Journal Information
Information Fusion
http://www.journals.elsevier.com/information-fusion
Impact Factor:
10.71
Publisher:
Elsevier
ISSN:
1566-2535
Viewed:
9305
Tracked:
12

Call For Papers
The journal is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source 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.
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Advances in Multi-Source Information Fusion for Epidemic Diseases
Submission Date: 2020-09-01

We are living in unprecedented times and as the coronavirus pandemic deepens and more restrictions are put in place around the world, the impact of the Covid-19 crisis on our lives can be troubling and confusing. With the continuous attempts of scientific research institutions, advanced information fusion technologies have been actively leveraged to comprehensively harness the multi-source epidemical information from medical devices, biomedical sensors, mobile terminals, social networks, etc., improving the efficiency for epidemic monitoring, virus tracking, prevention, control and treatment, and resource allocation. Although the information fusion technologies have unique advantages and can play an important role in responding to epidemic diseases, there are three main challenges to develop strategies in practice: i) gaps exist between researchers in different areas like computer science, bioinformatics, epidemiology and molecular modeling make it difficult to cognize the problem in depth from multi-source information; ii) epidemiological information is vast so that we need considerable and effective approaches to harness it; iii) there is still a lack of practical approaches, algorithms and tools for information fusion to fight the virus and save lives. This special issue aims to explore recent advances and disseminate state-of-the-art research on multi-source information fusion for epidemic monitoring, virus tracking, prevention, control and treatment, and resource allocation. Original, unpublished technical papers with novel and important contributions will be considered for the special issue; submitted papers must not be published, accepted or under review by another journal, and extended version of a conference paper must be so indicated and the extension must include a substantial improvement to the technical content of the paper. Some of the most important areas include, but are not limited to: Multi-Source information fusion in public health management Multi-Source information fusion for monitoring and predicting the spread of epidemic diseases Multi-Source information fusion for tracking infections and epidemical/medical analysis Multi-Source information fusion for rapid detection and diagnosis Medical knowledge graph construction assisted by multi-Source information fusion Information-fusion-aided drug discovery, treatment, prevention, and resource allocation Multi-Source information fusion for measuring the damage of the epidemic disease in terms of social behavior, industrial practices, and environmental impact Privacy and security in healthcare and medical information fusion Multi-Communication technologies to support Multi-Source information fusion to handle epidemic disease and people tracking
Last updated by Dou Sun in 2020-06-18
Special Issue on Advance Machine Learning Fusion Approaches for COVID-19
Submission Date: 2020-09-01

Undoubtedly, irrespective of the latest development in science and technology COVID-19 (Corona virus) is the biggest harmful buzzword throughout the globe. The threat of this virus is so dread that more than 3.12million people have lost their lives within a span of four months. World Health Organization (WHO) declared the virus outbreak a pandemic in the second week of March 2020. The major problem in the identification of COVID-19 is detection and diagnosis due to the nonavaliability of medicine. In this situation, only Reverse Transcription Polymerase Chain Reaction (RT-PCR) method is used for the diagnosis and bas been widely adopted. With the evolvement of COVID-19, the present research community has witnessed many machine learning and deep learning based approaches with incremental dataset over the month. However, the present scenario of COVID needs an effectual research with original clinical data rather a collection of random internet based data(limited for mathematical analysis). With the help of original real time data (under expert doctors and experts) the accurate identification and diagnosis of such pandemic is possible, which may help to provide a major breakthrough for this disease throughout the globe. Efforts for expedited data and results reporting should not be limited to only for the sake of clinical trials, but should include observational studies which may lead to many major developments for further studies on the virus. The main objective of this special issue is to develop innovative, state-of-the-art fusion of advance machine learning approaches with complex real life problems to protect from this hazardous pandemic. This special issue provides an ideal platform to submit manuscripts that discuss the prospective fusion based original developments of advance machine learning experimented on real/original COVID data and innovative ideas in the diagnosis of COVID-19. This issue encourages real time clinical and epidemiological investigations for COVID-19 with novel methodologies. The Issue will act as a resource of guidance to extend COVID research a step ahead to attract many clinical and epidemiological studies on this outbreak, ensuring a fast turnaround time for high quality research. Topics appropriate for this special issue include (but are not necessarily limited to): Identification and diagnosis of clinical characteristics of novel corona virus based on real data with expert supervision Novel machine learning techniques for tracking COVID with the help of original clinical data Novel methodologies for effective diagnosis of infection and transmission dynamics of the disease Advanced machine learning techniques fusion with clinical medical image analyses of COVID-19 Advanced machine learning techniques for long term and short term risk prediction of COVID-19 based on clinical data Advanced machine learning techniques for evaluation of impact of interventions including pharmaceutical and non-pharmaceutical approaches. Advance machine learning based fusion modelling of COVID-19 spread based on original data Advance machine learning, forecasting and inference from pandemic data Focused advance deep learning algorithms for infectious disease modelling based on clinical data Fusion of machine learning and quantitative social science approaches for epidemiological models Fusion of advanced machine learning techniques and real time big data for future challenges of COVID-19
Last updated by Dou Sun in 2020-07-08
Special Issue on Multi-Source Information Fusion for Smart Health with AI
Submission Date: 2020-10-31

Information fusion is the process of integrating multiple information sources to obtain more complex, reliable, consistent and accurate information for decision-making support. To achieve the goal, inference is essential, which comes from combination of data from multiple sources and transformation of multi-source information into discrete, actionable format for analysis. Artificial Intelligence with newly developed techniques in pattern recognition and image / natural language processing, such as deep learning, have largely improved the performance of processing massive data from multiple resources and leveraged the power of references because of more accurate and meaningful patterns being discovered. Smart Health, deemed as the revolutionary form of traditional health, is promised by recent advancement of science and technology, such as Internet of Things (IoT), Wisdom Web of Things (W2T), Brain Informatics, Big Data, Artificial Intelligence, and mobile Internet like 5G. Smart Health uses wearable devices, IoT, and mobile Internet to dynamically access information, connect people, materials and infrastructures related to healthcare, and then manages and responds to medical ecosystem demands actively and intelligently. Specifically, the core of Smart Health lies on the concept of P4 (predictive, preventive, personalized and participatory) medicine, which will make healthcare and medical systems to be evidence-based instead of traditional experience-based. Information Fusion has the potential technology and methodology to answer the demand, especially with the super accessibility to data and knowledge, as a power granted from the recent advances of Artificial Intelligence. While the whole world is suffering from the COVID-19 pandemic, the Special Issue will discuss the theories, methodologies and possible breakthroughs designed and adopted information fusion for smart health adopting recent Artificial Intelligence advances (e.g., learning models, representations, reasoning and metrics). How to achieve and realize human-level intelligence reflected in Smart Health systems and services by developing intelligent technologies using collective and fused information from multi-sources? The manuscript will be judged solely on the basis of new contributions excluding the contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein. Topics appropriate for this special issue include (but are not necessarily limited to): New AI techniques, models, algorithms for multi-source health and medical data fusion systems Deep learning models for multi-source health and medical data processing Feature fusion for intelligent health and medical systems Shared multi-source health and medical model learning Improved algorithms for multi-source health and medical data fusion systems Analysis on big health and medical data fusion Hierarchical intelligent systems for multi-source health and medical data fusion Multi-source data fusion applications for smart health and P4 medicine Computational issues in fusion methods for real-time bio-signal analysis Heterogeneous information fusion in big health and medical data context Tensor methods and constraint techniques for health and medical data fusion
Last updated by Dou Sun in 2020-07-08
Special Issue on Data Fusion for Trust Evaluation
Submission Date: 2020-12-01

Trust evaluation is a process to quantify trust by analyzing the data related to the factors that affect trust. It has been widely applied in many fields to facilitate decision making, system entity collaboration and security establishment, e.g., social networking, digital communications, e-commerce, cloud services, Peer-to-Peer networking, and so on. Nowadays, trust evaluation has become a useful technique that has benefited many emerging areas by playing as a significant compensation to other security technologies. With the rapid development of cyber systems and the huge volumes of data bursted in it, trust evaluation is evolving from simple mathematical calculation to data analytics based on data fusion, e.g., machine learning. Traditional trust evaluation methods determine trust by aggregating trust factors through weighting and relevant calculations. They become infeasible facing large amount of data with a complex structure. In response to this problem, many researchers suggested using data fusion to make trust evaluation intelligent and accurate. Comparing with the traditional methods, using data fusion for trust evaluation has some irreplaceable advantages. First, it can overcome “cold start” and “zero knowledge” problems. By training a model with available trust-related feature data, trust evaluation can be performed even though some valuable data are missed. Second, the recent advance of data fusion can help evaluating trust in an accurate way, especially when we need to handle enormous data with a complex structure. Third, data fusion can precisely simulate human decision making with regard to trust evaluation, thus evaluation results can be easily explained and accepted by human-beings. However, a number of new challenges are raised in data fusion for trust evaluation caused by practical demands and limitations of current data fusion technologies. First, a generic model for trust evaluation based on data fusion is still missed and hard to achieve. Second, trust evaluation requests a fine-grained evaluation result, which is hard to achieve with machine learning since it treats trust evaluation as a classification problem with two or a limited number of categories such as trusted and distrusted. Third, existing methods generally do not consider privacy protection on the data used for evaluation and seldom concern the robustness of evaluation. True data discovery and attack tracing are seldom studied. Last but not the least, most of the existing methods do not pay special attention to evaluation efficiency. This greatly impacts their applicability in practice. This special issue aims to bring together researchers and practitioners to discuss various aspects of data fusion for trust evaluation, explore key theories and technologies, investigate technology enablers and innovate new solutions for overcoming major challenges in this research field. Manuscripts (which should be original and not previously published either in full or in part or presented even in a more or less similar form at any other forum) covering unpublished research that report the advances in multimodality data fusion in neuroimaging are invited. The manuscript will be judged solely on the basis of new contributions excluding the contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein. Topics appropriate for this special issue include (but are not necessarily limited to): New theories and methods of data fusion for trust evaluation Clustering analysis for trust evaluation Machine learning, data mining and fusion related to trust evaluation Adversary learning in trust evaluation New models of trust evaluation based on data fusion Multimodal data fusion for trust evaluation based on machine learning Fragment knowledge fusion for trust evaluation Data fusion trust, security and privacy in trust evaluation True value discovery and attack tracing in trust evaluation and management Novel datasets and benchmarks for trust evaluation Data fusion for trust, security and privacy
Last updated by Dou Sun in 2020-07-30
Special Issue on Advances in Explainable (XAI) and Responsible (RAI) Artificial Intelligence
Submission Date: 2020-12-15

In the last few years, the interest in deriving complex AI models capable of achieving unprecedented levels of performance has been progressively displaced by a growing concern with alternative design factors, aimed at making such models more usable in practice. Indeed, in a manifold of applications complex AI models become of limited or even null practical utility. The reason lies on the fact that AI models are often designed with only performance as their design target, thus leaving aside other important aspects such as privacy awareness, transparency, confidence, fairness or accountability. Remarkably, all these aspects have acquired a great momentum in the Artificial Intelligence community, giving rise to exclusive sections devoted to all these concepts in prospective studies and reports delivered at the highest international levels (see e.g. “Ethics Guidelines for Trustworthy Artificial Intelligence”, by the High-Level Expert Group on AI, April 2019). In this context, Explainable AI (XAI) refers to those Artificial Intelligence techniques aimed at explaining, to a given audience, the details or reasons by which a model produces its output [1]. To this end, XAI borrows concepts from philosophy, cognitive sciences and social psychology to yield a spectrum of methodological approaches that can provide explainable decisions for users without a strong background on Artificial Intelligence. Therefore, XAI targets at bridging the gap between the complexity of the model to be explained, and the cognitive skills of the audience for which explainability is sought. Interdisciplinary XAI methods have so far embraced assorted elements from multiple disciplines, including signal processing, adversarial learning, visual analytics or cognitive modeling, to mention a few. Although reported XAI advances have risen sharply in recent times, there is global consensus around the need for further studies around the explainability of ML models. A major focus has been placed on XAI developments that involve the human in the loop and thereby, become human-centric. This includes the automated generation of counterfactuals, neuro-symbolic reasoning, or fuzzy rule-based systems, among others. A step beyond XAI is Responsible AI (RAI), which denotes a set of principles to be met when deploying AI-based systems in practical scenarios: Fairness, Explainability, Human-Centric, Privacy Awareness, Accountability, Safety and Security. Therefore, RAI extends further XAI by ensuring that other critical modeling aspects are taken into account when deploying AI-based systems in practice, including not only algorithmic proposals but also new procedures devoted to ensuring responsibility in the application and usage of AI models, including tools for accountability and data governance, methods to assess and explain the impact of decisions made by AI models, or techniques to detect, counteract or mitigate the effect of bias on the model’s output. It is only by carefully accounting for all these aspects when humans, through all processes and systems endowed with AI-based functionalities (e.g. Robotics, Machine Learning, Optimization and Reasoning), will fully trust and welcome the arrival of this technology. This special issue seeks original works and fresh studies dealing with research findings on XAI and RAI. The list of topics in this special issue include, but is not limited to: XAI: Post-hoc explainability techniques for AI models Neural-symbolic reasoning to explain AI models Fuzzy Rule-based Systems for explaining AI models Counterfactual explanations of AI models Explainability and data fusion Knowledge representation for XAI Human-centric XAI Visual explanations for AI models Contrastive explanation methods for XAI Natural Language generation for XAI Interpretability of other ML tasks (e.g. ranking, recommendation, reinforcement learning) Hybrid transparent-Blackbox modeling Quantitative evaluation (metrics) of the interpretability of AI models Quantitative evaluation (metrics) of the quality of explanations XAI and theory-guided data science RAI: Privacy-aware methods for AI models Accountability of decisions made by AI models Bias and fairness in AI models Methodology for an ethical and responsible use of AI based models AI models’ output confidence estimation Adversarial analysis for AI security (attack detection, explanation and defense) Causal reasoning, causal explanations, and causal inference
Last updated by Dou Sun in 2020-07-30
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