Aim: Stock price prediction remains a highly challenging task due to the complex and nonlinear nature of financial time series data. While deep learning (DL) has shown promise in capturing these nonlinear patterns, its effectiveness is often hindered...
Large language models (LLMs) and other foundation models are rapidly being woven into enterprise analytics workflows, where they assist with data exploration, forecasting, decision support, and automation. These systems can feel like powerful new tea...
Environmental, social and governance (ESG) metrics increasingly inform sustainable investment yet suffer from inter-rater heterogeneity and incomplete reporting, limiting their utility for forward-looking allocation. In this study, we developed and v...
Many fluid-injection sequences display burst-like seismicity with approximate power-law event-size distributions whose exponents drift between catalogs. Classical percolation models instead predict fixed, dimension-dependent exponents and do not spec...
Background: Existing evaluations of large language models (LLMs) largely emphasize linguistic and factual performance, while their psychometric characteristics and behavioral biases remain insufficiently examined, particularly beyond English-language...
This study proposes GSM, an interpretable and operational GAM-SHAP-MCDA framework for stroke risk stratification by integrating generalized additive models (GAMs), a point-based clinical scoring system, SHAP-based explainability, and multi-criteria d...
The App Store introduced a title-length limit for mobile apps in 2016, and similar policies were later adopted across the industry. This issue drew considerable attention from industry practitioners in the 2010s. Using both empirical and theoretical...
To support novice learners, the Java programming learning assistant system (JPLAS) has been developed with various features. Among them, code writing problem (CWP) assigns writing an answer code that passes a given test code. The correctness of an an...
Global optimization is a fundamental tool for addressing complex and nonlinear problems across scientific and technological domains. The primary objective of this work is to enhance the efficiency, stability, and convergence speed of the Magnificent...
This study investigates the relationship between capital expenditure (CAPEX) and long-term corporate profitability in South Korea’s electronics industry. Using panel data from 126 listed electronics firms covering 2005–2019, the research...
In this research, we summarize the results of implementing the market risk premium into the option valuation formulas of the Black–Scholes–Merton model for out-of-the-money (OTM) options. We show that derivative prices can partly depend o...
The COVID-19 pandemic disrupted traditional patterns of sport consumption, raising questions about whether fans would return to stadiums and how sensitive they would be to ticket prices in the recovery period. This study reconceptualizes ticket price...
This study presents the development of an AI-powered chatbot designed to facilitate accurate and efficient retrieval of information from the FDA drug labeling documents. Leveraging OpenAI’s GPT-3.5-turbo model within a controlled, document-grou...
Correspondence analysis is a dimension reduction technique for visualizing a non-negative matrix
In recent years, video games have become an increasingly popular form of entertainment and enjoyment for consumers of all ages. Given their rapid rise in production, projects such as PlayMyData aim to organize the immense amounts of data that accompa...
As modern power systems integrate more renewable and decentralised generation, maintaining grid stability has become increasingly challenging. This study proposes a data-driven machine learning framework for forecasting system inertia service costs&m...
We introduce a distributional CNN-LSTM framework for probabilistic multivariate modeling and heterogeneous treatment effect (HTE) estimation. The model jointly captures complex dependencies among multiple outcomes and enables precise estimation of in...
Reservoir computing is an approach to machine learning that leverages the dynamics of a complex system alongside a simple, often linear, machine learning model for a designated task. While many efforts have previously focused their attention on integ...
The UK electricity market is changing to adapt to Net Zero targets and respond to disruptions like the Russia–Ukraine war. This requires strategic planning to decide on the construction of new electricity generation plants for a resilient UK el...
As organizations increasingly turn to machine learning for customer segmentation and targeted marketing, concerns about fairness and algorithmic bias have become more urgent. This study presents a comprehensive fairness audit and mitigation framework...
The landscape of cybercrime has undergone significant transformations over the past decade. Present-day threats include AI-generated attacks, deep fakes, 5G network vulnerabilities, cryptojacking, and supply chain attacks, among others. To remain res...
Artificial intelligence adoption in financial services presents uncertain implications for competitive dynamics, particularly for smaller institutions. The literature on AI in finance is growing, but there remains a notable absence regarding the impa...
Maximal Extractable Value (MEV) presents a significant challenge to the fairness and efficiency of decentralized finance (DeFi). This paper provides a game-theoretic analysis of the strategic interactions within the MEV supply chain, involving search...
Bankruptcy prediction is critical for financial risk management. This study demonstrates that machine learning models, particularly Random Forest, can substantially improve prediction accuracy compared to traditional approaches. Using data from 8262...
In this paper, explicit exact representations of the Unit Step Function and Ramp Function are obtained. These important functions constitute fundamental concepts of operational calculus together with digital signal processing theory and are also invo...
This article introduces a computational tool for Bayesian estimation of the expected time until the next event occurs in both homogeneous Poisson processes (HPPs) and non-homogeneous Poisson processes (NHPPs), following a truncated time. The estimati...
Hydrological time series frequently exhibit periodic trends with variables such as rainfall, runoff, and evaporation rates often following annual cycles. Seasonal variations further contribute to the complexity of these data sets. A critical aspect o...
As digital transformation becomes an increasingly central focus of national and regional policy agendas, parallel efforts are intensifying to stimulate innovation as a critical driver of firm competitiveness and high-quality economic growth. However,...
Domestication is a translation theory in which the source text (to be translated) is matched to the foreign reader by erasing its original linguistic and cultural difference. This match aims at making the target text (translated text) more fluent. On...
We compare the “classical” equations of type-token systems, namely Zipf’s laws, Heaps’ law and the relationships between their indices, with data selected from the Standardized Project Gutenberg Corpus (SPGC). Selected items a...
Generally, following an omnibus (overall equality) test, multiple pairwise comparison (MPC) tests are typically conducted as the second step in a sequential testing procedure to identify which specific pairs (e.g., proportions) exhibit significant di...
Airlines profitability increasingly depends on the sale of ancillary items such as seat selection, baggage fees, etc. The modeling of ancillary items is becoming more important in the analytics literature. Much of the modeling is stylized and not imm...
The Holy Bible is the most read book in the world, originally written in Aramaic, Hebrew, and Greek over a time span in the order of centuries by many people, and formed by a combination of various literary styles, such as stories, prophecies, poetry...
Traffic prediction, as a core task to alleviate urban congestion and optimize the transport system, has limitations in the integration of multimodal data, making it difficult to comprehensively capture the complex spatio-temporal characteristics of t...
Deoxyribonucleic acid, more commonly known as DNA, is a fundamental genetic material in all living organisms, containing thousands of genes, but only a subset exhibit differential expression and play a crucial role in diseases. Microarray technology...
Graduate salaries are a significant concern for graduates, employers, and policymakers, as various factors influence them. This study investigates determinants of graduate salaries in the UK, utilising survey data from HESA (Higher Education Statisti...
Analytics [...]
This study explores the role of cognitive performance in the general health of older Europeans aged 50 and over, focusing on gender differences, using data from 336,500 respondents in the sixth wave of the Survey of Health, Aging, and Retirement in E...
As the levels of automation and reliance on modern artificial intelligence (AI) approaches increase across multiple industries, the importance of the human-centered perspective becomes more evident. Various actors in such industrial applications, inc...
This study investigates how the Federal Open Market Committee’s (FOMC) statements impact healthcare spending, mental health trends, and stock performance in healthcare and tech sectors By analyzing FOMC’s sentiment from 2018 to 2024, we f...
This study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. Th...
The ability to automate and personalize the recommendation of multimedia contents to consumers has been gaining significant attention recently. The burgeoning demand for digitization and automation of formerly analog communication processes has caugh...
Competitive intelligence (CI) is a critical tool for startups, enabling informed decision making through the systematic gathering and analysis of relevant information. This study aims to identify and prioritize the key factors influencing CI in start...
Determining the ‘best-fitting’ distribution for data is an important problem in data analysis. Specifically, observing how the distribution of data changes as values below (or above) a threshold are omitted from analyses can be of use in...
The rapid expansion of big data has transformed research practices across disciplines, yet disparities exist in its adoption among U.S. institutions of higher education. This study examines trends in NSF-funded big data-driven research across researc...
This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearing...
Predicting ncRNA–protein interactions (NPIs) is essential for understanding regulatory roles in cellular processes and disease mechanisms, yet experimental methods are costly and time-consuming. In this study, we propose NPI-WGNN, a novel weigh...
Breast cancer is the most prevalent type of disease among women. It has become one of the foremost causes of death among women globally. Early detection plays a significant role in administering personalized treatment and improving patient outcomes....
The Bayesian information criterion (BIC) is a widely used statistical tool originally derived for fully observed data. The BIC formula includes the sample size and the number of estimated parameters in the penalty term. However, not all variables are...
Recent technology and equipment advancements have provided us with opportunities to better analyze Alzheimer’s disease (AD), where we could collect and employ the data from different image and genetic modalities that may potentially enhance the...
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