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-USAI InsightsImage 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 ArslanOmid Zare Zeinab MohseniMahdi BeigzadehAbel Abebe BzuayeneAli Abbaszadeh Sori Javad Hassannataj JoloudariBulbula Kumeda Kussia
Copyright (c) 2025 Author(s)
2025-05-042025-05-04124668466810.18282/aii4668Beyond 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/
2025-07-242025-07-24124889488910.18282/aii4889