Explainable Artificial Intelligence: Concepts, Techniques, Analytics and Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 290
Special Issue Editors
Interests: computer vision; machine learning; deep learning; wireless sensor networks; IoT
Special Issues, Collections and Topics in MDPI journals
Interests: neuro-fuzzy technologies; fuzzy logic approaches; adaptive fuzzy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Explainable Artificial Intelligence (XAI) has emerged as a crucial field addressing the need for transparency and understanding in AI systems to foster trust and accountability. XAI aims to make AI decision-making processes comprehensible to humans by developing methodologies that clarify how AI systems arrive at their decisions, encompassing both final outputs and intermediate processes. Techniques to enhance explainability include creating inherently interpretable models like decision trees, applying post hoc methods such as LIME and SHAP to existing models, and using visualization tools like feature importance plots and heatmaps. Analytical frameworks in XAI evaluate the accuracy, comprehensibility, and actionability of explanations to ensure they are useful to end-users. XAI applications span healthcare, finance, autonomous systems, legal contexts, agriculture, and transportation, where transparency enhances trust, safety, regulatory compliance, and operational efficiency. This Special Issue explores the latest advancements in XAI, fostering collaboration and innovation in developing more explainable and reliable AI systems.
Topics of Interest:
- Bio-inspired methods, deep learning, convolutional neural networks, hybrid architectures, etc.
- Time series analysis, fractional-order controllers, gradient field methods, surface reconstruction, and other mathematical models for intelligent feature detection, extraction, and recognition.
- Embedded intelligent computer vision algorithms.
- Human, nature, technology, and various object activity recognition models.
- Hyper-parameter learning and tuning, automatic calibration, and hybrid and surrogate learning for computational intelligence in vision systems.
- Intelligent video and image acquisition techniques.
Dr. Mohit Mittal
Prof. Dr. Valentina E. Balas
Guest Editors
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Keywords
- explainable AI
- computer vision algorithms
- embedded AI
- AI approaches
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