Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by motor neuron degeneration, with alterations in neural excitability serving as key indicators. Recent advancements in induced pluripotent stem cell (iPSC) technology have enabled the generation of human iPSC-derived neuronal cultures, which, when combined with multi-electrode array (MEA) electrophysiology, provide rich spatial and temporal electrophysiological data. Traditionally, MEA data is analyzed using handcrafted features based on potentially imperfect domain knowledge, which while useful may not fully capture all useful characteristics inherent in the data. Machine learning, particularly deep learning, has the potential to automatically learn relevant characteristics from raw data without solely relying on handcrafted feature extraction. However, handcrafted features remain critical for encoding domain knowledge and improving interpretability, especially with limited or noisy data. This study introduces FRAME-C, a knowledge-augmented machine learning pipeline that combines domain knowledge, raw spike waveform data, and deep learning techniques to classify MEA signals and identify ALS-specific phenotypes. FRAME-C leverages deep learning to learn important features from spike waveforms while incorporating handcrafted features such as spike amplitude, inter-spike interval, and spike duration, preserving key spatial and temporal information. We validate FRAME-C on both simulated and real MEA data from human iPSC-derived neuronal cultures, demonstrating superior performance over existing classification methods. FRAME-C shows over 11% improvement on real data and up to 25% on simulated data. We also show FRAME-C can evaluate handcrafted feature importance, providing insights into ALS phenotypes.
Unattended scheduled appointments, defined as patient no-shows, adversely affect both healthcare providers and patients' health, disrupting the continuity of care, operational efficiency, and the efficient allocation of medical resources. Accurate predictive modelling is needed to reduce the impact of no-shows. Although machine learning methods, such as logistic regression, random forest models, and decision trees, are widely used in predicting patient no-shows, they often rely on hard decision splits and static feature importance, limiting their adaptability to specific or complex patient behaviors. To address this limitation, we propose a new hybrid Multi-Head Attention Soft Random Forest (MHASRF) model that integrates attention mechanisms into a random forest model using probabilistic soft splitting instead of hard splitting. The MHASRF model assigns attention weights differently across the trees, enabling attention on specific patient behaviors. The model exhibited 93.56% accuracy, 93.67% precision, 93.56% recall, and a 93.59% F1 score, surpassing the performance of decision tree, logistic regression, random forest, and naive Bayes models. Furthermore, MHASRF was able to identify key predictors of patient no-shows using two levels of feature importance (tree level and attention mechanism level), offering deeper insights into patient no-show predictors. The proposed model is a robust, adaptable, and interpretable method for predicting patient no-shows that will help healthcare providers in optimizing resources.
Assessing the visual quality of video game graphics presents unique challenges due to the absence of reference images and the distinct types of distortions, such as aliasing, texture blur, and geometry level of detail (LOD) issues, which differ from those in natural images or user-generated content. Existing no-reference image and video quality assessment (NR-IQA/VQA) methods fail to generalize to gaming environments as they are primarily designed for distortions like compression artifacts. This study introduces a semantically-aware NR-IQA model tailored to gaming. The model employs a knowledge-distilled Game distortion feature extractor (GDFE) to detect and quantify game-specific distortions, while integrating semantic gating via CLIP embeddings to dynamically weight feature importance based on scene content. Training on gameplay data recorded across graphical quality presets enables the model to produce quality scores that align with human perception. Our results demonstrate that the GDFE, trained through knowledge distillation from binary classifiers, generalizes effectively to intermediate distortion levels unseen during training. Semantic gating further improves contextual relevance and reduces prediction variance. In the absence of in-domain NR-IQA baselines, our model outperforms out-of-domain methods and exhibits robust, monotonic quality trends across unseen games in the same genre. This work establishes a foundation for automated graphical quality assessment in gaming, advancing NR-IQA methods in this domain.
Explainable AI (XAI) methods generally fall into two categories. Post-hoc approaches generate explanations for pre-trained models and are compatible with various neural network architectures. These methods often use feature importance visualizations, such as saliency maps, to indicate which input regions influenced the model's prediction. Unfortunately, they typically offer a coarse understanding of the model's decision-making process. In contrast, ante-hoc (inherently explainable) methods rely on specially designed model architectures trained from scratch. A notable subclass of these methods provides explanations through prototypes, representative patches extracted from the training data. However, prototype-based approaches have limitations: they require dedicated architectures, involve specialized training procedures, and perform well only on specific datasets. In this work, we propose EPIC (Explanation of Pretrained Image Classification), a novel approach that bridges the gap between these two paradigms. Like post-hoc methods, EPIC operates on pre-trained models without architectural modifications. Simultaneously, it delivers intuitive, prototype-based explanations inspired by ante-hoc techniques. To the best of our knowledge, EPIC is the first post-hoc method capable of fully replicating the core explanatory power of inherently interpretable models. We evaluate EPIC on benchmark datasets commonly used in prototype-based explanations, such as CUB-200-2011 and Stanford Cars, alongside large-scale datasets like ImageNet, typically employed by post-hoc methods. EPIC uses prototypes to explain model decisions, providing a flexible and easy-to-understand tool for creating clear, high-quality explanations.
Cooperative game theory methods, notably Shapley values, have significantly enhanced machine learning (ML) interpretability. However, existing explainable AI (XAI) frameworks mainly attribute average model predictions, overlooking predictive uncertainty. This work addresses that gap by proposing a novel, model-agnostic uncertainty attribution (UA) method grounded in conformal prediction (CP). By defining cooperative games where CP interval properties-such as width and bounds-serve as value functions, we systematically attribute predictive uncertainty to input features. Extending beyond the traditional Shapley values, we use the richer class of Harsanyi allocations, and in particular the proportional Shapley values, which distribute attribution proportionally to feature importance. We propose a Monte Carlo approximation method with robust statistical guarantees to address computational feasibility, significantly improving runtime efficiency. Our comprehensive experiments on synthetic benchmarks and real-world datasets demonstrate the practical utility and interpretative depth of our approach. By combining cooperative game theory and conformal prediction, we offer a rigorous, flexible toolkit for understanding and communicating predictive uncertainty in high-stakes ML applications.
Ensuring fairness in machine learning models is critical, particularly in high-stakes domains where biased decisions can lead to serious societal consequences. Existing preprocessing approaches generally lack transparent mechanisms for identifying which features or instances are responsible for unfairness. This obscures the rationale behind data modifications. We introduce FairSHAP, a novel pre-processing framework that leverages Shapley value attribution to improve both individual and group fairness. FairSHAP identifies fairness-critical instances in the training data using an interpretable measure of feature importance, and systematically modifies them through instance-level matching across sensitive groups. This process reduces discriminative risk - an individual fairness metric - while preserving data integrity and model accuracy. We demonstrate that FairSHAP significantly improves demographic parity and equality of opportunity across diverse tabular datasets, achieving fairness gains with minimal data perturbation and, in some cases, improved predictive performance. As a model-agnostic and transparent method, FairSHAP integrates seamlessly into existing machine learning pipelines and provides actionable insights into the sources of bias.Our code is on https://github.com/youlei202/FairSHAP.
Brain-Computer Interfaces enable direct communication between the brain and external systems, with functional Near-Infrared Spectroscopy emerging as a portable and non-invasive method for capturing cerebral hemodynamics. This study investigates the classification of rest and task states during a realistic, interactive tennis simulation using fNIRS signals and a range of machine learning approaches. We benchmarked traditional classifiers based on engineered features, Long Short-Term Memory networks on raw time-series data, and Convolutional Neural Networks applied to Gramian Angular Field-transformed images. Ensemble models like Extra Trees and Gradient Boosting achieved accuracies above 97 percent, while the ResNet-based CNN reached 95.0 percent accuracy with a near-perfect AUC of 99.2 percent, outperforming both LSTM and EfficientNet architectures. A novel data augmentation strategy was employed to equalize trial durations while preserving physiological integrity. Feature importance analyses revealed that both oxygenated and deoxygenated hemoglobin signals, particularly slope and RMS metrics, were key contributors to classification performance. These findings demonstrate the strong potential of fNIRS-based BCIs for deployment in dynamic, real-world environments and underscore the advantages of deep learning models in decoding complex neural signals.
There can be many competing and contradictory explanations for a single model prediction, making it difficult to select which one to use. Current explanation evaluation frameworks measure quality by comparing against ideal "ground-truth" explanations, or by verifying model sensitivity to important inputs. We outline the limitations of these approaches, and propose three desirable principles to ground the future development of explanation evaluation strategies for local feature importance explanations. We propose a ground-truth Agnostic eXplanation Evaluation framework (AXE) for evaluating and comparing model explanations that satisfies these principles. Unlike prior approaches, AXE does not require access to ideal ground-truth explanations for comparison, or rely on model sensitivity - providing an independent measure of explanation quality. We verify AXE by comparing with baselines, and show how it can be used to detect explanation fairwashing. Our code is available at https://github.com/KaiRawal/Evaluating-Model-Explanations-without-Ground-Truth.
The emergence of hybrid quantum-classical machine learning (HQML) models opens new horizons of computational intelligence but their fundamental complexity frequently leads to black box behavior that undermines transparency and reliability in their application. Although XAI for quantum systems still in its infancy, a major research gap is evident in robust global and local explainability approaches that are designed for HQML architectures that employ quantized feature encoding followed by classical learning. The gap is the focus of this work, which introduces QuXAI, an framework based upon Q-MEDLEY, an explainer for explaining feature importance in these hybrid systems. Our model entails the creation of HQML models incorporating quantum feature maps, the use of Q-MEDLEY, which combines feature based inferences, preserving the quantum transformation stage and visualizing the resulting attributions. Our result shows that Q-MEDLEY delineates influential classical aspects in HQML models, as well as separates their noise, and competes well against established XAI techniques in classical validation settings. Ablation studies more significantly expose the virtues of the composite structure used in Q-MEDLEY. The implications of this work are critically important, as it provides a route to improve the interpretability and reliability of HQML models, thus promoting greater confidence and being able to engage in safer and more responsible use of quantum-enhanced AI technology. Our code and experiments are open-sourced at: https://github.com/GitsSaikat/QuXAI
Traditional machine learning models often prioritize predictive accuracy, often at the expense of model transparency and interpretability. The lack of transparency makes it difficult for organizations to comply with regulatory requirements and gain stakeholders trust. In this research, we propose a fraud detection framework that combines a stacking ensemble of well-known gradient boosting models: XGBoost, LightGBM, and CatBoost. In addition, explainable artificial intelligence (XAI) techniques are used to enhance the transparency and interpretability of the model's decisions. We used SHAP (SHapley Additive Explanations) for feature selection to identify the most important features. Further efforts were made to explain the model's predictions using Local Interpretable Model-Agnostic Explanation (LIME), Partial Dependence Plots (PDP), and Permutation Feature Importance (PFI). The IEEE-CIS Fraud Detection dataset, which includes more than 590,000 real transaction records, was used to evaluate the proposed model. The model achieved a high performance with an accuracy of 99% and an AUC-ROC score of 0.99, outperforming several recent related approaches. These results indicate that combining high prediction accuracy with transparent interpretability is possible and could lead to a more ethical and trustworthy solution in financial fraud detection.
This paper presents a novel framework for prioritizing urban green space development in Tehran using diverse socio-economic, environmental, and sensitivity indices. The indices were derived from various sources including Google Earth Engine, air pollution measurements, municipal reports and the Weather Research & Forecasting (WRF) model. The WRF model was used to estimate the air temperature at a 1 km resolution due to insufficient meteorological stations, yielding RMSE and MAE values of 0.96{\deg}C and 0.92{\deg}C, respectively. After data preparation, several machine learning models were used for binary vegetation cover classification including XGBoost, LightGBM, Random Forest (RF) and Extra Trees. RF achieved the highest performance, exceeding 94% in Overall Accuracy, Recall, and F1-score. Then, the probability of areas lacking vegetation cover was assessed using socio-economic, environmental and sensitivity indices. This resulted in the RF generating an urban green space development prioritization map. Feature Importance Analysis revealed that the most significant indices were nightly land surface temperature (LST) and sensitive population. Finally, the framework performance was validated through microclimate simulation to assess the critical areas after and before the green space development by green roofs. The simulation demonstrated reducing air temperature by up to 0.67{\deg}C after utilizing the green roof technology in critical areas. As a result, this framework provides a valuable tool for urban planners to develop green spaces.
Vehicular Ad Hoc Networks (VANETs) play a key role in Intelligent Transportation Systems (ITS), particularly in enabling real-time communication for emergency vehicles. However, Distributed Denial of Service (DDoS) attacks, which interfere with safety-critical communication channels, can severely impair their reliability. This study introduces a robust and scalable framework to detect DDoS attacks in highway-based VANET environments. A synthetic dataset was constructed using Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban Mobility (SUMO) and further enriched with real-world mobility traces from Germany's A81 highway, extracted via OpenStreetMap (OSM). Three traffic categories were simulated: DDoS, VoIP, and TCP-based video streaming (VideoTCP). The data preprocessing pipeline included normalization, signal-to-noise ratio (SNR) feature engineering, missing value imputation, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Feature importance was assessed using SHapley Additive exPlanations (SHAP). Eleven classifiers were benchmarked, among them XGBoost (XGB), CatBoost (CB), AdaBoost (AB), GradientBoosting (GB), and an Artificial Neural Network (ANN). XGB and CB achieved the best performance, each attaining an F1-score of 96%. These results highlight the robustness of the proposed framework and its potential for real-time deployment in VANETs to secure critical emergency communications.
Metabolic syndrome (MetS) is a medication condition characterized by abdominal obesity, insulin resistance, hypertension and hyperlipidemia. It increases the risk of majority of chronic diseases, including type 2 diabetes mellitus, and affects about one quarter of the global population. Therefore, early detection and timely intervention for MetS are crucial. Standard diagnosis for MetS components requires blood tests conducted within medical institutions. However, it is frequently underestimated, leading to unmet need for care for MetS population. This study aims to use the least physiological data and free texts about exercises related activities, which are obtained easily in daily life, to diagnosis MetS. We collected the data from 40 volunteers in a nursing home and used data augmentation to reduce the imbalance. We propose a deep learning framework for classifying MetS that integrates natural language processing (NLP) and exercise monitoring. The results showed that the best model reported a high positive result (AUROC=0.806 and REC=76.3%) through 3-fold cross-validation. Feature importance analysis revealed that text and minimum heart rate on a daily basis contribute the most in the classification of MetS. This study demonstrates the potential application of data that are easily measurable in daily life for the early diagnosis of MetS, which could contribute to reducing the cost of screening and management for MetS population.
Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an ``interpretable'' feature representation. In tabular data, feature values themselves are often considered interpretable. This paper examines the impact of data engineering choices on local feature-based explanations. We demonstrate that simple, common data engineering techniques, such as representing age with a histogram or encoding race in a specific way, can manipulate feature importance as determined by popular methods like SHAP. Notably, the sensitivity of explanations to feature representation can be exploited by adversaries to obscure issues like discrimination. While the intuition behind these results is straightforward, their systematic exploration has been lacking. Previous work has focused on adversarial attacks on feature-based explainers by biasing data or manipulating models. To the best of our knowledge, this is the first study demonstrating that explainers can be misled by standard, seemingly innocuous data engineering techniques.
Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. We propose a novel approach to identify the most informative features within prototypes, termed alike parts. Using feature importance scores derived from an agnostic explanation method, it emphasizes the most relevant overlapping features between an instance and its nearest prototype. Furthermore, the feature importance score is incorporated into the objective function of the prototype selection algorithms to promote global prototypes diversity. Through experiments on six benchmark datasets, we demonstrate that the proposed approach improves user comprehension while maintaining or even increasing predictive accuracy.
Academic misconduct detection in biomedical research remains challenging due to algorithmic narrowness in existing methods and fragmented analytical pipelines. We present BMMDetect, a multimodal deep learning framework that integrates journal metadata (SJR, institutional data), semantic embeddings (PubMedBERT), and GPT-4o-mined textual attributes (methodological statistics, data anomalies) for holistic manuscript evaluation. Key innovations include: (1) multimodal fusion of domain-specific features to reduce detection bias; (2) quantitative evaluation of feature importance, identifying journal authority metrics (e.g., SJR-index) and textual anomalies (e.g., statistical outliers) as dominant predictors; and (3) the BioMCD dataset, a large-scale benchmark with 13,160 retracted articles and 53,411 controls. BMMDetect achieves 74.33% AUC, outperforming single-modality baselines by 8.6%, and demonstrates transferability across biomedical subfields. This work advances scalable, interpretable tools for safeguarding research integrity.
The present research tackles the difficulty of predicting osteoporosis risk via machine learning (ML) approaches, emphasizing the use of explainable artificial intelligence (XAI) to improve model transparency. Osteoporosis is a significant public health concern, sometimes remaining untreated owing to its asymptomatic characteristics, and early identification is essential to avert fractures. The research assesses six machine learning classifiers: Random Forest, Logistic Regression, XGBoost, AdaBoost, LightGBM, and Gradient Boosting and utilizes a dataset based on clinical, demographic, and lifestyle variables. The models are refined using GridSearchCV to calibrate hyperparameters, with the objective of enhancing predictive efficacy. XGBoost had the greatest accuracy (91%) among the evaluated models, surpassing others in precision (0.92), recall (0.91), and F1-score (0.90). The research further integrates XAI approaches, such as SHAP, LIME, and Permutation Feature Importance, to elucidate the decision-making process of the optimal model. The study indicates that age is the primary determinant in forecasting osteoporosis risk, followed by hormonal alterations and familial history. These results corroborate clinical knowledge and affirm the models' therapeutic significance. The research underscores the significance of explainability in machine learning models for healthcare applications, guaranteeing that physicians can rely on the system's predictions. The report ultimately proposes directions for further research, such as validation across varied populations and the integration of supplementary biomarkers for enhanced predictive accuracy.
Molecular odor prediction is the process of using a molecule's structure to predict its smell. While accurate prediction remains challenging, AI models can suggest potential odors. Existing methods, however, often rely on basic descriptors or handcrafted fingerprints, which lack expressive power and hinder effective learning. Furthermore, these methods suffer from severe class imbalance, limiting the training effectiveness of AI models. To address these challenges, we propose a Feature Contribution-driven Hierarchical Multi-Feature Mapping Network (HMFNet). Specifically, we introduce a fine-grained, Local Multi-Hierarchy Feature Extraction module (LMFE) that performs deep feature extraction at the atomic level, capturing detailed features crucial for odor prediction. To enhance the extraction of discriminative atomic features, we integrate a Harmonic Modulated Feature Mapping (HMFM). This module dynamically learns feature importance and frequency modulation, improving the model's capability to capture relevant patterns. Additionally, a Global Multi-Hierarchy Feature Extraction module (GMFE) is designed to learn global features from the molecular graph topology, enabling the model to fully leverage global information and enhance its discriminative power for odor prediction. To further mitigate the issue of class imbalance, we propose a Chemically-Informed Loss (CIL). Experimental results demonstrate that our approach significantly improves performance across various deep learning models, highlighting its potential to advance molecular structure representation and accelerate the development of AI-driven technologies.
We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and B content reduces MS from 1.81T (DFT~2.04 T) to ~1.54 T (DFT~1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09T), Fe-5Si-5B (2.01T) and Fe-10Si-10B (1.54T) alloy compositions further support our findings. These trends are consistent with density functional theory (DFT) predictions, which link increased electronic disorder and band broadening to lower MS values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveals that MS is governed by a nonlinear interplay between Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudo-quaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe84.8Si0.5B9.4Cu0.8 P3.5C1), FINEMET (Fe73.5Si13.5B9 Cu1Nb3), NANOPERM (Fe88Zr7B4Cu1), and HITPERM (Fe44Co44Zr7B4Cu1. Our fundings demonstrate the potential of ML framework for accelerated search of high-performance, Co- and Ni-free, soft magnetic materials.
Evaluating synthetic tabular data is challenging, since they can differ from the real data in so many ways. There exist numerous metrics of synthetic data quality, ranging from statistical distances to predictive performance, often providing conflicting results. Moreover, they fail to explain or pinpoint the specific weaknesses in the synthetic data. To address this, we apply explainable AI (XAI) techniques to a binary detection classifier trained to distinguish real from synthetic data. While the classifier identifies distributional differences, XAI concepts such as feature importance and feature effects, analyzed through methods like permutation feature importance, partial dependence plots, Shapley values and counterfactual explanations, reveal why synthetic data are distinguishable, highlighting inconsistencies, unrealistic dependencies, or missing patterns. This interpretability increases transparency in synthetic data evaluation and provides deeper insights beyond conventional metrics, helping diagnose and improve synthetic data quality. We apply our approach to two tabular datasets and generative models, showing that it uncovers issues overlooked by standard evaluation techniques.