Fusion System Architecture Research Articles

Text and Social Analytics with Fusion Techniques Enhance Hospital Health Management

the impact of social analytics on hospital health management: a multilevel fusion approach for data-driven decision-making and brand improvement. The hospital health management center should use feature extraction techniques to learn more about customers' feelings towards their services and optimize their business strategies and promotions accordingly. The proposed multi-levelhybrid level fusion system architectures can effectively integrate dataimages from multiple sources, including social networks, to collect and process essential data for score level and rank level decision-making. This approach leverages intelligent techniques, such as deep learning models, fuzzy logic, and optimization algorithms, to improve fusion scores and achieve optimal fusion performance. The proposed framework can also be extended to various applications, including multimedia data fusion, e-systems data fusion, and spatial data fusion, to enable intelligent systems for information fusion and decision-making in diverse domains. Therefore, this paper proposes Improved Customer Relation and Business Operations (ICR-BO) to enhance customer relationships in business development using text and social analytics. A case study is carried out to explore the online debate of computer brands operated in hospital environments and Twitter suppliers. The authors used text-mining strategies and social analytics to analyze business operations. Social Media uses data sets to view important observations and trends to identify consumer awareness after collecting critical tweets using Twitter search. The experimental results show that ICR-BO achieves the highest customer relation compared to other existing methods.

Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection

The healthcare sector's use of cyber-physical systems to provide high-quality patient treatment highlights the need for sophisticated security solutions due to the wide range of attack surfaces from medical and mobile devices, as well as body sensor nodes. Cyber-physical systems have various processing technologies to choose from, but these technical methods are as varied. Existing technologies are not well-suited for managing complex information about problem identification and diagnosis, which is distinct from technology. To address this issue, intelligent techniques for fusion processing, such as multi-sensor fusion system architectures and fusion optimization, can be used to improve fusion score and decision-making. Additionally, the use of deep learning models and multimedia data fusion applications can help to combine multiple models for intelligent systems and enhance machine learning for data fusion in E-Systems and cloud environments. Fuzzy approaches and optimization algorithms for data fusion can also be applied to robotics and other applications.. In this paper, a computer vision technology-based fault detection (CVT-FD) framework has been suggested for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behavior can be predicted using an artificial neural network (ANN), and the analysis of this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms with current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.

Cognitive Systems Engineering for Evolvable Human-in-the-Loop Data Fusion

Data fusion systems are increasingly being used to support military planning and decision making. Typically these systems are designed around the current capabilities of particular data collectors (e.g., sensors) and processing algorithms. They incorporate an ‘ontology’ that reflects the designer's perception of the key features of the world (e.g., types of threats, classes of vehicles to be tracked) and how these can be parsed by the data fusion systems. As a consequence they are limited in their ability to adapt to the dynamic changes that inevitably arise in the operational environment (e.g., new sensors, weapons, tactics). This is representative of a more generic problem with current approaches to system design that result in rigid systems that are unable to evolve to keep pace with changing operational conditions. In this paper we present the results of an analysis, design, and development effort intended to move away from traditional data fusion systems towards evolvable human-in-the-loop data fusion systems. We discuss the analysis we conducted in support of an evolvable system design and provide an overview of the prototype evolvable data fusion system architecture we are developing.

Information fusion in data association applications

There is a limitation to process data fusion by means of traditional deterministic or probabilistic data association algorithm of multisensor data fusion (MSDF). Those methods for data association do not adequately account for quantitative and qualitative information in an automated fashion. Fuzzy logic offers an enabling technology for automated quantitative and qualitative information in the data fusion process. We propose the fusion system architecture, called fuzzy gating approach, coordinating both quantitative and qualitative information which is realized using a fuzzy-based reasoning approach. This approach is composed of two stages in cascade. The first stage implements available quantitative information, namely target range, azimuth, and elevation angle, to form a subset of statistically likely target solutions via fuzzy validation. The second stage of the fuzzy similarity utilizes available qualitative information, namely infrared image area and brightness, to form another subset of returns. Finally the fuzzy gating approach was tested in a dense clutter environment. Results of test show that performance of fuzzy gating approach is superior to JPDA based on a Bayesian approach. Moreover, adding qualitative information to the tracking algorithm can improve tracker performance effectively.

Interoperability and Sensor Fusion ‐ Part III: Seeking Solutions

Part II: System Architecture developed the concept of sensor fusion system architecture as a directed graph that represents the flow of sensor data from sensors to users. The architecture was shown to be essentially the same thing as the track template developed in Part I: Track Management, and that it should be a tree. Each multiple-user-multiple-sensor (MUMS) vertex in the architecture requires a data link over which track partitioning decisions are shared using the hierarchical association method (HAM). Architectures are interoperable only if they are sub-trees of a common tree, and the similar sources principle was applied to develop the main features of an ideal common tree.

A statistical overview of recent literature in information fusion

The objective of this paper is to make a picture of the recent articles published on information fusion. Indeed, a great number of documents dealing with this technique are available in the literature. A classification scheme including application fields, fusion goals, fusion system architecture and mathematical tools is proposed. This overview of the last three years allows us to compute the article distribution into each class. Finally, some elements of preliminary analysis of this classification are drawn.

Sensor fusion potential exploitation-innovative architectures and illustrative applications

Sensor fusion models have been characterized in the literature in a number of distinctly different ways: in terms of information levels at which the fusion is accomplished; the objectives of the fusion process, the application domain; the types of sensors employed, the sensor suite configuration and so on. The characterization most commonly encountered in the rapidly growing sensor fusion literature based on level of detail in the information is that of the now well known triplet: data level, feature level, and decision level. We consider here a generalized input-output (I/O) descriptor pair based characterization of the sensor fusion process that can be looked upon as a natural out growth of the trilevel characterization. The fusion system design philosophy expounded here is that an exhaustive exploitation of the sensor fusion potential should explore fusion under all of the different I/O-based fusion modes conceivable under such a characterization. Fusion system architectures designed to permit such exploitation offer the requisite flexibility for developing the most effective fusion system designs for a given application. A second facet of this exploitation is aimed at exploring the new concept of self-improving multisensor fusion system architectures wherein the central (fusion system) and focal (individual sensor subsystems) decision makers mutually enhance the other's performance by providing reinforced learning. A third facet is that of investigating fusion system architectures for environments wherein the different local decision makers may only be capable of narrower decisions that span only a subset of decision choices. The paper discusses these flexible fusion system architectures along with related issues and illustrates them with examples of their application to real-world scenarios.