Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer

Querying large graphs in biomedicine with colored graphs and decomposition.

In situ mineralogical and chemical analyses of rock samples using a space-prototype laser ablation ionization mass spectrometer along with unsupervised machine learning are powerful tools for the study of surface samples on planetary bodies. This potential is demonstrated through the examination of a thin section of a terrestrial rock sample in the laboratory. Autonomous isolation of mineral phases within the acquired mass spectrometric data is achieved with two dimensionality reduction techniques: uniform manifold approximation and projection (UMAP) and density-preserving variation of UMAP (densMAP), and the density-based clustering algorithm Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Both densMAP and UMAP yield comparable outcomes, successfully isolating the major mineral phases fluorapatite, calcite, and forsterite in the studied rock sample. Notably, densMAP reveals additional insights into the composition of the sample through outlier detection, uncovering signals from the trace minerals pyrite, rutile, baddeleyite, and uranothorianite. Through a grid search, the stability of the methods over a broad model parameter space is confirmed, revealing a correlation between the level of data preprocessing and the resulting clustering quality. Consequently, these methods represent effective strategies for data reduction, highlighting their potential application on board spacecraft to obtain direct and quantitative information on the chemical composition and mineralogy of planetary surfaces and to optimize mission returns through the unsupervised selection of valuable data.

Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery in conjunction with topography and soil attribute data and employ a generalized cluster identification algorithm, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to study the underlying patterns of palms in two areas of Guyana, South America. The results of the HDBSCAN assessment were cross-validated with several point pattern analysis methods commonly used by ecologists (the quadrat test for complete spatial randomness, Morista Index, Ripley’s L-function, and the pair correlation function). A spatial logistic regression model was generated to understand the multivariate environmental influences driving the placement of cluster and outlier palms. Our results showed that palms are strongly clustered in the areas of interest and that the HDBSCAN’s clustering output correlates well with traditional analytical methods. The environmental factors influencing palm clusters or outliers, as determined by logistic regression, exhibit qualitative similarities to those identified in conventional ground-based palm surveys. These findings are promising for prospective research aiming to integrate remote flora identification techniques with traditional data collection studies.

The interplay between political campaigning and online discourse has emerged as a critical area of computational social science, particularly concerning the propagation of hate speech. This study investigates how Donald Trump’s 2024–2025 U.S. presidential campaign influenced the prevalence and thematic focus of hate speech on Reddit, a platform known for its politically engaged communities. Leveraging a dataset of over 55 million Reddit posts spanning two key periods (pre- and mid-campaign), we develop a large-scale Natural Language Processing (NLP) methodology to detect and analyse the dynamics of hate speech. Hate speech is identified using a fine-tuned BERT model optimized for multi-class classification, distinguishing hateful, offensive, and neutral content. To explore the structure of hate speech targets, we apply Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), a density-based clustering algorithm that accommodates variable data distributions in high-dimensional embeddings. A post-processing algorithm further refines overlapping categories by decomposing identity-based slurs into distinct subgroups. Our findings reveal a marked increase in hate speech volume during the campaign, along with significant shifts in targeted identity clusters, particularly toward ideological, gendered, and racial groups. These results contribute empirical evidence to debates around algorithmic content moderation, political communication, and the computational modeling of social polarization.

This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, k-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high k-path centrality have high node betweenness centrality. The randomized algorithm runs in time $O(\kappa^{3}n^{2-2\alpha}\log n)$ and outputs, for each vertex v, an estimate of its k-path centrality up to additive error of $\pm n^{1/2+ \alpha}$ with probability $1-1/n^2$. Experimental evaluations on real and synthetic social networks show improved accuracy in detecting high betweenness centrality nodes and significantly reduced execution time when compared with existing randomized algorithms.

Structural characteristics and spatiotemporal changes of a reticular river network based on complex network theory

Comparison of clinical symptoms and symptom structure across different onset ages in schizophrenia inpatients.

From the Ground Up-Materials Science in Singapore.

A Multiomic Approach to Reversing Therapy Resistance in Multiple Myeloma Using Paired Ex Vivo Drug Sensitivity Measures and RNA Sequencing Data

This research introduces an enhanced stratified sampling-density-based spatial clustering of applications with noise (SS-DBSCAN), a scalable and robust density-based clustering algorithm designed to tackle challenges in high-dimensional and complex data analysis. The algorithm integrates advanced parameter optimization techniques to improve clustering accuracy and interpretability. Key innovations include a fast grid search method for optimizing the search of optimal minimum points (MinPts) by keeping the ϵ parameter obtained constant. Notably, this study emphasizes the often-overlooked MinPts parameter, introducing a dynamic approach that initiates by calculating density metrics within a specified ϵ distance and adjusting the MinPts range based on the standard deviation of these metrics. This approach identifies optimal MinPts values based on the maximum allowed range. Comprehensive experiments on five real-world datasets demonstrate SS-DBSCAN’s superior performance compared to density-based spatial clustering of applications with noise (DBSCAN), hierarchical DBSCAN, and ordering points to identify the clustering structure (OPTICS), evidenced by higher silhouette and Davies–Bouldin index scores. The results highlight SS-DBSCAN’s ability to capture intrinsic clustering structures accurately, providing deeper insights across various research domains. SS-DBSCAN’s scalability and adaptability to diverse data densities make it a valuable tool for analyzing large, complex datasets.

Visualising patterns in clinicians' eye movements while interpreting fetal ultrasound imaging videos is challenging. Across and within videos, there are differences in size an d position of Areas-of-Interest (AOIs) due to fetal position, movement and sonographer skill. Currently, AOIs are manually labelled or identified using eye-tracker manufacturer specifications which are not study specific. We propose using unsupervised clustering to identify meaningful AOIs and bi-contour plots to visualise spatio-temporal gaze characteristics. We use Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to identify the AOIs, and use their corresponding images to capture granular changes within each AOI. Then we visualise transitions within and between AOIs as read by the sonographer. We compare our method to a standardised eye-tracking manufacturer algorithm. Our method captures granular changes in gaze characteristics which are otherwise not shown. Our method is suitable for exploratory data analysis of eye-tracking data involving multiple participants and AOIs.

Automatic individual recognition of wild Crested Ibis based on hybrid method of self-supervised learning and clustering

Traditional searchable encryption schemes construct document vectors based on the term frequency-inverse document frequency (TF-IDF) model. Such vectors are not only high-dimensional and sparse but also ignore the semantic information of the documents. The Sentence Bidirectional Encoder Representations from Transformers (SBERT) model can be used to train vectors containing document semantic information to realize semantic-aware multi-keyword search. In this paper, we propose a privacy-preserving searchable encryption scheme based on the SBERT model. The SBERT model is used to train vectors containing the semantic information of documents, and these document vectors are then used as input to the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering algorithm. The HDBSCAN algorithm generates a soft cluster membership vector for each document. We treat each cluster as a topic, and the vector represents the probability that the document belongs to each topic. According to the clustering process in the schemes, the topic-term frequency-inverse topic frequency (TTF-ITF) model is proposed to generate keyword topic vectors. Through the SBERT model, searchable encryption scheme can achieve more precise semantic-aware keyword search. At the same time, the special index tree is used to improve search efficiency. The experimental results on real datasets prove the effectiveness of our scheme.

Aphasia assessment plays a crucial role in rehabilitating people with aphasia (PWA), including classifying healthy individuals, identifying subtypes of aphasia, and assessing severity. This study explores unsupervised clustering methods in aphasic speech data, comparing five feature extraction approaches and four clustering algorithms. Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Agglomerative clustering, K-means, and Gaussian Mixture Models. The focus is on distinguishing between fluent and non-fluent aphasia and subtypes such as Broca's, Wernicke's, and anomic aphasia. The feature extraction methods include Mel-Frequency Cepstral Coefficients (MFCCs), OpenL3 embeddings, their combination, and a fusion of MFCC with chroma features, spectral contrast, zero-crossing rate, and a novel deep convolutional autoencoder-based (CAE) model. These methods are evaluated for their ability to capture key characteristics of aphasic speech, with the clustering results assessed using intrinsic evaluation metrics. Our findings highlight the effectiveness of different feature extraction methods, with CAE emerging as the most effective approach in distinguishing aphasic speech categories and improving clustering performance. This research highlights the potential of unsupervised learning in aphasia research and offers insight into automated diagnosis and speech therapy tools, contributing to enhanced aphasia assessment and rehabilitation.

Aiming at the trajectory clustering problem of observed data, an improved Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, named FB-HDBSCAN, is proposed. Firstly, the spatiotemporal features of the data are thoroughly exploited to construct a distance matrix based on Fast Dynamic Time Warping (FastDTW), ensuring an accurate measurement of similarity between data points. Then, the Bayesian Optimization (BO) algorithm is introduced for adaptive parameter optimization, and the optimal parameters, along with the distance matrix, are input into the HDBSCAN algorithm framework to achieve automatic clustering of trajectory data. Experimental results demonstrate that the proposed algorithm achieves superior performance across multiple datasets, validating its feasibility and applicability.