Fuzzy models are regarded as numeric constructs and such optimized evaluated at the level. In this study, we depart from commonly accepted position propose a granular evaluation of fuzzy present an augmentation by forming information granules around values parameters constructions models. The concepts algorithms discussed in setting Takagi-Sugeno rule-based architectures. We show how different protocols allocating lead to improvement performance Different standard measure coming form root...

Multi-view clustering has been received considerable attention due to the widespread collection of multi-view data from diverse domains and sources. However, storing across multiple devices in many real scenarios poses significant challenges for efficient analysis. Federated Learning framework enables collaborative machine learning on distributed while preserving privacy constraints. Even though there have intensive algorithms fuzzy clustering, federated not adequately investigated so far....

Incomplete data are frequently encountered and bring difficulties when it comes to further processing. The concepts of granular computing (GrC) help deliver a higher level abstraction address this problem. Most the existing imputation related modeling methods numeric nature require prior models be provided. underlying objective study is introduce novel straightforward approach that uses information granules as vehicle effectively represent missing build fuzzy directly from resulting hybrid...

Two 50Ah lithium iron phosphate (LiFePO4) lithium-ion batteries with different safety valve equivalent diameters (5mm, 11mm) were used for the study, and experimental analysis of thermal runaway was carried out in a confined environment by using combination sensing system an image recording high-speed photography. Among them, camera to capture battery opening, rupture electrolyte, mixture pulsation disturbance instability characteristics its turbulence fully developed flow characteristics,...

Multi-view fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices driven the emergence new challenge MVFC researches. Federated learning, a technique that can jointly train without directly using raw data, gain significant attention decentralized MVFC. However, their applicability depends on assumptions data integrity and independence between different views. In fact, while within...

Fuzzy rule-based models (FRBMs) are sound constructs to describe complex systems. However, in reality, we may encounter situations, where the user or owner of a system only owns either input output data that (the other part could be owned by another user); and due consideration privacy, he/she not obtain all needed build FRBMs. Since this type situation has been fully realized (noticed) studied before, our objective is come up with some strategy address challenge meet specific privacy during...

Multi-view learning becomes increasingly attractive and promising because multimodal or multi-view data are commonly encountered in real-world applications. In this study, we develop a novel Takagi–Sugeno–Kang (TSK) fuzzy system framework to handle classification problems for such data. We propose an anchor graph subspace clustering strategy discover represent the actual latent distribution each view separately. way, discriminate anchors (landmarks) learned capture main structure of This...

This article presents a design and realization of fuzzy rule-based regression models based on standard decision trees. A two-phase model is offered in this study to provide good alternative cope with high dimensional data. We first build tree the basis variables order discover homogeneous subsets Subsequently, collection rules induced by aim reflecting underlying phenomenon. The calculation membership degrees refinement data located each partition exhibit substantial level originality...

Detection of tiny object in complex environments is a matter urgency, not only because the high real-world demand, but also deployment and real-time requirements. Although many current single-stage algorithms have good detection performance under low computing power requirements, there are still significant challenges such as distinguishing background from features extracting small-scale target natural environments. To address this, we first created real datasets based on improved dataset...

Abstract Capturing the dynamics of urban fire situation is a basic but challenging task, which takes an indispensable role in field security and emergency decision. Traditional methods approach prediction via stochastic process based on physics or statistics, may be interpretable less practical real applications. Recently, some data-driven models, Convolutional Neural Network (CNN), Recurrent (RNN) Graph (GCN), seem to fruitful capturing spatio-temporal with massive high-dimension data. In...