Outputs (55)

Two-layer ensemble of deep learning models for medical image segmentation. [Article] (2024)
Journal Article
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2024. Two-layer ensemble of deep learning models for medical image segmentation. Cognitive computation [online], In Press. Available from: https://doi.org/10.1007/s12559-024-10257-5

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation al... Read More about Two-layer ensemble of deep learning models for medical image segmentation. [Article].

Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19. (2023)
Conference Proceeding
JOHNSTON, P., ZARB, M. and MORENO-GARCIA, C.F. 2023. Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) Frontiers in education conference (FIE 2023),18-21 October 2023, College Station, TX, USA. Piscataway: IEEE [online], article number 10343048. Available from: https://doi.org/10.1109/fie58773.2023.10343048

This paper presents an experience report of online attendance and associated behavioural patterns during a module in the first complete semester undertaken fully online in the autumn of 2020, and the corresponding module deliveries in 2021 and 2022.... Read More about Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19..

Robust cardiac segmentation corrected with heuristics. (2023)
Journal Article
CERVANTES-GUZMÁN, A., MCPHERSON, K., OLVERES, J., MORENO-GARCÍA, C.F., ROBLES, F.T., ELYAN, E. and ESCALANTE-RAMÍREZ, B. 2023. Robust cardiac segmentation corrected with heuristics. PLoS ONE [online], 18(10), article e0293560. https://doi.org/10.1371/journal.pone.0293560

Cardiovascular diseases related to the right side of the heart, such as Pulmonary Hypertension, are some of the leading causes of death among the Mexican (and worldwide) population. To avoid invasive techniques such as catheterizing the heart, improv... Read More about Robust cardiac segmentation corrected with heuristics..

Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. (2023)
Journal Article
OFORI-BOATENG, R., ACEVES-MARTINS, M., JAYNE, C., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2023. Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. Porcedia computer science [online], 222: selected papers from the 2023 International Neural Network Society workshop on deep learning innovations and applications (INNS DLIA 2023), co-located with the 2023 International joint conference on neural networks (IJCNN), 18-32 June 2023, Gold Coast, Australia, pages 114-126. Available from: https://doi.org/10.1016/j.procs.2023.08.149

Systematic Review (SR) presents the highest form of evidence in research for decision and policy-making. Nonetheless, the structured steps involved in carrying out SRs make it demanding for reviewers. Many studies have projected the abstract screenin... Read More about Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation..

AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics. (2023)
Conference Proceeding
PIRIE, C., WIRATUNGA, N., WIJEKOON, A. and MORENO-GARCIA, C.F. 2023. AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics. In Malburg, L. and Verma, D. (eds.) Workshop proceedings of the 31st International conference on case-based reasoning (ICCBR-WS 2023), 17 July 2023, Aberdeen, UK. CEUR workshop proceedings, 3438. Aachen: CEUR-WS [online], pages 184-199. Available from: https://ceur-ws.org/Vol-3438/paper_14.pdf

As deep learning models become increasingly complex, practitioners are relying more on post hoc explanation methods to understand the decisions of black-box learners. However, there is growing concern about the reliability of feature attribution expl... Read More about AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics..

Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections. (2023)
Conference Proceeding
TORAL-QUIJAS, L.A., ELYAN, E., MORENO-GARCÍA, C.F. and STANDER, J. 2023. Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections. In Iliadis, L, Maglogiannis, I., Alonso, S., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 24th International conference on engineering applications of neural networks (EAAAI/EANN 2023), 14-17 June 2023, León, Spain. Communications in computer and information science, 1826. Cham: Springer [online], pages 217-226. Available from: https://doi.org/10.1007/978-3-031-34204-2_19

Inspecting circumferential welds in caissons is a critical task for ensuring the safety and reliability of structures in the offshore industry. However, identifying and classifying different types of circumferential welds can be challenging in subsea... Read More about Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections..

A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews. (2023)
Journal Article
MORENO-GARCIA, C.F., JAYNE, C., ELYAN, E. and ACEVES-MARTINS, M. 2023. A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews. Decision analytics journal [online], 6, article 100162. Available from: https://doi.org/10.1016/j.dajour.2023.100162

Zero-shot classification refers to assigning a label to a text (sentence, paragraph, whole paper) without prior training. This is possible by teaching the system how to codify a question and find its answer in the text. In many domains, especially he... Read More about A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews..

Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search. (2022)
Conference Proceeding
RICA, E., ALVAREZ, S., MORENO-GARCIA, C.F. and SERRATOSA, F. 2022. Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search. In Krzyzak, A., Suen, C.Y., Torsello, A. and Nobile, N. (eds.) Structural, syntactic, and statistical pattern recognition: proceedings of the 2022 Joint International Association for Pattern Recognition (IAPR) international workshops on statistical techniques in pattern recognition, and structural and syntactic pattern recognition (S+SSPR 2022), 26-27 August 2022, Montréal, Canada. Lecture notes in computer science, 13813. Cham: Springer [online], pages 274-282. Available from: https://doi.org/10.1007/978-3-031-23028-8_28

Thousands of huge printed sheets depicting engineering drawings keep record of complex industrial structures from Oil & Gas facilities. Currently, there is a trend of digitising these drawings, having as final end the regeneration of the original com... Read More about Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search..

Obesity and its association with mental health among Mexican children and adolescents: systematic review. (2022)
Journal Article
GODINA-FLORES, N.L., GUTIERREZ-GÓMEZ, Y.Y., GARCÍA-BOTELLO, M., LÓPEZ-CRUZ, L., MORENO-GARCÍA, C.F. and ACEVES-MARTINS, M. 2023. Obesity and its association with mental health among Mexican children and adolescents: systematic review. Nutrition reviews [online], 81(6). pages 658-669. Available from: https://doi.org/10.1093/nutrit/nuac083

Obesity and mental health issues increasingly affect children and adolescents, but whether obesity is a risk factor for mental health issues is unclear. To systematically review the association between obesity and mental health issues (ie, anxiety a... Read More about Obesity and its association with mental health among Mexican children and adolescents: systematic review..

Cross domain evaluation of text detection models. (2022)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., MORENO-GARCÍA, C. and JAYNE, C. 2022. Cross domain evaluation of text detection models. In Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A. and Aydin, M. (eds.) Artificial neural networks and machine learning - ICANN 2022: proceedings of the 31st International conference on artificial neural networks (ICANN 2022), 6-9 September 2022, Bristol, UK, part III. Lecture notes in computer science, 13531. Cham: Springer [online], pages 50-61. Available from: https://doi.org/10.1007/978-3-031-15934-3_5

Text detection is a very common task across a wide range of domains, such as document image analysis, remote identity verification, amongst others. It is also considered an integral component of any text recognition system, where the performance of r... Read More about Cross domain evaluation of text detection models..

TransSLC: skin lesion classification in dermatoscopic images using transformers. (2022)
Conference Proceeding
SARKER, M.M.K., MORENO-GARCÍA, C.F., REN, J. and ELYAN, E. 2022. TransSLC: skin lesion classification in dermatoscopic images using transformers. In Yang, G., Aviles-Rivero, A., Roberts, M. and Schönlieb, C.-B. (eds.) Medical image understanding and analysis: proceedings of 26th Medical image understanding and analysis 2022 (MIUA 2022), 27-29 July 2022, Cambridge, UK. Lecture notes in computer sciences, 13413. Cham: Springer [online], pages 651-660. Available from: https://doi.org/10.1007/978-3-031-12053-4_48

Early diagnosis and treatment of skin cancer can reduce patients' fatality rates significantly. In the area of computer-aided diagnosis (CAD), the Convolutional Neural Network (CNN) has been widely used for image classification, segmentation, and rec... Read More about TransSLC: skin lesion classification in dermatoscopic images using transformers..

A general framework for partial to full image registration. (2022)
Working Paper
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2022. A general framework for partial to full image registration. arXiv [online]. Available from: https://doi.org/10.48550/arXiv.2207.06387

Image registration is a research field in which images must be compared and aligned independently of the point of view or camera characteristics. In some applications (such as forensic biometrics, satellite photography or outdoor scene identification... Read More about A general framework for partial to full image registration..

Cultural factors related to childhood and adolescent obesity in Mexico: a systematic review of qualitative studies. (2022)
Journal Article
ACEVES-MARTINS, M., LÓPEZ-CRUZ, L., GARCÍA-BOTELLO, M., GODINA-FLORES, N.L., GUTIERREZ-GÓMEZ, Y.Y. and MORENO-GARCÍA, C.F. 2022. Cultural factors related to childhood and adolescent obesity in Mexico: a systematic review of qualitative studies. Obesity reviews [online], 23(9), article e13461. Available from: https://doi.org/10.1111/obr.13461

Culture and culturally specific beliefs or practices may influence perceptions and decisions, potentially contributing to childhood obesity. The objective of this study is to identify the cultural factors (expressed through decisions, behaviors, indi... Read More about Cultural factors related to childhood and adolescent obesity in Mexico: a systematic review of qualitative studies..

Obesity and oral health in Mexican children and adolescents: systematic review and meta-analysis. (2022)
Journal Article
ACEVES-MARTINS, M., GODINA-FLORES, N.L., GUTIERREZ-GÓMEZ, Y.Y., RICHARDS, D., LÓPEZ-CRUZ, L., GARCÍA-BOTELLO, M. and MORENO-GARCÍA, C.F. 2021. Obesity and oral health in Mexican children and adolescents: systematic review and meta-analysis. Nutrition reviews [online], 80(6), pages 1694-1710. Available from: https://doi.org/10.1093/nutrit/nuab088

Context: A relationship between obesity and poor oral health has been reported. Objective: To investigate the association between overweight/obesity and oral health in Mexican children and adolescents. Data Sources: A literature search was conducted... Read More about Obesity and oral health in Mexican children and adolescents: systematic review and meta-analysis..

Implementation of NAO robot maze navigation based on computer vision and collaborative learning. (2022)
Journal Article
MAGALLÁN-RAMÍREZ, D., MARTÍNEZ-AGUILAR, J.D., RODRÍGUEZ-TIRADO, A., BALDERAS, D., LÓPEZ-CAUDANA, E.O. AND MORENO-GARCÍA, C.F. 2022. Implementation of NAO robot maze navigation based on computer vision and collaborative learning. Frontiers in robotics and AI [online], 9, article 834021. Available from: https://doi.org/10.3389/frobt.2022.834021

Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub, airports, or for evacuation of... Read More about Implementation of NAO robot maze navigation based on computer vision and collaborative learning..

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. (2022)
Journal Article
ELYAN, E., VUTTIPITTAYAMONGKOL, P., JOHNSTON, P., MARTIN, K., MCPHERSON, K., MORENO-GARCIA, C.F., JAYNE, C. and SARKER, M.M.K. 2022. Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artificial intelligence surgery [online], 2, pages 24-25. Available from: https://doi.org/10.20517/ais.2021.15

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmen... Read More about Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward..

Using artificial intelligence methods for systematic review in health sciences: a systematic review. [Appendices] (2022)
Dataset
BLAIZOT, A., VEETTIL, S.K., SAIDOUNG, P., MORENO-GARCIA, C.F., WIRATUNGA, N., ACEVES-MARTINS, M., LAI, N.M. and CHAIYAKUNAPRUK, N. 2022. Using artificial intelligence methods for systematic review in health sciences: a systematic review. [Appendices]. Research synthesis methods [online], 1393), pages 353-362. Available from: https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fjrsm.1553&file=jrsm1553-sup-0001-supinfo.docx

Systematic reviews are fundamental to evidence-based decision making, as they use a comprehensive search and synthesis of the available literature. Such an operation usually requires a team of reviewers to evaluate thousands of articles. With the exp... Read More about Using artificial intelligence methods for systematic review in health sciences: a systematic review. [Appendices].

Using artificial intelligence methods for systematic review in health sciences: a systematic review. (2022)
Journal Article
BLAIZOT, A., VEETTIL, S.K., SAIDOUNG, P., MORENO-GARCIA, C.F., WIRATUNGA, N., ACEVES-MARTINS, M., LAI, N.M. and CHAIYAKUNAPRUK, N. 2022. Using artificial intelligence methods for systematic review in health sciences: a systematic review. Research synthesis methods [online], 13(3), pages 353-362. Available from: https://doi.org/10.1002/jrsm.1553

The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the re... Read More about Using artificial intelligence methods for systematic review in health sciences: a systematic review..

Interventions to prevent obesity in Mexican children and adolescents: systematic review. (2021)
Journal Article
ACEVES-MARTINS, M., LÓPEZ-CRUZ, L., GARCÍA-BOTELLO, M., GUTIERREZ-GÓMEZ, Y.Y. and MORENO-GARCÍA, C.F. 2022. Interventions to prevent obesity in Mexican children and adolescents: systematic review. Prevention science [online], 23(4), pages 563-586. Available from: https://doi.org/10.1007/s11121-021-01316-6

The prevalence of overweight and obesity has been rising among Mexican children and adolescents in the last decades. To systematically review obesity prevention interventions delivered to Mexican children and adolescents. Thirteen databases and one s... Read More about Interventions to prevent obesity in Mexican children and adolescents: systematic review..

A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams. (2021)
Conference Proceeding
TORAL, L., MORENO-GARCIA, C.F., ELYAN, E. and MEMON, S. 2021. A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams. In Barney Smith, E.H. and Pal, U. (eds.) Document analysis and recognition: ICDAR 2021 workshops, part II: proceedings of 16th International conference on document analysis and recognition 2021 (ICDAR 2021), 5-10 September 2021, Lausanne, Switzerland. Lecture notes in computer science, 12917. Cham: Springer [online], pages 268-276. Available from: https://doi.org/10.1007/978-3-030-86159-9_18

Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In th... Read More about A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams..

Interventions to treat obesity in Mexican children and adolescents: systematic review and meta-analysis. (2021)
Journal Article
ACEVES-MARTINS, A., LÓPEZ-CRUZ, L., GARCÍA-BOTELLO, M., GUTIERREZ-GÓMEZ, Y.Y. and MORENO-GARCÍA, C.F. 2022. Interventions to treat obesity in Mexican children and adolescents: systematic review and meta-analysis. Nutrition reviews [online], 80(3), pages 544-560. Available from: https://doi.org/10.1093/nutrit/nuab041

Context: Prevalence of overweight and obesity has been rising in the past 3 decades among Mexican children and adolescents. Objective: To systematically review experimental studies evaluating interventions to treat obesity in Mexican children and ado... Read More about Interventions to treat obesity in Mexican children and adolescents: systematic review and meta-analysis..

Class-decomposition and augmentation for imbalanced data sentiment analysis. (2021)
Conference Proceeding
MORENO-GARCIA, C.F., JAYNE, C. and ELYAN, E. 2021. Class-decomposition and augmentation for imbalanced data sentiment analysis. In Proceedings of 2021 International joint conference on neural networks (IJCNN 2021), 18-22 July 2021, [virtual conference]. Piscataway: IEEE [online], article 9533603. Available from: https://doi.org/10.1109/IJCNN52387.2021.9533603

Significant progress has been made in the area of text classification and natural language processing. However, like many other datasets from across different domains, text-based datasets may suffer from class-imbalance. This problem leads to model's... Read More about Class-decomposition and augmentation for imbalanced data sentiment analysis..

Face detection with YOLO on edge. (2021)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., MORENO-GARCIA, C.F. and ZWIEGELAAR, J. 2021. Face detection with YOLO on edge. In Iliadis, L., Macintyre, J., Jayne, C. and Pimenidis, E. (eds.). Proceedings of the 22nd Enginering applications of neural networks conference (EANN2021), 25-27 June 2021, Halkidiki, Greece. Proceedings of the International Neural Networks Society (INNS), 3. Cham: Springer [online], pages 284-292. Available from: https://doi.org/10.1007/978-3-030-80568-5_24

Significant progress has been achieved in objects detection applications such as Face Detection. This mainly due to the latest development in deep learning-based approaches and especially in the computer vision domain. However, deploying deep-learnin... Read More about Face detection with YOLO on edge..

Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. (2021)
Conference Proceeding
DANG, T., NGUYEN, T.T., MORENO-GARCIA, C.F., ELYAN, E. and MCCALL, J. 2021. Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. In Proceeding of 2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021), 28 June - 1 July 2021, Kraków, Poland : [virtual conference]. Piscataway: IEEE [online], pages 744-751. Available from: https://doi.org/10.1109/CEC45853.2021.9504929

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorith... Read More about Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation..

Image pre-processing and segmentation for real-time subsea corrosion inspection. (2021)
Conference Proceeding
PIRIE, C. and MORENO-GARCIA, C.F. 2021. Image pre-processing and segmentation for real-time subsea corrosion inspection. In Iliadis, L., Macintyre, J., Jayne, C. and Pimenidis, E. (eds.). Proceedings of the 22nd Engineering applications of neural networks conference (EANN2021), 25-27 June 2021, Halkidiki, Greece. Proceedings of the International Neural Networks Society (INNS), 3. Cham: Springer [online], pages 220-231. Available from: https://doi.org/10.1007/978-3-030-80568-5_19

Inspection engineering is a highly important field in the Oil & Gas sector for analysing the health of offshore assets. Corrosion, a naturally occurring phenomenon, arises as a result of a chemical reaction between a metal and its environment, causin... Read More about Image pre-processing and segmentation for real-time subsea corrosion inspection..

Two layer ensemble of deep learning models for medical image segmentation. [Preprint] (2021)
Working Paper
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2021. Two layer ensemble of deep learning models for medical image segmentation. arXiv [online]. Available from: https://doi.org/10.48550/arXiv.2104.04809

In recent years, deep learning has rapidly become a method of choice for the segmentation of medical images. Deep Neural Network (DNN) architectures such as UNet have achieved state-of-the-art results on many medical datasets. To further improve the... Read More about Two layer ensemble of deep learning models for medical image segmentation. [Preprint].

Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems. (2021)
Journal Article
MEDINA, E.C.G., ESPITIA, V.M.V., SILVA, D.C., DE LAS CUEVAS, S.F.R., HIRATA, M.P., CHEN, A.Z., GONZÁLEZ, J.A.G., BUSTAMANTE-BELLO, R. and MORENO-GARCÍA, C.F. 2021. Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems. Applied sciences [online], 11(7), article 2925. Available from: https://doi.org/10.3390/app11072925

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional... Read More about Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems..

An IoT based industry 4.0 architecture for integration of design and manufacturing systems. (2020)
Journal Article
ANBALAGAN, A. and MORENO-GARCIA, C.F. 2021. An IoT based industry 4.0 architecture for integration of design and manufacturing systems. Materials today: proceedings [online], 46(17): proceedings of 3rd International conference on materials, manufacturing and modelling 2021 (ICMMM 2021), 19-21 March 2021, [virtual conference], pages 7135-7142. Available from: https://doi.org/10.1016/j.matpr.2020.11.196

This paper proposes an Internet of Things (IoT) based 5-stage Industry 4.0 architecture to integrate the design and manufacturing systems in a Cyber Physical Environment (CPE). It considers the transfer of design and manufacturing systems data throug... Read More about An IoT based industry 4.0 architecture for integration of design and manufacturing systems..

A pipeline framework for robot maze navigation using computer vision, path planning and communication protocols. (2020)
Conference Proceeding
RODRIGUEZ-TIRADO, A., MAGALLAN-RAMIREZ, D., MARTINEZ-AGUILAR, J.D., MORENO-GARCIA, C.F., BALDERAS, D. and LOPEZ-CAUDANA, E. 2020. A pipeline framework for robot maze navigation using computer vision, path planning and communication protocols. In Proceedings of 13th Developments in eSystems engineering international conference 2020 (DeSe 2020), 13-17 December 2020, [virtual conference]. Piscataway: IEEE [online], pages 152-157. Available from: https://doi.org/10.1109/DeSE51703.2020.9450731

Maze navigation is a recurring challenge in robotics competitions, where the aim is to design a strategy for one or several entities to traverse the optimal path in a fast and efficient way. To do so, numerous alternatives exist, relying on different... Read More about A pipeline framework for robot maze navigation using computer vision, path planning and communication protocols..

Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. (2020)
Conference Proceeding
MORENO-GARCÍA, C.F., DANG, T., MARTIN, K., PATEL, M., THOMPSON, A., LEISHMAN, L. and WIRATUNGA, N. 2020. Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. In Bach, K., Bunescu, R., Marling, C. and Wiratunga, N. (eds.) Knowledge discovery in healthcare data 2020: proceedings of the 5th Knowledge discovery in healthcare data international workshop 2020 (KDH 2020), co-located with 24th European Artificial intelligence conference (ECAI 2020), 29-30 August 2020, [virtual conference]. CEUR workshop proceedings, 2675. Aachen: CEUR-WS [online], pages 63-70. Available from: http://ceur-ws.org/Vol-2675/paper10.pdf

Fracture detection has been a long-standingparadigm on the medical imaging community. Many algo-rithms and systems have been presented to accurately detectand classify images in terms of the presence and absence offractures in different parts of the... Read More about Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection..

Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. (2020)
Conference Proceeding
MORENO-GARCÍA, C.F., JOHNSTON, P. and GARKUWA, B. 2020. Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207479. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207479

One of the key features of most document image digitisation systems is the capability of discerning between the main components of the printed representation at hand. In the case of engineering drawings, such as circuit diagrams, telephone exchanges... Read More about Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks..

Deep learning for text detection and recognition in complex engineering diagrams. (2020)
Conference Proceeding
JAMIESON, L, MORENO-GARCIA, C.F. and ELYAN, E. 2020. Deep learning for text detection and recognition in complex engineering diagrams. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207127. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207127

Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper... Read More about Deep learning for text detection and recognition in complex engineering diagrams..

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. (2020)
Journal Article
ELYAN, E., MORENO-GARCIA, C.F. and JAYNE, C. 2021. CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. Neural computing and applications [online], 33(7), pages 2839-2851. Available from: https://doi.org/10.1007/s00521-020-05130-z

Class-imbalanced datasets are common across several domains such as health, banking, security, and others. The dominance of majority class instances (negative class) often results in biased learning models, and therefore, classifying such datasets re... Read More about CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification..

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. (2020)
Conference Proceeding
ELYAN, E., MORENO-GARCÍA, C.F. and JOHNSTON, P. 2020. Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, 2. Cham: Springer [online], pages 215-224. Available from: https://doi.org/10.1007/978-3-030-48791-1_16

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents... Read More about Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks..

Reducing human effort in engineering drawing validation. (2020)
Journal Article
RICA, E., MORENO-GARCÍA, C.F., ÁLVAREZ, S. and SERRATOS, F. 2020. Reducing human effort in engineering drawing validation. Computers in industry [online], 117, article ID 103198. Available from: https://doi.org/10.1016/j.compind.2020.103198

Oil & Gas facilities are extremely huge and have complex industrial structures that are documented using thousands of printed sheets. During the last years, it has been a tendency to migrate these paper sheets towards a digital environment, with the... Read More about Reducing human effort in engineering drawing validation..

A comparison of feature extractors for panorama stitching in an autonomous car architecture. (2019)
Conference Proceeding
CORTÉS-GALLARDO, E., MORENO-GARCIA, C.F., ZHU, A., CHÍPULI-SILVA, D., GONZÁLEZ-GONZÁLEZ, J.A., MORALES-ORTIZ, D., FERNÁNDEZ, S., URRIZA, B., VALVERDE-LÓPEZ, J., MARÍN, A., PÉREZ, H., IZQUIERDO-REYES, J. and BUSTAMANTE-BELLO, R. 2019. A comparison of feature extractors for panorama stitching in an autonomous care architecture. In Proceedings of 2019 International conference on mechatronics, electronics and automotive engineering (ICMEAE 2019), 26-29 November 2019, Cuernavaca, Mexico. Piscataway: IEEE [online], page 50-55. Available from: https://doi.org/10.1109/ICMEAE.2019.00017

Panorama stitching consists on frames being put together to create a 360o view. This technique is proposed for its implementation in autonomous vehicles instead of the use of an external 360o camera, mostly due to its reduced cost and improved aerody... Read More about A comparison of feature extractors for panorama stitching in an autonomous car architecture..

Digitisation of assets from the oil and gas industry: challenges and opportunities. (2019)
Conference Proceeding
MORENO-GARCIA, C.F. and ELYAN, E. 2019. Digitisation of assets from the oil and gas industry: challenges and opportunities. In Proceedings of 2019 International conference on document analysis and recognition workshops (ICDARW), 22-25 September 2019, Sydney, Australia. Piscataway: IEEE [online], 7, pages 2-5. Available from: https://doi.org/10.1109/ICDARW.2019.60122

Automated processing and analysis of legacies of printed documents across the Oil & Gas industry provide a unique opportunity and at the same time pose a significant challenge. One particular example is the case of Piping and Instrumentation Diagrams... Read More about Digitisation of assets from the oil and gas industry: challenges and opportunities..

Generalised median of graph correspondences. (2019)
Journal Article
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2019. Generalised median of graph correspondences. Pattern recognition letters [online], 125, pages 389-395. Available from: https://doi.org/10.1016/j.patrec.2019.05.015

A graph correspondence is defined as a function that maps the elements of two attributed graphs. Due to the increasing availability of methods to perform graph matching, numerous graph correspondences can be deducted for a pair of attributed graphs.... Read More about Generalised median of graph correspondences..

Correspondence edit distance to obtain a set of weighted means of graph correspondences. (2018)
Journal Article
MORENO-GARCÍA, C.F., SERRATOSA, F. and XIAOYI, J. 2020. Correspondence edit distance to obtain a set of weighted means of graph correspondences. Pattern recognition letters [online], 134, pages 29-36. Available from: https://doi.org/10.1016/j.patrec.2018.08.027

Given a pair of data structures, such as strings, trees, graphs or sets of points, several correspondences (also referred in literature as labellings, matchings or assignments) can be defined between their local parts. The Hamming distance has been l... Read More about Correspondence edit distance to obtain a set of weighted means of graph correspondences..

Modelling the generalised median correspondence through an edit distance. (2018)
Conference Proceeding
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2018. Modelling the generalised median correspondence through an edit distance. In Bai, X., Hancock, E.R., Ho, T.K., Wilson, R.C., Biggio, B. and Robles-Kelly, A. (eds.) Structural, syntactic, and statistical pattern recognition: proceedings of the 2018 Joint International Association for Pattern Recognition (IAPR) international workshops on structural and syntactic pattern recognition (SSPR 2018), and statistical techniques in pattern recognition (SPR 2018) (S+SSPR 2018), 17-19 August 2018, Beijing, China. Lecture notes in computer science, 11004. Cham: Springer [online], pages 271-281. Available from: https://doi.org/10.1007/978-3-319-97785-0_26

On the one hand, classification applications modelled by structural pattern recognition, in which elements are represented as strings, trees or graphs, have been used for the last thirty years. In these models, structural distances are modelled as th... Read More about Modelling the generalised median correspondence through an edit distance..

Symbols classification in engineering drawings. (2018)
Conference Proceeding
ELYAN, E., MORENO GARCIA, C. and JAYNE, C. 2018. Symbols classification in engineering drawings. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489087. Available from: https://doi.org/10.1109/IJCNN.2018.8489087

Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we p... Read More about Symbols classification in engineering drawings..

Digital interpretation of sensor-equipment diagrams. (2018)
Conference Proceeding
MORENO-GARCÍA, C.F. 2018. Digital interpretation of sensor-equipment diagrams. In Martin, K., Wiratunga, N. and Smith, L.S. (eds.) Proceedings of the 2018 Scottish Informatics and Computer Science Alliance (SCISA) workshop on reasoning, learning and explainability (ReaLX 2018), 27 June 2018, Aberdeen, UK. CEUR workshop proceedings, 2151. Aachen: CEUR-WS [online], session 2, paper 1. Available from: http://ceur-ws.org/Vol-2151/Paper_s2.pdf

A sensor-equipment diagram is a type of engineering drawing used in the industrial practice that depicts the interconnectivity between a group of sensors and a portion of an Oil & Gas facility. The interpretation of these documents is not a straightf... Read More about Digital interpretation of sensor-equipment diagrams..

New trends on digitisation of complex engineering drawings. (2018)
Journal Article
MORENO-GARCIA, C.F., ELYAN, E. and JAYNE, C. 2019. New trends on digitisation of complex engineering drawings. Neural computing and applications [online], 31(6): selected papers from the proceedings of the 18th Engineering applications of neural networks conference (EANN 2017), 25-27 August 2017, Athens, Greece, pages 1695-1712. Available from: https://doi.org/10.1007/s00521-018-3583-1

Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that m... Read More about New trends on digitisation of complex engineering drawings..

Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings. (2017)
Conference Proceeding
MORENO-GARCÍA, C.F., ELYAN, E. and JAYNE, C. 2017. Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings. In Boracchi, G., Iliadis, L., Jayne, C. and Likas, A. (eds.) Engineering applications of neural networks: proceedings of the 18th International engineering applications of neural networks (EANN 2017), 25-27 August 2017, Athens, Greece. Communications in computer and information science, 744. Cham: Springer [online], pages 87-98. Available from: https://doi.org/10.1007/978-3-319-65172-9_8

The demand for digitisation of complex engineering drawings becomes increasingly important for the industry given the pressure to improve the efficiency and time effectiveness of operational processes. There have been numerous attempts to solve this... Read More about Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings..

An edit distance between graph correspondences. (2017)
Conference Proceeding
MORENO-GARCIA, C.F., SERRATOSA, F. and JIANG, X. 2017. An edit distance between graph correspondences. In Foggia, P., Liu, C.-L. and Vento, M (eds.) 2017. Graph-based representations in pattern recognition: proceedings of the 11th Image analysis in patter recognition technical committee 15th (IAPR-TC-15) international workshop (GbRPR 2017), 16-18 May 2017, Anacapri, Italy. Cham: Springer [online], pages 232-241. Available from: https://doi.org/10.1007/978-3-319-58961-9_21

The Hamming Distance has been largely used to calculate the dissimilarity of a pair of correspondences (also known as labellings or matchings) between two structures (i.e. sets of points, strings or graphs). Although it has the advantage of being sim... Read More about An edit distance between graph correspondences..

Generalised median of a set of correspondences based on the hamming distance. (2016)
Conference Proceeding
MORENO-GARCÍA, C.F., SERRATOSA, F. and CORTÉS, X. 2016. Generalised median of a set of correspondences based on the hamming distance. In: Robles-Kelly A., Loog M., Biggio B., Escolano F., Wilson R. (eds.) Structural, syntatic and statistical pattern recognition: proceedings of the 2016 Joint International Association of Pattern Recognition (IAPR) structural, syntatic and statistical pattern recognition international workshop (S+SSPR 2016), 29 November - 2 December 2016, Mérida, Mexico. Lecture Notes in Computer Science, vol 10029. Cham: Springer, pages 507-518. Available from: https://doi.org/10.1007/978-3-319-49055-7_45

A correspondence is a set of mappings that establishes a relation between the elements of two data structures (i.e. sets of points, strings, trees or graphs). If we consider several correspondences between the same two structures, one option to defin... Read More about Generalised median of a set of correspondences based on the hamming distance..

A graph repository for learning error-tolerant graph matching. (2016)
Conference Proceeding
MORENO-GARCÍA, C.F., CORTÉS, X. and SERRATOSA, F. 2016. A graph repository for learning error-tolerant graph matching. In Robles-Kelly, A., Loog, M., Biggio, B., Escolano, F. and Wilson, R. (eds.) Structural, syntactic and statistical pattern recognition: proceedings of 2016 Joint International Association of Pattern Recognition (IAPR) Structural and syntactic pattern recognition internaional workshops (SSPR 2016), and Statistical techniques in pattern recognition (SPR 2016) (S+SSPR 2016), 20 November - 2 December 2016, Mérida, Mexico. Lecture notes in computer science, 10029. Cham: Springer [online], pages 519-529. Available from: https://doi.org/10.1007/978-3-319-49055-7_46

In the last years, efforts in the pattern recognition field have been especially focused on developing systems that use graph based representations. To that aim, some graph repositories have been presented to test graph-matching algorithms or to lear... Read More about A graph repository for learning error-tolerant graph matching..

Obtaining the consensus of multiple correspondences between graphs through online learning. (2016)
Journal Article
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2017. Obtaining the consensus of multiple correspondences between graphs through online learning. Pattern recognition letters [online], 87, pages 79-86. Available from: https://doi.org/10.1016/j.patrec.2016.09.003

In structural pattern recognition, it is usual to compare a pair of objects through the generation of a correspondence between the elements of each of their local parts. To do so, one of the most natural ways to represent these objects is through att... Read More about Obtaining the consensus of multiple correspondences between graphs through online learning..

Semi-automatic pose estimation of a fleet of robots with embedded stereoscopic cameras. (2016)
Conference Proceeding
CORTÉS, X., SERRATOSA, F. and MORENO-GARCIA, C.-F. 2016. Semi-automatic pose estimation of a fleet of robots with embedded stereoscopic cameras. In Proceedings of 21st Institute of Electrical Electronic Engineers (IEEE) Emerging technologies and factory automation international conference 2016 (ETFA 2016), 6-9 September 2016, Berlin, Germany. Piscataway: IEEE [online], article ID 7733640. Available from: https://doi.org/10.1109/ETFA.2016.7733640

Given a fleet of robots, automatic estimation of the relative poses between them could be inaccurate in specific environments. We propose a framework composed by the fleet of robots with embedded stereoscopic cameras providing 2D and 3D images of the... Read More about Semi-automatic pose estimation of a fleet of robots with embedded stereoscopic cameras..

Unsupervised machine learning application to perform a systematic review and meta-analysis in medical research. (2016)
Journal Article
MORENO-GARCÍA, C.F., ACEVES-MARTINS, M. and SERRATOSA, F. 2016. Unsupervised machine learning application to perform a systematic review and meta-analysis in medical research. Computación y sistemas [online], 20(1), pages 7-17. Available from: https://doi.org/10.13053/CyS-20-1-2360

When trying to synthesize information from multiple sources and perform a statistical review to compare them, particularly in the medical research field, several statistical tools are available, most common are the systematic review and the meta-anal... Read More about Unsupervised machine learning application to perform a systematic review and meta-analysis in medical research..

Effectiveness of social marketing strategies to reduce youth obesity in European school-based interventions: A systematic review and meta-analysis. (2016)
Journal Article
ACEVES-MARTINS, M., LLAURDÓ, E., TARRO, L., MORENO-GARCÍA, C.F., TRUJILLO ESCOBAR, T.G., SOLÁ, R. and GIRALT, M. 2016. Effectiveness of social marketing strategies to reduce youth obesity in European school-based interventions: a systematic review and meta-analysis. Nutrition reviews [online], 74(5), pages 337-351. Available from: https://doi.org/10.1093/nutrit/nuw004

Context: The use of social marketing to modify lifestyle choices could be helpful in reducing youth obesity. Some or all of the 8 domains of the National Social Marketing Centre's social marketing benchmark criteria (SMBC) are often used but not alwa... Read More about Effectiveness of social marketing strategies to reduce youth obesity in European school-based interventions: A systematic review and meta-analysis..

Online learning the consensus of multiple correspondences between sets. (2015)
Journal Article
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2015. Online learning the consensus of multiple correspondences between sets. Knowledge-based systems [online], 90, pages 49-57. Available from: https://doi.org/10.1016/j.knosys.2015.09.034

When several subjects solve the assignment problem of two sets, differences on the correspondences computed by these subjects may occur. These differences appear due to several factors. For example, one of the subjects may give more importance to som... Read More about Online learning the consensus of multiple correspondences between sets..

Correspondence consensus of two sets of correspondences through optimisation functions. (2015)
Journal Article
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2017. Correspondence consensus of two sets of correspondences through optimisation functions. Pattern analysis and applications [online], 20(1), pages 201-213. Available from: https://doi.org/10.1007/s10044-015-0486-y

We present a consensus method which, given the two correspondences between sets of elements generated by separate entities, enounces a final correspondence consensus considering the existence of outliers. Our method is based on an optimisation techni... Read More about Correspondence consensus of two sets of correspondences through optimisation functions..

An interactive model for structural pattern recognition based on the Bayes classifier. (2015)
Conference Proceeding
CORTÉS, X., SERRATOSA, F. and MORENO-GARCÍA, C.F. 2015. An interactive model for structural pattern recognition based on the Bayes classifier. In De Marsico, M., Figueiredo, M. and Fred, A. (eds.) Proceedings of 4th International conference on pattern recognition applications and methods (ICPRAM 2015), 10-12 January 2015, Lisbon, Portugal, vol 1. Setubal: SCITEPRSS [online], pages 240-247. Available from: https://doi.org/10.5220/0005201602400247

This paper presents an interactive model for structural pattern recognition based on a naïve Bayes classifier. In some applications, the automatically computed correlation between local parts of two images is not good enough. Moreover, humans are ver... Read More about An interactive model for structural pattern recognition based on the Bayes classifier..

Fast and efficient palmprint identification of a small sample within a full image. (2014)
Journal Article
MORENO-GARCIA, C.F. and SERRATOSA, F. 2014. Fast and efficient palmprint identification of a small sample within a full image. Computación y sistemas [online], 18(4), pages 683-691. Available from: https://doi.org/10.13053/CyS-18-4-2059

In some fields like forensic research, experts demand that a found sample of an individual can be matched with its full counterpart contained in a database. The found sample may present several characteristics that make this matching more difficult t... Read More about Fast and efficient palmprint identification of a small sample within a full image..