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Concept-wise granular computing for explainable artificial intelligence. GRANULAR COMPUTING 2022. [DOI: 10.1007/s41066-022-00357-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Singh P, Wa Torek M, Ceglarek A, Fąfrowicz M, Lewandowska K, Marek T, Sikora-Wachowicz B, Oświȩcimka P. Analysis of fMRI Signals from Working Memory Tasks and Resting-State of Brain: Neutrosophic-Entropy-Based Clustering Algorithm. Int J Neural Syst 2022; 32:2250012. [PMID: 35179104 DOI: 10.1142/s0129065722500125] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This study applies a neutrosophic-entropy-based clustering algorithm (NEBCA) to analyze the fMRI signals. We consider the data obtained from four different working memory tasks and the brain's resting state for the experimental purpose. Three non-overlapping clusters of data related to temporal brain activity are determined and statistically analyzed. Moreover, we used the Uniform Manifold Approximation and Projection (UMAP) method to reduce system dimensionality and present the effectiveness of NEBCA. The results show that using NEBCA, we are able to distinguish between different working memory tasks and resting-state and identify subtle differences in the related activity of brain regions. By analyzing the statistical properties of the entropy inside the clusters, the various regions of interest (ROIs), according to Automated Anatomical Labeling (AAL) atlas crucial for clustering procedure, are determined. The inferior occipital gyrus is established as an important brain region in distinguishing the resting state from the tasks. Moreover, the inferior occipital gyrus and superior parietal lobule are identified as necessary to correct the data discrimination related to the different memory tasks. We verified the statistical significance of the results through the two-sample t-test and analysis of surrogates performed by randomization of the cluster elements. The presented methodology is also appropriate to determine the influence of time of day on brain activity patterns. The differences between working memory tasks and resting-state in the morning are related to a lower index of small-worldness and sleep inertia in the first hours after waking. We also compared the performance of NEBCA to two existing algorithms, KMCA and FKMCA. We showed the advantage of the NEBCA over these algorithms that could not effectively accumulate fMRI signals with higher variability.
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Affiliation(s)
- Pritpal Singh
- Institute of Theoretical Physics, Jagiellonian University, Kraków 30-348, Poland
| | - Marcin Wa Torek
- Institute of Theoretical Physics, Jagiellonian University, Kraków 30-348, Poland.,Faculty of Computer Science and Telecommunications, Cracow University of Technology, Kraków 31-155, Poland
| | - Anna Ceglarek
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Magdalena Fąfrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Koryna Lewandowska
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Barbara Sikora-Wachowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Paweł Oświȩcimka
- Institute of Theoretical Physics, Jagiellonian University, Kraków 30-348, Poland.,Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Kraków 31-342, Poland
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031353] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. Therefore, this paper presents a systematic literature review (SLR) on the recent developments of XAI methods and evaluation metrics concerning different application domains and tasks. This study considers 137 articles published in recent years and identified through the prominent bibliographic databases. This systematic synthesis of research articles resulted in several analytical findings: XAI methods are mostly developed for safety-critical domains worldwide, deep learning and ensemble models are being exploited more than other types of AI/ML models, visual explanations are more acceptable to end-users and robust evaluation metrics are being developed to assess the quality of explanations. Research studies have been performed on the addition of explanations to widely used AI/ML models for expert users. However, more attention is required to generate explanations for general users from sensitive domains such as finance and the judicial system.
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Arco JE, Ortiz A, Ramírez J, Zhang YD, Górriz JM. Tiled Sparse Coding in Eigenspaces for Image Classification. Int J Neural Syst 2021; 32:2250007. [PMID: 34967705 DOI: 10.1142/s0129065722500071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. blackThese alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are blackfirst partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. blackThen, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. blackOur system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.
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Affiliation(s)
- Juan E Arco
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Andrés Ortiz
- Department of Communications Engineering, University of Malaga 29010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester LE1 7RH, UK
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
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Explaining clinical decision support systems in medical imaging using cycle-consistent activation maximization. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.05.081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Amodeo M, Arpaia P, Buzio M, Di Capua V, Donnarumma F. Hysteresis Modeling in Iron-Dominated Magnets Based on a Multi-Layered Narx Neural Network Approach. Int J Neural Syst 2021; 31:2150033. [PMID: 34296651 DOI: 10.1142/s0129065721500337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics for computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy of classical and recent approaches for accelerator magnets, that combine hybridization of standard hysteretic models and neural network architectures. By means of an incremental procedure, different Deep Neural Network Architectures are selected, fine-tuned and tested in order to predict magnetic hysteresis in the context of electromagnets. Tests and results show that the proposed NARX architecture best fits the measured magnetic field behavior of a reference quadrupole at CERN. In particular, the proposed modeling framework leads to a percent error below 0.02% for the magnetic field prediction, thus outperforming state of the art approaches and paving a very promising way for future real time applications.
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Affiliation(s)
- Maria Amodeo
- Department of Electronics and Telecommunications (DET), Polytechnic University of Turin, Turin 10129, Italy.,Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Pasquale Arpaia
- Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Marco Buzio
- Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Vincenzo Di Capua
- Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Francesco Donnarumma
- Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Via San Martino della Battaglia, 44, Rome 00185, Italy
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