Tabular foundation models (TFMs) are pretrained once across many tables and applied to a new table by conditioning on its labelled rows, with no training on that table. They challenge gradient-boosted decision trees, the long-standing default for tab...
Tabular foundation models (TFMs) have moved tabular machine learning from per-dataset training towards amortised in-context inference, fitting a small-to-medium table in a single forward pass without a training run. The 2024–2026 release train,...
Chronic Kidney Disease (CKD) represents a considerable global health challenge, emphasizing the need for precise and prompt prediction of disease progression to enable early intervention and enhance patient outcomes. As per this study, we introduce a...
In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the exi...
As more machine learning models are used in sensitive fields like healthcare, finance, and smart infrastructure, protecting structured tabular data from privacy attacks is a key research challenge. Although several privacy-preserving methods have bee...
Recent advancements in natural language processing (NLP) have been significantly driven by the development of large language models (LLMs). Despite their impressive performance across various language tasks, these models still encounter challenges wh...
Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce an interpretable neural network-based regression model for tabular data in this study. Our proposed model uses ordinary least squa...
While traditional ensemble methods have dominated tabular intrusion detection systems (IDSs), recent advances in foundation models present new opportunities for enhanced cybersecurity applications. This paper presents a comprehensive multi-modal eval...
Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolut...
Applying large language models (LLMs) to industrial fault classification is hindered by the mismatch between tabular sensor data and text-based inputs and by the high memory cost of fine-tuning billion-parameter models on edge hardware. This paper pr...
Early detection of leaks, bursts, process faults, and cyber-physical attacks is a direct lever for the sustainable management of water infrastructures: it reduces non-revenue water, the energy spent on treating and pumping lost water, and service int...
Accurately predicting traffic crash severity is critical for intelligent transportation systems, where outcomes emerge from the interaction of infrastructure, environment, traffic control, and human behavior. However, existing approaches face three k...
Intrusion detection in industrial cyber-physical systems is constrained by small labelled-attack corpora and by the subtler signal of physical-process attacks compared with classical IT-network intrusions, motivating renewed interest in foundation-mo...
Deep learning has shown promise in tabular data modeling, yet challenges such as feature heterogeneity, sparse interactions, and expert prediction collapse remain unresolved. To address these issues, we propose DETTab (Diversity-Enhanced Tabular Expe...
This study proposes a time series-aware Large Language Model (LLM)-driven feature engineering framework for tabular prediction tasks. Existing automated feature engineering methods, including LLM-based approaches such as CAAFE and OCT-Tree and establ...
Deep learning models for tabular data often behave unpredictably during training, and these internal dynamics directly influence the reliability of post hoc interpretability methods. Static techniques such as SHapley Additive exPlanations (SHAP) and...
Credit risk assessment is a core component of financial decision-making. This study develops an explainable machine learning framework for modeling loan approval decisions on heterogeneous tabular data, centered on a Cross-Attentional Tabular Transfo...
Keeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of f...
Traffic incidents pose substantial hazards to public safety and wellbeing, and accurately estimating their duration is pivotal for efficient resource allocation, emergency response, and traffic management. However, existing research often faces limit...
While large language models (LLMs) have shown impressive performance on free-text questions, their reliability on tabular question answering (Tabular QA) is constrained, especially for multi-step reasoning and numerical aggregation. These challenges...
Fiber-reinforced polymer (FRP) composites offer high specific strength, high stiffness, and good corrosion resistance, which makes them suitable for aerospace, automotive, marine, and wind-energy structures. Their interlaminar fracture behavior is ha...
Background: Osteoarthritis (OA) is a prevalent chronic degenerative disorder, with coxarthrosis (hip OA) and gonarthrosis (knee OA) representing its most significant clinical manifestations. While diagnosis typically relies on imaging, such methods c...
Rare event detection in large tabular data remains a computationally challenging problem due to class imbalance, heterogeneous feature distributions, and unstable thresholds. Traditional machine learning approaches based on individual models and fixe...
The lack of clinical data for chronic kidney disease (CKD) prediction frequently results in model overfitting and inadequate generalization to novel samples. This research mitigates this constraint by utilizing a Conditional Tabular Generative Advers...
Class imbalance in aircraft predictive maintenance data causes classifiers to favor frequent actions and miss rare but operationally important outcomes. This study proposes a hybrid framework that combines a Class-Conditional Tabular Denoising Diffus...
Background/Objectives: In-context learning enables large language models (LLMs) to perform clinical prediction without task-specific training; however, their performance on structured tabular data relative to conventional machine learning remains unc...
Precise coagulant dosage control is essential for stable drinking-water treatment, yet conventional machine learning (ML) methods can be sensitive to data conditions. This study evaluates a large language model (LLM)-based in-context workflow for thi...
Knowledge distillation transfers information from a high-capacity teacher to a smaller student, but its behavior across regression, classification, and heterogeneous tabular model families remains insufficiently understood. This paper presents a comp...
Reliable uncertainty quantification matters for high-stakes tabular classification, yet the comparative influence of architecture and post-hoc calibration on uncertainty quality remains underexplored, particularly outside in-distribution conditions....
Static, machine learning-based malware detection is widely used in Android security products, where even small increases in false-positive rates can impose significant burdens on analysts and cause unacceptable disruptions for end users. Both tabular...
Anomaly detection on tabular data is widely used in fraud detection, predictive maintenance, and medical screening. While heterogeneous ensembles combining multiple detection paradigms achieve strong performance, their computational cost limits deplo...
Accreditation and quality assurance in technical and vocational education and training (TVET) remain largely dependent on manual review and expert judgment. This study presents AccreditNet, an explainable tabular AI model embedded in a broader accred...
Combinatorial Route Optimization (CRO) problems, such as the Vehicle Routing Problem (VRP) or the Travelling Salesman Problem (TSP), are commonly seen in scheduling, logistics, and transportation. While current machine learning (ML) methods can overc...
Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional...
Thyroid diseases are very common endocrine diseases that afflict millions of people around the world and need proper and timely diagnosis to ensure proper treatment. Although machine learning and hybrid metaheuristic methods have advanced, current mo...
Background/Objectives: Anemia is a complex hematological disorder with various subtypes, and its accurate diagnosis is of critical importance for clinical decision-making processes. Traditional machine learning algorithms and standard deep learning m...
This study presents NORIA, an availability-aware multitask learning framework for clinical prediction from incomplete electronic health records (EHRs). We analyzed a public dataset of 77,724 patients and 107 numerical features. To prevent temporal le...
Accurate agricultural yield prediction is crucial for crop management and data-driven decision-making. This study proposes a machine learning framework for predicting total lentil seed yield using 264 observations collected during a single growing se...
Despite the numerous advancements that have been made in the treatment and management of breast cancer, it continues to be a source of mortality in millions of female patients across the world each year; thus, there is a need for proper and reliable...
Existing unsupervised anomaly detection methods suffer from insufficient parameter precision, poor robustness to noise, and limited generalization capability. To address these issues, this paper proposes an Adaptive Diffusion Adversarial Evolutionary...
Predicting compressive strength from constituent quantities and curing conditions supports the assessment of candidate mixtures in ultra-high-performance concrete (UHPC) mixture design. Existing machine learning models usually process recorded variab...
This study presents a hybrid artificial intelligence system for latent cardiovascular risk stratification based on publicly available clinical and laboratory data. The proposed system integrates data preprocessing, auxiliary target modeling, latent p...
Rockburst intensity prediction from heterogeneous engineering databases is constrained by small sample sizes and inconsistent grading practices across data sources. This study develops a framework that couples the pre-trained tabular foundation model...
Reliable pre-season yield forecasting remains a persistent challenge in avocado (Persea americana Mill.) production, due to strong year-to-year variation and alternate bearing. This study developed an early-season, block-level yield forecasting frame...
Reliable, explainable prediction of the maternal mortality ratio (MMR) is challenging in global health because country-level indicators are heterogeneous and missingness is often informative rather than random. This study aims to develop and validate...
This research entails comparative analysis and optimisation of machine learning models for regression and classification tasks on structured tabular datasets. The primary target audience for this analysis comprises researchers and practitioners worki...
With increasing traffic loads and increasingly complex climate conditions, accurate prediction of the International Roughness Index (IRI) of asphalt pavements is crucial for developing effective maintenance plans. However, traditional regression mode...
Gradient-boosted decision trees (GBDTs) are among the leading methods for tabular data; however, their comparative robustness to corrupted labels remains unclear. This study resolves that uncertainty with a controlled benchmark protocol in which libr...
Peach maturity at harvest is a critical factor influencing fruit quality and postharvest life. Traditional destructive methods for maturity assessment, although effective, compromise fruit integrity and are unsuitable for practical implementation in...
This study introduces a Romanian–English bilingual corpus and a fine-tuned cross-lingual embedding framework aimed at improving retrieval performance in Retrieval-Augmented Generation (RAG) systems. The dataset integrates over 130,000 unstructu...
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