IEEE Intelligent Systems is a bimonthly publication of the IEEE Computer Society that provides peer-reviewed, cutting-edge articles on the theory and applications of systems that perceive, reason, learn, and act intelligently. The editorial staff collaborates with authors to produce technically accurate, timely, useful, and readable articles as part of a consistent and consistently valuable editorial product.

IEEE Intelligent Systems emphasizes current practice and experience and promising new ideas that will likely see use in the near future. We welcome articles on topics across the spectrum of related work. Example topics include

    Knowledge-based systems
    Intelligent software agents
    Natural language processing
    Knowledge management
    Machine learning
    Data mining
    Adaptive and intelligent robotics
    The Semantic Web
    Social issues relevant to intelligent systems

Special Issue on Deep Learning for Health and Medicine
Submission Date: 2023-05-23

Nowadays, deep learning has spread over almost all fields. In healthcare and medicine, an immense amount of data is being generated by distributed sensors and cameras, as well as multi-modal digital health platforms that support audio, video, image, and text. The availability of data from medical devices and digital record systems has greatly increased the potential for automated diagnosis. The past several years have witnessed an explosion of interest in, and a dizzyingly fast development of, computer-aided medical investigations using MRI, CT, and X-ray images. Researchers, having reached a deeper understanding of the methods, on one hand are proposing elegant ways to better integrate machine learning with neural networks in complex problems (such as image reconstruction), and on the other hand are advancing the learning algorithms themselves. Note that medical imaging data may include 2D images, image volumes, and 3D geometric data (such as point cloud).

This special issue focuses on deep learning techniques for health and medicine, including but not limited to:
- Intelligent medical and health systems
- Novel theories and methods of deep learning for medical imaging
- Drug discovery with deep learning
- Pandemic (such as COVID-19) management with deep learning
- Health and medical behavior analytics with deep learning
- Medical visual question and answering
- Un/semi/weakly/fully- supervised medical data (text/images)
- Graph learning on medical data (text/images)
- Generating diagnostic reports from medical images
- Fewer labels in clinical informatics
- Summarization of clinical information
- Knowledge transfer under various clinical environments
- Multimodal medical image analysis
- Medical image registration
- Organ and lesion segmentation/detection
- Image classification with MRI/CT/PET
- Medical image enhancement/denoising
- Learning robust medical image representation with noisy annotation
- Predicting clinical outcomes from multimodal medical data
- Anomaly detection in medical images
- Active learning and life-long learning in medical computer vision
- User/patient psychometric modeling from video, image, audio, and text
Guest Editors:
- Imran Razzak, University of New South Wales (Australia)
- Xuequan Lu, Deakin University (Australia)
- Ahmed Abbasi, University of Notre Dame (USA)
- Zongyuan Ge, Monash University (Australia)
- Yuejie Zhang, Fudan University (China)

Special Issue on Advanced Multimedia and Multimodal AI
Submission Date: 2023-06-01

With the development of Internet technologies, large volumes of multimedia and more generally, multimodal data, can easily be shared among different users or entities for the purpose of diagnosis, analysis, search, recommendation, and prediction etc. in a variety of critical application contexts with no hard-copy backups being kept. Over the past few years, multimedia and multimodal applications have continued to utilize Artificial Intelligence (AI) techniques to bring revolutionary advancement in numerous applications and domains including multimedia content analysis, multimedia social networks, multimodal learning, and multimedia database management. Although AI is not new to multimedia, the researchers are trying to take advantage of the most recent AI technologies in multimedia research to further advance the field. However, many challenges remain such as obtaining, analyzing and managing clean, large, and comprehensive multimedia and multimodal data, security, and applications, etc., which calls for advanced multimedia and multimodal AI to tackle these problems and to lead to great opportunities in the near future. Topics include, but are not limited to

- Multimedia and multimodal deep learning
- Multimedia and multimodal data analytics
- AI-based multimedia security and privacy processing
- Intelligent computational techniques for multimedia and multimodal data
- AI-based multimodal detection, retrieval, fusion, analysis, and recommendation
- AI for remote and mobile multimedia processing and systems
- Multimedia and multimodal intelligent systems
- Explainable AI (XAI) on multimedia and multimodal applications
- AI-enabled multimedia and multimodal applications
- Multimedia and multimodal AI for social good

Guest Editors:
- Amit Kumar Singh, National Institute of Technology Patna (India)
- Stefano Berretti, University of Florence (Italy)
- Abderrahim BENSLIMANE, University of Avignon (France)
- Mohamed Younis, University of Maryland Baltimore County (USA) 

Special Issue on Adversarial Learning for Intelligent Cyber-Physical Systems
Submission Date: 2023-09-01

Cyber-physical systems (CPS) are computer systems where the used mechanisms can be monitored and/or controlled by computer algorithms. CPS is also the collaboration of computing entities that are intensively connected to the surrounding physical world and its ongoing processes, while providing and using data access and processing services available on the Internet, which is designed to make the physical world more accessible to computational entities. In CPS, both software and physical components tend to be intertwined, enable operations on a variety of temporal as well as spatial scales, show tendencies towards multiple distinct behavioural modalities, and open gateways of interaction between Intelligent Systems in aspects that may be able to change with context. Both current machine learning and deep learning (ML/DL) techniques with their strong learning capabilities can be beneficial to CPS in a number of ways (e.g., spam filtering, intrusion detection, process monitoring). However, most ML/DL techniques rely on large data sets to achieve good performance, thus it has been a challenging issue to secure data collected by CPS, while preserving its confidentiality (privacy). In addition, well-trained ML models used in CPS are highly vulnerable to malicious attacks (e.g., adversarial/poisoning attacks) due to the distributed property of data sources and the inherent physical constraints imposed by CPS. As ML/DL techniques are integrated into a greater number of CPSs currently, those malicious attacks could become a serious problem. To overcome the above limitations and develop a trustworthiness of ML/DL models applied in CPS under malicious attacks, adversarial learning has been proposed to study these attacks, as well as the defenses on ML/DL algorithms in which to identify and spot the intentionally misleading data or behaviours, which is important for CPS.

This special issue aims to study the critical ML/DL theoretical and practical issues in adversarial environments and for CPS. The following interests are considered in this special issue, but not limited to:

- Foundational theory development of adversarial learning in CPS
- Managing dynamic configurations in CPS using adversarial learning
- Representation learning, knowledge discovery and model generalizability in CPS
- Distributed and federated adversarial learning in CPS
- Large-scale adversarial learning developments in CPS
- Adversarial learning of CPS interactions, behaviors, and impacts
- Malware and intrusion detection in CPS that use adversarial learning
- Detecting data poisoning and evasion attacks in CPS
- Adversarial learning-based risk assessment and risk-aware decision making in CPS
- Ethical and data protection issues by adversarial learning in CPS
- Explainable, transparent, or interpretable CPS systems via adversarial learning
- Robustness and reliability in adversarial learning systems in CPS