Information Fusion - Elsevier - Impact Factor

Information Fusion - Elsevier | 2025 Impact Factor:17.4 | Cite Score:26.7 | Q1

Impact Factor and Journal Rank of Information Fusion - Elsevier

About: Information Fusion is an interdisciplinary field that focuses on integrating data and information from multiple sources to provide a more comprehensive understanding of complex phenomena. This process involves combining data, knowledge, and models from diverse sources and domains to produce more accurate, reliable, and actionable information than can be obtained from any single source alone.
Sensor Networks: In sensor networks, information fusion involves combining data from multiple sensors to improve the accuracy and reliability of measurements. This is particularly important in applications such as environmental monitoring, surveillance, and healthcare.
Data Science and Analytics: In data science and analytics, information fusion techniques are used to integrate data from multiple sources, such as structured databases, unstructured text, and streaming data, to derive insights and make informed decisions.
Machine Learning and Artificial Intelligence: Information fusion plays a crucial role in machine learning and artificial intelligence systems by combining information from different modalities, such as text, images, and sensor data, to improve the performance of models and algorithms.
Remote Sensing and Earth Observation: In remote sensing and earth observation, information fusion techniques are used to combine data from satellite sensors, aerial platforms, and ground-based sensors to monitor and analyze the Earth-s surface, atmosphere, and oceans.
Cybersecurity and Defense: Information fusion is essential for cybersecurity and defense applications, where it is used to integrate data from various sources, such as network traffic, logs, and threat intelligence feeds, to detect and respond to cyber threats and security breaches.
Healthcare and Biomedical Informatics: In healthcare and biomedical informatics, information fusion techniques are used to integrate data from electronic health records, medical imaging, genomics, and wearable sensors to support personalized medicine, disease diagnosis, and treatment planning.
Intelligent Transportation Systems: Information fusion is critical for intelligent transportation systems, where it is used to integrate data from traffic sensors, GPS devices, and vehicle-to-vehicle communication systems to optimize traffic flow, improve safety, and reduce congestion.

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Editor-in-Chief: Salvador Garcia-Lopez

Scope: The field of Information Fusion is dedicated to the integration of information from multiple sources to provide a more accurate, complete, and reliable understanding of a given situation or phenomenon. Its scope encompasses a wide range of topics related to the fusion of data, knowledge, and evidence from diverse sources, with applications in various domains.
Here are some key areas of interest within the scope of Information Fusion:
Sensor Fusion: Integration of information from multiple sensors, including radar, lidar, cameras, GPS, and other types of sensors, to improve the accuracy and reliability of measurements and observations. This includes topics such as data fusion algorithms, multi-sensor calibration, and fusion architectures for sensor networks.
Data Fusion: Integration of information from heterogeneous data sources, including structured databases, unstructured text documents, multimedia content, and streaming data streams. This includes topics such as data integration techniques, data matching and reconciliation, and semantic data fusion.
Knowledge Fusion: Integration of knowledge from multiple experts, databases, and knowledge repositories to create a unified and consistent representation of domain knowledge. This includes topics such as ontology alignment, knowledge graph construction, and expert systems for knowledge fusion.
Decision Fusion: Integration of evidence and opinions from multiple decision-makers, including humans and automated decision support systems, to make informed and robust decisions. This includes topics such as fusion of classifiers, ensemble learning, and group decision-making processes.
Information Fusion Architectures: Design and implementation of architectures and frameworks for integrating and processing heterogeneous information sources. This includes topics such as distributed fusion systems, cloud-based fusion platforms, and edge computing for fusion applications.
Uncertainty Modeling and Management: Representation and management of uncertainty inherent in the fusion process, including uncertainty in sensor measurements, data quality, and model predictions. This includes topics such as probabilistic reasoning, Bayesian networks, and uncertainty propagation in fusion systems.
Applications of Information Fusion: Application of fusion techniques and methodologies to various domains, including surveillance and reconnaissance, intelligence analysis, healthcare informatics, environmental monitoring, robotics, autonomous systems, and cybersecurity.
Evaluation and Performance Metrics: Metrics and methodologies for evaluating the performance of information fusion systems, including measures of accuracy, reliability, robustness, and computational efficiency. This includes topics such as benchmark datasets, simulation frameworks, and real-world case studies.