{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:54:24Z","timestamp":1775120064526,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T00:00:00Z","timestamp":1635552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>This paper explores the use of paraconsistent analysis for assessing neural networks from an explainable AI perspective. This is an early exploration paper aiming to understand whether paraconsistent analysis can be applied for understanding neural networks and whether it is worth further develop the subject in future research. The answers to these two questions are affirmative. Paraconsistent analysis provides insightful prediction visualisation through a mature formal framework that provides proper support for reasoning. The significant potential envisioned is the that paraconsistent analysis will be used for guiding neural network development projects, despite the performance issues. This paper provides two explorations. The first was a baseline experiment based on MNIST for establishing the link between paraconsistency and neural networks. The second experiment aimed to detect violence in audio files to verify whether the paraconsistent framework scales to industry level problems. The conclusion shown by this early assessment is that further research on this subject is worthful, and may eventually result in a significant contribution to the field.<\/jats:p>","DOI":"10.3390\/electronics10212660","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:21:08Z","timestamp":1635805268000},"page":"2660","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Neural Network Explainable AI Based on Paraconsistent Analysis: An Extension"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2221-2261","authenticated-orcid":false,"given":"Francisco S.","family":"Marcondes","sequence":"first","affiliation":[{"name":"ALGORITMI Centre, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8313-7023","authenticated-orcid":false,"given":"Dalila","family":"Dur\u00e3es","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2378-5376","authenticated-orcid":false,"given":"Fl\u00e1vio","family":"Santos","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0722-2031","authenticated-orcid":false,"given":"Jos\u00e9 Jo\u00e3o","family":"Almeida","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3549-0754","authenticated-orcid":false,"given":"Paulo","family":"Novais","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre, University of Minho, 4710-057 Braga, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"ref_1","unstructured":"Larsen, E., Noever, D., MacVittie, K., and Lilly, J. (2021). Overhead-MNIST: Machine Learning Baselines for Image Classification. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Farooq, A., Jia, X., Hu, J., and Zhou, J. (2021). Transferable Convolutional Neural Network for Weed Mapping with Multisensor Imagery. IEEE Trans. Geosci. Remote. Sens.","DOI":"10.1109\/TGRS.2021.3102243"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farooq, A., Hu, J., and Jia, X. (2018, January 22\u201327). Weed classification in hyperspectral remote sensing images via deep convolutional neural network. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518541"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Oliveira, P., Fernandes, B., Analide, C., and Novais, P. (2021). Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities. Electronics, 10.","DOI":"10.3390\/electronics10101149"},{"key":"ref_5","first-page":"723","article-title":"Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches","volume":"31","author":"Fernandes","year":"2020","journal-title":"Informatica"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.inffus.2020.11.002","article-title":"Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy","volume":"68","author":"Fernandes","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Martins, R., Almeida, J.J., Henriques, P.R., and Novais, P. (2021, January 4\u20136). Identifying Depression Clues using Emotions and AI. Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021), Online.","DOI":"10.5220\/0010332811371143"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., and Yang, G.Z. (2019). XAI\u2014Explainable artificial intelligence. Sci. Robot., 4.","DOI":"10.1126\/scirobotics.aay7120"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Csisz\u00e1r, O., Csisz\u00e1r, G., and Dombi, J. (2020). Interpretable neural networks based on continuous-valued logic and multicriteria decision operators. Knowl.-Based Syst., 199.","DOI":"10.1016\/j.knosys.2020.105972"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Souza, S., and Abe, J.M. (2015). Paraconsistent Artificial Neural Networks and Aspects of Pattern Recognition. Paraconsistent Intelligent-Based Systems, Springer.","DOI":"10.1007\/978-3-319-19722-7_9"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Barrio, E., Pailos, F., and Szmuc, D. (2018). What is a paraconsistent logic?. Contradictions, from Consistency to Inconsistency, Springer.","DOI":"10.1007\/978-3-319-98797-2_5"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Marcondes, F.S., Dur\u00e3es, D., Gomes, M., Santos, F., Almeida, J.J., and Novais, P. (2021). Neural Network eXplainable AI Based on Paraconsistent Analysis-an Initial Approach. Sustainable Smart Cities and Territories International Conference, Springer.","DOI":"10.1007\/978-3-030-78901-5_13"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Abe, J.M., Akama, S., and Nakamatsu, K. (2015). Introduction to Annotated Logics: Foundations for Paracomplete and Paraconsistent Reasoning, Springer.","DOI":"10.1007\/978-3-319-17912-4"},{"key":"ref_15","unstructured":"Fridy, J.A. (2000). Introductory Analysis: The Theory of Calculus, Gulf Professional Publishing."},{"key":"ref_16","unstructured":"Boult, T.E., Cruz, S., Dhamija, A.R., Gunther, M., Henrydoss, J., and Scheirer, W.J. (February, January 27). Learning and the unknown: Surveying steps toward open world recognition. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4023","DOI":"10.1007\/s11229-013-0246-8","article-title":"Some topological properties of paraconsistent models","volume":"190","year":"2013","journal-title":"Synthese"},{"key":"ref_18","unstructured":"Matan, O., Kiang, R., Stenard, C., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L., and Le Cun, Y. (1990, January 5\u20137). Handwritten character recognition using neural network architectures. Proceedings of the 4th USPS Advanced Technology Conference, Washington, DC, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Linden, A., and Kindermann, J. (1989, January 18\u201322). Inversion of multilayer nets. Proceedings of the International Joint Conference on Neural Networks, Washington, DC, USA.","DOI":"10.1109\/IJCNN.1989.118277"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T.E. (2016, January 27\u201330). Towards open set deep networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"G\u00fcnther, M., Hu, P., Herrmann, C., Chan, C.H., Jiang, M., Yang, S., Dhamija, A.R., Ramanan, D., Beyerer, J., and Kittler, J. (2017, January 1\u20134). Unconstrained face detection and open-set face recognition challenge. Proceedings of the 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA.","DOI":"10.1109\/BTAS.2017.8272759"},{"key":"ref_22","unstructured":"Heyting, A. (1966). Intuitionism: An Introduction, Elsevier."},{"key":"ref_23","unstructured":"Chollet, F. (2018). Deep Learning with Python, Manning."},{"key":"ref_24","unstructured":"LeCun, Y. (2021, September 25). The MNIST Database of Handwritten Digits. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Soliman, M.M., Kamal, M.H., Nashed, M.A.E.M., Mostafa, Y.M., Chawky, B.S., and Khattab, D. (2019, January 8\u20139). Violence Recognition from Videos using Deep Learning Techniques. Proceedings of the 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS46948.2019.9014714"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mateen, M., Wen, J., Song, S., and Huang, Z. (2019). Fundus image classification using VGG-19 architecture with PCA and SVD. 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