In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not explain why the model classifies specific data instances as an anomaly. This study combines explainable AI techniques with anomaly detection algorithm to overcome the limitation above. As an explainable AI method, this study adopts Shapley Additive exPlanations (SHAP), which is theoretically solid and compatible with any kind of machine learning algorithm. SHAP enables us to measure the marginal contribution of each sensor variable to an anomaly. Thus, one can easily specify which sensor is responsible for the specific anomaly. To illustrate our framework, the actual sensor stream obtained from the cargo vessel collected over 10 months was analyzed. In this analysis, we performed hierarchical clustering analysis with transformed SHAP values to interpret and group common anomaly patterns. We showed that anomaly interpretation and segmentation using SHAP value provides more useful interpretation compared to the case without using SHAP value.

This study used a detailed explainable AI for automatic machine learning, AutoGluon, to predict pedestrian injury severity using data collected over five years (2016-2021) in Louisiana. The final dataset includes forty variables related to pedestrian characteristics, environmental circumstances, and vehicle specifications. Pedestrian injury severity was divided into three categories: fatal, injury, and no injury. The novelty of this approach lies in the application of explainable AI (XAI), specifically SHAP (SHapley Additive exPlanations) values, to interpret the AutoML model’s predictions. This combination not only addressed the opaqueness typically associated with AI “black box” models but also illuminated the critical variables influencing pedestrian injury severity outcomes. The results revealed that the weighted ensemble model emerged as top performers, showcasing high accuracy with minimal prediction times, demonstrating the potential of ensemble methods in improving prediction outcomes by integrating the strengths of various individual models. Furthermore, the global and local explainability analyses provided by SHAP values afforded us an in-depth understanding of the variables influencing pedestrian injury severity. This dual-level explanation offered valuable insights into the complex dynamics at play, ranging from pedestrian impairment and driver condition to environmental variables like lighting and weather conditions. These findings underscore the importance of specific variables in crash outcomes, offering actionable intelligence for targeted interventions.

Tabular data, widely used in industries like healthcare, finance, and transportation, presents unique challenges for deep learning due to its heterogeneous nature and lack of spatial structure. This survey reviews the evolution of deep learning models for tabular data, from early fully connected networks (FCNs) to advanced architectures like TabNet, SAINT, TabTranSELU, and MambaNet. These models incorporate attention mechanisms, feature embeddings, and hybrid architectures to address tabular data complexities. TabNet uses sequential attention for instance-wise feature selection, improving interpretability, while SAINT combines self-attention and intersample attention to capture complex interactions across features and data points, both advancing scalability and reducing computational overhead. Hybrid architectures such as TabTransformer and FT-Transformer integrate attention mechanisms with multi-layer perceptrons (MLPs) to handle categorical and numerical data, with FT-Transformer adapting transformers for tabular datasets. Research continues to balance performance and efficiency for large datasets. Graph-based models like GNN4TDL and GANDALF combine neural networks with decision trees or graph structures, enhancing feature representation and mitigating overfitting in small datasets through advanced regularization techniques. Diffusion-based models like the Tabular Denoising Diffusion Probabilistic Model (TabDDPM) generate synthetic data to address data scarcity, improving model robustness. Similarly, models like TabPFN and Ptab leverage pre-trained language models, incorporating transfer learning and self-supervised techniques into tabular tasks. This survey highlights key advancements and outlines future research directions on scalability, generalization, and interpretability in diverse tabular data applications.

Tabular data remains one of the most challenging modalities for deep learning due to its heterogeneity, lack of spatial or sequential inductive bias, and small-sample regimes. Key complexities include mixed feature types, high-cardinality categoricals, missing or sparse entries, and weak or irregular feature interactions-conditions that make representation learning and generalization difficult for neural networks. While early neural approaches struggled to match tree-based methods, recent advances demonstrate measurable progress across representation learning, regularization, and architectural design. This survey synthesizes findings from more than a decade of research, highlighting tabular data across two major families of approaches: classical models centered on decision trees and ensembles, and deep learning architectures. We identify three major trends: (1) the shift toward attention-based and hybrid architectures capable of modeling non-local interactions; (2) growing use of pretraining, self-supervision, and foundation-model-style priors to mitigate overfitting; and (3) emerging diffusion, graph-based, and multimodal frameworks that improve robustness and generalization. Despite these advances, tree-based methods remain strong baselines in low-dimensional and small-data contexts. By distilling empirical patterns across benchmarks and applications, this survey clarifies when deep learning delivers real gains for tabular data. We close with a prioritized research agenda on scalability, interpretability, and unified modeling.