International Journal of Applied Mathematics and Computer Science最新文献

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Semi–Supervised vs. Supervised Learning for Mental Health Monitoring: A Case Study on Bipolar Disorder 心理健康监测的半监督学习与监督学习：躁郁症案例研究

International Journal of Applied Mathematics and Computer Science Pub Date : 2023-09-01 DOI: 10.34768/amcs-2023-0030

Gabriella Casalino, Giovanna Castellano, O. Hryniewicz, Daniel Leite, Karol Opara, Weronika Radziszewska, Katarzyna Kaczmarek-Majer

{"title":"Semi–Supervised vs. Supervised Learning for Mental Health Monitoring: A Case Study on Bipolar Disorder","authors":"Gabriella Casalino, Giovanna Castellano, O. Hryniewicz, Daniel Leite, Karol Opara, Weronika Radziszewska, Katarzyna Kaczmarek-Majer","doi":"10.34768/amcs-2023-0030","DOIUrl":"https://doi.org/10.34768/amcs-2023-0030","url":null,"abstract":"Abstract Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patients. Nonetheless, the psychiatric assessment of the patient’s mental state is typically a sporadic occurrence that takes place every few months. Consequently, only a slight fraction of the acoustic data is labeled and applicable for supervised learning. The majority of the related work on mental health monitoring limits the considerations only to labeled data using a predefined ground-truth period. On the other hand, semi-supervised methods make it possible to utilize the entire dataset, exploiting the regularities in the unlabeled portion of the data to improve the predictive power of a model. To assess the applicability of semi-supervised learning approaches, we discuss selected state-of-the-art semi-supervised classifiers, namely, label spreading, label propagation, a semi-supervised support vector machine, and the self training classifier. We use real-world data obtained from a bipolar disorder patient to compare the performance of the different methods with that of baseline supervised learning methods. The experiment shows that semi-supervised learning algorithms can outperform supervised algorithms in predicting bipolar disorder episodes.","PeriodicalId":502322,"journal":{"name":"International Journal of Applied Mathematics and Computer Science","volume":"245 1","pages":"419 - 428"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139345754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event–Triggered Cooperative Control for High–Order Nonlinear Multi–Agent Systems with Finite–Time Consensus 具有有限时间共识的高阶非线性多代理系统的事件触发合作控制

International Journal of Applied Mathematics and Computer Science Pub Date : 2023-09-01 DOI: 10.34768/amcs-2023-0032

Shiyin Gong, Meirong Zheng, Jing Hu, Anguo Zhang

引用次数: 0

Optimal Control Problems without Terminal Constraints: The Turnpike Property with Interior Decay 无终端约束的最优控制问题：具有内部衰减的岔道特性

International Journal of Applied Mathematics and Computer Science Pub Date : 2023-09-01 DOI: 10.34768/amcs-2023-0031

M. Gugat, Martin Lazar

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引用次数: 0

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