{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:34:39Z","timestamp":1776976479350,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>At present, early warning of students' emotional crises mostly relies on single data sources and traditional models, making it difficult to achieve high-precision, real-time monitoring with cross-individual generalization. To address this, we propose a pipeline integrating multi-modal physiological signals and deep transfer learning: 1) Signal preprocessing (adaptive denoising via attention mechanism and z-score normalization) to improve quality of ECG, EEG, GSR, and EMG signals; 2) Attention-guided cross-modal feature fusion using a physiological behavior mapping matrix to unify feature spaces; Evaluation metrics include accuracy, true positive rate (TPR), false positive rate (FPR), and single-sample processing latency. Baseline models for comparison include traditional CNN-LSTM and standard BERT-BASE. System tests show that the accuracy of feature extraction of the heart rate signal is 78.2%, and that of the skin electro cutaneous signal is 34.5%. After deep transfer learning optimization, the emotional crisis early warning accuracy on the small-sample cross-subject dataset (n=80) increased from 45.3% (baseline) to 88.1%, the false positive rate (FPR) dropped to 12.75%, the true positive rate (TPR) reached 87.7%, and the false negative rate (FNR) was 12.3%. The single-sample processing latency was 23.45 ms.<\/jats:p>","DOI":"10.31449\/inf.v50i11.12049","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:20Z","timestamp":1776972980000},"source":"Crossref","is-referenced-by-count":0,"title":["Attention-Guided Multimodal Signal Fusion with Transformer-Based Deep Transfer Learning for Real-Time Emotional Crisis Prediction in Students"],"prefix":"10.31449","volume":"50","author":[{"given":"Wanhao","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaxin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,23]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12049\/6653","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12049\/6653","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:20Z","timestamp":1776972980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,4,23]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i11.12049","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}