Topic: Responsible Artificial Intelligence for Multidimensional Signal and Image Processing
Guest Editor(s)
Assistant Guest Editor(s)
Guest Editor Assistant(s)
Special Issue Introduction
The special issue embarks on an exhilarating journey at the intersection of Responsible Artificial Intelligence (AI) and multidimensional signal and image processing. We invite you to contribute your state-of-the-art research to the special issue, designed to showcase advancements in AI methodologies, including causal machine learning, explainable artificial intelligence, and the integration of large language models, such as GPT, applied to the nuanced challenges of multidimensional data processing. This special issue explores groundbreaking applications of Responsible AI, encompassing the following topics but not limited to:
● Causal Machine Learning: Novel approaches leveraging causal reasoning within machine learning models applied to multidimensional data, and explore how causality enhances predictive modeling, interpretability, and decision-making in complex systems.
● Explainable AI (XAI): Techniques that render AI models interpretable and transparent, ensuring that decisions made in the realm of multidimensional signal and image processing are not only accurate but also understandable, highlighting how XAI methods contribute to building trust in AI systems.
● Large Language Models (LLM): Transformative capabilities of LLM, such as GPT, in analyzing and generating responsible textual and contextual information relevant to multidimensional data and investigate how these models can be adapted to enhance understanding and processing in diverse domains.
● Meta-Learning for Multidimensional Tasks: Exploring meta-learning approaches that enable responsible AI models to adapt and learn from various multidimensional tasks, fostering more efficient and effective learning paradigms.
● Neuro-Inspired Computing: Investigating the integration of principles inspired by the human brain into Responsible AI models, exploring neuromorphic computing for enhanced capabilities in processing multidimensional data.
● Multimodal Data Fusion: Integrating diverse data modalities, including audio, video, and 3D imaging, utilizing Responsible AI, causal reasoning, explainability techniques, and large language models to unravel hidden patterns and relationships.
● Interpretable Time-Series Models: Enhancing the interpretability of time-series models is a growing concern and the need to developing methods to make complex models more transparent, aiding practitioners in understanding the rationale behind predictions.
● Responsible Medical Imaging and Video Diagnostics: Showcasing breakthroughs in AI-driven medical image analysis, disease detection, and diagnostic decision support systems with a focus on causality, interpretability, and leveraging language models.
● Responsible Human-AI Collaboration: Exploring how Responsible AI can seamlessly collaborate with human experts in multidimensional signal and image processing tasks, emphasizing synergies in decision-making and develop AI systems that dynamically adapt to changing conditions and interact effectively with multidimensional datasets in real-time.
This special issue contributes to the evolution of Responsible AI algorithms tailored for the challenges posed by multidimensional data, incorporating causal reasoning, XAI principles, and the unique capabilities of large language models. Your research will directly address real-world problems, fostering practical applications in healthcare, finance, robotics, and beyond, with a focus on causality, interpretability, and language understanding.
● Causal Machine Learning: Novel approaches leveraging causal reasoning within machine learning models applied to multidimensional data, and explore how causality enhances predictive modeling, interpretability, and decision-making in complex systems.
● Explainable AI (XAI): Techniques that render AI models interpretable and transparent, ensuring that decisions made in the realm of multidimensional signal and image processing are not only accurate but also understandable, highlighting how XAI methods contribute to building trust in AI systems.
● Large Language Models (LLM): Transformative capabilities of LLM, such as GPT, in analyzing and generating responsible textual and contextual information relevant to multidimensional data and investigate how these models can be adapted to enhance understanding and processing in diverse domains.
● Meta-Learning for Multidimensional Tasks: Exploring meta-learning approaches that enable responsible AI models to adapt and learn from various multidimensional tasks, fostering more efficient and effective learning paradigms.
● Neuro-Inspired Computing: Investigating the integration of principles inspired by the human brain into Responsible AI models, exploring neuromorphic computing for enhanced capabilities in processing multidimensional data.
● Multimodal Data Fusion: Integrating diverse data modalities, including audio, video, and 3D imaging, utilizing Responsible AI, causal reasoning, explainability techniques, and large language models to unravel hidden patterns and relationships.
● Interpretable Time-Series Models: Enhancing the interpretability of time-series models is a growing concern and the need to developing methods to make complex models more transparent, aiding practitioners in understanding the rationale behind predictions.
● Responsible Medical Imaging and Video Diagnostics: Showcasing breakthroughs in AI-driven medical image analysis, disease detection, and diagnostic decision support systems with a focus on causality, interpretability, and leveraging language models.
● Responsible Human-AI Collaboration: Exploring how Responsible AI can seamlessly collaborate with human experts in multidimensional signal and image processing tasks, emphasizing synergies in decision-making and develop AI systems that dynamically adapt to changing conditions and interact effectively with multidimensional datasets in real-time.
This special issue contributes to the evolution of Responsible AI algorithms tailored for the challenges posed by multidimensional data, incorporating causal reasoning, XAI principles, and the unique capabilities of large language models. Your research will directly address real-world problems, fostering practical applications in healthcare, finance, robotics, and beyond, with a focus on causality, interpretability, and language understanding.
Submission Deadline
30 Apr 2024
Submission Information
For Author Instructions, please refer to https://www.oaepublish.com/ir/author_instructions
For Online Submission, please login at https://oaemesas.com/login?JournalId=ir&IssueId=IR231130
Submission Deadline: 30 Apr 2024
Contacts: Amber Ren, Assistant Editor, editorial@intellrobot.com
