AI Insights https://ojs.piscomed.com/index.php/AII <p>AI Insights (AII) is an international, open-access journal that welcomes original scientific contributions across the entire spectrum of artificial intelligence (AI). It covers a wide range to AI and its diverse applications including machine learning, natural language processing, computer vision, intelligent agents and multi-agent systems, robotics and so on.</p> <p>AII publishes research articles, review papers, short communications and so on. Full experimental details should be provided so that the results can be reproduced.</p> PiscoMed Publishing Pte. Ltd. en-US AI Insights Image Quality Assessment for Gaussian Blur using Siamese Network combined with ResNet-18 https://ojs.piscomed.com/index.php/AII/article/view/4668 <p>This paper presents a novel Image Quality Assessment (IQA) framework, SNR (Siamese Network with ResNet-18), specifically designed for Gaussian blur detection. The approach leverages a Siamese network architecture combined with the ResNet-18 backbone to process image pairs—one blurred and one reference—to predict image quality based on their differences. The model effectively captures high-frequency features lost due to blur, such as edges and gradients. We conduct extensive experiments on the TID2013 dataset, showing that SNR achieves superior performance in blur-specific IQA tasks compared to other full-reference methods. Data augmentation techniques significantly improve model generalization, resulting in a test accuracy of 97.37% for ResNet-18. The proposed method demonstrates a strong correlation with human judgment and robust generalization across various image contents, with future work focusing on expanding its applicability to other distortions and optimizing computational efficiency.</p> Emrah Arslan Omid Zare Zeinab Mohseni Mahdi Beigzadeh Abel Abebe Bzuayene Ali Abbaszadeh Sori Javad Hassannataj Joloudari Bulbula Kumeda Kussia Copyright (c) 2025 Author(s) 2025-05-04 2025-05-04 1 2 4668 4668 10.18282/aii4668 Beyond the black box: How fuzzy logic and multi-modal AI are revolutionizing personalized education https://ojs.piscomed.com/index.php/AII/article/view/4889 <p>In classrooms around the world, educators are drowning in data—but starving for insight. Quiz scores, video engagement, homework submissions, and login timestamps: all are logged, analyzed, and visualized. Yet these numbers rarely answer the deeper questions. Why is a student struggling? What kind of support do they need? Most educational AI systems treat data points as isolated facts, ignoring the tangled web of factors that shape learning.</p> Zongwen Fan Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/ 2025-07-24 2025-07-24 1 2 4889 4889 10.18282/aii4889