We make use of data from a Facebook application where hundreds of thousands of people played a simultaneous move, zero-sum game—rock-paper-scissors—with varying information to analyze whether play in strategic settings is consistent with...
A data-driven method, the truncated LS-SVM, is proposed for estimating the nondimensional hydrodynamic coefficients of a nonlinear manoeuvring model. Experimental data collected in a shallow water towing tank are utilized in this study. To assess the...
Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to s...
The paper discusses the use of parallel computation to obtain rough set approximations from large-scale information systems where missing data exist in both condition and decision attributes. To date, many studies have focused on missing condition da...
Modern convolutional neural networks (CNNs) are often trained on pre-set data sets with a fixed size. As for the large-scale applications of satellite images, for example, global or regional mappings, these images are collected incrementally by multi...
The use of distribution-based data representation to handle large-scale scientific datasets is a promising approach. Distribution-based approaches often transform a scientific dataset into many distributions, each of which is calculated from a small...
Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective...
Addressing the demands of agricultural resource digitization and facility crop monitoring, precise extraction of plastic greenhouses using high-resolution remote sensing imagery demonstrates pivotal significance for implementing refined farmland mana...
Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the dist...
Background Hospitals invest extensive resources in large-scale initiatives to improve patient safety and quality at an organizational level. However, initial success, if any, does not guarantee longer-term improvement. Empirical and theoretical knowl...
In recent years, the rapid development of the internet and the advancement of information technology have produced a large amount of large-scale data, some of which are presented in the form of large-scale graphs, such as social networks and sensor n...
The increasing integration of high-frequency power electronic converters in renewable energy-grid systems has escalated reliability concerns, necessitating FPGA-accelerated large-scale real-time electromagnetic transient (EMT) computation to prevent...
Large-scale landslides often cause severe damage to lives and properties; therefore, their identification is essential in order to adopt proper mitigation measures. The objective of this study was to set up a methodological approach to help identify...
This paper investigates spatial relationships regarding the accessibility of urban green space, the overall yearly vitality of the surrounding vegetation, and additional indicators such as air and noise pollution, in urban areas. The analysis uses so...
We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a te...
The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction a...
As large-scale poultry farming becomes more intensive and concentrated, a deeper understanding of poultry meat production processes is crucial for achieving maximum economic and ecological efficiency. The transmission and analysis of data collected o...
29 January 2022
The Delta variant (B.1.617.2) has dominated in many countries over the world. Its sudden outbreak in China has led the government to quickly carry out large-scale nucleic acid testing to curb its spread. This qualitative study aims to find the challe...
Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is...
Attribute reduction is a crucial research area within concept lattices. However, the existing works are mostly limited to either increment or decrement algorithms, rather than considering both. Therefore, dealing with large-scale streaming attributes...
Large-scale assays, such as microarrays, next-generation sequencing and various “omics” technologies, have explored multiple aspects of the immune response following virus infection, often from a public health perspective. Yet a lack of similar data...
Conifers are an ecologically and economically important seed plant group that can provide significant insights into the evolution of land plants. Molecular phylogenetics has developed as an important approach in evolutionary studies, although there h...
Urban landmarks are frequently used in way-finding and representations of spatial knowledge. However, assessing the salience of urban landmarks is difficult. Moreover, no method exists to rapidly extract urban landmarks from basic geographic informat...
Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This pap...
We present a panoramic view of several scaling relations (ScRs) of galaxies of different morphology. The ScRs are obtained from the data of two large surveys (WINGS and MANGA). We analyze the distribution (parameterized by the percent over the total)...
The rapid growth of remote sensing data offers unprecedented opportunities for global environmental monitoring and resource assessment, yet poses significant challenges for efficient selection of large-scale image datasets. Traditional conditional re...
25 September 2024
This paper investigates the feasibility of downscaling within high-dimensional Lorenz models through the use of machine learning (ML) techniques. This study integrates atmospheric sciences, nonlinear dynamics, and machine learning, focusing on using...
The analysis of infrared video images is becoming one of the methods used to detect thermal hazards in many large-scale engineering sites. The fusion of infrared thermal imaging and visible image data in the target area can help people to identify an...
Genomic medicine attempts to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients’ genomic information. Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through exami...
Frequent and granular population data are essential for decision making. Further-more, for progress monitoring towards achieving the sustainable development goals (SDGs), data availability at global scales as well as at different disaggregated levels...
Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to sca...
In this work, a sub-modeling technique is proposed for the analysis of large-scale masonry structures. The approach couples an anisotropic macroscale formulation, derived by incorporating the notion of a fabric tensor for an orthotropic material, wit...
With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection h...
This article examines the impact of the stability of the management rights of transferred land (TLMR) on the adoption of technologies aiming to reduce the use of chemical fertilizers (ARFTs) based on the survey data of large-scale grain growing house...
Background: Chest X-ray (CXR) imaging is crucial for diagnosing thoracic abnormalities; however, the rising demand burdens radiologists, particularly in resource-limited settings. Method: We used large-scale, diverse public CXR datasets with noisy la...
Animation visualization is one of the primary methods for analyzing unsteady flow fields. In this paper, we addressed the issue of data visualization for large-scale unsteady flow fields using animation. Loading and rendering individual time steps se...
Transformer models have achieved great results in the field of computer vision over the past 2 years, drawing attention from within the field of remote sensing. However, there are still relatively few studies on this model in the field of remote sens...
The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspir...
Smart farming is essential to increasing crop production, and there is a need to consider the technological advancements of this era; modern technology has helped us to gain more accuracy in fertilizing, watering, and adding pesticides to the crops,...
A new sweep-search algorithm (SSA) is developed and tested to identify the channel geometry transitions responsible for numerical convergence failure in a Saint-Venant equation (SVE) simulation of a large-scale open-channel network. Numerical instabi...
Standardization alliance networks serve as crucial channels for firms to sponsor standards and access external resources, exerting a substantial impact on their standard-setting competitiveness and their ability to foster a sustainable innovation eco...
Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recog...
Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) senso...
This paper explores the use of machine learning (ML) to analyze borehole data aiming to enhance geotechnical insights, using the Gaza Strip as a case study. The data set consists of 632 boreholes, with features including spatial coordinates, ground l...
This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal func...
Fruit brandy equipment commonly uses partial condensation (dephlegmation) to generate reflux in the distillation column. Here, we examined the effect of dephlegmation on the composition of fruit brandies in both lab-scale and large-scale settings. In...
Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subsp...
Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national land-cover maps. This has led to complex pre- and post-processing and e...
The Italian school building stock is largely outdated, with structural and technological inadequacies leading to low comfort and high energy consumption. Addressing this challenge requires large-scale renovation supported by an integrated, data-drive...
We are faced with an unprecedented production in scholarly publications worldwide. Stakeholders in the digital libraries posit that the document-based publishing paradigm has reached the limits of adequacy. Instead, structured, machine-interpretable,...
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