Multiview subspace clustering is one of the most widely used methods for exploiting the internal structures of multiview data. Most previous studies have performed the task of learning multiview representations by individually constructing an affinity matrix for each view without simultaneously exploiting the intrinsic characteristics of multiview data. In this article, we propose a multiview low-rank representation (MLRR) method to comprehensively discover the correlation of multiview data for multiview subspace clustering. MLRR considers symmetric low-rank representations (LRRs) to be an approximately linear spatial transformation under the new base, that is, the multiview data themselves, to fully exploit the angular information of the principal directions of LRRs, which is adopted to construct an affinity matrix for multiview subspace clustering, under a symmetric condition. MLRR takes full advantage of LRR techniques and a diversity regularization term to exploit the diversity and consistency of multiple views, respectively, and this method simultaneously imposes a symmetry constraint on LRRs. Hence, the angular information of the principal directions of rows is consistent with that of columns in symmetric LRRs. The MLRR model can be efficiently calculated by solving a convex optimization problem. Moreover, we present an intuitive fusion strategy for symmetric LRRs from the perspective of spectral clustering to obtain a compact representation, which can be shared by multiple views and comprehensively represents the intrinsic features of multiview data. Finally, the experimental results based on benchmark datasets demonstrate the effectiveness and robustness of MLRR compared with several state-of-the-art multiview subspace clustering algorithms.

In a dynamic stream there is an assumption that the underlying process generating the stream is non-stationary and that concepts within the stream will drift and change as the stream progresses. Concepts learned by a classification model are prone to change and non-adaptive models are likely to deteriorate and become ineffective over time. The challenge of recognising and reacting to change in a stream is compounded by thescarcity of labelsproblem. This refers to the very realistic situation in which the true class label of an incoming point is not immediately available (or might never be available) or in situations where manually annotating data points are prohibitively expensive. In a high-velocity stream, it is perhaps impossible to manually label every incoming point and pursue a fully supervised approach. In this article, we formally describe the types of change, which can occur in a data-stream and then catalogue the methods for dealing with change when there is limited access to labels. We present an overview of the most influential ideas in the field along with recent advancements and we highlight trends, research gaps, and future research directions.

Historic buildings and urban streetscapes face increasing threats from climate change, development, and aging infrastructure, creating a pressing need for accurate and scalable documentation methods. This review assesses the combined use of photogrammetry and unmanned aerial vehicle (UAV) technologies in preserving built cultural heritage. We systematically analyze the end-to-end workflow, from the sophisticated processing of imagery into highly detailed and accurate 3D models in photogrammetry software via data acquisition using diverse UAV platforms and sensor payloads. Through case studies, including the mapping of ancient Maya sites in the Yucatán Peninsula and the conservation of the Notre Dame Cathedral, the review highlights the accuracy, efficiency, and accessibility offered by this technological synergy, underscoring its significance for heritage conservation, research, and the development of digital twins. Furthermore, it explores how these advancements foster public engagement and virtual accessibility, enabling immersive experiences and enriched educational opportunities. The paper also critically assesses the inherent technical, ethical, and legal challenges associated with this methodology, offering a balanced perspective on its application. By synthesizing the current knowledge, this review proposes future research trajectories and advocates for best practices, aiming to guide heritage professionals in leveraging photogrammetry and UAVs for the effective documentation and safeguarding of global cultural heritage.

Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise.

The integration of large language models (LLMs) into remote healthcare has the potential to revolutionize medication management by enhancing communication, improving medication adherence, and supporting clinical decision-making. This study aims to explore the role of LLMs in remote medication management, focusing on their impact. This paper comprehensively reviews the existing literature, medical LLM cases, and the commercial applications of LLMs in remote healthcare. It also addresses technical, ethical, and regulatory challenges related to the use of artificial intelligence (AI) in this context. The review methodology includes analyzing studies on LLM applications, comparing their impact, and identifying gaps for future research and development. The review reveals that LLMs have shown significant potential in remote medication management by improving communication between patients and providers, enhancing medication adherence monitoring, and supporting clinical decision-making in medication management. Compared to traditional reminder systems, AI reminder systems have a 14% higher rate in improving adherence rates in pilot studies. However, there are notable challenges, including data privacy concerns, system integration issues, and the ethical dilemmas of AI-driven decisions such as bias and transparency. Overall, this review offers a comprehensive analysis of LLMs in remote medication management, identifying both their transformative potential and the key challenges to be addressed. It provides insights for healthcare providers, policymakers, and researchers on optimizing the use of AI in medication management.

Ensemble techniques are a powerful method for recognising and reacting to changes in non-stationary data. However, most researches into dynamic classification with ensembles assume that the true class label of each incoming point is available or easily obtained. This is unrealistic in most practical applications, especially in high-velocity streams where manually labeling each point is prohibitively expensive. To address this challenge, this paper proposes an algorithm, named Clustering and One-Class Classification Ensemble Learning (COCEL), which incorporates a stream clustering algorithm and an ensemble of one-class classifiers with active learning, for classification in dynamic data streams. The method exploits the intuitive relationship between clusters and one-class classifiers to cope with a small training set (or no training set) and improve with experience, self-modifying its internal state to cope with changes in the data stream. The proposed method is evaluated on synthetic data streams exhibiting concept evolution and concept drift and a collection of high-velocity real data streams where manually labeling each incoming point is infeasible or expensive and labor intensive. Finally, a comparative evaluation with peer stream classification ensembles shows that COCEL can achieve superior or comparative accuracy while typically requiring less than 0.01% of the stream labels.