Effective Brain–Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This pape...
One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D cluster variation method...
Regional terrain complexity assessment (TCA) is an important theoretical foundation for geological feature identification, hydrological information extraction and land resources utilization. However, the previous TCA models have many disadvantages; f...
Black soils are vital agricultural resources, and assessing heavy metal contamination in black soils is of great significance to the sustainable development of agriculture and the environment. In this study, 1246 surface soil samples were collected f...
This paper proposes a methodology for establishing base values for short-term voltage variation indices. The work is focused on determining which variables best describe the disturbance and based on that, establish clusters that allow a more adequate...
Accurate ground deformation monitoring with interferometric synthetic aperture radar (InSAR) is often hindered by tropospheric delays caused by atmospheric pressure, temperature, and water vapor variations. While models such as ERA5 (European Centre...
The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam defor...
Configuration entropy is believed to stabilize disordered solid solution phases in multicomponent systems at elevated temperatures over intermetallic compounds by lowering the Gibbs free energy. Traditionally, the increment of configuration entropy w...
Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A nove...
Biological forms depend on a progressive specialization of pluripotent stem cells. The differentiation of these cells in their spatial and functional environment defines the organism itself; however, cellular mutations may disrupt the mutual balance...
The sorting problem in the Multi-criteria Decision Analysis (MCDA) has been used to address issues whose solutions involve the allocation of alternatives in classes. Traditional multi-criteria methods are commonly used for this task, such as ELECTRE...
Copy number variation (CNV) detection based on second-generation sequencing technology is the basis of much gene research, but the read depth is affected by mapping errors, repeated reads, and GC bias. The existing methods have low sensitivity to var...
Climatic classification is essential for evaluating climate parameters that allow sustainable urban planning and resource management in countries with difficult access to meteorological information. Clustering methods are on trend to identify climate...
Clustering plays a vital role in process mining as it organizes complex event logs into meaningful groups, helping to identify common patterns, outliers, and inefficiencies. This simplification enables organizations to detect bottlenecks and optimize...
The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are t...
Current models in design of urban water management systems and their corresponding infrastructure using centralized designs have commonly failed from the perspective of cost effectiveness and inability to adapt to the future changes. These challenges...
Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognoses and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and the r...
Single-cell RNA sequencing (scRNA-seq) is now a successful technology for identifying cell heterogeneity, revealing new cell subpopulations, and predicting developmental trajectories. A crucial component in scRNA-seq is the precise identification of...
Traditional clustering methods are often ineffective in extracting relevant features from high-dimensional, nonlinear near-infrared (NIR) spectra, resulting in poor accuracy of detecting lactose-free milk adulteration. In this paper, we introduce a c...
This study conducts an in-depth analysis of clustering small molecules using spectral geometry and deep learning techniques. We applied a spectral geometric approach to convert molecular structures into triangulated meshes and used the Laplace–...
This paper presents an approach for creating online assessment power curves by calculating the variations between the baseline and actual power curves. The actual power curve is divided into two regions based on the operation rules of a wind turbine,...
The objectives of this study were to assess the agronomic performance of common bean genotypes, previously selected for their response to infestation, by Mexican bean weevil and to identify promising lines that can be used as parents in a downstream...
Electricity smart meter consumption data is enabling utilities to analyze consumption information at unprecedented granularity. Much focus has been directed towards consumption clustering for diversifying tariffs; through modern clustering methods, c...
Spatial cluster detection is one of the focus areas of spatial analysis, whose objective is the identification of clusters from spatial distributions of point events aggregated in districts with small areas. Choi et al. (2018) formulated cluster dete...
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Introduction: Variation in the proportion of individuals living in a stable HIV sero-discordant partnership (SDP), and the potential drivers of such variability across sub Saharan Africa (SSA), are still not well-understood. This study aimed to exami...
Machine learning-based models have been widely used in the early drug-design pipeline. To validate these models, cross-validation strategies have been employed, including those using clustering of molecules in terms of their chemical structures. Howe...
Background: The anatomical variability of the nasal cavity affects intranasal drug delivery, especially to the olfactory region for nose-to-brain treatments. While previous studies used average models or 2D measurements to account for inter-individua...
Estimates of gross domestic product (GDP) play a significant role in evaluating the economic performance of a country or region. Understanding the spatiotemporal process of GDP growth is important for estimating or monitoring the economic state of a...
Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SS...
In this work, a novel entropy-weighted fuzzy c-means variation, referred to as Group-based Entropy Weighted Fuzzy C-Means (GEWFCM), is proposed. This variation introduces a semantic level of partitioning of features into groups. This approach enables...
We use two cloud screening methods—the clustering method and the multiplet method—to process the measurements of a sun photometer from March 2020 to April 2021 in Shouxian. The aerosol optical depth (AOD) and Angström parameters α and β are retrieved...
Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not necessarily b...
We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, ci...
Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In t...
Most existing methods for determining log decay levels normally use variations in log surface characteristics, and the results are subject to human subjectivity, which is uncertain and inaccurate. In order to investigate a novel method for the quanti...
The rapid developments in high-throughput sequencing technologies have allowed researchers to analyze the full genomic sequence of organisms faster and cheaper than ever before. An important application of such advancements is to identify the impact...
This paper uses on-line railway travel requests from the iRail schedule-finder application for assessing the suitability of that kind of big data for transportation planning and to examine the temporal and regional variations of the travel demand by...
Graph clustering is an efficient method for deep clustering that utilizes graph convolution. Graph convolution effectively combines structure and content information, and lots of recent graph convolution-based methods have shown promising results in...
In this study, alfalfa (Medicago sativa L.) is evaluated for suitability of variable rate irrigation (VRI) by analyzing within-field variation in crop water productivity (CWP) under uniform irrigation. The objectives were to (1) measure within-field...
Wind power plays an increasingly important role in the global energy transition. However, its power output exhibits significant uncertainty due to rapid variations in meteorological conditions. Existing forecasting methods still face challenges in la...
Efficient monitoring of the blast furnace system is crucial for maintaining high production efficiency and ensuring product quality. This article introduces a hybrid cluster variational autoencoder model for monitoring the blast furnace ironmaking pr...
Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens’ quality of life and ensure an organizatio...
We present a method for clustering genomic sequences based on variations in local entropy. We have analyzed the distributions of the block entropies of viruses and plant genomes. A distinct pattern for viruses and plant genomes is observed. These di...
Many animal species have unique patterns on their body that allow for individual identification, and these patterns often have a genetic basis. This individual-level pattern variation can be leveraged in ecological applications in a number of ways, s...
Climatic comfortability serves as a crucial factor in tourism decision making; however, there remains a gap in evaluating the climate comfort conditions specifically for forest therapy. We developed a new index—Forest Therapy Climate Comfort In...
Genetic variation in wild relatives, GenBank accessions, landraces, and cultivars can unlock key alleles for the traits of interest for breeding programs. Breeding programs often utilize different strategies to quantify the source of heritable variat...
Morphometric disparities were employed to characterize the geographical variations in and elucidate the phylogeography of Portunus sanguinolentus in East Asia. In this study, a comprehensive assessment was undertaken based on measurements from 833 in...
(1) Background: HIV-1 sub-subtype A1 is common in parts of Africa, Russia, former Soviet Union countries, and Eastern Europe. In Pakistan, sub-subtype A1 is the predominant HIV-1 subtype. Preliminary evidence suggests that distinct strains of HIV-1 s...
The number of litchi flower clusters is an important indicator for predicting the fruit set rate and yield of litchi trees. However, their dense distribution, scale variation, and occlusion make it very challenging to achieve high-precision intellige...
This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network...
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