Hybrid Artificial Intelligence for Systems and Applications
A special issue of Digital (ISSN 2673-6470).
Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 4888
Special Issue Editor
Interests: deep learning; XAI; human-centric AI; case-based reasoning; data mining; fuzzy logic and other machine learning and machine intelligence approaches for analytics—especially in big data
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Special Issue Information
Dear Colleagues,
This Special Issue contains extended papers from the sixth International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI′ 2024), 17–19 April 2024, Funchal (Madeira Island), Portugal (https://aspai-conference.com).
In recent years, the field of artificial intelligence (AI) has witnessed remarkable advancements, with systems and applications spanning various domains such as healthcare, finance, transportation, and manufacturing. One of the emerging paradigms within AI is hybrid artificial intelligence (HAI), which combines the strengths of different AI techniques to effectively address complex real-world problems. The concerns mentioned above related to trustworthy AI cannot be addressed through a single paradigm. We must incorporate various AI paradigms, such as learning, reasoning, optimization, inference, and meta-heuristics. Thus, the concept of “hybrid AI” is introduced that, computationally and mathematically, integrates different paradigms. Hybrid AI integrates multiple AI approaches, including symbolic reasoning, machine learning, evolutionary computation, expert systems, and fuzzy logic, among others, to create more robust and adaptive systems. The concept of hybrid AI stems from the recognition that no single AI technique can excel in all scenarios. While machine learning algorithms, such as deep neural networks, excel at pattern recognition and classification tasks, they may struggle with explainability and reasoning. Conversely, symbolic reasoning approaches are adept at logical inference and decision making but may lack the scalability and flexibility offered by machine learning techniques. By integrating these complementary approaches, hybrid AI endeavors to overcome the limitations of individual techniques and harness their combined capabilities to tackle complex problems more effectively. The systems and applications in hybrid AI are diverse and far-reaching. In healthcare, hybrid AI systems can assist in medical diagnoses and treatment recommendations by combining clinical expertise with data-driven insights from patient records and medical imaging. In finance, hybrid AI models can enhance risk assessments and portfolio optimization by integrating predictive analytics with expert knowledge of market dynamics. Similarly, in autonomous vehicles, hybrid AI enables robust decision making by combining sensor data processing with rule-based reasoning and machine learning for adaptive behavior in dynamic environments. Thus, this Special Issue, “Hybrid Artificial Intelligence for Systems and Applications”, aims to provide insights into principles, methodologies, and applications in this interdisciplinary field. Through a deeper understanding of hybrid AI, researchers, practitioners, and enthusiasts can leverage its potential to develop innovative solutions to complex real-world challenges, ultimately advancing the frontier of artificial intelligence and its practical applications across diverse domains.
Prof. Dr. Mobyen Uddin Ahmed
Guest Editor
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Keywords
- artificial intelligence (AI)
- hybrid artificial intelligence (HAI)
- learning
- reasoning
- optimization
- inference
- meta-heuristics
- symbolic reasoning
- machine learning
- evolutionary computation
- expert systems
- fuzzy logic
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