japanese medical device adverse events terminology, published by the Japan Federation of Medical Devices Associations (JFMDA terminology), contains entries for 89 terminology items, with each of the terminology entries created independently. It is necessary to establish and verify the consistency of these terminology [...] Read more.
Japanese medical device adverse events terminology, published by the Japan Federation of Medical Devices Associations (JFMDA terminology), contains entries for 89 terminology items, with each of the terminology entries created independently. It is necessary to establish and verify the consistency of these terminology entries and map them efficiently and accurately. Therefore, developing an automatic synonym detection tool is an important concern. Such tools for edit distances and distributed representations have achieved good performance in previous studies. The purpose of this study was to identify synonyms in JFMDA terminology and evaluate the accuracy using these algorithms. A total of 125 definition sentence pairs were created from the terminology as baselines. Edit distances (Levenshtein and Jaro–Winkler distance) and distributed representations (Word2vec, fastText, and Doc2vec) were employed for calculating similarities. Receiver operating characteristic analysis was carried out to evaluate the accuracy of synonym detection. A comparison of the accuracies of the algorithms showed that the Jaro–Winkler distance had the highest sensitivity, Doc2vec with DM had the highest specificity, and the Levenshtein distance had the highest value in area under the curve. Edit distances and Doc2vec makes it possible to obtain high accuracy in predicting synonyms in JFMDA terminology. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Article
A General Framework Based on Machine Learning for Algorithm Selection in Constraint Satisfaction Problems
Appl. Sci. 2021, 11(6), 2749; https://doi.org/10.3390/app11062749 - 18 Mar 2021
Viewed by 566
Abstract
Many of the works conducted on algorithm selection strategies—methods that choose a suitable solving method for a particular problem—start from scratch since only a few investigations on reusable components of such methods are found in the literature. Additionally, researchers might unintentionally omit some [...] Read more.
Many of the works conducted on algorithm selection strategies—methods that choose a suitable solving method for a particular problem—start from scratch since only a few investigations on reusable components of such methods are found in the literature. Additionally, researchers might unintentionally omit some implementation details when documenting the algorithm selection strategy. This makes it difficult for others to reproduce the behavior obtained by such an approach. To address these problems, we propose to rely on existing techniques from the Machine Learning realm to speed-up the generation of algorithm selection strategies while improving the modularity and reproducibility of the research. The proposed solution model is implemented on a domain-independent Machine Learning module that executes the core mechanism of the algorithm selection task. The algorithm selection strategies produced in this work are implemented and tested rapidly compared against the time it would take to build a similar approach from scratch. We produce four novel algorithm selectors based on Machine Learning for constraint satisfaction problems to verify our approach. Our data suggest that these algorithms outperform the best performing algorithm on a set of test instances. For example, the algorithm selectors Multiclass Neural Network (MNN) and Multiclass Logistic Regression (MLR), powered by a neural network and linear regression, respectively, reduced the search cost (in terms of consistency checks) of the best performing heuristic (KAPPA), on average, by 49% for the instances considered for this work. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Article
Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering
Appl. Sci. 2021, 11(6), 2510; https://doi.org/10.3390/app11062510 - 11 Mar 2021
Viewed by 429
Abstract
Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies. However, there has been little attention in previous research on location-based recommender systems for [...] Read more.
Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies. However, there has been little attention in previous research on location-based recommender systems for generating recommendations considering the locations of target users. Such recommender systems sometimes recommend places that are far from the target user’s current location. In this paper, we explore the issues of generating location recommendations for users who are traveling overseas by taking into account the user’s social influence and also the native or local expert’s knowledge. Accordingly, we have proposed a collaborative filtering recommendation framework called the Friend-And-Native-Aware Approach for Collaborative Filtering (FANA-CF), to generate reasonable location recommendations for users. We have validated our approach by systematic and extensive experiments using real-world datasets collected from Foursquare TM. By comparing algorithms such as the collaborative filtering approach (item-based collaborative filtering and user-based collaborative filtering) and the personalized mean approach, we have shown that our proposed approach has slightly outperformed the conventional collaborative filtering approach and personalized mean approach. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Article
A Study of Multilayer Perceptron Networks Applied to Classification of Ceramic Insulators Using Ultrasound
Appl. Sci. 2021, 11(4), 1592; https://doi.org/10.3390/app11041592 - 10 Feb 2021
Cited by 5 | Viewed by 678
Abstract
Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they [...] Read more.
Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator’s experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks’ application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Article
Split-Based Algorithm for Weighted Context-Free Grammar Induction
Appl. Sci. 2021, 11(3), 1030; https://doi.org/10.3390/app11031030 - 24 Jan 2021
Viewed by 493
Abstract
The split-based method in a weighted context-free grammar (WCFG) induction was formalised and verified on a comprehensive set of context-free languages. WCFG is learned using a novel grammatical inference method. The proposed method learns WCFG from both positive and negative samples, whereas the [...] Read more.
The split-based method in a weighted context-free grammar (WCFG) induction was formalised and verified on a comprehensive set of context-free languages. WCFG is learned using a novel grammatical inference method. The proposed method learns WCFG from both positive and negative samples, whereas the weights of rules are estimated using a novel Inside–Outside Contrastive Estimation algorithm. The results showed that our approach outperforms in terms of F1 scores of other state-of-the-art methods. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence (AI))
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Article
Interferometric Wavefront Sensing System Based on Deep Learning
Appl. Sci. 2020, 10(23), 8460; https://doi.org/10.3390/app10238460 - 27 Nov 2020
Viewed by 443
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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Article
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
Cited by 1 | Viewed by 584
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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Article
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
Cited by 1 | Viewed by 725
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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Article
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
Cited by 4 | Viewed by 913
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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Article
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
Cited by 2 | Viewed by 764
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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Article
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
Cited by 1 | Viewed by 867
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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