Explainable Artificial Intelligence (XAI) has primarily focused on explaining model predictions, yet a critical gap remains in explaining semantic structure discovery within knowledge graphs derived from concept maps (CMs). This study extends the OBO...
This study presents a data-driven and explainable artificial intelligence (XAI) framework for quantitatively analyzing research trends in the seismic performance of timber structures. Unlike conventional bibliometric approaches based on descriptive s...
Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling t...
Citizen-reporting platforms generate high-volume, multilingual streams of service requests, yet operational triage often relies on coarse category labels and manual inspection. This study develops an explainable analytics pipeline with probability ca...
Text that accumulates over time does not only drift: topics are born, they die, they fragment into competing sub-narratives (splits), and they fuse into broader frames (merges). Our companion system paper, BERTilda, detects these events with transpar...
13 November 2022
Interactive Machine Learning (IML) can enable intelligent systems to interactively learn from their end-users, and is quickly becoming more and more relevant to many application domains. Although it places the human in the loop, interactions are most...
The development of the World University Rankings (WURs) has produced the following paradox. On the one hand, the WURs are often criticized for their ranking methodology and logic. On the other hand, the WURs are growing in influence worldwide. Univer...
This article aims to perform a literature review on the topic of farmland valuation, covering the determinants of farmland value and the models that are used to price land. To do so, recent literature on the topic was combined with classical and well...
In this paper we propose the Recurrent Embedded Topic Model (RETM) which is a modification of the Embedded Topic Modelling (ETM) by reusing the Continuous Bag of Words (CBOW) that the model had implemented and applying it to a recurrent neural networ...
Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning...
This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with d...
In a previous work, a parsimonious topic model (PTM) was proposed for text corpora. In that work, unlike LDA, the modeling determined a subset of salient words for each topic, with topic-specific probabilities, with the rest of the words in the dicti...
This paper proposes a new methodology to study sequential corpora by implementing a two-stage algorithm that learns time-based topics with respect to a scale of document positions and introduces the concept of Topic Scaling, which ranks learned topic...
In this paper, we applied an explainable AI model (SHAP feature importance measures) to study the dynamic relationship between stock market returns and the US presidential election outcomes. More specifically, we wanted to study how the market would...
Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review...
Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whe...
With the continuous accumulation of biological data, more and more machine learning algorithms have been introduced into the field of gene function prediction, which has great significance in decoding the secret of life. Recently, a multi-label super...
Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological d...
Journalists navigating large digital archives face a tension between speed and thoroughness. Keyword search often returns unmanageable volumes of results, while topic-only recommendation may fail to identify contextually appropriate content. This pap...
This paper aims to research the topics related to risk included in non-financial disclosure (NFD) of companies listed on the Warsaw Stock Exchange (WSE) and explore factors that influence the risk topics ratio in NFD. We applied a content analysis us...
Industry 4.0 technologies are causing a paradigm shift in supply chain process management. The digital transformation of the supply chains provides enormous benefits to organizations by empowering collaboration among multiple internal and external or...
This review provides a succinct overview of the basic aspects of neutrino physics. The topics covered include neutrinos in the standard model and the three-neutrino mixing scheme; the current status of neutrino oscillation measurements and what remai...
There is a growing need to provide AI risk management models that can assess whether AI applications are safe and trustworthy, to make them responsible. To date, there are a few research papers on this topic. To fill the gap, in this paper we extend...
This paper contains the description of a successful system dynamics (SD) modeling approach used for almost a quarter-century in secondary schools, both in algebra classes and in a year-long SD modeling course. Secondary school students have demonstra...
The impact of COVID-19 across the United States (US) has been heterogeneous, with rapid spread and greater mortality in some areas compared with others. We used geographically-linked data to test the hypothesis that the risk for COVID-19 was defined...
The influence of the vehicle in topical treatment adherence remains to be elucidated. The aim of this study is to analyze the influence of the pharmaceutical dosage form on adherence to topical treatment in psoriasis patients, taking into considerati...
Advances in artificial intelligence (AI) are revolutionizing agriculture through applications in crop monitoring, precision agriculture, automation, and environmental management. With the rapid development of AI in agriculture, there is an increasing...
In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories...
Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rat...
In a cross-sectional study among 623 employees of a higher education institution, we examined the relations between perceived competence, autonomy, relatedness, intrinsic motivation, and productivity during the first lockdown in the spring of 2020. T...
Biodiversity maintenance is a crucial ecosystem service. Due to time limits and data availability, assessing biodiversity using indicators or models has become a hot topic in recent decades. However, whether some proposed indicators can explain biodi...
Remote sensing is used in an increasingly wide range of applications. Models and methodologies based on artificial intelligence (AI) are commonly used to increase the performance of remote sensing technologies. Deep learning (DL) models are the most...
Social and academic integration variables have been shown to be relevant for the understanding of university dropout. However, there is less evidence regarding the influence of these variables on dropout intention, as well as the predictive models th...
This paper investigates the variables that may explain the views of stakeholders regarding the need for a simplified non-financial reporting (NFR) standard for small and medium-sized enterprises (SMEs) in the European Union (EU) and whether this stan...
This paper examines certain implications from the literature on Tiebout’s model of local government service provision, particularly Hamilton’s extension of the model to include local control of land use and property taxation. Our empirica...
This paper reviews research and theory on the important topic of labour turnover resulting from issues related to job performance and/or job satisfaction which have, in turn, been initiated by changes in work motivation. We focus on labour turnover i...
This paper proposes the use of agent-based models (ABMs) as “interested amateurs” in policy making, and uses the example of the SWAP model of soil and water conservation adoption to demonstrate the potential of this approach. Daniel Dennett suggests...
Background/Objectives: This study analyzed ten years of domestic research on medical artificial intelligence (AI) from 2015 to 2024 using topic modeling and keyword network analysis. Chronological comparison showed that the research emphasis evolved...
We introduce a dynamic model of emotional behavior regulation that can generalize to a wide range of decision dilemmas. Dilemmas are characterized by availability of mutually exclusive goals that a decision maker is dually motivated to pursue. In our...
Background: Same-day surgery implies patient discharge on the same day after the surgery. The main aim of the research was to determine which predisposing factors lead to children treated with same-day surgery not being able to be discharged on the s...
The link between leverage and growth opportunities has been a topic issue in corporate finance for many years. The present paper aims to investigate the link between credit risk, leverage, and growth opportunities in a sample of Italian innovative sm...
This study introduces an explainable framework for Automated Fact Verification (AFV) systems, integrating a novel Context-Aware Retrieval and Explanation Generation (CARAG) methodology. CARAG enhances evidence retrieval by leveraging thematic embeddi...
Over the last few decades, a significant amount of literature on the preservation and adaptive reuse of historical buildings has been published. More recently, the use of digital technologies in heritage projects and academic research has become incr...
There is great interest in school leadership models, with their potential to explain and interpret leadership structures and processes. Models developed in the Global North, notably in the UK and USA, are now widely cited by scholars, policy-makers,...
Currently, organizations are experiencing rapid growth in the digitization of their processes, which generates a high availability of data and metadata in information systems generated by the activities of operations and support areas. This is import...
We provide an overview of the use of species distribution modeling to address research questions related to parrot ecology and conservation at a global scale. We conducted a literature search and applied filters to select the 82 most relevant studies...
It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be ab...
The rapid evolution of cities has brought new challenges to urban planning and management. The accurate evaluation of urban functional structure and mixed use is critical, especially at a fine scale such as by blocks. The composition and mixing of ur...
The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or...
The interest in studies aimed at understanding the integration of the stock market with the economic performance of countries has been growing in recent years, perhaps driven by the recent economic crises faced by the world. Although several studies...
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