EXPERT SYSTEMS WITH APPLICATIONS
Scope & Guideline
Advancing Knowledge in AI and Engineering
Introduction
Aims and Scopes
- Artificial Intelligence and Machine Learning Applications:
The journal emphasizes the application of AI and machine learning techniques across diverse fields including healthcare, finance, transportation, and environmental monitoring. - Data Mining and Knowledge Discovery:
It covers methodologies for extracting meaningful patterns and insights from large datasets, focusing on techniques like clustering, classification, and predictive modeling. - Decision Support Systems:
Research on systems that aid in decision-making processes, integrating various data sources and expert knowledge to improve outcomes in complex scenarios. - Robustness and Security in Intelligent Systems:
The journal addresses the challenges of ensuring the reliability and security of intelligent systems, including methods for adversarial attack detection and mitigation. - Sustainability and Environmental Applications:
It explores applications of expert systems in promoting sustainability, energy efficiency, and environmental protection. - Human-Centric Applications:
Research that focuses on enhancing user experience and interaction with intelligent systems, including emotion recognition and personalized recommendations.
Trending and Emerging
- Explainable AI (XAI):
There is a growing emphasis on developing explainable AI systems that provide transparency and interpretability, allowing users to understand the decision-making processes of intelligent systems. - Federated Learning and Privacy-Preserving Techniques:
Research on federated learning is gaining momentum, focusing on collaborative learning methods that maintain data privacy and security, especially in sensitive domains like healthcare. - Integration of AI with IoT Technologies:
The convergence of AI and IoT is a prominent theme, with studies exploring how intelligent systems can enhance data processing and decision-making in real-time IoT applications. - Ethics and Fairness in AI:
Emerging discussions around the ethical implications of AI systems, including fairness, bias mitigation, and the societal impact of intelligent technologies. - Multi-Modal Learning:
Research focusing on the integration of various data modalities (e.g., text, audio, and visual) for improved learning and decision-making capabilities. - Blockchain Applications in Expert Systems:
The application of blockchain technology in ensuring data integrity and security for intelligent systems is becoming a significant area of interest.
Declining or Waning
- Traditional Statistical Methods:
There has been a decline in the publication of papers focused solely on traditional statistical analysis, as more researchers favor machine learning and AI-based approaches for predictive modeling. - Basic Heuristic Algorithms:
The interest in basic heuristic optimization methods has waned, with a growing preference for hybrid approaches that combine various advanced techniques for better performance. - Single-Domain Applications:
There is a noticeable reduction in the number of studies focusing on single-domain applications, as interdisciplinary approaches gain traction and researchers seek to address more complex, multi-faceted problems. - Manual Feature Engineering:
Research that relies heavily on manual feature extraction is declining, as automated methods and deep learning techniques become more prevalent and effective. - Conventional Rule-Based Systems:
The focus on conventional rule-based systems has decreased, as more adaptive and learning-based systems are developed to handle dynamic environments.
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