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International Joint Conference on Neural Networks (IJCNN)

作者： J. Lopez-Martinez Purificación Checa José Manuel Soto-Hidalgo Isaac Triguero Alberto Fernández Department of Computer Science and Artificial Intelligence University of Granada Spain Dept. of Educational and Evolutive Psychology University of Granada Spain Dept. of Comput. Eng. Automation and Robotics University of Granada Spain DaSCI Andalusian Institute in Data Science and Computational Intelligence School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Early detection of Autism Spectrum Disorder (ASD) is crucial to facilitate timely interventions, improve outcomes, and enhance the quality of life for individuals on the spectrum. Artificial intelligence and machine learning have significantly advanced the study of ASD by enabling sophisticated analyses of complex behavioural data, providing more accurate and timely detection methods. Nevertheless, existing research is mostly focused on either screening manual methods via psychological surveys, and/or the application of functional Magnetic Resonance Imaging. In the former case, there is a limitation due the subjectivity of the procedure; whereas in the latter the high cost represents a barrier to widespread use in diagnosis. In consideration of the aforementioned factors, this research contributes significantly to the field by integrating commodity wearable technology with psycho-healthcare, employing eye-tracking capabilities as a novel, non-invasive approach for early ASD diagnosis. Our methodology stands out by offering a comprehensive, four-step process that includes sophisticated image preprocessing, innovative feature extraction, precise classification, and a novel multi-instance aggregation strategy. We show its potential via a compelling case study that attests to the feasibility and efficacy of this innovative paradigm. Additionally, the work underscores prevailing challenges in the field, stressing some factors such as the acquisition of extensive and diverse datasets to allow for a multi-modal approach and the application of Trustworthy Artificial intelligence.

关键词： Surveys Autism Neural networks Psychology Machine learning Gaze tracking Manuals

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Explaining Agent Behavior in Agent-Based Modeling Using Subgroup Discovery

SSRN

引用

SSRN 2023年

作者： Romero, Elena Carmona del Jesús, Cristóbal José Cordón, Oscar del Jesus, María José Damas, Sergio Chica, Manuel Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Computer Science and Artificial Intelligence DECSAI University of Granada Granada18071 Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Jaen Jaen23071 Spain Leicester School of Pharmacy De Montfort University LeicesterLEI 9BH United Kingdom Dept. of Computer Science University of Jaen Jaen23071 Spain Dept. of Software Engineering University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia

Agent-based modeling (ABM) is a popular tool for simulating complex systems. In addition to the key performance indicators ABM provides as main outputs of the modeling process, it also generates valuable information about the agents' roadmap, but the large volume of generated data is difficult to analyze and understand. Explainable Artificial intelligence (XAI) techniques can improve the transparency and interpretability of these models. Subgroup Discovery (SD) is a useful tool for XAI that can facilitate the analysis of the data with respect to a variable of interest. In this contribution, we propose a methodology for applying SD to ABM for extracting useful knowledge of agents behavior from the model output. We compare the performance of different SD algorithms and show the effectiveness of our methodology through a case study in the marketing area. We analyze the obtained set of rules from the point of view of a marketing expert. The results show that our methodology can provide a deeper understanding of consumer behavior and market dynamics, providing potential areas of opportunity for marketers. © 2023, The Authors. All rights reserved.

关键词： Consumer behavior

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Using clustering methods to deal with high number of alternatives on Group Decision Making

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Procedia Computer science 2019年 162卷 316-323页

作者： J.A. Morente-Molinera S. Ríos Aguilar R. González-Crespo E. Herrera-Viedma Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain. School of Engineering and Technology Universidad Internacional de la Rioja (UNIR) Logroño Spain.

Novel Group Decision Making methods and Web 2.0 have augmented the quantity of data that experts have to discuss about. Nevertheless, experts are only capable of dealing with a reduced set of information. In this paper, a novel method for dealing with decision environments that include a large set of alternatives is presented. By the use of clustering methods, the available alternatives are combined into clusters according to their similarity. Afterwards, one Group Decision Making process is employed for choosing a cluster and another one for selecting the final alternative.

关键词： Clustering methods large-scale Group Decision Making linguistic modelling

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Exploring Automated Feature Engineering for Energy Consumption Forecasting with AutoML

Exploring Automated Feature Engineering for Energy Consumpti...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Nasser Alkhulaifi Alexander L. Bowler Direnc Pekaslan Isaac Triguero Nicholas J. Watson Computational Optimisation and Learning (COL) Lab School of Computer Science University of Nottingham Nottingham UK Faculty of Environment School of Food Science and Nutrition University of Leeds Leeds UK Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham Nottingham UK Department of Computer Science and Artificial Intelligence University of Granada Spain DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada Spain

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Machine learning methods are widely used to predict energy consumption, aiming to enhance efficiency and support environmental goals. However, developing these models is traditionally time-consuming and expert-dependent. While Automated Machine Learning (AutoML) has emerged as a valuable approach to streamlining machine learning pipelines, including appropriate preprocessing and learning algorithms, it may still require human experts. These experts might be needed to generate new, interpretable features that could significantly improve model performance. This is particularly relevant in complex settings such as the energy domain, where deep learning's automatic feature extraction lacks interpretability. To address this challenge, this exploratory work introduces an automated feature engineering method tailored for energy forecasting problems. It involves generating a comprehensive set of features that can be fed into AutoML, thereby reducing the need for domain knowledge in feature engineering. The proposed method has been validated using eleven datasets from various energy domains, including residential buildings, renewable energy, and regional energy consumption, with state-of-the-art AutoML methods, namely H20, TPOT, AutoGluon, and FLAML. The results demonstrate a noticeable reduction in prediction errors across all the examined datasets.

关键词： Knowledge engineering Deep learning Energy consumption Renewable energy sources Machine learning algorithms Pipelines Automated machine learning Prediction algorithms Feature extraction Forecasting

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Learning Agents' Behavioral Patterns in Agent-Based Modeling by Means of Evolutionary Algorithms

Learning Agents' Behavioral Patterns in Agent-Based Modeling...

引用

Congress on Evolutionary Computation

作者： Péricles B.C. Miranda Jesús Giráldez-Cru Moésio W. Silva-Filho Carmen Zarco Oscar Cordón Departamento de Computação Universidade Federal Rural de Pernambuco Brazil Dept. of Computer Science and Artificial Intelligence (DECSAI) University of Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain Department of Marketing and Market Research University of Granada Spain

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Agent-based models (ABM) stand as a well-established paradigm in developing computational models. ABM enable the simulation of complex systems by consolidating individual-level interactions through an underlying artificial social network. Upon the accurate construction of a model, experts can employ it as a decision support system for assessing policies in hypothetical what-if scenarios and gaining insights into the operational dynamics of the target system. However, ABM still face diverse challenges such as learning realistic agents' behavioral patterns to model real-world conducts and habits. Existing research shows that machine learning techniques, when used in ABM, can address such challenges. This work focuses on using evolutionary algorithms (EAs) to learn agents' behavior through the definition of their micro-rules, aiming to accurately model them, and thus improving the global performance of the system. To do so, we model the machine learning problem and undertake a comparative analysis of seven EAs, encompassing both classical and recent approaches, to learn agents' micro-rules with a specific focus on consumers' behavior for brand selection. Our investigation involves the assessment of the performance of each EA across problem instances derived from an ABM applied to real-world marketing domains such as dairy products and automakers. The findings of our study reveal a distinct dominance of SHADE-ILS and DECC-g over the remaining algorithms considered. Furthermore, we underline the advantages of these methods to assist modelers in selecting among the best solutions.

关键词： Decision support systems Analytical models Dairy products Consumer behavior Social networking (online) Evolutionary computation Machine learning

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Standardization of Agnostic Learning Techniques in Neuroimaging: a Case Study in EEG

Standardization of Agnostic Learning Techniques in Neuroimag...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： C. Jimenez-Mesa J. M. Peñalver D. Lopez-Garcia J. Ramirez C. Gonzalez-Garcia F. Segovia J. Suckling M. Ruz J.M. Gorriz Data Science and Computational Intelligence (DASCI) Institute Spain Mind Brain and Behavior Research Center University of Granada Granada Spain Department of Psychiatry University of Cambridge United Kingdom

Over the last few years, the use of multivariate pattern analysis (MVPA) has been growing exponentially in cognitive neuroscience. This methodology offers the possibility to perform more detailed studies in electroencephalography (EEG) and other neuroimaging modalities. Usually, machine learning-based algorithms along with empirical out-of sample generalization approaches, e.g. K-fold cross validation (CV), are implemented. In this work we applied a validation approach based on resubstitution with upper bound correction to address the instability problem concerning limited sample sizes, particularly typical in neuroimaging, when estimating accuracy via CV. Moreover, a statistical assessment of the accuracies is conducted. To this end, the methodology underlying Statistical Agnostic Mapping (SAM) to perform spatial detection of regions of interest (ROIs) in neuromaging modalities, such as magnetic resonance imaging (MRI), has been applied to a temporal EEG study. Instead of estimating ROIs by means of a significance maps, the time points where trials are most significant are highlighted. The analyses were implemented on an EEG dataset obtained from 48 participants that performed a simple go-nogo task, in which they were presented names and faces. Here, we classified name and face perception at the subject level. The findings suggest that the use of resubstitution-based approaches can reduce the computational burden and obtain similar, albeit more conservative, results than CV.

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Ensemble Learning Using Weighted Convolutional Neural Networks for Parkinson's Disease

Ensemble Learning Using Weighted Convolutional Neural Networ...

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2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Castillo-Barnes, D. Garcia-Diaz, R. Merino-Chica, J. Segovia, F. Jimenez-Mesa, C. Arco, J.E. Ramírez, J. Górriz, J.M. University of Granada Andalusian Research Institute on Data Science and Computational Intelligence Dept. of Signal Theory Networking and Communications C. Periodista Daniel Saucedo Aranda S/N Granada18071 Spain University of Malaga Dept. of Communications Engineering Blvr. Louis Pasteur 35 Malaga29004 Spain

ISBN: (纸本)9781665488723

In recent years, there has been a growing interest on Computer-Aided-Diagnosis (CAD) systems based on Deep Learning to disentangle how neurodegenerative disorders such as Parkinson's Disease (PD) behave. Although these models are quite accurate, they are often criticized for their lack of interpretability, high complexity and tendency to overfitting (as a result of the amount of input features -intensity voxels from brain scans- in comparison with the reduced number of input samples). Though there are some proposals that are trying to solve these limitations, such as data augmentation and/or interpretable models, it would be interesting, from the point of view of neuroimaging, to develop a CAD model able to 1) split the problem of analyzing the full brain scans and 2) combine the contribution of smaller Regions of Interest (ROIs). This could help us not only to reduce the complexity of the neural networks focused on the analysis of each ROI, but also as a way to quantify the contribution of each region to CAD decisions. In this context, we have proposed an ensemble learning methodology with penalization of non-reliable input sources focused on the analysis of 7 ROIs (including: accumbens, amygdala, anterior cingulate cortex, cingulate, striatum, substantia nigra and thalamus). This method is able to combine the resulting predictions obtained for each ROI by means of a Weighted Majority Voting decision schema using the scores derived from a two-sample Welch's test. As shown by our results, this proposal achieves classification rates of 97.5% and is an easily adaptable solution for the analysis of other pathologies. © 2022 IEEE.

关键词： convolutional neural networks deep learning ensemble learning neuroimaging Parkinson's Disease signal processing

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Big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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The evolution of cooperation in spatial public goods game with tolerant punishment based on reputation threshold

arXiv

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arXiv 2024年

作者： Zhang, Gui Yao, Yichao Zeng, Ziyan Feng, Minyu Chica, Manuel The College of Artificial Intelligence Southwest University No.2 Tiansheng Road Beibei Chongqing400715 China Department of Computer Science A.I. Andalusian Research Institute DaSCI Data Science and Computational Intelligence University of Granada Granada18071 Spain School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

Reputation and punishment are significant guidelines for regulating individual behavior in human society, and those with a good reputation are more likely to be imitated by others. In addition, society imposes varying degrees of punishment for behaviors that harm the interests of groups with different reputations. However, conventional pairwise interaction rules and the punishment mechanism overlook this aspect. Building on this observation, this paper enhances a spatial public goods game in two key ways: 1) We set a reputation threshold and use punishment to regulate the defection behavior of players in low-reputation groups while allowing defection behavior in high-reputation game groups. 2) Differently from pairwise interaction rules, we combine reputation and payoff as the fitness of individuals to ensure that players with both high payoff and reputation have a higher chance of being imitated. Through simulations, we find that a higher reputation threshold, combined with a stringent punishment environment, can substantially enhance the level of cooperation within the population. This mechanism provides deeper insight into the widespread phenomenon of cooperation that emerges among individuals. Copyright © 2024, The Authors. All rights reserved.

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Group decision making and soft consensus: Analyzing citation classics by means of H-Classics

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Procedia Computer science 2019年 162卷 251-259页

作者： Francisco Javier Cabrerizo María Ángeles Martínez Javier López-Gijón Jesús Cascón-Katchadourian Enrique Herrera-Viedma Andalusian Research Institute on Data Science and Computational Intelligence University of Granada Granada 18071 Spain Department of Social Work and Services University of Granada Granada 18071 Spain Department of Library Science University of Granada Granada 18071 Spain

Citation classics provide a view on the documents that could be considered the basis of a scientific area and that have therefore attracted historical and great interest by the researchers. Because of the potential relation that can be established between quality research and citation counts, the identification of citation classics also is a main way used to carry out a methodical assessment of research performance. In recent times, the H-Classics, a methodology that is founded on the popular h-index, has been introduced to determine the highly cited papers. This research aims to provide an insight into the development of soft consensus in group decision making via H-Classics. Particularly, we identify both the scientific actors (researchers, institutions, countries, and journals) making the biggest research contribution to the development of this scientific area and the topics covered.

关键词： Citation classics h-index H-Classics consensus group decision making

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