International Journal of Applied Mathematics and Computer Science's Cover Image
International Journal of Applied Mathematics and Computer Science
Mathematical Modeling in Medical Problems (Special section, pp. 349-428), Urszula Foryś, Katarzyna Rejniak, Barbara Pękala, Agnieszka Bartłomiejczyk (Eds.)

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Alanazi, A. (2022). Using machine learning for healthcare challenges and opportunities, Informatics in Medicine Unlocked 30: 100924. Search in Google Scholar

Antosik-Wójcińska, A.Z., Dominiak, M., Chojnacka, M., Kaczmarek-Majer, K., Opara, K.R., Radziszewska, W., Olwert, A. and Łukasz Świecicki (2020). Smartphone as a monitoring tool for bipolar disorder: A systematic review including data analysis, machine learning algorithms and predictive modelling, International Journal of Medical Informatics 138: 104131. Search in Google Scholar

Ao, X., Luo, P., Ma, X., Zhuang, F., He, Q., Shi, Z. and Shen, Z. (2014). Combining supervised and unsupervised models via unconstrained probabilistic embedding, Information Sciences 257: 101–114. Search in Google Scholar

Arevian, A.C., Bone, D., Malandrakis, N., Martinez, V.R., Wells, K.B., Miklowitz, D.J. and Narayanan, S. (2020). Clinical state tracking in serious mental illness through computational analysis of speech, PLoS ONE 15(1): e0225695. Search in Google Scholar

Arshad, A., Riaz, S. and Jiao, L. (2019). Semi-supervised deep fuzzy c-mean clustering for imbalanced multi-class classification, IEEE Access 7: 28100–28112. Search in Google Scholar

Basu, S., Banerjee, A. and Mooney, R. (2002). Semi-supervised clustering by seeding, Proceedings of the 19th International Conference on Machine Learning (ICML-2002), Sydney, Australia. Search in Google Scholar

Bennett, K. and Demiriz, A. (1998). Semi-supervised support vector machines, in M. Kearns et al. (Eds), Advances in Neural Information Processing Systems, Vol. 11, MIT Press, Cambridge, pp. 368–374. Search in Google Scholar

Bezdek, J.C. (2013). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York. Search in Google Scholar

Bilenko, M., Basu, S. and Mooney, R.J. (2004). Integrating constraints and metric learning in semi-supervised clustering, Proceedings of the 21st International Conference on Machine Learning, Banff, Canada, p. 11. Search in Google Scholar

Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (2017). Classification and Regression Trees, Routledge, New York. Search in Google Scholar

Cai, J., Hao, J., Yang, H., Zhao, X. and Yang, Y. (2023). A review on semi-supervised clustering, Information Sciences 632: 164–200. Search in Google Scholar

Casalino, G., Castellano, G., Galetta, F. and Kaczmarek-Majer, K. (2020). Dynamic incremental semi-supervised fuzzy clustering for bipolar disorder episode prediction, in A. Appice et al. (Eds), Discovery Science, DS 2020, Lecture Notes in Computer Science, Vol. 12323, Springer, Cham, pp. 79–93. Search in Google Scholar

Dominiak, M., Kaczmarek-Majer, K., Antosik-Wojcinska, A.Z., Opara, K.R., Wojnar, M., Olwert, A., Radziszewska, W., Hryniewicz, O., Swiecicki, L. and Mierzejewski, P. (2022). Behavioural data collected from smartphones in the assessment of depressive and manic symptoms for bipolar disorder patients: Prospective observational study, Journal of Medical Internet Research 24(1): e28647. Search in Google Scholar

Espinola, C.W., Gomes, J.C., Pereira, J.M.S. and dos Santos, W.P. (2021). Detection of major depressive disorder using vocal acoustic analysis and machine learning—An exploratory study, Research on Biomedical Engineering 37: 53–64. Search in Google Scholar

Eyben, F., Weninger, F., Gross, F. and Schuller, B. (2013). Recent developments in openSMILE, the Munich open-source multimedia feature extractor, Proceedings of the 21st ACM International Conference on Multimedia, Barcelona, Spain, pp. 835–838. Search in Google Scholar

Faurholt-Jepsen, M., Busk, J., Frost, M., Bardram, J.E., Vinberg, M. and Kessing, L.V. (2019). Objective smartphone data as a potential diagnostic marker of bipolar disorder, Australian & New Zealand Journal of Psychiatry 53(2): 119–128, PMID: 30387368. Search in Google Scholar

Faurholt-Jepsen, M., Vinberg, M., Debel, S., Bardram, J.E. and Kessing, L.V. (2016). Behavioral activities collected through smartphones and the association with illness activity in bipolar disorder, International Journal of Methods in Psychiatric Research 25(4): 309–323. Search in Google Scholar

Gomes, H.M., Grzenda, M., Mello, R., Read, J., Le Nguyen, M.H. and Bifet, A. (2022). A survey on semi-supervised learning for delayed partially labelled data streams, ACM Computing Surveys 55(4): 1–42. Search in Google Scholar

González-Almagro, G., Peralta, D., De Poorter, E., Cano, J.-R. and García, S. (2023). Semi-supervised constrained clustering: An in-depth overview, ranked taxonomy and future research directions, arXiv: 2303.00522. Search in Google Scholar

Grande, I., Berk, M., Birmaher, B. and Vieta, E. (2016). Bipolar disorder, The Lancet 387(10027): 1561–1572. Search in Google Scholar

Grünerbl, A., Muaremi, A. and Osmani, V. (2015). Smartphone-based recognition of states and state changes in bipolar disorder patients, IEEE Journal of Biomedical and Health Informatics 19(1): 140–148. Search in Google Scholar

Hryniewicz, O. and Kaczmarek-Majer, K. (2021). Possibilistic aggregation of inhomogeneous streams of data, 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, pp. 1–6, DOI: 10.1109/FUZZ45933.2021.9494583. Search in Google Scholar

Kaczmarek-Majer, K., Casalino, G., Castellano, G., Dominiak, M., Hryniewicz, O., Kamińska, O., Vessio, G. and Díaz-Rodríguez, N. (2022a). Plenary: Explaining black-box models in natural language through fuzzy linguistic summaries, Information Sciences 614: 374–399. Search in Google Scholar

Kaczmarek-Majer, K., Casalino, G., Castellano, G., Hryniewicz, O. and Dominiak, M. (2022b). Explaining smartphone-based acoustic data in bipolar disorder: Semi-supervised fuzzy clustering and relative linguistic summaries, Information Sciences 588: 174–195. Search in Google Scholar

Kaczmarek-Majer, K., Casalino, G., Castellano, G., Leite, D. and Hryniewicz, O. (2022c). Fuzzy linguistic summaries for explaining online semi-supervised learning, 2022 IEEE 11th International Conference on Intelligent Systems, Warsaw, Poland, pp. 1–8. Search in Google Scholar

Kamińska, O., Kaczmarek-Majer, K., Opara, K., Jakuczun, W., Dominiak, M., Antosik-Wójcińska, A., Święcicki, Ł. and Hryniewicz, O. (2019). Self-organizing maps using acoustic features for prediction of state change in bipolar disorder, in M. Marcos et al. (Eds), Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems, Springer, Berlin/Heidelberg, pp. 148–160. Search in Google Scholar

Kamińska, O., Kaczmarek-Majer, K. and Hryniewicz, O. (2020). Acoustic feature selection with fuzzy clustering, self organizing maps and psychiatric assessments, Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, Lisbon, Portugal, pp. 342–355. Search in Google Scholar

Kmita, K., Casalino, G., Castellano, G., Hryniewicz, O. and Kaczmarek-Majer, K. (2022). Confidence path regularization for handling label uncertainty in semi-supervised learning: Use case in bipolar disorder monitoring, 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Padua, Italy, pp. 1–8. Search in Google Scholar

Kusy, M. and Zajdel, R. (2021). A weighted wrapper approach to feature selection, International Journal of Applied Mathematics and Computer Science 31(4): 685–696, DOI: 10.34768/amcs-2021-0047. Search in Google Scholar

Lai, D.T.C. and Garibaldi, J.M. (2011). A comparison of distance-based semi-supervised fuzzy c-means clustering algorithms, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Taipei, Taiwan, pp. 1580–1586. Search in Google Scholar

Leite, D., Decker, L., Santana, M. and Souza, P. (2020). EGFC: Evolving Gaussian fuzzy classifier from never-ending semi-supervised data streams—With application to power quality disturbance detection and classification, 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, pp. 1–9. Search in Google Scholar

Li, K., Cao, Z., Cao, L. and Zhao, R. (2009a). A novel semi-supervised fuzzy c-means clustering method, Chinese Control and Decision Conference, Guilin, China, pp. 3761–3765. Search in Google Scholar

Li, Y.-F., Kwok, J.T. and Zhou, Z.-H. (2009b). Semi-supervised learning using label mean, Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, Canada, pp. 633–640. Search in Google Scholar

Low, D., Bentley, K. and Ghosh, S.K. (2020). Automated assessment of psychiatric disorders using speech: A systematic review, Laryngoscope Investigative Otolaryngology 315(1): 96–116. Search in Google Scholar

Mai, D.S. and Ngo, L.T. (2015). Semi-supervised fuzzy c-means clustering for change detection from multispectral satellite image, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, Turkey, pp. 1–8. Search in Google Scholar

Otsu, N. (1979). A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics 9(1): 62–66. Search in Google Scholar

Panek, D., Skalski, A., Gajda, J. and Tadeusiewicz, R. (2015). Acoustic analysis assessment in speech pathology detection, International Journal of Applied Mathematics and Computer Science 25(3): 631–643, DOI: 10.1515/amcs-2015-0046. Search in Google Scholar

Pedrycz, W. and Waletzky, J. (1997). Fuzzy clustering with partial supervision., IEEE Transactions on Systems, Man and Cybernetics B: Cybernetics 27(5): 787–95. Search in Google Scholar

Ruiz, D. and Finke, J. (2019). Lyapunov-based anomaly detection in preferential attachment networks, International Journal of Applied Mathematics and Computer Science 29(2): 363–373, DOI: 10.2478/amcs-2019-0027. Search in Google Scholar

Vapnik, V. (2006). Estimation of Dependences Based on Empirical Data, Springer Berlin/Heidelberg. Search in Google Scholar

Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods, 33rd Annual Meeting of the Association for Computational Linguistics, Cambridge, USA, pp. 189–196. Search in Google Scholar

Zhou, D., Bousquet, O., Lal, T., Weston, J. and Schölkopf, B. (2003). Learning with local and global consistency, in S. Thrun et al. (Eds), Advances in Neural Information Processing Systems, MIT Press, Cambridge. Search in Google Scholar

Zhu, X. and Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation, Report CMUCALD-02-107, Carnegie Mellon University, Pittsburgh. Search in Google Scholar

eISSN:
2083-8492
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Mathematics, Applied Mathematics