2025 September - 50 articles
In real-world visual recognition tasks, long-tailed distributions pose a widespread challenge, with extreme class imbalance severely limiting the representational learning capability of deep models. In practice, due to this imbalance, deep models oft...
Background: Liquidity crises pose significant risks to financial stability, and missing data in predictive models increase the uncertainty in decision-making. This study aims to develop a robust Bayesian Model Averaging (BMA) framework using decision...
Background: Customer churn significantly impacts business revenues. Machine Learning (ML) and Deep Learning (DL) methods are increasingly adopted to predict churn, yet a systematic synthesis of recent advancements is lacking. Objectives: This systema...
In the technology-assisted review (TAR) area, most research has focused on ranking effectiveness and active learning strategies within individual topics, often assuming unconstrained review effort. However, real-world applications such as legal disco...
Students must navigate large catalogs of courses and make appropriate enrollment decisions in many online learning environments. In this context, identifying key concepts and their relationships is essential for understanding course content and infor...
Power transformers represent critical assets within the electrical power system, and their unexpected failures may result in substantial financial losses for both utilities and consumers. Dissolved Gas Analysis (DGA) is a well-established diagnostic...
To carry out data analysis, it is necessary to implement a model that guides the process in an orderly and sequential manner, with the aim of maintaining control over software development and its documentation. One of the most widely used tools in th...
Interpretable decision-making algorithms are important when used in the context of production optimization. While concepts like Petri nets are inherently interpretable, they are not straightforwardly learnable. This paper presents a novel approach to...
Accurate correspondence of complex neuroanatomical surfaces under non-rigid deformations remains a formidable challenge in computational neuroimaging, owing to inter-subject topological variability, partial occlusions, and non-isometric distortions....
Curriculum Learning (CL) is a potent field in Machine Learning that provides several excellent techniques for enhancing the performance of the training process given the same data points, regardless of the training method used. In this research, we p...
Automated test case generation aims to improve software testing by reducing the manual effort required to create test cases. Recent advancements in large language models (LLMs), with their ability to understand natural language and generate code, hav...
Bioinspired computing methods, such as Artificial Neural Networks (ANNs), play a significant role in machine learning. This is particularly evident in smart manufacturing, where ANNs and their derivatives, like deep learning, are widely used for patt...
Deep neural networks (DNNs) are highly effective across many domains but are sensitive to noisy or corrupted training data. Existing noise mitigation strategies often rely on strong assumptions about noise distributions or require costly retraining,...
Knee osteoarthritis (KOA) is a most prevalent chronic muscoloskeletal disorder causing pain and functional impairment. Accurate predictions of KOA evolution are important for early interventions and preventive treatment planning. In this paper, we pr...
While neural networks can solve complex geometric problems, as demonstrated by systems like AlphaGeometry, we have limited understanding of how they internally represent and reason about spatial relationships. In this work, we investigate how neural...
Multimodal medical data provides a wide and real basis for disease diagnosis. Computer-aided diagnosis (CAD) powered by artificial intelligence (AI) is becoming increasingly prominent in disease diagnosis. CAD for multimodal medical data requires add...
Nanoparticles have found widespread application across diverse fields, including agriculture and animal husbandry. However, a persistent challenge in laboratory-based studies involving nanoparticle exposure is the limited availability of experimental...
This paper introduces a novel framework for Distributionally Robust Bayesian Optimization (DRBO) with continuous context that integrates optimal transport theory and entropic regularization. We propose the sampling from the Wasserstein Barycenter Bay...
Accurate and high-resolution spatio-temporal prediction of PM2.5 concentrations remains a significant challenge for air pollution early warning and prevention. Advanced artificial intelligence (AI) technologies, however, offer promising solutions to...
Recent advancements in generative AI have fostered the development of highly adept Large Language Models (LLMs) that integrate diverse data types to empower decision-making. Among these, multimodal retrieval-augmented generation (RAG) applications ar...
The data landscape has changed, as more and more information is produced in the form of continuous data streams instead of stationary datasets. In this context, several online machine learning techniques have been proposed with the aim of automatical...
Novel techniques in evolutionary optimization, simulation, and machine learning enable a broad analysis of domains like fluid dynamics, in which computation is expensive and flow behavior is complex. This paper introduces the concept of full domain a...
This paper examines the integration of machine learning (ML) techniques in professional football, focusing on two key areas: (i) player and team performance, and (ii) match outcome prediction. Using a systematic methodology, this study reviews 172 pa...
Accurate segmentation of deep brain structures is critical for preoperative planning in such neurosurgical procedures as Deep Brain Stimulation (DBS). Previous research has showcased successful pipelines for segmentation from T1-weighted (T1w) Magnet...
Interactions between human behavior, legal regulations, and monitoring technology in road traffic systems provide an everyday example of complex biosocial–technical systems. In this paper, a study is reported that investigated the potential for...
According to a recent experimental phenomenology–information processing theory, the sensory strength, or vividness, of visual mental images self-reported by human observers reflects the intensive variation in subjective time duration during the...
Vision-based navigation is a common solution for the critical challenge of GPS-denied Unmanned Aerial Vehicle (UAV) operation, but a research gap remains in the autonomous discovery of robust landmarks from aerial survey imagery needed for such syste...
Predicting undergraduate student success is critical for informing timely interventions and improving outcomes in higher education. This study leverages over a decade of historical data from Louisiana State University (LSU) to forecast graduation out...
In this study, we introduce an enhanced hybrid Autoencoder–Dense–Transformer Neural Network (AE-DTNN) model for developing an effective intrusion detection system (IDS) aimed at improving the performance and robustness of threat detection...
Rust’s growing popularity in high-integrity systems requires automated vulnerability detection in order to maintain its strong safety guarantees. Although Rust’s ownership model and compile-time checks prevent many errors, sometimes unexp...
Generative Adversarial Networks (GANs) using Long Short-Term Memory (LSTM) provide a computationally cheaper approach for text generation compared to large language models (LLMs). The low hardware barrier of training GANs poses a threat because it me...
Machine learning modeling is a valuable tool for gap-filling or prediction, and its performance is typically evaluated using standard metrics. To enable more precise assessments for time-series data, this study emphasizes the importance of considerin...
Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues...
Large language models (LLMs) often tend to hallucinate, especially in domain-specific tasks and tasks that require reasoning. Previously, we introduced SubGraph Retrieval Augmented Generation (SG-RAG) as a novel Graph RAG method for multi-hop questio...
The increasing complexity and scale of electronic health records (EHRs) demand advanced tools for efficient data retrieval, summarization, and comparative analysis in clinical practice. MERA (Medical Electronic Records Assistant) is a Retrieval-Augme...
VisRep (Visualisation Report) is an AI-powered system for capturing and structuring the early stages of the visualisation design process. It addresses a critical gap in predesign: the lack of tools that can naturally record, organise, and transform r...
Applying language models (LMs) and generative artificial intelligence (GenAI) to the study of Ancient Greek offers promising opportunities. However, it faces substantial challenges due to the language’s morphological complexity and lack of anno...
Random Forests are powerful machine learning models widely applied in classification and regression tasks due to their robust predictive performance. Nevertheless, traditional Random Forests face computational challenges during tree construction, par...
In earthquake-prone areas such as Tokyo, accurate estimation of bearing stratum depth is crucial for foundation design, liquefaction assessment, and urban disaster mitigation. However, traditional methods such as the standard penetration test (SPT),...
We present a pipeline for synthetic simplification of text in French that combines large language models with structured semantic guidance. Our approach enhances data generation by integrating contextual knowledge from Wikipedia and Vikidia articles...
Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimension...
Generalising deep-learning models to perform well on unseen data domains with minimal retraining remains a significant challenge in computer vision. Even when the target task—such as quantifying the number of elements in an image—stays th...
This study investigates the application of the Dueling Double Deep Q-Network (3DQN) algorithm to optimize traffic signal control at a major urban intersection in Danang City, Vietnam. The objective is to enhance signal timing efficiency in response t...
The underground coal mine environment is complex and dynamic, making the application of visual algorithms for object detection a crucial component of underground safety management as well as a key factor in ensuring the safe operation of workers. We...
Predicting company bankruptcy is a critical task in financial risk assessment. This study introduces a novel approach using Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to enhance bankruptcy prediction accuracy. By...
Work-related well-being is an important research topic, as it is linked to various aspects of individuals’ lives, including job performance. To measure it effectively, unobtrusive sensors are desirable to minimize the burden on employees. Becau...
The integration of artificial intelligence (AI) and advanced deep learning techniques is reshaping intelligent financial forecasting and decision-support systems. This study presents a comprehensive comparative analysis of advanced deep learning mode...
High-dimensional experimental spaces and resource constraints challenge modern science. We introduce a hybrid machine-learning (ML) framework that combines Ordinary Least Squares (OLS) for global surface estimation, Gaussian Process (GP) regression f...
Preserving cultural heritage can be efficiently supported by structured and semantic representation of historical artifacts. Bookbinding, a critical aspect of book history, provides valuable insights into past craftsmanship, material use, and conserv...
Machine Learning (ML) is increasingly applied across various domains, addressing tasks such as predictive analytics, anomaly detection, and decision-making. Many of these applications share similar underlying tasks, offering potential for systematic...
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