Publications

@article{Garrido-Labrador2024,

title = {Ensemble methods and semi-supervised learning for information fusion: A review and future research directions},

author = {José Luis Garrido-Labrador and Ana Serrano-Mamolar and Jesús Maudes-Raedo and Juan J. Rodríguez and César García-Osorio},

doi = {10.1016/j.inffus.2024.102310},

issn = {1566-2535},

year  = {2024},

date = {2024-07-00},

urldate = {2024-07-00},

journal = {Information Fusion},

volume = {107},

publisher = {Elsevier BV},

keywords = {p_humanaid, PID2020-119894GB-I00, SSL},

pubstate = {published},

tppubtype = {article}

}

@article{Guillen-Sanz2024b,

title = {A systematic review of wearable biosensor usage in immersive virtual reality experiences},

author = {Henar Guillen-Sanz and David Checa and Ines Miguel-Alonso and Andres Bustillo},

doi = {10.1007/s10055-024-00970-9},

issn = {1434-9957},

year  = {2024},

date = {2024-06-00},

journal = {Virtual Reality},

volume = {28},

number = {2},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractWearable biosensors are increasingly incorporated in immersive Virtual Reality (iVR) applications. A trend that is attributed to the availability of better quality, less costly, and easier-to-use devices. However, consensus is yet to emerge over the most optimal combinations. In this review, the aim is to clarify the best examples of biosensor usage in combination with iVR applications. The high number of papers in the review (560) were classified into the following seven fields of application: psychology, medicine, sports, education, ergonomics, military, and tourism and marketing. The use of each type of wearable biosensor and Head-Mounted Display was analyzed for each field of application. Then, the development of the iVR application is analyzed according to its goals, user interaction levels, and the possibility of adapting the iVR environment to biosensor feedback. Finally, the evaluation of the iVR experience was studied, considering such issues as sample size, the presence of a control group, and post-assessment routines. A working method through which the most common solutions, the best practices, and the most promising trends in biofeedback-based iVR applications were identified for each field of application. Besides, guidelines oriented towards good practice are proposed for the development of future iVR with biofeedback applications. The results of this review suggest that the use of biosensors within iVR environments need to be standardized in some fields of application, especially when considering the adaptation of the iVR experience to real-time biosignals to improve user performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Guillen-Sanz2024c,

title = {A systematic review of wearable biosensor usage in immersive virtual reality experiences},

author = {Henar Guillen-Sanz and David Checa and Ines Miguel-Alonso and Andres Bustillo},

doi = {10.1007/s10055-024-00970-9},

issn = {1434-9957},

year  = {2024},

date = {2024-06-00},

urldate = {2024-06-00},

journal = {Virtual Reality},

volume = {28},

number = {2},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>Wearable biosensors are increasingly incorporated in immersive Virtual Reality (iVR) applications. A trend that is attributed to the availability of better quality, less costly, and easier-to-use devices. However, consensus is yet to emerge over the most optimal combinations. In this review, the aim is to clarify the best examples of biosensor usage in combination with iVR applications. The high number of papers in the review (560) were classified into the following seven fields of application: psychology, medicine, sports, education, ergonomics, military, and tourism and marketing. The use of each type of wearable biosensor and Head-Mounted Display was analyzed for each field of application. Then, the development of the iVR application is analyzed according to its goals, user interaction levels, and the possibility of adapting the iVR environment to biosensor feedback. Finally, the evaluation of the iVR experience was studied, considering such issues as sample size, the presence of a control group, and post-assessment routines. A working method through which the most common solutions, the best practices, and the most promising trends in biofeedback-based iVR applications were identified for each field of application. Besides, guidelines oriented towards good practice are proposed for the development of future iVR with biofeedback applications. The results of this review suggest that the use of biosensors within iVR environments need to be standardized in some fields of application, especially when considering the adaptation of the iVR experience to real-time biosignals to improve user performance.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{rodriguez-garcia2024,

title = {A systematic review of virtual 3D reconstructions of Cultural Heritage in immersive Virtual Reality},

author = {Bruno Rodríguez-García and Henar Guillen-Sanz and David Checa and Andrés Bustillo},

url = {https://link.springer.com/article/10.1007/s11042-024-18700-3},

doi = {https://doi.org/10.1007/s11042-024-18700-3},

issn = {1573-7721},

year  = {2024},

date = {2024-04-02},

urldate = {2024-04-02},

journal = {Multimedia Tools and Applications},

abstract = {Immersive Virtual Reality (iVR) devices are increasingly affordable and accessible to consumers. The widespread adoption of this technology for professional training is now finding its way into various other fields. One field that is gaining significant popularity is Cultural Heritage (CH), where iVR enables the reconstruction and exploration of lost heritage. However, an up-to-date systematic review of iVR within this field will be of great benefit. Hence, the present review of 94 papers published between 2013 and 2022 that follows PRISMA methodology on virtual reconstruction of CH for iVR. The aim is to identify the key factors behind the development of these applications and their standards. To do so, a statistical analysis on the following topics was performed: (1) nationality, publication date, and article type; (2) heritage type and its current state of preservation; (3) the area of final application and the features of the reconstructions; (4) the characteristics of the iVR experience; and (5) the assessment of the iVR applications. Finally, a roadmap of best practices is outlined for the virtual reconstruction of CH using iVR and some of the most promising future research lines are outlined.},

keywords = {3D model, Cultural Heritage, head mounted display, Virtual Reality, Virtual Reconstruction},

pubstate = {published},

tppubtype = {article}

}

@article{Kuncheva2024,

title = {Semi-supervised classification with pairwise constraints: A case study on animal identification from video},

author = {Ludmila I. Kuncheva and José Luis Garrido-Labrador and Ismael Ramos-Pérez and Samuel L. Hennessey and Juan J. Rodríguez},

doi = {10.1016/j.inffus.2023.102188},

issn = {1566-2535},

year  = {2024},

date = {2024-04-00},

urldate = {2024-04-00},

journal = {Information Fusion},

volume = {104},

publisher = {Elsevier BV},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Ramos-Pérez2024,

title = {An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data},

author = {Ismael Ramos-Pérez and José Antonio Barbero-Aparicio and Antonio Canepa-Oneto and Álvar Arnaiz-González and Jesús Maudes-Raedo},

doi = {10.3390/info15040223},

issn = {2078-2489},

year  = {2024},

date = {2024-04-00},

urldate = {2024-04-00},

journal = {Information},

volume = {15},

number = {4},

publisher = {MDPI AG},

abstract = {<jats:p>The most common preprocessing techniques used to deal with datasets having high dimensionality and a low number of instances—or wide data—are feature reduction (FR), feature selection (FS), and resampling. This study explores the use of FR and resampling techniques, expanding the limited comparisons between FR and filter FS methods in the existing literature, especially in the context of wide data. We compare the optimal outcomes from a previous comprehensive study of FS against new experiments conducted using FR methods. Two specific challenges associated with the use of FR are outlined in detail: finding FR methods that are compatible with wide data and the need for a reduction estimator of nonlinear approaches to process out-of-sample data. The experimental study compares 17 techniques, including supervised, unsupervised, linear, and nonlinear approaches, using 7 resampling strategies and 5 classifiers. The results demonstrate which configurations are optimal, according to their performance and computation time. Moreover, the best configuration—namely, k Nearest Neighbor (KNN) + the Maximal Margin Criterion (MMC) feature reducer with no resampling—is shown to outperform state-of-the-art algorithms.</jats:p>},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Maestro-Prieto2024,

title = {Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults},

author = {Jose Alberto Maestro-Prieto and José Miguel Ramírez-Sanz and Andrés Bustillo,and Juan José Rodriguez-Díez},

url = {https://doi.org/10.1007/s10489-024-05373-6},

doi = {10.1007/s10489-024-05373-6},

issn = {1573-7497},

year  = {2024},

date = {2024-03-28},

urldate = {2024-03-28},

journal = {Applied Intelligence},

abstract = {Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and diagnosis solution. Firstly, a dataset is generated containing both normal operating patterns and seven different failure classes of the two aforementioned failure modes that vary in intensity. Several datasets are then generated, maintaining different numbers of labeled instances and unlabeling the others, in order to evaluate the number of labeled instances needed for the desired accuracy level. Subsequently, different types of SSL algorithms and combinations of algorithms are trained and then evaluated with the test data. The results showed that an SSL approach could improve the accuracy of trained classifiers when a small number of labeled instances were used together with many unlabeled instances to train a Co-Training algorithm or combinations of such algorithms. When a few labeled instances (fewer than 10% or 327 instances, in this case) were used together with unlabeled instances, the SSL algorithms outperformed the result obtained with the Supervised Learning (SL) techniques used as a benchmark. When the number of labeled instances was sufficient, the SL algorithm (using only labeled instances) performed better than the SSL algorithms (accuracy levels of 87.04% vs. 86.45%, when labeling 10% of instances). A competitive accuracy of 97.73% was achieved with the SL algorithm processing a subset of 40% of the labeled instances.},

keywords = {Bearing failures, Fault detection and diagnosis, PID2020-119894GB-I00, Powertrain failures, Semi-supervised learning, SSL, Wind turbine},

pubstate = {published},

tppubtype = {article}

}

@article{guillen-sanz2024,

title = {A systematic review of wearable biosensor usage in immersive virtual reality experiences},

author = {Henar Guillen-Sanz and David Checa and Inés Miguel-Alonso and Andrés Bustillo},

url = {https://rdcu.be/dAEEn},

doi = {10.1007/s10055-024-00970-9},

issn = {1434-9957},

year  = {2024},

date = {2024-03-08},

urldate = {2024-03-08},

journal = {Virtual Reality},

volume = {28},

number = {74},

abstract = {Wearable biosensors are increasingly incorporated in immersive Virtual Reality (iVR) applications. A trend that is attributed to the availability of better quality, less costly, and easier-to-use devices. However, consensus is yet to emerge over the most optimal combinations. In this review, the aim is to clarify the best examples of biosensor usage in combination with iVR applications. The high number of papers in the review (560) were classified into the following seven fields of application: psychology, medicine, sports, education, ergonomics, military, and tourism and marketing. The use of each type of wearable biosensor and Head-Mounted Display was analyzed for each field of application. Then, the development of the iVR application is analyzed according to its goals, user interaction levels, and the possibility of adapting the iVR environment to biosensor feedback. Finally, the evaluation of the iVR experience was studied, considering such issues as sample size, the presence of a control group, and post-assessment routines. A working method through which the most common solutions, the best practices, and the most promising trends in biofeedback-based iVR applications were identified for each field of application. Besides, guidelines oriented towards good practice are proposed for the development of future iVR with biofeedback applications. The results of this review suggest that the use of biosensors within iVR environments need to be standardized in some fields of application, especially when considering the adaptation of the iVR experience to real-time biosignals to improve user performance.},

keywords = {Biofeedback, Biosensors, Head-mounted displays, Heart rate, Physiology, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2024e,

title = {Evaluation of the novelty effect in immersive Virtual Reality learning experiences},

author = {Ines Miguel-Alonso and David Checa and Henar Guillen-Sanz and Andres Bustillo},

doi = {10.1007/s10055-023-00926-5},

issn = {1434-9957},

year  = {2024},

date = {2024-03-00},

urldate = {2024-03-00},

journal = {Virtual Reality},

volume = {28},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>In this study, the novelty effect or initial fascination with new technology is addressed in the context of an immersive Virtual Reality (iVR) experience. The novelty effect is a significant factor contributing to low learning outcomes during initial VR learning experiences. The aim of this research is to measure the effectiveness of a tutorial at mitigating the novelty effect of iVR learning environments among first-year undergraduate students. The iVR tutorial forms part of the iVR learning experience that involves the assembly of a personal computer, while learning the functions of the main components. 86 students participated in the study, divided into a Control group (without access to the tutorial) and a Treatment group (completing the tutorial). Both groups showed a clear bimodal distribution in previous knowledge, due to previous experience with learning topics, giving us an opportunity to compare tutorial effects with students of different backgrounds. Pre- and post-test questionnaires were used to evaluate the experience. The analysis included such factors as previous knowledge, usability, satisfaction, and learning outcomes categorized into remembering, understanding, and evaluation. The results demonstrated that the tutorial significantly increased overall satisfaction, reduced the learning time required for iVR mechanics, and improved levels of student understanding, and evaluation knowledge. Furthermore, the tutorial helped to homogenize group behavior, particularly benefiting students with less previous experience in the learning topic. However, it was noted that a small number of students still received low marks after the iVR experience, suggesting potential avenues for future research.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{garrido2024ensemble,

title = {Ensemble methods and semi-supervised learning for information fusion: A review and future research directions},

author = {José Luis Garrido-Labrador and Ana Serrano-Mamolar and Jesús Maudes-Raedo and Juan José Rodríguez and César García-Osorio},

url = {https://doi.org/10.1016/j.inffus.2024.102310},

doi = {10.1016/j.inffus.2024.102310},

year  = {2024},

date = {2024-02-02},

urldate = {2024-02-02},

journal = {Information Fusion},

volume = {107},

abstract = {Advances over the past decade at the intersection of information fusion methods and Semi-Supervised Learning (SSL) are investigated in this paper that grapple with challenges related to limited labelled data. To do so, a bibliographic review of papers published since 2013 is presented, in which ensemble methods are combined with new machine learning algorithms. A total of 128 new proposals using SSL algorithms for ensemble construction are identified and classified. All the methods are categorised by approach, ensemble type, and base classifier. Experimental protocols, pre-processing, dataset usage, unlabelled ratios, and statistical tests are also assessed, underlining the major trends, and some shortcomings of particular studies. It is evident from this literature review that foundational algorithms such as self-training and co-training are influencing current developments, and that innovative ensemble …

},

keywords = {Bibliographic review, Ensemble learning, Experimental protocol, Information fusion, Label scarsity, PID2020-119894GB-I00, Research trends, Semi-supervised ensemble classification, Semi-supervised learning, SSL},

pubstate = {published},

tppubtype = {article}

}

@article{miguel-alonso2024,

title = {Evaluation of the novelty effect in immersive Virtual Reality learning experiences},

author = {Ines Miguel-Alonso and David Checa and Henar Guillen-Sanz and Andres Bustillo},

url = {https://doi.org/10.1007/s10055-023-00926-5 },

doi = {https://doi.org/10.1007/s10055-023-00926-5 },

issn = {1434-9957},

year  = {2024},

date = {2024-01-21},

urldate = {2024-01-21},

journal = {Virtual Reality},

volume = {28},

number = {27},

abstract = {In this study, the novelty effect or initial fascination with new technology is addressed in the context of an immersive Virtual Reality (iVR) experience. The novelty effect is a significant factor contributing to low learning outcomes during initial VR learning experiences. The aim of this research is to measure the effectiveness of a tutorial at mitigating the novelty effect of iVR learning environments among first-year undergraduate students. The iVR tutorial forms part of the iVR learning experience that involves the assembly of a personal computer, while learning the functions of the main components. 86 students participated in the study, divided into a Control group (without access to the tutorial) and a Treatment group (completing the tutorial). Both groups showed a clear bimodal distribution in previous knowledge, due to previous experience with learning topics, giving us an opportunity to compare tutorial effects with students of different backgrounds. Pre- and post-test questionnaires were used to evaluate the experience. The analysis included such factors as previous knowledge, usability, satisfaction, and learning outcomes categorized into remembering, understanding, and evaluation. The results demonstrated that the tutorial significantly increased overall satisfaction, reduced the learning time required for iVR mechanics, and improved levels of student understanding, and evaluation knowledge. Furthermore, the tutorial helped to homogenize group behavior, particularly benefiting students with less previous experience in the learning topic. However, it was noted that a small number of students still received low marks after the iVR experience, suggesting potential avenues for future research.},

keywords = {head mounted display, Learning, novelty effect, serious games, tutorial, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{martinez2024,

title = {Development of the Engagement Playability and User eXperience (EPUX) Metric for 2D-Screen and VR Serious Games: A Case-Study Validation of Hellblade: Senua’s Sacrifice },

author = {Kim Martinez and David Checa and Andres Bustillo},

url = {https://doi.org/10.3390/electronics13020281},

doi = {electronics13020281},

year  = {2024},

date = {2024-01-08},

urldate = {2024-01-08},

journal = {Electronics},

volume = {13},

number = {2},

issue = {281},

pages = {281},

abstract = {Research into the design of serious games still lacks metrics to evaluate engagement with the experience so that users can achieve the learning aims. This study presents the new EPUX metric, based on playability and User eXperience (UX) elements, to measure the capability of any serious game to maintain the attention of players. The metric includes (1) playability aspects: game items that affect the emotions of users and that constitute the different layers of the game, i.e., mechanics, dynamics and aesthetics; and (2) UX features: motivation, meaningful choices, usability, aesthetics and balance both in the short and in the long term. The metric is also adapted to evaluate virtual reality serious games (VR-SGs), so that changes may be considered to features linked to playability and UX. The case study for the assessment of the EPUX metric is Hellblade, developed in two versions: one for 2D-screens and the other for VR devices. The comparison of the EPUX metric scores for both versions showed that (1) some VR dynamics augmented the impact of gameplay and, in consequence, engagement capacity; and (2) some game design flaws were linked to much lower scores. Among those flaws were low numbers of levels, missions, and items; no tutorial to enhance usability; and lack of strategies and rewards to increase motivation in the long term.},

keywords = {game design, game engagement, game evaluation, serious games, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{MARTICORENASANCHEZ2024101727,

title = {UBUMonitor: Desktop application for visual e-learning student clustering with Moodle},

author = {Raúl Marticorena-Sánchez and Carlos López-Nozal and Ana Serrano-Mamolar and Alicia Olivares-Gil},

url = {https://www.sciencedirect.com/science/article/pii/S2352711024000980},

doi = {https://doi.org/10.1016/j.softx.2024.101727},

issn = {2352-7110},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {SoftwareX},

volume = {26},

pages = {101727},

abstract = {UBUMonitor is an open-source Java desktop tool designed to analyse student interactions and grades within courses in the Moodle learning management system. Its primary goal is to assist educational researchers in making informed decisions. The application offers flexible configuration of educational datasets, enabling educational researchers to customize features of their analyses based on filters such as time, participants, e-resources, e-forums and teaching e-activities. With a modular architecture developed iteratively in Github with 35 releases, UBUMonitor has demonstrated its usability in teaching through several published use cases. This paper focuses specifically on the student clustering module, which allows customization of course activity access and grading, supports multiple clustering algorithms, and incorporates techniques for validating both the optimal number of clusters and the quality of cluster result.},

keywords = {e-learning analytic, Educational data clustering process, Educational software, Learning management systems, Unsupervised machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{barbero-aparicio2023b,

title = {Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques},

author = {José A. Barbero-Aparicio and Alicia Olivares-Gil and Juan J. Rodríguez and César García-Osorio and José F. Díez-Pastor},

url = {https://www.sciencedirect.com/science/article/pii/S1566253523003512},

doi = {10.1016/j.inffus.2023.102035},

issn = {1566-2535},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Information Fusion},

volume = {102},

pages = {102035},

abstract = {This paper presents a comprehensive analysis of deep transfer learning methods, supervised methods, and semi-supervised methods in the context of protein fitness prediction, with a focus on small datasets. The analysis includes the exploration of the combination of different data sources to enhance the performance of the models. While deep learning and deep transfer learning methods have shown remarkable performance 

in situations with abundant data, this study aims to address the more realistic scenario faced by wet lab researchers, where labeled data is often limited. The novelty of this work lies in its examination of deep transfer learning in the context of small datasets and its consideration of semi-supervised methods and multi-view strategies. While previous research has extensively explored deep transfer learning in large dataset scenarios, little attention has been given to its efficacy in small dataset settings or its comparison with semi-supervised approaches. Our findings suggest that deep transfer learning, exemplified by ProteinBERT, shows promising performance in this context compared to the rest of the methods across various evaluation metrics, not only in small dataset contexts but also in large dataset scenarios. This highlights the robustness and versatility of deep transfer learning in protein fitness prediction tasks, even with limited labeled data. The results of this study shed light on the potential of deep transfer learning as a state-of-the-art approach in the field of protein fitness prediction. By leveraging pre-trained models and fine-tuning them on small datasets, researchers can achieve competitive performance surpassing traditional supervised and semi-supervised methods. These findings provide valuable insights for wet lab researchers who face the challenge of limited labeled data, enabling them to make informed decisions when selecting the most effective methodology for their specific protein fitness prediction tasks. Additionally, the study investigated the combination of two different sources of information (encodings) through our enhanced semi-supervised methods, yielding noteworthy results improving their base model and providing valuable insights for further research. The presented analysis contributes to a better understanding of the capabilities and limitations of different learning approaches in small dataset scenarios, ultimately aiding in the development of improved protein fitness prediction methods},

keywords = {bioinformatics, Machine learning, PID2020-119894GB-I00, Protein fitness prediction, Semi-supervised learning, Small datasets, SSL, Transfer learning},

pubstate = {published},

tppubtype = {article}

}

@article{Ramírez-Sanz2023e,

title = {Semi-supervised learning for industrial fault detection and diagnosis: A systemic review},

author = {José Miguel Ramírez-Sanz and Jose-Alberto Maestro-Prieto and Álvar Arnaiz-González and Andrés Bustillo},

doi = {10.1016/j.isatra.2023.09.027},

issn = {0019-0578},

year  = {2023},

date = {2023-12-00},

urldate = {2023-12-00},

journal = {ISA Transactions},

volume = {143},

pages = {255--270},

publisher = {Elsevier BV},

keywords = {p_humanaid, PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Ramírez-Sanz2023f,

title = {Semi-supervised learning for industrial fault detection and diagnosis: A systemic review},

author = {José Miguel Ramírez-Sanz and Jose-Alberto Maestro-Prieto and Álvar Arnaiz-González and Andrés Bustillo},

doi = {10.1016/j.isatra.2023.09.027},

issn = {0019-0578},

year  = {2023},

date = {2023-12-00},

journal = {ISA Transactions},

volume = {143},

pages = {255--270},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2023e,

title = {Design and development of a gamified tutorial for iVR serious games},

author = {Ines Miguel-Alonso and Henar Guillen-Sanz and Bruno Rodriguez-Garcia and Andres Bustillo},

doi = {10.34190/ecgbl.17.1.1563},

issn = {2049-100X},

year  = {2023},

date = {2023-09-29},

urldate = {2023-09-29},

journal = {ECGBL},

volume = {17},

number = {1},

pages = {411--417},

publisher = {Academic Conferences International Ltd},

abstract = {<jats:p>Serious games, including immersive Virtual Reality (iVR) experiences, can be challenging for players due to their unfamiliar control systems and mechanics. This study focuses on designing a gamified tutorial for iVR serious games that not only teaches iVR interactions but also enhances user enjoyment and engagement. The tutorial consists of progressively challenging mini-games that adapt to the user's performance. Tips and recommendations are provided through a robot avatar if users struggle or make mistakes. An optional narrative is included to enhance user engagement, but it is not mandatory for the iVR experience. Gamification elements, such as point collection and progress updates, are incorporated into the tutorial. It can be played independently or as an introduction to iVR serious games. The goal is to use gamification principles to maintain user engagement and flow while enhancing the learning experience in the virtual world.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{Mena-Alonso2023,

title = {A cost-effective stereo camera-based system for measuring crack propagation in fibre-reinforced concrete},

author = {Álvaro Mena-Alonso and Pedro Latorre-Carmona and Dorys C. González and José F. Díez-Pastor and Juan J. Rodríguez and Jesús Mínguez and Miguel A. Vicente},

doi = {10.1007/s43452-023-00723-6},

issn = {2083-3318},

year  = {2023},

date = {2023-08-00},

urldate = {2023-08-00},

journal = {Archiv.Civ.Mech.Eng},

volume = {23},

number = {3},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>This paper shows a new low-cost technology for the measurement of crack propagation in quasi-fragile materials based on a stereo pair of cameras and LED light spots. The two cameras record the displacement experienced by a series of LED white lights. For each frame, the <jats:italic>X</jats:italic>, <jats:italic>Y</jats:italic> and <jats:italic>Z</jats:italic> 3D coordinates of all the centroids of the LED points are obtained. From this information, it is possible to determine the variation of the distance between any two of them. In this case, 2 strips of 12 LED lights each were arranged in such a way that the points of both strips coincided in pairs in height. The algorithm made it possible to monitor the increase in distance that occurred between each pair of lights at the same height. The paper shows the mathematical basis of this technological solution. A test has been carried out by installing this system in a concrete cube 150 mm side and subjected to a wedge-splitting test. The results show that it is possible to monitor the crack propagation (position of the crack front) during the test and to know the crack width too. At present, the accuracy of this technique is only limited by the camera resolution and the computer processing capability.</jats:p>},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Serrano-Mamolar2023,

title = {Towards learner performance evaluation in iVR learning environments using eye-tracking and Machine-learning},

author = {Ana Serrano-Mamolar and Ines Miguel-Alonso and David Checa and Carlos Pardo-Aguilar},

doi = {10.3916/c76-2023-01},

issn = {1988-3293},

year  = {2023},

date = {2023-07-01},

urldate = {2023-07-01},

journal = {Comunicar: Media Education Research Journal},

volume = {31},

number = {76},

publisher = {Oxbridgepublishinghouse},

abstract = {<jats:p>At present, the use of eye-tracking data in immersive Virtual Reality (iVR) learning environments is set to become a powerful tool for maximizing learning outcomes, due to the low-intrusiveness of eye-tracking technology and its integration in commercial iVR Head Mounted Displays. However, the most suitable technologies for data processing should first be identified before their use in learning environments can be generalized. In this research, the use of machine-learning techniques is proposed for that purpose, evaluating their capabilities to classify the quality of the learning environment and to predict user learning performance. To do so, an iVR learning experience simulating the operation of a bridge crane was developed. Through this experience, the performance of 63 students was evaluated, both under optimum learning conditions and under stressful conditions. The final dataset included 25 features, mostly temporal series, with a dataset size of up to 50M data points. The results showed that different classifiers (KNN, SVM and Random Forest) provided the highest accuracy when predicting learning performance variations, while the accuracy of user learning performance was still far from optimized, opening a new line of future research. This study has the objective of serving as a baseline for future improvements to model accuracy using complex machine-learning techniques.</jats:p>

<jats:p>Actualmente, el uso de los datos del seguimiento de la mirada en entornos de aprendizaje de Realidad Virtual inmersiva (iVR) está destinado a ser una herramienta fundamental para maximizar los resultados de aprendizaje, dada la naturaleza poco intrusiva del eye-tracking y su integración en las gafas comerciales de Realidad Virtual. Pero, antes de que se pueda generalizar el uso del eye-tracking en entornos de aprendizaje, se deben identificar las tecnologías más adecuadas para el procesamiento de datos. Esta investigación propone el uso de técnicas de aprendizaje automático para este fin, evaluando sus capacidades para clasificar la calidad del entorno de aprendizaje y predecir el rendimiento de aprendizaje del usuario. Para ello, se ha desarrollado una experiencia docente en iVR para aprender el manejo de un puente-grúa. Con esta experiencia se ha evaluado el rendimiento de 63 estudiantes, tanto en condiciones óptimas de aprendizaje como en condiciones con factores estresores. El conjunto de datos final incluye 25 características, siendo la mayoría series temporales con un tamaño de conjunto de datos superior a 50 millones de puntos. Los resultados muestran que la aplicación de diferentes clasificadores como KNN, SVM o Random Forest tienen una alta precisión a la hora de predecir alteraciones en el aprendizaje, mientras que la predicción del rendimiento del aprendizaje del usuario aún está lejos de ser óptima, lo que abre una nueva línea de investigación futura. Este estudio tiene como objetivo servir como línea de base para futuras mejoras en la precisión de los modelos mediante el uso de técnicas de aprendizaje automático más complejas.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{romero2023,

title = {Tuning 3D-printing parameters to produce vertical ultra-hydrophobic PETG parts with low ice adhesion: A food industry case study},

author = {Pablo E. Romero and Barrios Juan M and Esther Molero and Andres Bustillo},

url = {https://doi.org/10.1177/09544054231178970},

doi = {10.1177/09544054231178970},

year  = {2023},

date = {2023-06-06},

urldate = {2023-06-06},

journal = {Proc IMechE Part B: J Engineering Manufacture},

pages = {1-9},

abstract = {The food industry is a dynamic component of the European economy. A wide variety of products and small batch are demanded in a market that is accustomed to frequenting changes in food packaging formats. Cheaper and lighter 3D-printed tools are replacing expensive metallic ones, producing previously impossible product geometries and processing fish and meat products more quickly and in more reliable ways. In addition to food contact, these printed parts are often required to have hydrophobic surfaces that facilitate cleaning and have low adhesion both foodstuffs and ice. In this study, the surface wettability of PolyEthylene Terephthalate Glycol (PETG) printed parts via fused filament fabrication is assessed. Specifically, several printing parameters (layer height, extrusion temperature, printing speed, acceleration, and flow) and their influence on the hydrophobicity of 3D printed parts with vertical orientation are analyzed. The experimental results indicated that the parameter with the strongest influence on the wettability of the XZ parts was flow: low-flow values generated ultra-hydrophobic surfaces, with contact angles higher than 120°. Acceleration had no influence at low flow values; however, for high flow values, low acceleration rates yielded higher contact angles. In addition, it was experimentally proven that the 3D-printed PETG parts with high-contact angle surfaces showed lower adhesion to ice than those with low contact-angle surfaces. The technology was applied to a case study of a 3D-printed hopper for the ice duct of an ice-cube machine.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kuncheva2023,

title = {An experiment on animal re-identification from video},

author = {Ludmila I. Kuncheva and José Luis Garrido-Labrador and Ismael Ramos-Pérez and Samuel L. Hennessey and Juan J. Rodríguez},

doi = {10.1016/j.ecoinf.2023.101994},

issn = {1574-9541},

year  = {2023},

date = {2023-05-00},

urldate = {2023-05-00},

journal = {Ecological Informatics},

volume = {74},

publisher = {Elsevier BV},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{barbero-aparicio2023,

title = {Deep learning and support vector machines for transcription start site identification},

author = {José A. Barbero-Aparicio and Alicia Olivares-Gil and José F. Díez-Pastor and César García-Osorio},

editor = {Carlos Fernandez-Lozano},

url = {https://doi.org/10.7717/peerj-cs.1340},

doi = {10.7717/peerj-cs.1340},

issn = {2376-5992},

year  = {2023},

date = {2023-04-17},

urldate = {2023-04-17},

journal = {PeerJ Computer Science},

volume = {9},

issue = {e1340},

abstract = {Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments},

keywords = {bioinformatics, Convolutional neural network, Deep learning, Long short-term memory, Machine learning, PID2020-119894GB-I00, Support vector machines, transcription start site},

pubstate = {published},

tppubtype = {article}

}

@article{ramirez-sanz2023,

title = {A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept},

author = {José Miguel Ramírez-Sanz and José Luis Garrido-Labrador and Alicia Olivares-Gil and Álvaro García-Bustillo and Álvar Arnaiz-González and José-Francisco Díez-Pastor and Maha Jahouh and Josefa González-Santos and Jerónimo J. González-Bernal and Marta Allende-Río and Florita Valiñas-Sieiro and Jose M. Trejo-Gabriel-Galan and Esther Cubo},

editor = {Maria-Esther Vidal and José Alberto Benítez Andrades and Alejandro Rodríguez-González},

url = {https://www.mdpi.com/2227-9032/11/4/507},

doi = {10.3390/healthcare11040507},

issn = {2227-9032},

year  = {2023},

date = {2023-02-09},

urldate = {2023-02-09},

journal = {Healthcare},

volume = {11},

number = {507},

issue = {4},

abstract = {first_page 

settings 

Order Article Reprints 

Open AccessArticle 

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept 

by José Miguel Ramírez-Sanz 

1 [ORCID] , José Luis Garrido-Labrador 

1 [ORCID] , Alicia Olivares-Gil 

1 [ORCID] , Álvaro García-Bustillo 

2 [ORCID] , Álvar Arnaiz-González 

1,* [ORCID] , José-Francisco Díez-Pastor 

1, Maha Jahouh 

3, Josefa González-Santos 

3, Jerónimo J. González-Bernal 

3 [ORCID] , Marta Allende-Río 

4, Florita Valiñas-Sieiro 

4, Jose M. Trejo-Gabriel-Galan 

4 and Esther Cubo 

4 

1 

Escuela Politécnica Superior, Departamento de Ingeniería Informática, Universidad de Burgos, Avda. Cantabria s/n, 09006 Burgos, Spain 

2 

Fundación Burgos por la Investigación de la Salud, 09006 Burgos, Spain 

3 

Departamento de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, Paseo Comendadores s/n, 09001 Burgos, Spain 

4 

Servicio de Neurología, Hospital Universitario de Burgos, 09006 Burgos, Spain 

* 

Author to whom correspondence should be addressed. 

Healthcare 2023, 11(4), 507; https://doi.org/10.3390/healthcare11040507 (registering DOI) 

Received: 19 December 2022 / Revised: 20 January 2023 / Accepted: 7 February 2023 / Published: 9 February 2023 

(This article belongs to the Special Issue Analysis of Healthcare Big Data and Health Informatics) 

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Versions Notes 

The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.},

keywords = {artificial intelligence in healthcare, Big data, Parkinson's disease, telemedicine, telerehabilitation},

pubstate = {published},

tppubtype = {article}

}

@article{miguel-alonso2023,

title = {Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments},

author = {Inés Miguel-Alonso and Bruno Rodriguez-Garcia and David Checa and Andrés Bustillo},

editor = {MDPI},

url = {https://www.mdpi.com/2076-3417/13/1/593 

https://www.mdpi.com/journal/applsci/special_issues/HK43Y20XK5},

doi = {10.3390/app13010593},

issn = {2076-3417},

year  = {2023},

date = {2023-01-02},

urldate = {2023-01-02},

journal = {Applied Sciences},

volume = {13},

number = {593},

issue = {1},

abstract = {Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.},

keywords = {cybersickness, education, novelty effect, tutorial, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2023b,

title = {Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments},

author = {Ines Miguel-Alonso and Bruno Rodriguez-Garcia and David Checa and Andres Bustillo},

doi = {10.3390/app13010593},

issn = {2076-3417},

year  = {2023},

date = {2023-01-00},

journal = {Applied Sciences},

volume = {13},

number = {1},

publisher = {MDPI AG},

abstract = {Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miguel-Alonso2023d,

title = {Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments},

author = {Ines Miguel-Alonso and Bruno Rodriguez-Garcia and David Checa and Andres Bustillo},

doi = {10.3390/app13010593},

issn = {2076-3417},

year  = {2023},

date = {2023-01-00},

urldate = {2023-01-00},

journal = {Applied Sciences},

volume = {13},

number = {1},

publisher = {MDPI AG},

abstract = {<jats:p>Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.</jats:p>},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{Arnau-González2023b,

title = {Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems},

author = {Pablo Arnau-González and Ana Serrano-Mamolar and Stamos Katsigiannis and Turke Althobaiti and Miguel Arevalillo-Herráez},

doi = {10.1109/access.2023.3290478},

issn = {2169-3536},

year  = {2023},

date = {2023-00-00},

urldate = {2023-00-00},

journal = {IEEE Access},

volume = {11},

pages = {67030--67039},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {p_humanaid},

pubstate = {published},

tppubtype = {article}

}

@article{Setó-Rey2023,

title = {Vulnerability of Package Dependency Networks},

author = {Daniel Setó-Rey and José Ignacio Santos-Martín and Carlos López-Nozal},

doi = {10.1109/tnse.2023.3260880},

issn = {2327-4697},

year  = {2023},

date = {2023-00-00},

urldate = {2023-00-00},

journal = {IEEE Trans. Netw. Sci. Eng.},

pages = {1--13},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Barbero-Aparicio2022,

title = {Nonlinear physics opens a new paradigm for accurate transcription start site prediction},

author = {José Antonio Barbero-Aparicio and Santiago Cuesta-Lopez and César Ignacio García-Osorio and Javier Pérez-Rodríguez and Nicolás García-Pedrajas},

editor = {José Manuel Benítez},

url = {https://doi.org/10.1186/s12859-022-05129-4},

doi = {10.1186/s12859-022-05129-4},

issn = {1471-2105},

year  = {2022},

date = {2022-12-30},

urldate = {2022-12-30},

journal = {BMC Bioinformatics},

volume = {23},

number = {565},

abstract = {There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learning (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.},

keywords = {DNA breathing, DNA modelling, Machine learning, String kernels, SVM, TSS prediction},

pubstate = {published},

tppubtype = {article}

}

@article{kim2022,

title = {A New Measure for Serious Games Evaluation: Gaming Educational Balanced (GEB) Model},

author = {Kim Martinez and María Isabel Menéndez-Menéndez and Andres Bustillo},

editor = {Maya Satratzemi and Stelios Xinogalos},

doi = {10.3390/app122211757},

issn = {2076-3417},

year  = {2022},

date = {2022-11-19},

urldate = {2022-11-19},

journal = {Applied Sciences},

volume = {12},

number = {11757},

issue = {22},

abstract = {Serious games have to meet certain characteristics relating to gameplay and educational content to be effective as educational tools. There are some models that evaluate these aspects, but they usually lack a good balance between both ludic and learning requirements, and provide no guide for the design of new games. This study develops the Gaming Educational Balanced (GEB) Model which addresses these two limitations. GEB is based on the Mechanics, Dynamics and Aesthetics framework and the Four Pillars of Educational Games theory. This model defines a metric to evaluate serious games, which can also be followed to guide their subsequent development. This rubric is tested with three indie serious games developed using different genres to raise awareness of mental illnesses. This evaluation revealed two main issues: the three games returned good results for gameplay, but the application of educational content was deficient, due in all likelihood to the lack of expert educators participating in their development. A statistical and machine learning validation of the results is also performed to ensure that the GEB metric features are clearly explained and the players are able to evaluate them correctly. These results underline the usefulness of the new metric tool for identifying game design strengths and weaknesses. Future works will apply this metric to more serious games to further test its effectiveness and to guide the design of new serious games.},

keywords = {game design, game evaluation, metrics, serious games},

pubstate = {published},

tppubtype = {article}

}

@article{Pimenov2022,

title = {Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review},

author = {Danil Yurievich Pimenov and Andrés Bustillo and Szymon Wojciechowski and Vishal Santosh Sharma and Munish Kumar Gupta and Mustafa Kuntğlu},

url = {https://link.springer.com/article/10.1007/s10845-022-01923-2#citeas},

doi = {10.1007/s10845-022-01923-2},

issn = {0956-5515},

year  = {2022},

date = {2022-03-12},

urldate = {2022-03-12},

journal = {Journal of Intelligent Manufacturing},

volume = {2022},

abstract = {The wear of cutting tools, cutting force determination, surface roughness variations and other machining responses are of keen interest to latest researchers. The variations of these machining responses results in change in dimensional accuracy and productivity upto great extent. In addition, an excessive increase in wear leads to catastrophic consequences, exceeding the tool breakage. Therefore, this article discusses the online trend of modern approaches in tool condition monitoring while different machining operations. For this purpose, the effective use of new sensors and artificial intelligence (AI) is considered and followed during this holistic review work. The sensor systems used for monitoring tool wear are dynamometers, accelerometers, acoustic emission sensors, current and power sensors, image sensors, other sensors. These systems allow to solve the problem of automation and modeling of technological parameters of the main types of cutting, such as turning, milling, drilling and grinding. The modern artificial intelligence methods are considered, such as: Neural networks, Image recognition, Fuzzy logic, Adaptive neuro-fuzzy inference systems, Bayesian Networks, Support vector machine, Ensembles, Decision and regression trees, k-nearest neighbors, Artificial Neural Network, Markov model, Singular Spectrum Analysis, Genetic algorithms. Discussions also includes the main advantages, disadvantages and prospects of using various AI methods for tool wear monitoring. Moreover, the problems and future directions of the main processing methods using AI models are also highlighted.},

keywords = {Artificial intelligence, Machining, PID2020-119894GB-I00, Sensor, tool condition monitoring, Tool life, Wear},

pubstate = {published},

tppubtype = {article}

}

@article{Ramos-Pérez2022,

title = {When is resampling beneficial for feature selection with imbalanced wide data?},

author = {Ismael Ramos-Pérez and Álvar Arnaiz-González and Juan José Rodríguez and César García-Osorio},

url = {https://www.sciencedirect.com/science/article/pii/S0957417421013622},

doi = {https://doi.org/10.1016/j.eswa.2021.116015},

issn = {0957-4174},

year  = {2022},

date = {2022-02-01},

urldate = {2022-02-01},

journal = {Expert Systems with Applications},

volume = {188},

pages = {116015},

abstract = {This paper studies the effects that combinations of balancing and feature selection techniques have on wide data (many more attributes than instances) when different classifiers are used. For this, an extensive study is done using 14 datasets, 3 balancing strategies, and 7 feature selection algorithms. The evaluation is carried out using 5 classification algorithms, analyzing the results for different percentages of selected features, and establishing the statistical significance using Bayesian tests. 

Some general conclusions of the study are that it is better to use RUS before the feature selection, while ROS and SMOTE offer better results when applied afterwards. Additionally, specific results are also obtained depending on the classifier used, for example, for Gaussian SVM the best performance is obtained when the feature selection is done with SVM-RFE before balancing the data with RUS.},

keywords = {Feature selection, High dimensional data, Machine learning, PID2020-119894GB-I00, SELECTED, Unbalanced, Very low sample size, Wide data},

pubstate = {published},

tppubtype = {article}

}

@article{Olivares-Gil2022,

title = {Mapping the scientific structure of organization and management of enterprises using complex networks},

author = {Alicia Olivares-Gil and Adrián Arnaiz-Rodríguez and José Miguel Ramírez-Sanz and José Luis Garrido-Labrador and Virginia Ahedo and César García-Osorio and José Ignacio Santos and José Manuel Galán},

doi = {10.4995/ijpme.2022.16666},

issn = {2340-4876},

year  = {2022},

date = {2022-01-31},

urldate = {2022-01-31},

journal = {Int. J. Prod. Manag. Eng.},

volume = {10},

number = {1},

pages = {65--76},

publisher = {Universitat Politecnica de Valencia},

abstract = {<jats:p>Understanding the scientific and social structure of a discipline is a fundamental aspect for scientific evaluation processes, identifying trends and niches, and balancing the trade-off between exploitation and exploration in research. In the present contribution, the production of doctoral theses is used as a proxy to analyze the scientific structure of the knowledge area of business organization in Spain. To that end, a complex networks approach is selected, and two different networks are built: (i) the social network of co-participation in thesis examining committees and thesis supervision, and (ii) a bipartite network of theses and thesis descriptors. The former has a modular structure that is partially explained by thematic specialization in different subdisciplines. The latter serves to assess the interdisciplinary structure of the discipline, as it enables the characterization of affinity levels between fields, research poles and thematic clusters. Our results have implications for the scientific evaluation and formal definition of related fields.</jats:p>},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Cruz2022b,

title = {Using Machine-Learning techniques and Virtual Reality to design cutting tools for energy optimization in milling operations},

author = {David Checa Cruz and Gorka Urbikain and Aitor Beranoagirre and Andrés Bustillo and Luis Norberto López Lacalle},

url = {https://www.tandfonline.com/doi/full/10.1080/0951192X.2022.2027020},

doi = {10.1080/0951192X.2022.2027020},

issn = {0951-192X},

year  = {2022},

date = {2022-01-19},

urldate = {2022-01-19},

journal = {International Journal of Computer Integrated Manufacturing},

volume = {35},

number = {1},

pages = {1-21},

abstract = {The selection of a proper cutting tool in machining operations is a critical issue. Tool geometric parameters are essential for milling performance. However, the process engineer has very limited experience of the best parameter combination, due to the high cost of cutting tool tests. The same holds true for bachelor studies on machining processes. This study proposes a new strategy that combines experimental tests, machine-learning modelling and Virtual Reality visualization to overcome these limitations. First, tools with different geometric parameters are tested. Second, the experimental data are modeled with different machine-learning techniques (regression trees, multilayer perceptrons, bagging and random forest ensembles). An in-depth analysis of the influence of each input on model accuracy is performed to reduce experimental costs. The results show that the best model with no cutting-force inputs performed worse than the best model with all the inputs. Third, the most accurate model is used to build 3D graphs of special interest to engineering students as well as process engineers, for the optimization of power consumption under different cutting conditions. Finally, a Virtual Reality environment is presented to train engineering students in the study of the best tool design and cutting parameter optimization.},

keywords = {energy optimization, Ensembles, Multilayer perceptron, PID2020-119894GB-I00, serrated cutters, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Cruz2022,

title = {Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors},

author = {David Checa Cruz and Juan José Saucedo-Dorantes and Roque Alfredo Osorno Ríos and José Alfonso Antonio-Daviu and Andrés Bustillo},

url = {https://www.mdpi.com/2076-3417/12/1/414},

doi = {10.3390/app12010414},

issn = {2076-3417},

year  = {2022},

date = {2022-01-01},

journal = {Applied Sciences},

volume = {12},

number = {1},

pages = {414},

abstract = {The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors.},

keywords = {eye tracking, fault detection, FFT, induction motors, SELECTED, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Cruz2021b,

title = {Immersive virtual-reality computer-assembly serious game to enhance autonomous learning},

author = {David Checa Cruz and Inés Miguel Alonso and Andrés Bustillo},

url = {https://link.springer.com/article/10.1007%2Fs10055-021-00607-1},

doi = {10.1007/s10055-021-00607-1},

issn = {1434-9957},

year  = {2021},

date = {2021-12-23},

journal = {Virtual Reality},

abstract = {Immersive virtual reality (VR) environments create a very strong sense of presence and immersion. Nowadays, especially when student isolation and online autonomous learning is required, such sensations can provide higher satisfaction and learning rates than conventional teaching. However, up until the present, learning outcomes with VR tools have yet to prove their advantageous aspects over conventional teaching. The project presents a VR serious game for teaching concepts associated with computer hardware assembly. These concepts are often included in any undergraduate’s introduction to Computer Science. The learning outcomes are evaluated using a pre-test of previous knowledge, a satisfaction/usability test, and a post-test on knowledge acquisition, structured with questions on different knowledge areas. The results of the VR serious game are compared with another two learning methodologies adapted to online learning: (1) an online conventional lecture; and (2) playing the same serious game on a desktop PC. An extensive sample of students (n = 77) was formed for this purpose. The results showed the strong potential of VR serious games to improve student well-being during spells of confinement, due to higher learning satisfaction. Besides, ease of usability and the use of in-game tutorials are directly related with game-user satisfaction and performance. The main novelty of this research is related to academic performance. Although a very limited effect was noted for learning theoretical knowledge with the VR application in comparison with the other methodologies, this effect was significantly improved through visual knowledge, understanding and making connections between different concepts. It can therefore be concluded that the proposed VR serious game has the potential to increase student learning and therefore student satisfaction, by imparting a deeper understanding of the subject matter to students.},

keywords = {active learning, computer science, e-learning, educational game, game engine, head mounted display, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Martínez2021,

title = {A systematic review of serious games for children and adolescents: awareness, prevention, detection and therapy for depression and anxiety},

author = {Kim Martínez and María Isabel Menéndez Menéndez and Andrés Bustillo},

url = {https://games.jmir.org/2021/4/e30482},

doi = {10.2196/30482},

issn = {2291-9279},

year  = {2021},

date = {2021-12-16},

journal = {JMIR Serious Games},

volume = {9},

number = {4 (2021)},

abstract = {Background: 

Depression and anxiety in children and adolescents are major health problems worldwide. In recent years, serious games research has advanced in the development of tools to address these mental health conditions. However, there has not been an extensive analysis of these games, their tendencies, and capacities. 

Objective: 

This review aims to gather the most current serious games, published from 2015 to 2020, with a new approach focusing on their applications: awareness, prevention, detection, and therapy. The purpose is also to analyze the implementation, development, and evaluation of these tools to obtain trends, strengths, and weaknesses for future research lines. 

Methods: 

The identification of the serious games through a literature search was conducted on the databases PubMed, Scopus, Wiley, Taylor and Francis, Springer, PsycINFO, PsycArticles, Web of Science, and Science Direct. The identified records were screened to include only the manuscripts meeting these criteria: a serious game for PC, smartphone, or virtual reality; developed by research teams; targeting only depression or anxiety or both; aiming specifically at children or adolescents. 

Results: 

A total of 34 studies have been found that developed serious games for PC, smartphone, and virtual reality devices and tested them in children and adolescents. Most of the games address both conditions and are applied in prevention and therapy. Nevertheless, there is a trend that anxiety is targeted more in childhood and depression targeted more in adolescence. Regarding design, the game genres arcade minigames, adventure worlds, and social simulations are used, in this order. For implementation, these serious games usually require sessions of 1 hour and are most often played using a PC. Moreover, the common evaluation tools are normalized questionnaires that measure acquisition of skills or reduction of symptoms. Most studies collect and compare these data before and after the participants play. 

Conclusions: 

The results show that more awareness and detection games are needed, as well as games that mix the awareness, prevention, detection, and therapy applications. In addition, games for depression and anxiety should equally target all age ranges. For future research, the development and evaluation of serious games should be standardized, so the implementation of serious games as tools would advance. The games should always offer support while playing, in addition to collecting data on participant behavior during the game to better analyze their learning. Furthermore, there is an open line regarding the use of virtual reality for these games due to the capabilities offered by this technology.},

keywords = {adolescents, anxiety, awareness, children, depression, detection, mental health, prevention, serious games, therapy, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Juez-Gil2021bb,

title = {Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark},

author = {Mario Juez-Gil and Álvar Arnaiz-González and Juan José Rodríguez and Carlos López-Nozal and César García-Osorio},

url = {https://www.sciencedirect.com/science/article/pii/S0925231221012832},

doi = {https://doi.org/10.1016/j.neucom.2021.08.086},

issn = {0925-2312},

year  = {2021},

date = {2021-11-13},

journal = {Neurocomputing},

volume = {464},

pages = {432-437},

abstract = {One of the main goals of Big Data research, is to find new data mining methods that are able to process large amounts of data in acceptable times. In Big Data classification, as in traditional classification, class imbalance is a common problem that must be addressed, in the case of Big Data also looking for a solution that can be applied in an acceptable execution time. In this paper we present Approx-SMOTE, a parallel implementation of the SMOTE algorithm for the Apache Spark framework. The key difference with the original SMOTE, besides parallelism, is that it uses an approximated version of k-Nearest Neighbor which makes it highly scalable. Although an implementation of SMOTE for Big Data already exists (SMOTE-BD), it uses an exact Nearest Neighbor search, which does not make it entirely scalable. Approx-SMOTE on the other hand is able to achieve up to 30 times faster run times without sacrificing the improved classification performance offered by the original SMOTE.},

keywords = {Big data, Data Mining, imbalance, SMOTE, Spark},

pubstate = {published},

tppubtype = {article}

}

@article{Cruz2021,

title = {Virtual Reality opportunities in the reduction of occupational hazards in industry 4.0},

author = {David Checa Cruz and Kim Martínez and Roque Alfredo Osorno Ríos and Andrés Bustillo},

url = {https://www.revistadyna.com/search/virtual-reality-opportunities-in-the-reduction-of-occupational-hazards-in-industry-40},

doi = {https://doi.org/10.6036/10241},

issn = {0012-7361},

year  = {2021},

date = {2021-11-01},

journal = {DYNA},

volume = {96},

number = {6},

pages = {620-626},

abstract = {This work discuss the possibilities of Immersive Virtual Reality (iVR) environments in occupational risk prevention in the manufacturing industry. Firstly, a framework for iVR experiences design is presented. Secondly, two examples to demonstrate the usefulness of this scheme for the detection of occupational hazards are discussed. In the first one, the worker controls an overhead crane in a realistic iVR environment. Realism is searched to intensify user´s presence in the iVR and, therefore, learning effectiveness. Visual quality is maximized and natural movements and load´s inertias are programmed with this objective. The user performs different critical operations in this application. The tasks becomes more complex while the user gets used to the iVR serious game: noise level, bad lighting, presence of other workers in the working area and, especially, load unbalance. Under these conditions, the user must carry out different common tasks while avoiding accidents. In the second one, the worker moves through a factory and identifies different risk situations, taking the corresponding corrective measures. While in the first application, the user interacts with the virtual environment using a real overhead crane keypad to increase his immersion, in the second one a standard iVR interface is used, because it simulates in a natural way the interaction with virtual objects. In both cases, a data acquisition system, including positioning and eyetracking, allows the trainer to directly provide feedback to the user on his performance. 

Keywords: Virtual Reality; Occupational Risk Prevention; Industry 4.0; Overhead crane; Educational Games},

keywords = {Educational Games, Industria 4.0, Industry 4.0, Juegos educativos, Occupational Risk Prevention, Overhead crane, Prevención de Riesgos Laborales, Puente grúa, Realidad Virtual, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Juez-Gil2021,

title = {Rotation Forest for Big Data},

author = {Mario Juez-Gil and Álvar Arnaiz-González and Juan José Rodríguez and Carlos López-Nozal and César García-Osorio},

url = {https://www.sciencedirect.com/science/article/pii/S1566253521000634},

doi = {10.1016/j.inffus.2021.03.007},

issn = {1566-2535},

year  = {2021},

date = {2021-10-01},

journal = {Information Fusion},

volume = {74},

pages = {39-49},

abstract = {The Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this paper, a MapReduce Rotation Forest and its implementation under the Spark framework are presented. The proposed MapReduce Rotation Forest behaves in the same way as the standard Rotation Forest, training the base classifiers on a rotated space, but using a functional implementation of the rotation that enables its execution in Big Data frameworks. Experimental results are obtained using different cloud-based cluster configurations. Bayesian tests are used to validate the method against two ensembles for Big Data: Random Forest and PCARDE classifiers. Our proposal incorporates the parallelization of both the PCA calculation and the tree training, providing a scalable solution that retains the performance of the original Rotation Forest and achieves a competitive execution time (in average, at training, more than 3 times faster than other PCA-based alternatives). In addition, extensive experimentation shows that by setting some parameters of the classifier (i.e., bootstrap sample size, number of trees, and number of rotations), the execution time is reduced with no significant loss of performance using a small ensemble.},

keywords = {Big data, Ensemble learning, Machine learning, Random forest, Rotation forest, SELECTED, Spark},

pubstate = {published},

tppubtype = {article}

}

@article{Juez-Gil2021b,

title = {Experimental evaluation of ensemble classifiers for imbalance in Big Data},

author = {Mario Juez-Gil and Álvar Arnaiz-González and Juan José Rodríguez and César García-Osorio},

url = {https://www.sciencedirect.com/science/article/pii/S1568494621003707?via%3Dihub},

doi = {10.1016/j.asoc.2021.107447},

issn = {1568-4946},

year  = {2021},

date = {2021-09-01},

journal = {Applied Soft Computing},

volume = {108},

number = {107447},

abstract = {Datasets are growing in size and complexity at a pace never seen before, forming ever larger datasets known as Big Data. A common problem for classification, especially in Big Data, is that the numerous examples of the different classes might not be balanced. Some decades ago, imbalanced classification was therefore introduced, to correct the tendency of classifiers that show bias in favor of the majority class and that ignore the minority one. To date, although the number of imbalanced classification methods have increased, they continue to focus on normal-sized datasets and not on the new reality of Big Data. In this paper, in-depth experimentation with ensemble classifiers is conducted in the context of imbalanced Big Data classification, using two popular ensemble families (Bagging and Boosting) and different resampling methods. All the experimentation was launched in Spark clusters, comparing ensemble performance and execution times with statistical test results, including the newest ones based on the Bayesian approach. One very interesting conclusion from the study was that simpler methods applied to unbalanced datasets in the context of Big Data provided better results than complex methods. The additional complexity of some of the sophisticated methods, which appear necessary to process and to reduce imbalance in normal-sized datasets were not effective for imbalanced Big Data.},

keywords = {Big data, ensemble, imbalance, resampling, Spark, unbalance},

pubstate = {published},

tppubtype = {article}

}

@article{Díez-Pastor2021b,

title = {Experimental Assessment of Feature Extraction Techniques Applied to the Identification of Properties of Common Objects, Using a Radar System},

author = {José Francisco Díez-Pastor and Pedro Latorre-Carmona and José Luis Garrido-Labrador and José Miguel Ramírez-Sanz and Juan J. Rodríguez},

doi = {10.3390/app11156745},

issn = {2076-3417},

year  = {2021},

date = {2021-08-00},

urldate = {2021-08-00},

journal = {Applied Sciences},

volume = {11},

number = {15},

publisher = {MDPI AG},

abstract = {<jats:p>Radar technology has evolved considerably in the last few decades. There are many areas where radar systems are applied, including air traffic control in airports, ocean surveillance, and research systems, to cite a few. Other types of sensors have recently appeared, which allow tracking sub-millimeter motion with high speed and accuracy rates. These millimeter-wave radars are giving rise to myriad new applications, from the recognition of the material close objects are made, to the recognition of hand gestures. They have also been recently used to identify how a person interacts with digital devices through the physical environment (Tangible User Interfaces, TUIs). In this case, the radar is used to detect the orientation, movement, or distance from the objects to the user’s hands or the digital device. This paper presents a thoughtful comparative analysis of different feature extraction techniques and classification strategies applied on a series of datasets that cover problems such as the identification of materials, element counting, or determining the orientation and distance of objects to the sensor. The results outperform previous works using these datasets, especially when the accuracy was lowest, showing the benefits feature extraction techniques have on classification performance.</jats:p>},

keywords = {PID2020-119894GB-I00},

pubstate = {published},

tppubtype = {article}

}

@article{Cerro2021,

title = {Use of machine learning algorithms for surface roughness prediction of printed parts in polyvinyl butyral via fused deposition modeling},

author = {Azahara Cerro and Pablo E. Romero and Okan Yiğit and Andrés Bustillo},

url = {https://link.springer.com/article/10.1007/s00170-021-07300-2},

doi = {https://doi.org/10.1007/s00170-021-07300-2},

issn = {0268-3768},

year  = {2021},

date = {2021-05-25},

journal = {The International Journal of Advanced Manufacturing Technology},

abstract = {Machine learning algorithms for classification are employed in this study to generate different models that can predict the surface roughness of parts manufactured from polyvinyl butyral by means of Fused Deposition Modeling (FDM). Five input variables are defined (layer height, print speed, number of perimeters, wall angle, and extruder temperature), and 16 parts are 3D printed, each with three different surfaces (48 surfaces in total). The print values used to print each part were defined by a fractionated orthogonal experimental design. Using a perthometer, the average value of surface roughness, Ra, on each surface was obtained. From these experimental values, 40 models were trained and validated. The model with the best prediction results was the one generated by bagging and Multilayer Perceptron (BMLP), with a Kappa statistic of 0.9143. The input variables with the highest influence on the surface finish are the wall angle and the layer height.},

keywords = {3d printing, Fused deposition modeling Fused filament fabrication, Machine learning, surface roughness, WEKA},

pubstate = {published},

tppubtype = {article}

}

@article{Rodríguez2021,

title = {Rotation Forest for multi-target regression},

author = {Juan José Rodríguez and Mario Juez-Gil and Carlos López-Nozal and Álvar Arnaiz-González},

url = {https://link.springer.com/article/10.1007/s13042-021-01329-1},

doi = {https://doi.org/10.1007/s13042-021-01329-1},

issn = {1868-808X},

year  = {2021},

date = {2021-04-22},

journal = {International Journal of Machine Learning and Cybernetics},

abstract = {The prediction of multiple numeric outputs at the same time is called multi-target regression (MTR), and it has gained attention during the last decades. This task is a challenging research topic in supervised learning because it poses additional difficulties to traditional single-target regression (STR), and many real-world problems involve the prediction of multiple targets at once. One of the most successful approaches to deal with MTR, although not the only one, consists in transforming the problem in several STR problems, whose outputs will be combined building up the MTR output. In this paper, the Rotation Forest ensemble method, previously proposed for single-label classification and single-target regression, is adapted to MTR tasks and tested with several regressors and data sets. Our proposal rotates the input space in an efficient and novel fashion, avoiding extra rotations forced by MTR problem decomposition. Four approaches for MTR are used: single-target (ST), stacked-single target (SST), Ensembles of Regressor Chains (ERC), and Multi-target Regression via Quantization (MRQ). For assessing the benefits of the proposal, a thorough experimentation with 28 MTR data sets and statistical tests are used, concluding that Rotation Forest, adapted by means of these approaches, outperforms other popular ensembles, such as Bagging and Random Forest.},

keywords = {ensemble, multi-target regression, Rotation forest, SELECTED},

pubstate = {published},

tppubtype = {article}

}

@article{Díez-Pastor2021,

title = {High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning},

author = {José Francisco Díez-Pastor and Alain Gil Val and Fernando Veiga and Andrés Bustillo},

url = {https://www.sciencedirect.com/science/article/pii/S0263224120308654},

doi = {https://doi.org/10.1016/j.measurement.2020.108328},

issn = {0263-2241},

year  = {2021},

date = {2021-01-15},

journal = {Measurement},

volume = {168},

number = {108328},

abstract = {Industrial threading processes that use cutting taps are in high demand. However, industrial conditions differ markedly from laboratory conditions. In this study, a machine-learning solution is presented for the correct classification of threads, based on industrial requirements, to avoid expensive manual measurement of quality indicators. First, quality states are categorized. Second, process inputs are extracted from the torque signals including statistical parameters. Third, different machine-learning algorithms are tested: from base classifiers, such as decision trees and multilayer perceptrons, to complex ensembles of classifiers especially designed for imbalanced datasets, such as boosting and bagging decision-tree ensembles combined with SMOTE and under-sampling balancing techniques. Ensembles demonstrated the lowest sensitivity to window sizes, the highest accuracy for smaller window sizes, and the greatest learning ability with small datasets. Fourth, the combination of models with both high Recall and high Precision resulted in a reliable industrial tool, tested on an extensive experimental dataset.},

keywords = {Bagging, Cutting taps, Imbalanced datasets, Quality assessment, SELECTED, Threading},

pubstate = {published},

tppubtype = {article}

}

@article{Díez-Pastor2020,

title = {“You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification},

author = {José Francisco Díez-Pastor and Pedro Latorre-Carmona and Álvar Arnaiz-González and Javier Ruiz-Pérez and Débora Zurro},

url = {https://www.cambridge.org/core/journals/microscopy-and-microanalysis/article/you-are-not-my-type-an-evaluation-of-classification-methods-for-automatic-phytolith-identification/48F88E9407086B797BBE383B8BC15904},

doi = {https://doi.org/10.1017/S1431927620024629},

issn = {1431-9276},

year  = {2020},

date = {2020-11-10},

journal = {Microscopy and Microanalysis},

volume = {26},

pages = {1158-1167},

abstract = {Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.},

keywords = {Feature extraction, Machine learning, Microfossils, Morphometry, Proxy},

pubstate = {published},

tppubtype = {article}

}