MAKE MDPI

🔥 XAI Paper Highlight from Make

📝 Using Segmentation to Boost Classification Performance and Explainability in CapsNets

👥 Authors: Dominik Vranay, Maroš Hliboký, László Kovács, and Peter Sinčák

Explainability remains a key challenge in image classification, especially when using complex neural network architectures.

This paper introduces Combined-CapsNet, a novel approach that integrates segmentation masks as reconstruction targets within Capsule Neural Networks. By focusing on significant image regions, the method aims to improve both classification performance and model explainability.

The study shows that segmentation-based reconstruction can help produce clearer and more interpretable explanations while reducing model complexity, supporting the development of more transparent image classification systems.

🔗 Read more: https://www.mdpi.com/2504-4990/6/3/68

📢 New paper published in MAKE

📝 Algorithmic Insights into Human Irrationality: Machine Learning Approaches to Detecting Cognitive Biases and Motivated Reasoning

👥 Authors: Sarthak Pattnaik, Chhayank Jain, and Eugene Pinsky

This study applies machine learning methods to large-scale public opinion data to examine cognitive biases and motivated reasoning.

Drawing on dual-process theory and nudge architecture, the authors use hierarchical unsupervised clustering and supervised predictive models to detect patterns related to loss aversion, availability heuristic, and partisan motivated reasoning.

The findings offer insights into political cognition, digital engagement, and the ethical use of AI-augmented behavioral analysis in an era of affective polarization.

🔗 Read more: https://www.mdpi.com/2504-4990/8/4/98

🎉 We are delighted to share that Machine Learning and Knowledge Extraction has achieved a CiteScore of 12.7 in the 2025 release.

This places MAKE among the top 10% of journals in the Scopus category Engineering (miscellaneous).

The new score represents a +28% increase from last year’s CiteScore of 9.9, reflecting the growing impact and visibility of the research published in the journal.

We are grateful to our authors, reviewers, Guest Editors, Editorial Board Members, and readers for their valuable contributions and continued support. 👏

📊 Full details: https://bit.ly/4e25s0c