In this work, we study the problem of inferring a discrete probability distribution using both expert knowledge and empirical data. This is an important issue for many applications where the scarcity of data prevents a purely empirical approach. In t...
In this theoretical paper, we explore Big Data ethics in the broader context of general data ethics, stakeholder groups, demand for governance and regulation, social norms, and human values. We follow and expand on the digital divide, governance, and...
Experts’ beliefs embody a present state of knowledge. It is desirable to take this knowledge into account when making decisions. However, ranking experts based on the merit of their beliefs is a difficult task. In this paper, we show how expert...
8 December 2014
Spatially-based scientific data visualizations are becoming widely available, yet they are often not optimized for novice audiences. This study follows after an investigation of ex-pert and novice meaning-making from scaffolded data visualizations us...
Accurate imputation of missing pavement-condition data is critical for proactive infrastructure management, yet it is complicated by spatial non-stationarity—deterioration patterns and data quality vary markedly across regions. This study propo...
In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. T...
In the world of big data, there is a need to investigate how data-driven approaches can support expert-based analyses during a technology planning process. To meet this goal, we examined opportunities and challenges for big data analytics in the soci...
Credit card fraud detection is a critical challenge in the financial sector due to the rapidly evolving tactics of fraudsters and the significant class imbalance betweenegitimate and fraudulent transactions. Traditional models, while effective to som...
Problems of modeling uncertainty and imprecision for the analysis of insufficient expert data (IED) are considered in the environment of interactive multi-group decision-making (MGDM). Based on the Choquet finite integral, a moments’ method for...
Municipal decision-makers must define and quantitatively analyze full-renovation scenarios adapted to specific districts and buildings to achieve the European Union (EU) target of saving 60% to 90% of energy by renovating 75% of building stock. Howev...
Artificial intelligence is one of the fastest-developing areas of science that covers a remarkably wide range of problems to be solved. It has found practical application in many areas of human activity, also in medicine. One of the directions of coo...
Today, there is growing interest in the automatic classification of a variety of tasks, such as weather forecasting, product recommendations, intrusion detection, and people recognition. “Mixture-of-experts” is a well-known classification...
We explored how presence data and expert opinions performed with respect to identifying the ecological preferences and the spatial needs of six butterfly species in the Federal State of Saxony, Germany. We used presence records and a land-cover map....
Intensive Care Units (ICUs) face unprecedented challenges in resource allocation, particularly during health crises in which algorithmic systems may be exposed to adversarial manipulation. A transformer-based expert system, ICU-Transformer, is presen...
In the last decade data-based decision making has been promoted to stimulate school improvement and student learning. However, many teachers struggle with one or more elements of data-based decision making, as they are often not data literate. In thi...
Continual learning (CL) is a key technology for enabling data-driven autonomous guidance systems to operate stably and persistently in complex and dynamic environments. Its core goal is to enable the model to continuously learn new scenarios and task...
31 January 2023
Occupational exposure assessment is important in preventing occupational coal worker’s diseases. Methods have been proposed to assess compliance with exposure limits which aim to protect workers from developing diseases. A Bayesian framework wi...
11 January 2024
Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy...
Background: Limited availability of high-quality labeled biomedical image datasets presents a significant challenge for training deep learning models in medical diagnostics. This study proposes a novel image generation framework combining conditional...
In any productive sector, predictive tools are crucial for optimal management and decision-making. In the health sector, it is especially important to have information available in advance, as this not only means optimizing resources, but also improv...
As artificial intelligence (AI) and data science education gain importance in K-12 curricula, there is a growing need for well-designed sustainable educational datasets tailored to different school levels. Sustainable datasets should be reusable, ada...
The navigation waterways to the harbors of Rostock (Warnow Estuary), Germany, and Szczecin (Oder/Szczecin Lagoon), Poland, were recently deepened. Both activities required Environmental Impact Assessments. We conducted expert- and data-based ecosyste...
Expert systems (ESs) can be seen as a perspective method for risk analysis process automation, especially in the case of small- and medium-sized enterprises that lack internal security resources. Expert system practical applicability is limited by th...
Climate change brings significant challenges to developing countries whose primary livelihood is agriculture. Farmers are directly perceiving and being affected by climate change, and their correct perception of climate change is critical for choosin...
Citizen science data are easily accessible, which has led to their wide use for scientific data collection such as mapping invasive plant species. However, the crowd-sourced nature of citizen science data has led to criticism over the quality of the...
Expert systems, a form of artificial intelligence (AI), are typically designed to solve many real-world problems by reasoning through knowledge, which is primarily represented as IF–THEN rules, with the information acquired from humans or domain expe...
Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian i...
The development of new sequencing technologies has led to a significant increase in biological data. The exponential increase in data has exceeded increases in computing power. The storage and analysis of the huge amount of data poses challenges for...
The integration of human–cyber–physical systems (HCPSs), IoT, digital twins, and big data analytics underpins Industry 4.0, transforming traditional manufacturing into smart manufacturing with capabilities for real-time monitoring, qualit...
Strategic financial decision-making is critical for organizational sustainability and competitive advantage. However, traditional approaches that rely solely on human expertise or isolated machine learning (ML) models often fall short in capturing th...
Tables serve as a widely adopted data format, attracting considerable academic interest concerning semantic understanding and logical inference of tables. In recent years, the prevailing paradigm of pre-training and fine-tuning on tabular data has be...
This study presents a comparative analysis between simulated results obtained using PVSol Expert software and real operational data from a functioning photovoltaic power plant (PVPP) located in Plovdiv, Bulgaria. The primary objective is to evaluate...
The scarcity of underwater acoustic data in deep and remote sea environments poses a significant challenge to data-driven target recognition models, severely restricting their performance. To address this challenge, this study presents a ray-theory-b...
The continuous development of machine learning procedures and the development of new ways of mapping based on the integration of spatial data from heterogeneous sources have resulted in the automation of many processes associated with cartographic pr...
Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative mann...
When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls. A principal curve acts as a nonlinear generalization of PCA, and the present paper proposes a novel...
8 October 2023
Recent advances in mathematical modeling and artificial intelligence have challenged the use of traditional regression analysis in biomedical research. This study examined artificial data sets and biomedical data sets from cancer research using binom...
In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to...
Rapid expansion of Torreya forests in the mountainous region in Zhejiang Province in the past three decades has produced many environmental problems such as soil erosion and poor water quality, requiring an update of its spatial distribution in a tim...
Predicting suspended sediment concentration (SSC) profiles with high accuracy remains a critical challenge for environmental and engineering applications. This study presents a novel, data-driven expert system that leverages a knowledge-based framewo...
Fine-grained building function identification (BFI) is essential for sustainable urban development, land-use analysis, and data-driven spatial planning. Recent progress in fully supervised semantic segmentation has advanced multimodal BFI; however, m...
Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES). We aim to identify and compare methods, models, frameworks, and tools used to forecast earthquakes using...
Interior wall paints and coatings may evaporate hazardous emissions such as volatile organic compound and formaldehyde. Hence, for these products, Material Safety Data Sheets (MSDSs) with information about toxicological and environmental properties a...
Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure p...
Funded pensions have become a key focus in strategies to ensure supplementary income during retirement. This paper explores two distinct approaches for estimating replacement rates: a deep learning model and a Mamdani Fuzzy Inference System (FIS). Us...
In this article, we introduce a dataset of curated learning paths (LPs) to support search as learning. LPs were obtained through an online survey delivered to experts in different domains. Data were then analyzed and described in terms of a set of va...
Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the...
Academicians and practitioners have recently begun to accord Artificial Intelligence (AI) and Big Data Analytics (BDA) significant consideration when exploring emerging research trends in different fields. The technique of bibliometric review has bee...
Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, d...
The core driver of enterprise operations is data, making data lineage crucial for data management. It not only tracks data flow but also links data sources, workflows, applications, and decision-making, improving efficiency and governance. However, c...
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