Scopus Index Publications
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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Item Embargo Early Detection of Student Mental Health and Academic Burnout Using Multimodal AI-Based Behavioral, Physiological, and Emotional Analysis(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Indrapala W.V.H.; Kumarasinghe K.D.K.Y.; De Silva A.H.H.; Ranathunga A.K.M.; Weerasinghe, L; Weerathunga, IMental health issues such as stress, anxiety, depression, and academic burnout are increasingly common among university students and have a significant impact on academic performance and long-term well-being. Existing assessment approaches rely mainly on self-reported questionnaires and periodic evaluations, which are reactive, subjective, and ineffective for early intervention. This paper presents a multimodal artificial intelligence-based system for early identification of student mental health conditions by analyzing behavioral, physiological, emotional, and academic data. The proposed framework integrates facial emotion recognition, wearable sensor data analysis, natural language processing of reflective text to continuously monitor student well-being in a privacy-aware manner. Machine learning and deep learning models are employed to detect stress, anxiety, and burnout indicators and to predict future mental health risks. Experimental results obtained from real and synthetic datasets demonstrate that multimodal analysis provides more reliable and accurate predictions than single-source methods. The proposed system enables early risk identification and supports timely intervention in academic environments.Item Embargo Stealth Eye: Behavioral Analysis for Fileless Malware Detection(Institute of Electrical and Electronics Engineers Inc., 2025) Bandara H.M.H.M; Ayeshani K.M.N; Kumari M.M.P.M; Wijerathna D.M.S.T; Abeywardena, K.Y; Wijesooriya, AFileless malware is a significant cybersecurity threat as it is entirely present in system memory and evades traditional signature-based detection methods. This paper introduces STEALTH EYE, an endpoint behavioral analysis framework for detecting fileless malware, such as ransomware, spyware, trojans, and RedLine Stealer, in real time. The framework utilizes an endpoint agent that monitors system activity in real time and captures snapshots of behavior every 60 seconds for real- time threat analysis. These captures track memory injections, DLL loading and execution, file and handle operations, service activity, process and thread behavior, registry modifications, network communications, cryptographic function usage, keystroke logging, and clipboard access. The data that is collected is analyzed through supervised machine learning mechanisms to detect patterns that indicate fileless malware activity. In contrast to traditional post-infection forensic approaches, STEALTH EYE provides real-time monitoring, notification, and active response with enhanced cybersecurity resilience against the widespread fileless attacks.