5,419,468 AI resources indexedWorking toward the world’s largest AI resource index.
Publications about AI methods and applications.
Research paper by Michael Angello Qadosy Riyadi and collaborators, published 2026 in Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). Research area: Privacy-Preserving Technologies in Data.
Research paper by Desi Ramayanti and collaborators, published 2026 in Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). Research area: Seismology and Earthquake Studies.
Research paper by Akhmad Dahlan and collaborators, published 2026 in Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). Research area: Text and Document Classification Technologies.
Research paper by Margareta Valencia Suci Handayani and collaborators, published 2026 in Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). Research area: Sentiment Analysis and Opinion Mining.
Research paper by Alexandros Ntagiantas and collaborators. arXiv record dated 2026-08-05. Research area: Machine learning.
Research paper by Dwija Wisnu Brata and collaborators, published 2026 in Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). Research area: Hate Speech and Cyberbullying Detection.
Research paper by Muhammad Rafi Athallah and collaborators, published 2026 in Jurnal Rekayasa Material Manufaktur dan Energi. Research area: Solar Radiation and Photovoltaics.
Research paper by Marhani Amalia Putri and collaborators, published 2025 in Jurnal Rekayasa dan Manajemen Sistem Industri. Research area: Text and Document Classification Technologies.
Research paper by Muhammad Ilham Arsyam and collaborators, published 2026 in Jurnal Informatika dan Rekayasa Elektronik. Research area: Sentiment Analysis and Opinion Mining.
This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks and identifying the dominant causes for such behaviours. Data augmentation techniques and ML models are utilized to predict the associated risk. Furthermore, SHapley Additive exPlanations (SHAP) and correlation analysis are used to rank the importance of variables in predictions. Experimental results indicate that Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models achieve the best results while DT has the best performance with an accuracy of 95:23% and an Area Under Curve (AUC) of 0.9
Research paper by Bahareh Golchin and collaborators. arXiv record dated 2025-04-03. Research area: Machine learning.
Research paper by Roger Waleffe and collaborators. arXiv record dated 2025-02-25. Research area: Machine learning.
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