{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:55:04Z","timestamp":1773856504231,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This study aims to predict anxiety among college students using optimization-enhanced neural network models instead of conventional machine learning. The student anxiety and depression dataset from Kaggle (6,982 records) was analyzed through comprehensive text preprocessing (tokenization, stop-word removal, TF-IDF feature extraction, and SMOTE balancing), exploratory data analysis, and a set of baseline models including ANN, RNN, ES-ANN + LOF, Harmony-Search ANN, and GA-BP ANN. To address the limitations of earlier approaches, a new SSO-FFNN (Scalable Seeker Optimization-Enhanced Feedforward Neural Network) framework was introduced, where the SSO algorithm optimizes network weights and biases to avoid local minima and improve convergence speed. Results show that the proposed SSO-FFNN achieved 97 % accuracy, an F1-score of 0.84, and a ROC-AUC of 0.97, outperforming all baseline models. These findings highlight the potential of optimization-driven neural networks for early anxiety detection and timely preventive counselling in academic settings.<\/jats:p>","DOI":"10.31449\/inf.v50i10.12124","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:46Z","timestamp":1773832366000},"source":"Crossref","is-referenced-by-count":0,"title":["SSO-FFNN: A Scalable Seeker Optimization-Enhanced Feedforward Neural Network for Predicting Anxiety Levels in College Students"],"prefix":"10.31449","volume":"50","author":[{"given":"Wenjing","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longhua","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12124\/6590","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12124\/6590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:47Z","timestamp":1773832367000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i10.12124","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}