Traditional vehicle fault diagnosis methods rely heavily on the expertise of mechanics or diagnostic tools available at service centers, which can be costly, time-consuming, and may not always provide accurate results. This study presents a comprehensive vehicle fault diagnosis framework, which utilized Mel-Frequency Cepstral Coefficients (MFCCs), Discrete Wavelet Transform (DWT)-based features, and the Extreme Learning Machine (ELM) classifier. To address the limitations of previous works, the proposed framework leverages a large, diverse dataset encompassing various vehicle models and real-world operating conditions. Significantly improved robustness and generalizability of the fault diagnosis system were achieved. The results of the experiments demonstrate the superiority of the MFCC-based features combined with the ELM classifier, achieving the highest performance metrics in terms of accuracy, precision, recall, F1-score, macro F1-score, and weighted F1-score, which are 92.17%, 92.24%, 92.22%, 92.10%, and 92.06%, respectively. Slightly lower performance was obtained while employing the DWT-based features compared to employing MFCC-based features. Additionally, frequency analysis was conducted to identify specific frequency bins, which are the most indicative of different fault types in providing valuable guidance for future diagnostic efforts. Overall, the proposed framework provides a reliable and practical solution for accurate vehicle fault detection, paving the way for future advancements in automotive diagnostics.

Fall risk assessment (FRA) helps clinicians make decisions about the best preventative measures to lower the risk of falls by identifying the different risks that are specific to an individual. With the development of wearable technologies such as inertial measurement units (IMUs), several free-living FRA methods based on fall predictors derived from IMU-based data have been introduced. The performance of such methods could be improved by increasing awareness of the individuals’ walking environment. This study aims to introduce and analyze a 25-layer convolutional neural network model for classifying nine walking surface conditions using IMU-based gait data, providing a basis for environment-aware FRAs. A database containing data collected from thirty participants who wore six IMU sensors while walking on nine surface conditions was employed. A systematic analysis was conducted to determine the effects of gait signals (acceleration, magnetic field, and rate of turn), sensor placement, and signal segment size on the method’s performance. Accuracies of 0.935 and 0.969 were achieved using a single and dual sensor, respectively, reaching an accuracy of 0.971 in the best-case scenario with optimal settings. The findings and analysis can help to develop more reliable and interpretable fall predictors, eventually leading to environment-aware FRA methods.

Background/Objectives: Monkeypox is a zoonotic virus that presents with smallpox-like symptoms, making visual diagnosis challenging due to overlap with other dermatological conditions. Existing AI-based studies on monkeypox classification have largely relied on Convolutional Neural Networks (CNNs), with limited exploration of Transformer architectures or robust interpretability frameworks. Moreover, most explainability research still depends on conventional heatmap techniques without systematic evaluation. This study addresses these gaps by applying Transformer-based models and introducing a novel hybrid explainability approach. Methods: We fine-tuned Vision Transformer (ViT) and Data-Efficient Image Transformer (DeiT) models for both binary and multi-class classification of monkeypox and other skin lesions. To improve interpretability, we integrated multiple explainable AI techniques—Gradient-weighted Class Activation Mapping (Grad-CAM), Layer-wise Relevance Propagation (LRP), and Attention Rollout (AR)—and proposed a hybrid method that combines these heatmaps using Principal Component Analysis (PCA). The reliability of explanations was quantitatively assessed using deletion and insertion metrics. Results: ViT achieved superior performance with an AUC of 0.9192 in binary classification and 0.9784 in multi-class tasks, outperforming DeiT. The hybrid approach (Grad-CAM + LRP) produced the most informative explanations, achieving higher insertion scores and lower deletion scores than individual methods, thereby enhancing clinical reliability. Conclusions: This study is among the first to combine Transformer models with systematically evaluated hybrid explainability techniques for monkeypox classification. By improving both predictive performance and interpretability, our framework contributes to more transparent and clinically relevant AI applications in dermatology. Future work should expand datasets and integrate clinical metadata to further improve generalizability.

Background/Objectives: Dementia is a progressive neurodegenerative disorder for which accurate and timely diagnosis remains a major clinical challenge. Electroencephalography (EEG) offers a noninvasive and cost-effective means of capturing neurophysiological alterations, motivating the development of reliable EEG-based automated diagnostic frameworks. This study aims to systematically examine how different time–frequency representations (TFRs) affect dementia classification performance within a unified multi-channel EEG image fusion framework. Methods: Resting-state, eyes-closed EEG recordings from 88 subjects, including Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls, were preprocessed and segmented. Channel-wise signals were converted into two-dimensional time–frequency images using Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), Wigner–Ville Distribution (WVD), or Constant-Q Transform (CQT). Images from 19 EEG channels were fused into a structured grid and classified using pretrained convolutional neural networks, including MobileNetV2, ResNet-50, and InceptionV3. Results: Results indicate that classification performance is highly dependent on the chosen TFR. The STFT-based representation combined with InceptionV3 achieved the highest accuracy, reaching 98.8% with random splitting and 84.3% with subject-wise splitting, outperforming previous studies. CQT also showed competitive performance, whereas HHT and WVD were less effective. Gradient-weighted class activation mapping provided interpretable visualization of physiologically relevant EEG channel contributions. Conclusions: The proposed framework demonstrates the importance of structured multi-channel fusion and systematic TFR evaluation for robust and interpretable EEG-based dementia classification and serves as a foundation for future cross-dataset validation.

Background/Objectives: Automated diabetic retinopathy (DR) severity grading from fundus images is a challenging task due to the high resolution of retinal images and the spatially sparse nature of pathological lesions. Methods: In this study, we propose a patch-based attention framework for DR severity grading that exploits localized information while avoiding aggressive global downsampling. Each fundus image is partitioned into non-overlapping patches, which are processed using a shared ResNet-18 encoder to extract patch-level feature representations. An attention-based aggregation mechanism then assigns adaptive importance weights to the extracted features and produces an image-level representation for severity classification. The proposed framework is evaluated on the APTOS 2019 dataset. Results: An analysis of different patch configurations identifies 25 patches as the best-performing setting, achieving a multi-class accuracy of 0.852, compared with 0.817 for the whole-image baseline. A controlled ablation using the same effective input resolution further shows that attention-based aggregation achieves the highest accuracy and F1-score among the evaluated whole-image and patch-based aggregation configurations. The model also achieves a binary DR/No-DR accuracy of 0.986. Attention visualizations provide qualitative insight into the regions contributing to model predictions. Conclusions: These results demonstrate the potential of attention-guided patch-based feature learning for accurate and interpretable diabetic retinopathy severity grading.