An International Journal on Multi-Sensor, Multi-Source Information Fusion

The journal is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source, multi-process information fusion and thereby promote the synergism among the many disciplines that are contributing to its growth. The journal is the premier vehicle for disseminating information on all aspects of research and development in the field of information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome. The journal publishes original papers, letters to the Editors and from time to time invited review articles, in all areas related to the information fusion arena including, but not limited to, the following suggested topics:

• Data/Image, Feature, Decision, and Multilevel Fusion
• Multi-classifier/Decision Systems
• Multi-Look Temporal Fusion
• Multi-Sensor, Multi-Source Fusion System Architectures
• Distributed and Wireless Sensor Networks
• Higher Level Fusion Topics Including Situation Awareness And Management
• Multi-Sensor Management and Real-Time Applications
• Adaptive And Self-Improving Fusion System Architectures
• Active, Passive, And Mixed Sensor Suites
• Multi-Sensor And Distributed Sensor System Design
• Fusion Learning In Imperfect, Imprecise And Incomplete Environments
• Intelligent Techniques For Fusion Processing
• Fusion System Design And Algorithmic Issues
• Fusion System Computational Resources and Demands Optimization
• Special Purpose Hardware Dedicated To Fusion Applications
• Mining Remotely Sensed Multi-Spectral/Hyper-Spectral Image Data Bases
• Information Fusion Applications in Intrusion Detection, Network Security, Information Security and Assurance arena
• Applications such as Robotics, Space, Bio-medical, Transportation, Economics, and Financial Information Systems
• Real-World Issues such as Computational Demands, Real-Time Constraints in the context of Fusion systems.

Special Issue on Multi-Sensor Data Fusion for Smart Healthcare
截稿日期: 2024-12-15

Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images and 3D volumes or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make an auscultation and produce a report in text format; an electrocardiogram can be made and printed in time series format, an x- ray can be performed and saved as an image; a volume can be provided through an angiography; temporal information can be given by echocardiograms, 4D information can be extracted through flow MRI. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance. This high and diverse amount of information needs to be organized and mined in an appropriate way so that meaningful information can be extracted. In recent times, multimodal medical data fusion and analysis can combine salient information into a single source to ensure better diagnostic accuracy and assessment, and ultimately personalized healthcare services delivery. It is established that multi-sensor data fusion provides a valuable solution for healthcare tele monitoring requirements. These healthcare applications demand the usage of wide range of heterogeneous sensors/devices. Further, the multi-sensor fusion approaches increase the reliability and robustness of the healthcare systems by reducing the threatens posed by various malfunctions in the sensors and environment itself such as the power and communication. In addition, global decisions can be taken based on the dependability between the data sources. Finally, exploiting and fusing generative AI techniques for medical data generation could be fundamental to create new opportunities and tackle complex issues in the healthcare area.

Motivated by these facts, this special issue targets the researchers from both academia and industry to explore and share new ideas, approaches, theories and practices with focus on multi-sensor data fusion for smart healthcare.Authors are invited to submit high quality proposal on topics including, but not limited to:

    Multi-sensor fusion for healthcare
    Combining multiple models for medical data
    Combining multiple sources in medical data
    Data Fusion in the Internet of medical things
    AI techniques/algorithms for multi-source medical data fusion
    Deep learning for multi-source medical data processing
    Feature fusion for medical data
    Hierarchical models for medical information fusion
    Fusion algorithm for Bio-Inspired data
    Tensor schemes and constraint algorithms for health and medical data fusion
    Heterogeneous information fusion for healthcare
    Medical data fusion trust, security and privacy in trust evaluation
    Fusing generative AI techniques for generating healthcare data

Guest editors:

Giancarlo Fortino, PhD
University of Calabria, Arcavacata di Rende, Italy. 
g.fortino@unical.it

David Camacho, PhD
Universidad Politécnica Madrid, Madrid, Spain. david.camacho@upm.es

Min Chen, PhD
South China University of Technology, Guangzhou, China.minchen@ieee.org

Amit Kumar Singh, PhD
National Institute of Technology Patna, Patna, India.amit.singh@nitp.ac.in

Xiaohui Tao, PhD 
University of Southern Queensland, Toowoomba, Australia. 
Xiaohui.Tao@unisq.edu.au

Manuscript submission information:

The journal's submission platform (Editorial Manager®) will be available for receiving submissions to this Special Issue from July 1st, 2024. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: Multi-Sensor Fusion” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Information Fusion | Journal | ScienceDirect.com by Elsevier.

Timeline:

Submission Open Date *01/07/2024
Final Manuscript Submission Deadline *15/12/2024
Editorial Acceptance Deadline *31/03/2025

Keywords:

Sensor Fusion, Healthcare, Generative AI, Medical data, Deep Learning

Special Issue on Underwater Optical Applications of Multi-source Information Fusion
截稿日期: 2024-12-15

The exploration and study of underwater environments crucially depend on acquiring high-quality imagery, challenged by factors like limited visibility and light scattering. Multi-source information fusion stands out as a key strategy, integrating multi-modal data and multi-model outputs to significantly enhance the analysis of underwater images. This approach leverages diverse sensor data—such as sonar, optical, and infrared—and employs advanced machine learning and artificial intelligence technologies. It aims to improve the accuracy of object detection, classification, and environmental understanding. We invite scholars to share their innovative research on multi-source information fusion for enhancing underwater imaging and surveillance capabilities. Submissions are encouraged to explore multi-modal processing, multi-model fusion techniques, and the application of large models in underwater environment analysis. This special issue aims to highlight technological advancements and expand applications in underwater imaging, addressing the complex challenges of accurately capturing and analyzing underwater scenes.

This special issue will focus on the following topics:

    Create an underwater multi-modal dataset (open source);
    Develop high-quality standard datasets for water-related optical images, underwater object detection, and underwater video;
    Implementing constrained underwater image fusion using deep learning or traditional techniques;
    Enhance the image quality of complex underwater environments (murky, low-light, blurry, etc.) through information fusion;
    Implement underwater object detection using acoustic and optical data;
    Enhance the quality of water-related optical images using deep learning-based multi-modal data fusion;
    Enhance water-related optical image information using optical imaging models；
    Utilize data fusion in AUVs for marine environmental monitoring, aquaculture, and underwater security;
    Employ advanced technology to decouple the interlinked degradation clues in underwater environments.

Guest editors:

Guang-Yong Chen, PhD
Fuzhou University, Fuzhou, China
gychen@fzu.edu.cn
(Image enhancement/restoration, computational intelligence, system identification)

Lin Wang, PhD
University of Jinan, Jinan, China
wangplanet@gmail.com
(Systems and cybernetics, computational intelligence, computer vision)

Long Chen, PhD
University of Macau, Macau, China
longchen@umac.mo
(Machine learning, computer vision, pattern recognition)

Vivone Gemine, PhD
National Research Council, Tito, Italy
gemine.vivone@imaa.cnr.it
(Image fusion, statistical signal processing, deep learning)

C. L. Philip Chen, PhD
South China University of Technology, Guangzhou, China
philip.chen@ieee.org
(Machine learning, system identification, systems and cybernetics)

Manuscript submission information:

The journal's submission platform (Editorial Manager®) will be available for receiving submissions to this Special Issue from June 15th, 2024. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: Underwater Optical APP” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Information Fusion | Journal | ScienceDirect.com by Elsevier.

Timeline:

Submission Open Date *15/06/2024
Final Manuscript Submission Deadline *15/12/2024
Editorial Acceptance Deadline *15/04/2025

Keywords:

Underwater, Information Fusion, Multi-source, Multi-model, Computer vision

Special Issue on Mixture of Experts (MoE) and Ensemble Learning for Big Data
截稿日期: 2024-12-31

In the age of artificial intelligence and the digital economy, data analysis and model building are considered compulsory processes in many applications. In this process, machine learning algorithms become important and valuable tools because pattern recognition, knowledge discovery from data for decision-making, and models learned from data for intelligent operations are enabled by efficient and effective algorithms. As the complexity of the operational processes increases, the complexity and diversity of the collected data also increase, which results in more algorithms being developed to satisfy the needs of data analysis. There are several theoretical and technical challenges in developing efficient, scalable, and approximate computing methods for complex big data.

This special issue aims to address the most recent developments and research findings related to the intersection of AI, NLP, and big data computing, focusing on areas like mixture of experts (MoE), parallel and distributed processing, approximate computing, intelligent data analysis, dynamic information systems, ensemble learning, large language model (LLM), etc. This issue strives to provide a platform for researchers and practitioners worldwide, encouraging innovative, cutting-edge, and state-of-the-art theoretical and applied solutions that can tackle the challenges associated with big data computing. It endeavors to promote discussions, stimulate ideas, and foster collaborations towards developing advanced, scalable, and intelligent computing systems.

The articles will provide cross-disciplinary innovative research ideas and application results for mixture of experts (MoE) and ensemble learning for big data including novel theory, algorithms, and applications. Manuscript submissions are encouraged for a broad theme of related topics, including but not limited to:

    Emerging mixture of experts (MoE) model
    Ensemble distributed machine learning algorithms
    Simplification and compression techniques for machine learning models
    Data and model parallelism for large language model (LLM)
    Distributed computing for low-consumption computing
    Scalable data mining for big data analytics
    Energy-efficient AI models and applications
    Large and multi-modal data processing
    Feature fusion-based data augmentation in a resource-limited computing scenario
    Data-centric perspective to build efficient and trustworthy information service applications

Guest editors:

Mohammad Sultan Mahmud, Ph.D
Shenzhen University, Shenzhen, China. Email: sultan@szu.edu.cn

Diego Garcia-Gil, Ph.D
University of Granada, Granada, Spain. Email: djgarcia@decsai.ugr.es

Joshua Zhexue Huang, Ph.D
Shenzhen University, Shenzhen, China. Email: zx.huang@szu.edu.cn

Alladoumbaye Ngueilbaye, Ph.D
Shenzhen University, Shenzhen, China. Email: angueilbaye@szu.edu.cn

Xudong Sun, Ph.D
Shenzhen University, Shenzhen, China. Email: sunxudong2016@email.szu.edu.cn

Manuscript submission information:

The journal's submission platform (Editorial Manager®) will be available for receiving submissions to this Special Issue from July 8th, 2024. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: MoE and Ensemble learning” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Information Fusion | Journal | ScienceDirect.com by Elsevier.

Timeline:

Submission Open Date *08/07/2024
Final Manuscript Submission Deadline *31/12/2024
Editorial Acceptance Deadline *15/04/2025

Keywords:

Artificial Intelligence, Big Data Mining, Ensemble Learning, Mixture of Experts (MoE), Parallel and Distributed Processing, Large Language Model (LLM)