An ACO-based personalized learning technique in support of people with acquired brain injury

This is the author’s version of a work that was accepted for publication in Applied Soft Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing 47 (2016) 316–331. DOI 10.1016/j.asoc.2016.04.039The ever-increasing cases of acquired brain injury (ABI), especially among young people, have prompted a rapid progress in research involving neurological disorders. One important path is the concept of relearning, which attempts to help people regain basic motor and cognitive skills lost due to illness or accident. The goals of relearning are twofold. First, there must exist a way to properly assess the necessities of an affected person, leading to a diagnosis, followed by a recommendation regarding the exercises, tests and tasks to perform; and second, there must be a way to confirm the results obtained from these recommendations in order to fine-tune and personalize the relearning process. This presents a challenge, as there is a deeply-rooted duality between the personalized and the generalized approach. In this work we propose a personalization algorithm based on the ant colony optimization (ACO), which is a bio-inspired meta-heuristic. As we show, the stochastic nature of ants has certain similarities to the human learning process. We combine the adaptive and exploratory capabilities of ACO systems to respond to rapidly changing environments and the ubiquitous human factor. Finally, we test the proposed solution extensively in various scenarios, achieving high quality results. © 2016 Elsevier B.V. All rights reservedThis research has been funded by the Spanish Ministry of Economy and Competitiveness and by the FEDER funds of the EU under the project SUPEREMOS (TIN2014-60077-R) and insPIre (TIN2012-34003). Kamil Krynicki is supported by the FPI fellowship from Universitat Politecnica de Valencia.Krynicki, K.; Jaén Martínez, FJ.; Navarro, E. (2016). An ACO-based personalized learning technique in support of people with acquired brain injury. Applied Soft Computing. 47:316-331. doi:10.1016/j.asoc.2016.04.039S3163314

Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

Tesis por compendio[EN] The long-lasting trend in the field of computation of stress and resource distribution has found its way into computer networks via the concept of peer-to-peer (P2P) connectivity. P2P is a symmetrical model, where each network node is enabled a comparable range of capacities and resources. It stands in a stark contrast to the classical, strongly asymmetrical client-server approach. P2P, originally considered only a complimentary, server-side structure to the straightforward client-server model, has been shown to have the substantial potential on its own, with multiple, widely known benefits: good fault tolerance and recovery, satisfactory scalability and intrinsic load distribution. However, contrary to client-server, P2P networks require sophisticated solutions on all levels, ranging from network organization, to resource location and managing. In this thesis we address one of the key issues of P2P networks: performing efficient resource searches of semantic nature under realistic, dynamic conditions. There have been numerous solutions to this matter, with evolutionary, stigmergy-based, and simple computational foci, but few attempt to resolve the full range of challenges this problem entails. To name a few: real-life P2P networks are rarely static, nodes disconnect, reconnect and change their content. In addition, a trivial incorporation of semantic searches into well-known algorithms causes significant decrease in search efficiency. In our research we build a solution incrementally, starting with the classic Ant Colony System (ACS) within the Ant Colony Optimization metaheuristic (ACO). ACO is an algorithmic framework used for solving combinatorial optimization problems that fits contractually the problem very well, albeit not providing an immediate solution to any of the aforementioned problems. First, we propose an efficient ACS variant in structured (hypercube structured) P2P networks, by enabling a path-post processing algorithm, which called Tabu Route Optimization (TRO). Next, we proceed to resolve the issue of network dynamism with an ACO-compatible information diffusion approach. Consequently, we attempt to incorporate the semantic component of the searches. This initial approximation to the problem was achieved by allowing ACS to differentiate between search types with the pheromone-per-concept idea. We called the outcome of this merger Routing Concept ACS (RC-ACS). RC-ACS is a robust, static multipheromone implementation of ACS. However, we were able to conclude from it that the pheromone-per-concept approach offers only limited scalability and cannot be considered a global solution. Thus, further progress was made in this respect when we introduced to RC-ACS our novel idea: dynamic pheromone creation, which replaces the static one-to-one assignment. We called the resulting algorithm Angry Ant Framework (AAF). In AAF new pheromone levels are created as needed and during the search, rather than prior to it. The final step was to enable AAF, not only to create pheromone levels, but to reassign them to optimize the pheromone usage. The resulting algorithm is called EntropicAAF and it has been evaluated as one of the top-performing algorithms for P2P semantic searches under all conditions.[ES] La popular tendencia de distribución de carga y recursos en el ámbito de la computación se ha transmitido a las redes computacionales a través del concepto de la conectividad peer-to-peer (P2P). P2P es un modelo simétrico, en el cual a cada nodo de la red se le otorga un rango comparable de capacidades y recursos. Se trata de un fuerte contraste con el clásico y fuertemente asimétrico enfoque cliente-servidor. P2P, originalmente considerado solo como una estructura del lado del servidor complementaria al sencillo modelo cliente-servidor, ha demostrado tener un potencial considerable por sí mismo, con múltiples beneficios ampliamente conocidos: buena tolerancia a fallos y recuperación, escalabilidad satisfactoria y distribución de carga intrínseca. Sin embargo, al contrario que el modelo cliente-servidor, las redes P2P requieren de soluciones sofisticadas a todos los niveles, desde la organización de la red hasta la gestión y localización de recursos. Esta tesis aborda uno de los problemas principales de las redes P2P: la búsqueda eficiente de recursos de naturaleza semántica bajo condiciones dinámicas y realistas. Ha habido numerosas soluciones a este problema basadas en enfoques evolucionarios, estigmérgicos y simples, pero pocas han tratado de resolver el abanico completo de desafíos. En primer lugar, las redes P2P reales son raramente estáticas: los nodos se desconectan, reconectan y cambian de contenido. Además, la incorporación trivial de búsquedas semánticas en algoritmos conocidos causa un decremento significativo de la eficiencia de la búsqueda. En esta investigación se ha construido una solución de manera incremental, comenzando por el clásico Ant Colony System (ACS) basado en la metaheurística de Ant Colony Optimization (ACO). ACO es un framework algorítmico usado para búsquedas en grafos que encaja perfectamente con las condiciones del problema, aunque no provee una solución inmediata a las cuestiones mencionadas anteriormente. En primer lugar, se propone una variante eficiente de ACS para redes P2P estructuradas (con estructura de hipercubo) permitiendo el postprocesamiento de las rutas, al que hemos denominado Tabu Route Optimization (TRO). A continuación, se ha tratado de resolver el problema del dinamismo de la red mediante la difusión de la información a través de una estrategia compatible con ACO. En consecuencia, se ha tratado de incorporar el componente semántico de las búsquedas. Esta aproximación inicial al problema ha sido lograda permitiendo al ACS diferenciar entre tipos de búsquedas através de la idea de pheromone-per-concept. El resultado de esta fusión se ha denominado Routing Concept ACS (RC-ACS). RC-ACS es una implementación multiferomona estática y robusta de ACS. Sin embargo, a partir de esta implementación se ha podido concluir que el enfoque pheromone-per-concept ofrece solo escalabilidad limitada y que no puede ser considerado una solución global. Por lo tanto, para lograr una mejora a este respecto, se ha introducido al RC-ACS una novedosa idea: la creación dinámica de feromonas, que reemplaza la asignación estática uno a uno. En el algoritmo resultante, al que hemos denominado Angry Ant Framework (AAF), los nuevos niveles de feromona se crean conforme se necesitan y durante la búsqueda, en lugar de crearse antes de la misma. La mejora final se ha obtenido al permitir al AAF no solo crear niveles de feromona, sino también reasignarlos para optimizar el uso de la misma. El algoritmo resultante se denomina EntropicAAF y ha sido evaluado como uno de los algoritmos más exitosos para las búsquedas semánticas P2P bajo todas las condiciones.[CA] La popular tendència de distribuir càrrega i recursos en el camp de la computació s'ha estès cap a les xarxes d'ordinadors a través del concepte de connexions d'igual a igual (de l'anglès, peer to peer o P2P). P2P és un model simètric on cada node de la xarxa disposa del mateix nombre de capacitats i recursos. P2P, considerat originàriament només una estructura situada al servidor complementària al model client-servidor simple, ha provat tindre el suficient potencial per ella mateixa, amb múltiples beneficis ben coneguts: una bona tolerància a errades i recuperació, una satisfactòria escalabilitat i una intrínseca distribució de càrrega. No obstant, contràriament al client-servidor, les xarxes P2P requereixen solucions sofisticades a tots els nivells, que varien des de l'organització de la xarxa a la localització de recursos i la seua gestió. En aquesta tesi s'adreça un dels problemes clau de les xarxes P2P: ser capaç de realitzar eficientment cerques de recursos de naturalesa semàntica sota condicions realistes i dinàmiques. Existeixen nombroses solucions a aquest tema basades en la computació simple, evolutiva i també basades en l'estimèrgia (de l'anglès, stigmergy), però pocs esforços s'han realitzat per intentar resoldre l'ampli conjunt de reptes existent. En primer lloc, les xarxes P2P reals són rarament estàtiques: els nodes es connecten, desconnecten i canvien els seus continguts. A més a més, la incorporació trivial de cerques semàntiques als algorismes existents causa una disminució significant de l'eficiència de la cerca. En aquesta recerca s'ha construït una solució incremental, començant pel sistema clàssic de colònia de formigues (de l'anglés, Ant Colony System o ACS) dins de la metaheurística d'optimització de colònies de formigues (de l'anglès, Ant Colony Optimization o ACO). ACO és un entorn algorísmic utilitzat per cercar en grafs i que aborda el problema de forma satisfactòria, tot i que no proveeix d'una solució immediata a cap dels problemes anteriorment mencionats. Primer, s'ha proposat una variant eficient d'ACS en xarxes P2P estructurades (en forma d'hipercub) a través d'un algorisme de processament post-camí el qual s'ha anomenat en anglès Tabu Route Optimization (TRO). A continuació, s'ha procedit a resoldre el problema del dinamisme de les xarxes amb un enfocament de difusió d'informació compatible amb ACO. Com a conseqüència, s'ha intentat incorporar la component semàntica de les cerques. Aquest enfocament inicial al problema s'ha realitzat permetent a ACS diferenciar entre tipus de cerques amb la idea de ''feromona per concepte'', i s'ha anomenat a aquest producte Routing Concept ACS o RC-ACS. RC-ACS és una implementació multi-feromona robusta i estàtica d'ACS. No obstant, s'ha pogut concloure que l'enfocament de feromona per concepte ofereix només una escalabilitat limitada i no pot ser considerada una solució global. En aquest respecte s'ha realitzat progrés posteriorment introduint una nova idea a RC-ACS: la creació dinàmica de feromones, la qual reemplaça a l'assignació un a un de les mateixes. A l'algorisme resultant se l'ha anomenat en anglès Angry Ant Framework (AAF). En AAF es creen nous nivells de feromones a mesura que es necessiten durant la cerca, i no abans d'aquesta. El progrés final s'ha aconseguit quan s'ha permès a AAF, no sols crear nivells de feromones, sinó reassignar-los per optimitzar la utilització de feromones. L'algorisme resultant s'ha anomenat EntropicAAF i ha sigut avaluat com un dels algorismes per a cerques semàntiques P2P amb millors prestacions.Krynicki, KK. (2016). Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61293TESISPremios Extraordinarios de tesis doctoralesCompendi

Fuzzy-description logic for supporting the rehabilitation of the elderly

[EN] According to the latest statistics, the proportion of the elderly (+65) is increasing and is expected to double within the European Union in a period of 50 years. This ageing is due to the improvement of quality of life and advances in medicine in the last decades. Gerontechnology is receiving a great deal of attention as a way of providing the elderly with sustainable products, environments, and services combining gerontology and technology. One of the most important aspects to consider by gerontechnology is the mobility/rehabilitation technologies, because there is an important relationship between mobility and the elderly's quality of life. Telerehabilitation systems have emerged to allow the elderly to perform their rehabilitation exercises remotely. However, in many cases, the proposed systems assist neither the patients nor the experts about the progress of the rehabilitation. To address this problem, we propose in this paper, a fuzzy-semantic system for evaluating patient's physical state during the rehabilitation process based on well-known standard for patients' evaluation. Moreover, a tool called FINE has been developed that facilitates the evaluation be accomplished in a semi-automatic way first asking patients to carry out a set of standard tests and then inferencing their state by means of a fuzzy-semantic approach using the data captured during the rehabilitation tasks.This research was funded by the Spanish Ministry of Economy and Competitiveness and by EU FEDER funds under project grants TIN2016-79100-R and TIN2015-72931-EXP. It has also been funded by the Junta de Comunidades de Castilla¿La Mancha scholarship 2018-UCLM1-9131Moya, A.; Navarro, E.; Jaén Martínez, FJ.; González, P. (2020). Fuzzy-description logic for supporting the rehabilitation of the elderly. Expert Systems. 37(2):1-16. https://doi.org/10.1111/exsy.12464116372Alamri, A., Cha, J., & El Saddik, A. (2010). AR-REHAB: An Augmented Reality Framework for Poststroke-Patient Rehabilitation. IEEE Transactions on Instrumentation and Measurement, 59(10), 2554-2563. doi:10.1109/tim.2010.2057750Antoniou, G., & van Harmelen, F. (2004). Web Ontology Language: OWL. Handbook on Ontologies, 67-92. doi:10.1007/978-3-540-24750-0_4Bobillo F.(2008).Managing vagueness in ontologies. Universidad de Granada.Bobillo F. (2015).The fuzzyDL system. Retrieved July 10 2018 fromhttp://www.umbertostraccia.it/cs/software/fuzzyDL/fuzzyDL.htmlBobillo, F., Delgado, M., & Gómez-Romero, J. (2012). DeLorean: A reasoner for fuzzy OWL 2. Expert Systems with Applications, 39(1), 258-272. doi:10.1016/j.eswa.2011.07.016Bobillo, F., & Straccia, U. (2016). The fuzzy ontology reasoner fuzzyDL. Knowledge-Based Systems, 95, 12-34. doi:10.1016/j.knosys.2015.11.017Boucenna, S., Narzisi, A., Tilmont, E., Muratori, F., Pioggia, G., Cohen, D., & Chetouani, M. (2014). Interactive Technologies for Autistic Children: A Review. Cognitive Computation, 6(4), 722-740. doi:10.1007/s12559-014-9276-xCarter J. E. L.(2002).The Heath‐Carter anthropometric somatotype—Instruction manual. San Diego:State University.Chiu, Y.-H., Chen, T.-W., Chen, Y. J., Su, C.-I., Hwang, K.-S., & Ho, W.-H. (2018). Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury. Technology and Health Care, 26(1), 17-27. doi:10.3233/thc-171403Fernández-Caballero, A., González, P., & Navarro, E. (2017). Gerontechnologies - Current achievements and future trends. Expert Systems, 34(2), e12203. doi:10.1111/exsy.12203Giles, R. (1976). Łukasiewicz logic and fuzzy set theory. International Journal of Man-Machine Studies, 8(3), 313-327. doi:10.1016/s0020-7373(76)80003-xHsieh, Y.-W., Hsueh, I.-P., Chou, Y.-T., Sheu, C.-F., Hsieh, C.-L., & Kwakkel, G. (2007). Development and Validation of a Short Form of the Fugl-Meyer Motor Scale in Patients With Stroke. Stroke, 38(11), 3052-3054. doi:10.1161/strokeaha.107.490730Karime, A., Eid, M., Alja’am, J. M., Saddik, A. E., & Gueaieb, W. (2014). A Fuzzy-Based Adaptive Rehabilitation Framework for Home-Based Wrist Training. IEEE Transactions on Instrumentation and Measurement, 63(1), 135-144. doi:10.1109/tim.2013.2277536Krynicki, K., Jaen, J., & Navarro, E. (2016). An ACO-based personalized learning technique in support of people with acquired brain injury. Applied Soft Computing, 47, 316-331. doi:10.1016/j.asoc.2016.04.039Leap Motion INC. (2018).Leap Motion. Retrieved July 10 2018 fromhttps://www.leapmotion.com/Lukasiewicz, T., & Straccia, U. (2008). Managing uncertainty and vagueness in description logics for the Semantic Web. Journal of Web Semantics, 6(4), 291-308. doi:10.1016/j.websem.2008.04.001Metz, D. . (2000). Mobility of older people and their quality of life. Transport Policy, 7(2), 149-152. doi:10.1016/s0967-070x(00)00004-4Nassabi M. H. Den Akker H. &Vollenbroek‐Hutten M. (2014).An ontology‐based recommender system to promote physical activity for pre‐frail elderly 181–184.Navarro, E., González, P., López-Jaquero, V., Montero, F., Molina, J. P., & Romero-Ayuso, D. (2018). Adaptive, Multisensorial, Physiological and Social: The Next Generation of Telerehabilitation Systems. Frontiers in Neuroinformatics, 12. doi:10.3389/fninf.2018.00043OpenNI Pioneering Members. (2018).OpenNI. Retrieved July 10 2018 fromhttp://openni.ru/about/index.htmlOrbbec 3D. (2018).Orbbec Astra Pro. Retrieved July 10 2018 fromhttps://orbbec3d.com/product‐astra‐pro/Rodríguez, A. C., Roda, C., Montero, F., González, P., & Navarro, E. (2015). An Interactive Fuzzy Inference System for Teletherapy of Older People. Cognitive Computation, 8(2), 318-335. doi:10.1007/s12559-015-9356-6Shaughnessy, M., Resnick, B. M., & Macko, R. F. (2006). Testing a Model of Post-Stroke Exercise Behavior. Rehabilitation Nursing, 31(1), 15-21. doi:10.1002/j.2048-7940.2006.tb00005.xSu, C.-J., Chiang, C.-Y., & Huang, J.-Y. (2014). Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic. Applied Soft Computing, 22, 652-666. doi:10.1016/j.asoc.2014.04.020Velozo, C. A., & Woodbury, M. L. (2011). Translating measurement findings into rehabilitation practice: An example using Fugl-Meyer Assessment-Upper Extremity with patients following stroke. The Journal of Rehabilitation Research and Development, 48(10), 1211. doi:10.1682/jrrd.2010.10.0203W3C. (2012).OWL 2 web ontology language. Retrieved July 10 2018 from https://www.w3.org/TR/owl2‐overview/Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xZhang, Z., Fang, Q., & Gu, X. (2014). Fuzzy inference system based automatic Brunnstrom stage classification for upper-extremity rehabilitation. Expert Systems with Applications, 41(4), 1973-1980. doi:10.1016/j.eswa.2013.08.09

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

Baystate Medical Practices Annual Report - 2017

https://scholarlycommons.libraryinfo.bhs.org/bmpannual_report/1001/thumbnail.jp

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Financiado para publicación en acceso aberto: Universidad de Granada / CBUA.[Abstract]: Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes. This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovacón y Universidades”), P18-RT-1624, UMA20-FEDERJA-086, CV20-45250, A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”. The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”. The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019. Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00. José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC (ED431G 2019/01), GPC ED431B 2022/33, and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). In [247], the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain). In [248], the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4, 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies”BG05M2OP001-1.002-0023. The work reported here has been partially funded by the support of MICIN project PID2020-116346GB-I00. The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III; by MinCiencias project 1222-852-69927, contract 495-2020. The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb. This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058. S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)).Junta de Andalucía; CV20-45250Junta de Andalucía; A-TIC-080-UGR18Junta de Andalucía; B-TIC-586-UGR20Junta de Andalucía; P20-00525Junta de Andalucía; P18-RT-1624Junta de Andalucía; UMA20-FEDERJA-086Portugal. Fundação para a Ciência e a Tecnologia; DSAIPA/AI/0099/2019Xunta de Galicia; ED431G 2019/01Xunta de Galicia; GPC ED431B 2022/33Chile. Agencia Nacional de Investigación y Desarrollo; 1201572Generalitat Valenciana; PROMETEO/2019/119Bulgarian National Science Fund; KP-06-N42/4Bulgaria. Operational Programme Science and Education for Smart Growth; BG05M2OP001-1.002-0023Colombia. Ministerio de Ciencia, Tecnología e Innovación; 1222-852-69927Junta de Andalucía; UMA18-FEDERJA-084Suíza. State Secretariat for Education, Research and Innovation; 22 00058Institute of Information & Communications Technology Planning & Evaluation (Corea del Sur); 2020-0-0136

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Medicine + Health Magazine, Fall 2018

https://hsrc.himmelfarb.gwu.edu/smhs_medhealth/1016/thumbnail.jp

Baystate Medical Practices Annual Report - 2017

https://scholarlycommons.libraryinfo.bhs.org/bmpannual_report/1001/thumbnail.jp