• Flow-Based Ensemble Learning for Intrusion Detection in Software-Defined Networks 

  Karunarathne, K.M.G.B.C.; Dissanayaka, D.M.H.V.; Senanayake, P.S.R.P.S.; Mayuran, P.; Senthooran, V. (Korea Database Strategy Society (KDSS), 2026)

  We propose a machine learning-based intrusion detection system for SDN, considering the special vulnerability of the centralized control plane. It is trained on a publicly available dataset for SDN network traffic, which ...
• A YOLOv8 Based Recognition System for Sri Lankan Road Traffic Signs 

  Praveen, S.G.M.; Nanayakkara, R.S.; Fernando, W.B.D.A.; Jayaweera, A.L.L. (Korea Database Strategy Society (KDSS), 2026)

  Road traffic signs are crucial for road safety, providing drivers with essential information on speed limits, road conditions, and other important instructions. Accurate recognition of these signs is vital in preventing ...
• Edge-AI-Enabled Passenger Tracking for Public Buses with Face Recognition and GPS 

  Sanjeevan, S.; Caldera, H.P.S.N.; Gimhani, M.M.C.; Mayuran, P. (Korea Database Strategy Society (KDSS), 2026)

  In many developing countries, the counting of passengers in buses is done manually, resulting in a loss of revenue for the transportation companies. This paper proposes an IoT-based tracking system for passengers in buses ...
• Efficient and Interpretable Machine Learning for Encrypted Malicious Traffic Classification 

  Chamuditha, P.G.L.; Madhuwantha, P.L.G.H.K; Senarathna, P.K.C.; Mayuran, P.; Senthooran, V. (Korea Database Strategy Society (KDSS), 2026)

  Communications that are encrypted, such as HTTPS, TLS, and VPN, have become popular tools for ensuring privacy; yet, they can be used for hiding malicious payloads, making intrusion detection more challenging. This study ...
• An AI-Based Chatbot Framework for Intercultural Conflict Resolution in Online Communities 

  Vinthuja, T.; Libisanan, J.A.; Anitha, J.; Nusky Ahamed, T. (Korea Database Strategy Society (KDSS), 2026)

  The online social media facilitates easy communication between people from various cultures, but language varieties, emotional expression differences and cultural norms confusion could cause misunderstandings and conflicts. ...
• Recognizing Preschool Children Handwriting Using CNN 

  Abirami, S.; Jayarathna, J.H.S.S.M.; Abeysinghe, A.K.U.S.; Sakuntharaj, R. (Korea Database Strategy Society (KDSS), 2026)

  The major difficulty of identifying handwritten numbers and alphabetic letters produced by preschoolers is addressed in this study. It can be challenging for conventional automated recognition systems to correctly interpret ...
• A Classification and Grading Framework for Social Conflict Resolution Based on Knowledge Extraction from Large Language Models 

  Li, Y.; Liu, Z.; Li, W.; Hu, S.; Qi, M.; Tong, P.; Dai, W. (Korea Database Strategy Society (KDSS), 2026)

  Due to cultural and institutional differences, social conflicts in East countries are mainly addressed through mediation mechanisms embedded in community governance, while Western societies tend to rely more on formal ...
• An LLM-based Hierarchical Framework for Constructing the Disciplinary Knowledge System of Neuromanagement and Neuroengineering 

  Liu, Z.; Fu, Y.; Tong, P.; Dai, W. (Korea Database Strategy Society (KDSS), 2026)

  The concept of the discipline "Neuromanagement" was first introduced by Chinese scholar in 2006, aiming to apply neuroscience and technological methods to study psychological and behavioral issues in management. Over the ...
• Extract and Analyze the Performance Data from Research Papers using Generative AI 

  Abeysekara, W.A.D.S.S.; Yasotha, R. (Korea Database Strategy Society (KDSS), 2026)

  This study explores automated extraction and analysis of tabular data from research papers to streamline researchers’ workflows. It integrates Generative AI and Optical Character Recognition within an end-to-end pipeline ...
• Attendance Marking System using QR codes with Facial Features 

  Adikaram, K.K.J.K.B.; Mohomed Wazeem, S.K.; Ranaweera, S.D.; Sakuntharaj, R. (Korea Database Strategy Society (KDSS), 2026)

  This research presents the design and implementation of a secure, AI-powered attendance system utilizing dual authentication through facial recognition and QR code verification. The study addresses the prevalent issues ...
• Detecting Hate Speech on YouTube Comments: A Comparative Study of Machine Learning and Deep Learning Techniques 

  Sweshthika, S.; Yasotha, R. (Korea Database Strategy Society (KDSS), 2026)

  The proliferation of social media has significantly amplified user interactions, but it also poses serious threats to communities through the spread of harmful content such as hate speech. The emotionally charged and ...
• An Advanced Non-Invasive Blood Group Detection Using Fingerprint Biometrics: A Deep Learning Approach 

  Weerasinghe, W.D.; Banu, N.F.N.; Sewmini, A.R.M.N.; Venuja, N.; Saliny, N. (Korea Database Strategy Society (KDSS), 2026)

  The correct blood group is important in healthcare, forensics and emergency care, but traditional approaches are invasive and can only be applied in laboratory facilities with trained personnel, which is not applicable ...
• AI Agent-Based Framework for Integrated Chinese–Western Medicine Self Management: A Case Study of Type 2 Diabetes 

  Chen, Y.; Ma, Z.; Zong, J.; Huang, Y.; Kang, Y.; Dai, W. (Korea Database Strategy Society (KDSS), 2026)

  Chronic diseases require long-term, individualized, and adaptive self-management; however, existing digital health solutions often suffer from fragmented medical knowledge, limited personalization, and insufficient ...
• Emotion-Predictive Modeling for Personalized Music Recommendation in Individuals with Depression 

  Warakagoda, R.N.A.M.M.K.; Jayasinghe, J.K.C.S.; Kavishka, W.P.G.; Sakuntharaj, R. (Korea Database Strategy Society (KDSS), 2026)

  Depression is a prevalent mental health disorder that significantly affects emotional well-being, daily functioning, and quality of life. Although traditional treatments such as psychotherapy and medication are widely ...
• An Empirical Evaluation of Deep Learning Architectures for Music Emotion Recognition Using Audio Data 

  Amrithaa, T.; Himaathri, P.; Gunawardhana, M.U.K.; Senthooran, V. (Korea Database Strategy Society (KDSS), 2026)

  The Music Emotion Recognition (MER) system aims to automatically recognize the emotional content of music. MER has gained significant interest in recent times due to its applicability in music recommendation systems, ...
• Sociology of Poverty: A conceptual discussion and empirical realities 

  Thapa, A. (Korea Database Strategy Society (KDSS), 2026)

  This article explores the multidimensional nature of poverty from a sociological perspective, contrasting theoretical definitions with empirical realities. While the monetary approach remains the dominant global and ...
• An EEG-Informed Deep Reinforcement Learning Approach for Surveillance Video Understanding in Residential Communities 

  Zou, B.; Xiong, Z.; Liu, Y.; Zhuob, J.; Liu, J.; Dai, W. (Korea Database Strategy Society (KDSS), 2026)

  Surveillance video has been widely applied in residential communities, serving as a critical basis for abnormal situation detection, security early warning, and emergency assistance. However, existing machine learning ...
• Sparse-Gated Band Distillation for Single-Channel EEG Emotion Recognition in Neuroeducation 

  Gu, Y.; Dai, Y.; Ouyang, Z.; Dai, W.; Sun, H. (Korea Database Strategy Society (KDSS), 2026)

  Student emotion recognition in classroom and field practice training scenarios is a frequently encountered problem in neuroeducation. Multichannel EEG emotion recognition can achieve high reliability by exploiting spatial ...
• When Adoption Fades: Post-Adoption Dynamics of Electric Two-Wheeler Riders 

  Ashok, K.; Arora, S. (Korea Database Strategy Society (KDSS), 2026)

  The current study shifts the focus of sustainability researchers to potential rejectors, the set of consumers who intend to abandon a technology (electric vehicles, EVs in this case) after sufficient use. Recent research ...
• Solar and Wind Energy Generation Forecasting Using Deep Learning Time-Series Models 

  Musharrif, M.A.M.A.; Madhusanka, G.S.; Nashan, Y.M.; Venuja, N. (Korea Database Strategy Society (KDSS), 2026)

  Since solar and wind energy are becoming more and more integrated into electrical grids, accurate forecasting of renewable energy generation is essential for modern power systems. However, accurate prediction is difficult ...

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