# Authors Title Details Date Pdf/Links/Bibtex Keywords
18Vilone G., Longo L. Evaluating Argumentation Graphs as Global Explainable Surrogate Models for Dense Neural Networks and Their Comparison with Decision Trees eXplainable Artificial Intelligence, The World Conference (xAI-2025) 2025 Logical Analysis • Graph Theory • Graph Theory in Probability • Machine Learning • Reasoning • Symbolic AI • Explainable AI • Surrogate models • Computational Argumentation • Rule-based systems • Decision-trees • Dense Neural Networks • Deep learning
10.1007/978-3-032-08333-3_5
17Davydko O., Pavlov V., Longo L. A Combination of Integrated Gradients and SRFAMap for Explaining Neural Networks Trained with High-Order Statistical Radiomic Features eXplainable Artificial Intelligence, The World Conference (xAI-2025) 2025 Explainable artificial intelligence • Radiomics • Texture analysis • Medical image processing • Saliency map • Integrated Gradients • Neural Networks • Interpretable Machine Learning
10.1007/978-3-032-08317-3_17
16Marochko V., Rogala J., Longo L. Integrated Gradients for Enhanced Interpretation of P3b-ERP Classifiers Trained with EEG-superlets in Traditional and Virtual Environments Joint Proceedings of the xAI 2025 Late-breaking Work, Demos and Doctoral Consortium co-located with the 3rd World Conference on eXplainable Artificial Intelligence (xAI 2025) 2025 Event-related potentials • Deep learning • Convolutional neural networks • Explainable Artificial Intelligence • Integrated Gradients • P3b • Oddball paradigm • time-frequency super-resolution • Superlets.
15Gupta G., Qureshi M.A., Longo L.A Global Post Hoc XAI Method For Interpreting LSTM Using Deterministic Finite State Automata The Irish conference on Artificial Intelligence and Cognitive Science 2025 RNN • interpretability • Explainable AI • LSTM • Deterministic Finite State Automata • k-means clustering • Recurrent Neural Networks
14Marochko V., and Longo L.Enhancing the analysis of the P300 event-related potential with integrated gradients on a convolutional neural network trained with superlets Joint Proceedings of the xAI 2024 Late-breaking Work, Demos and Doctoral Consortium co-located with the 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024) 2024 Event-related potentials • Deep learning • Convolutional neural networks • Explainable Artificial Intelligence • Integrated gradients • P3b • Oddball paradigm • time-frequency super-resolution • Superlets
13Raufi B., Finnegan C., Longo L. A Comparative Analysis of SHAP, LIME, ANCHORS, and DICE for Interpreting a Dense Neural Network in Credit Card Fraud Detection eXplainable Artificial Intelligence, The World Conference (xAI-2024) 2024 Explainable Artificial Intelligence • Credit Card Fraud Detection • Interpretability • methods comparison • SHapley Additive exPlanations • Local Interpretable • Model-agnostic Explanation • ANCHORS • Diverse Counterfactual Explanations
10.1007/978-3-031-63803-9_20
12Hryniewska-Guzik W., Longo L., Biecek P.CNN-Based Explanation Ensembling for Dataset, Representation and Explanations Evaluation eXplainable Artificial Intelligence, The World Conference (xAI-2024) 2024 Explainable Artificial Intelligence • XAI • Convolutional Neural Network • model evaluation • data evaluation • representation learning • ensemble • deep learning • machine learning
10.1007/978-3-031-63797-1_18
11Vilone G., Longo L. An Examination of the Effect of the Inconsistency Budget in Weighted Argumentation Frameworks and their Impact on the Interpretation of Deep Neural Networks Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023) 2023 Explainable artificial intelligence • Argumentation • Non-monotonic reasoning • Automatic attack extraction • Weighted argumentation frameworks • Inconsistency budget • Machine Learning • Neural Networks
10Davydko O., Pavlov V., Longo L.Selecting textural characteristics of chest X-Rays for pneumonia lesions classification with the integrated gradients XAI attribution method eXplainable Artificial Intelligence, The World Conference (xAI-2023) 2023 Explainable artificial intelligence • Neural networks • Texture analysis • Medical image processing • Classification • Machine Learning
10.1007/978-3-031-44064-9_36
9Gómez Tapia C., Bozic B., Longo L. Investigating the Effect of Pre-processing Methods on Model Decision-Making in EEG-Based Person Identification eXplainable Artificial Intelligence, The World Conference (xAI-2023) 2023 Electroencephalography • eXplainable Artificial Intelligence • Deep Learning • Signal processing • attribution xAI methods • Graph-Neural Network • Biometrics • signal-to-noise ratio
10.1007/978-3-031-44070-0_7
8Longo L., O'Reilly R.Artificial Intelligence and Cognitive Science 30th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2022), Revised selected papers. 2023 information retrieval • computer vision • artificial intelligence • machine learning • agent systems • collaborative networks • neural networks • image processing • patter recognition • neural computing
10.1007/978-3-031-26438-2
7Davydko O., Horodetska O., Nastenko I., Hladkyi Y., Pavlov V., Linnik M., Galkin O., Longo L.A Pipeline for the Diagnosis and Classification of Lung Lesions for Patients with COVID-19 IEEE 17th International Conference on Computer Sciences and Information Technologies 2022 COVID-19 • classification • segmentation • neural network • logistic self-organized forest • texture analysis
10.1109/CSIT56902.2022.10000435
6Chikkankod A.V., Longo L. On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps Machine Learning and Knowledge Extraction 2022 electroencephalography • latent space analysis • sliding windowing • convolutional autoencoders • automatic feature extraction • dense neural network
10.3390/make4040053
5Longo L.Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning Brain Sciences 2022 cognitive load • deep learning • self-supervision • brain rate • convolutional neural network • recurrent neural network • mental workload • EEG bands • electroencephalography • spectral topology-preserving head-maps
10.3390/brainsci12101416
4Ahmed T., Longo L. Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands IEEE Access 2022 Electroencephalography • convolutional variational autoencoder • latent space • deep learning • frequency bands • spectral topographic maps • neural networks
10.1109/ACCESS.2022.3212777
3Gómez-Tapia C., Bozic B., Longo L.On the Minimal Amount of EEG Data Required for Learning Distinctive Human Features for Task-Dependent Biometric Applications Frontiers Neuroinformatics 2022 biometrics • EEG • feature extraction • machine learning • deep learning • graph neural networks • Electroencephalography
10.0.13.61/fninf.2022.844667
2Jindala K., Upadhyaya R., Padhyb P.K., Longo L.Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals Artificial Intelligence-Based Brain-Computer Interface 2022 bi-directional LSTM • Long-Short Term Memory • Deep Learning • Schizophrenia • Spectral analysis • Convolutional neural network • Electroencephalography
10.1016/B978-0-323-91197-9.00011-4
1Marochko V., Leonard J., Mazzara M., Longo L.Pseudorehearsal in actor-critic agents with neural network function approximation IEEE 32nd International Conference on Advanced Information Networking and Applications 2018 Reinforcement learning • Neural Networks • Catastrophic Forgetting • Pseudorehearsal • Artificial Intelligence
10.1109/AINA.2018.00099
# Authors Title Details Date Pdf/Links/Bibtex Keywords