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arXiv 2024年

作者： Khaldi, Rohaifa Tabik, Siham Puertas-Ruiz, Sergio de Giles, Julio Peñas Hódar correa, José Antonio Zamora, Regino Segura, Domingo Alcaraz LifeWatch-ERIC ICT Core Seville41071 Spain Dept. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Botany Faculty of Science University of Granada Granada18071 Spain Dept. of Ecology Faculty of Science University of Granada Granada18071 Spain Granada18071 Spain Zaragoza50059 Spain

Monitoring the distribution and size of long-living large shrubs, such as junipers, is crucial for assessing the long-term impacts of global change on high-mountain ecosystems. While deep learning models have shown remarkable success in object segmentation, adapting these models to detect shrub species with polymorphic nature remains challenging. In this research, we release a large dataset of individual shrub delineations on freely available satellite imagery and use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve (Sierra Nevada, Spain). To optimize performance, we introduced a novel dual data construction approach: using photo-interpreted (PI) data for model development and fieldwork (FW) data for validation. To account for the polymorphic nature of junipers during model evaluation, we developed a soft version of the Intersection over Union metric. Finally, we assessed the uncertainty of the resulting map in terms of canopy cover and density of shrubs per size class. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data. The R2 and RMSE of the observed versus predicted relationship were 0.63 and 6.67% for canopy cover, and 0.90 and 20.62 for shrub density. The greater density of larger shrubs in lower altitudes and smaller shrubs in higher altitudes observed in the model outputs was also present in the PI and FW data, suggesting an altitudinal uplift in the optimal performance of the species. This study demonstrates that deep learning applied on freely available high-resolution satellite imagery is useful to detect medium to large shrubs of high ecological value at the regional scale, which could be expanded to other high-mountains worldwide and to historical and forthcoming imagery. © 2024, cc BY-Nc-ND.

关键词： Vegetation mapping

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RSAFormer: A method of polyp segmentation with region self-attention transformer

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computers in Biology and Medicine 2024年 172卷 108268-108268页

作者： Yin, Xuehui Zeng, Jun Hou, Tianxiao Tang, chao Gan, chenquan Jain, Deepak Kumar García, Salvador School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune412115 India Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain

colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ability due to the lack of clear and highly similar boundaries between normal tissue and polyps. To deal with this problem, we propose a region self-attention enhancement network (RSAFormer) with a transformer encoder to capture more robust features. Different from other excellent methods, RSAFormer uniquely employs a dual decoder structure to generate various feature maps. contrasting with traditional methods that typically employ a single decoder, it offers more flexibility and detail in feature extraction. RSAFormer also introduces a region self-attention enhancement module (RSA) to acquire more accurate feature information and foster a stronger interplay between low-level and high-level features. This module enhances uncertain areas to extract more precise boundary information, these areas being signified by regional context. Extensive experiments were conducted on five prevalent polyp datasets to demonstrate RSAFormer's proficiency. It achieves 92.2% and 83.5% mean Dice on Kvasir and ETIS, respectively, which outperformed most of the state-of-the-art models. © 2024 Elsevier Ltd

关键词： Decoding

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Multi-Granular and Multi-criteria Large-Scale Group Decision-Making Method Using the Aggressive Ordered Aggregation Operator created Using Sentiment Analysis

SSRN

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SSRN 2022年

作者： Morfeq, Ali Trillo, José Ramón Morente-Molinera, Juan Antonio Al-Hmouz, Rami Herrera-Viedma, Enrique Department of Electrical and Computer Engineering The Faculty of Engineering The King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

currently, human beings make different decisions daily, from choosing where to invest their money to decide how they can improve their business. Helping make the decision, Decision-Making processes can be used, in which several experts can participate. Nevertheless, when you want to make a Decision-Making process with large number of experts, Large-Scale Group Decision-Making methods are used, but several problems arise when using them. The first problem is how to manage all the information that is generated in the Group Decision-Making process, as the number of participants is larger than in a previous Group Decision-Making process. The second problem is knowing how to control the aggressiveness that the debate can generate when experts give their information. The third problem is the management of relationships in the debate, as differences can generate controversy and arguments. Solving these problems, this paper proposes a novel Large-Scale Group Decision-Making process applied to a multi-criteria environment and has a multi-granular fuzzy linguistic modelling that uses sentiment analysis to aggregate the criteria and optimises the consensus value. With this method, the experts can express themselves as they wish and it increases the relevance of the debate, as extra information is extracted and used during the process. © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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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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IEEE Symposium on Nuclear science (NSS/MIc)

作者： D. castillo-Barnes R. Garcia-Diaz J. Merino-chica F. Segovia c. Jimenez-Mesa J. E. Arco J. Ramírez J. M. Górriz Dept. of Signal Theory Networking and Communications and Andalusian Research Institute on Data Science and Computational Intelligence University of Granada Granada Spain Dept. of Communications Engineering University of Malaga Malaga Spain

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.

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Age-at-Death Estimation based on Symbolic Regression Ensemble Learning from Multiple Annotations

Age-at-Death Estimation based on Symbolic Regression Ensembl...

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congress on Evolutionary computation

作者： Enrique Bermejo Oscar cordón Javier Irurita Inmaculada Alemán Ángel Rubio Salvador Dept. of Computer Science and Artificial Intelligence University of Granada Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence (DASCI) University of Granada Granada Spain Dept. of Legal Medicine Toxicology and Physical Anthropology University of Granada Granada Spain Catalan Institute of Human Paleoecology and Social Evolution (IPHES-CERCA) Tarragona Spain Dept. of History and History of Art Rovira i Virgili University Tarragona Spain

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The present study addresses the problem of semiautomatic age-at-death estimation from pubic symphysis, a crucial yet complex task in forensic anthropology. Its accuracy directly depends on the quality of the pubic bone trait labeling developed by the forensic practitioners, affected by an inherent uncertainty in their definition. As interpretability is a mandatory requirement, we propose an approach where the model design is based on evolutionary learning, considering genetic programming to frame the problem as a symbolic regression task. Additionally, ensemble learning is considered to address the challenges posed by noise, uncertainty, and conflicting annotations inherent in data collected from multiple subjects. Ensemble learning provides an effective approach to navigate these challenges by facilitating consensus-building through decision making and information fusion. Hence, observer committees are formed, comprising multiple forensic specialists with different skills and expertise which provide alternative annotations. Several ensemble configurations combining different weak learners and aggregation operators are tested to assess their effectiveness in improving accuracy and reliability in age-at-death predictions. Their performance is compared against models trained on single annotations, revealing an improvement in predictive accuracy. The obtained results also highlight the benefits of incorporating diverse perspectives to address the complexities associated with human variability and anatomical assessments.

关键词： Uncertainty Accuracy Annotations Forensics Decision making Predictive models Mathematical models

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Tf-Gan: Text Feature Fusion Gan for Text-to-Image Generation

SSRN

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SSRN 2022年

作者： Jiang, Xiaoyan Fang, Zhijun chen, Jize Fujita, Hamido School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China School of i-somet Incorporation Association Morioka Japan School of Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain

Generating high-resolution realistic images from text descriptions is a challenging topic in computer vision. Most existing text-to-image generation methods follow the multi-stage generative adversarial network (GAN) framework, which can produce relatively high-resolution images. However, the image quality of current stages relies heavily on the images generated in the previous stages. Moreover, the semantic consistency between text description and the generated image is not guaranteed by the state-of-the-art text-to-image generators. To solve the above problems, we propose a novel architecture called Text Feature Fusion GAN (TF-GAN), emphasizing on local words and global sentence feature. Keywords are extracted in the text description by the sentence fusion attention mechanism (SFAttn) to optimize image features in early stages and provide fine-grained details for the images of the later stage. The conditional fusion block (cFBlock) constraints the generated images in the global semantic level for deep information fusion. Multiple fusions in cFBlock make the network more non-linear, so the model can fuse the sentence feature and the image features more deeply and improve the semantic consistency of the generated images. Extensive experiments and comparison with other state-of-the-art methods on the caltech-UcSD Birds 200 dataset and the Microsoft common Objects in context dataset show that our generated images are more photo-realistic and closer to the text descriptions. © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Z-Number-Valued Rule-Based classification System

SSRN

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SSRN 2022年

作者： Li, Yangxue Herrera-Viedma, Enrique Pérez, Ignacio Javier Barragán-Guzmán, Mónica Morente-Molinera, Juan Antonio Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

The fuzzy rule-based classification system (FRBcS) is a popular tool in classification problems due to its interpretability and comprehensibility. As an extension of fuzzy numbers, the concept of Z-number is a more appropriate formal structure to describe uncertain and partially reliable information. A Z-number is an ordered pair of fuzzy numbers, where the second fuzzy number describes the reliability of the first one. As a result of its representation capability, it can receive better classification results. However, there is still a gap in the application of Z-numbers to classification problems due to its high computation complexity. To take advantage of the Z-number, we design a simple way to make Z-numbers apply to classification problems. Using the second fuzzy number to adjust the first fuzzy number to fit the unevenly distributed data. Then we create a Z-number-valued if-then rule by extending the fuzzy if-then rule. In addition, a Z-number-valued rule-based classification system (ZRBcS) is developed, including two main processes: rules generation and new pattern classification. compared to the classic fuzzy rule-based system, the proposed system can cover more information that can improve classification results. We evaluated the proposed method on 20 datasets to compare its learning and predictive abilities against the classic fuzzy rule-based system. The results demonstrate the Z-number-valued rule-based classification system is superior to the fuzzy rule-based classification system. © 2022, The Authors. All rights reserved.

关键词： classification (of information)

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Depth Estimation of the Object of Interest in challenging cross-Domain Scenes

SSRN

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SSRN 2023年

作者： Xu, chenglong chen, chu Xing, Wen Herrera-Viedma, Enrique College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Qingdao Innovation and Development Center Harbin Engineering University Qingdao266000 China Shenyang SIASUN Robot & Automation Co. LTD Shenyang110168 China Andalusian Research Institute of Data Science and Computational Intelligence Department of Computer Science and AI University of Granada Granada18071 Spain

Depth estimation plays an important role in 3D visual perception, autonomous vehicles, and near-field optical detection. At present, there are mainly learning-based methods and traditional geometric constraint reasoning based methods. However, most learning-based methods have poor cross-domain generalization, the accuracy will be greatly reduced when tested on new scenes. Although the traditional geometric methods have strong scene adaptability, they have more marginal noise in depth discontinuity areas, and require a large amount of memory at a high bandwidth. In order to solve the above problems, we present a disparity confidence propagation and edge smoothing framework, which combines robust semantic mask with tree diffusion model. compared with those of state-of-the-art fast geometric constraint based methods and learning-based methods, the proposed method has a great improvement in cross-domain adaptability and efficiency. Experiments on multiple data sets verify the effectiveness of the method, and a good balance is achieved between generalization and accuracy. © 2023, The Authors. All rights reserved.

关键词： Semantics

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pyDML: a python library for distance metric learning

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2020年第1期21卷 3808-3814页

作者： Juan Luis Suárez Salvador García Francisco Herrera DaSCI Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain

pyDML is an open-source python library that provides a wide range of distance metric learning algorithms. Distance metric learning can be useful to improve similarity learning algorithms, such as the nearest neighbors classifier, and also has other applications, like dimensionality reduction. The pyDML package currently provides more than 20 algorithms, which can be categorized, according to their purpose, in: dimensionality reduction algorithms, algorithms to improve nearest neighbors or nearest centroids classifiers, information theory based algorithms or kernel based algorithms, among others. In addition, the library also provides some utilities for the visualization of classifier regions, parameter tuning and a stats website with the performance of the implemented algorithms. The package relies on the scipy ecosystem, it is fully compatible with scikit-learn, and is distributed under GPLv3 license. Source code and documentation can be found at https://***/jlsuarezdiaz/pyDML.

关键词： distance metric learning classification mahalanobis distance dimensionality Python

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Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms

arXiv

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arXiv 2021年

作者： carrasco, Jacinto López, David Aguilera-Martos, Ignacio García-Gil, Diego Markova, Irina García-Barzana, Marta Arias-Rodil, Manuel Luengo, Julián Herrera, Francisco Department of Computer Science and AI Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain ArcelorMittal Global R&D New Frontier Digital Portfolio

The research in anomaly detection lacks a unified definition of what represents an anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple paradigms of algorithms design and experimentation. Predictive maintenance is a special case, where the anomaly represents a failure that must be prevented. Related time series research as outlier and novelty detection or time series classification does not apply to the concept of an anomaly in this field, because they are not single points which have not been seen previously and may not be precisely annotated. Moreover, due to the lack of annotated anomalous data, many benchmarks are adapted from supervised scenarios. To address these issues, we generalise the concept of positive and negative instances to intervals to be able to evaluate unsupervised anomaly detection algorithms. We also preserve the imbalance scheme for evaluation through the proposal of the Preceding Window ROc, a generalisation for the calculation of ROc curves for time series scenarios. We also adapt the mechanism from a established time series anomaly detection benchmark to the proposed generalisations to reward early detection. Therefore, the proposal represents a flexible evaluation framework for the different scenarios. To show the usefulness of this definition, we include a case study of Big data algorithms with a real-world time series problem provided by the company ArcelorMittal, and compare the proposal with an evaluation method. © 2021, cc0.

关键词： Anomaly detection

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