{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:47:55Z","timestamp":1781714875864,"version":"3.54.5"},"reference-count":29,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Campus safety is an essential concern as schools, colleges, and universities work to create secure environments for students, staff, and visitors. Many existing security systems are not fully effective at detecting unusual behaviors or sending fast alerts, which can delay responses to potential threats. To improve this, the research introduces DeepCARE(Deep-learning-based Campus Anomaly &amp; Risk Evaluation), a deep learning-based framework designed to enhance behavior recognition and early warning for campus security. DeepCARE combines convolutional neural networks (CNNs) with long short-term memory (LSTM) networks to process real-time video footage and detect abnormal activities such as aggression, unauthorized access, and people staying too long in restricted areas. The system\u2019s main feature is its hybrid model, where CNNs extract key visual features from surveillance footage while LSTM networks analyze these features over time to recognize behavior patterns. DeepCARE also includes an anomaly detection module using autoencoders, which helps improve the system\u2019s accuracy and reduces false alarms. This makes DeepCARE a flexible and scalable solution, suitable not only for educational campuses but also for public spaces, transport hubs, and smart cities. By applying deep learning, DeepCARE supports early risk detection and faster response times, helping security teams create safer spaces. Experimental results show that DeepCARE achieves a behavior recognition accuracy of 94.5%, performs 8% better than traditional methods, and shortens emergency response times by 30%.<\/jats:p>","DOI":"10.1371\/journal.pone.0335040","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T18:39:30Z","timestamp":1762281570000},"page":"e0335040","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Application of deep learning in behavior recognition and early warning system for campus safety 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