Abstract Understanding the temporal evolution of human emotions is critical for developing empathetic and adaptive Human Robot Interaction (HRI) systems. While existing research predominantly focuses on instantaneous emotion classification, it often overlooks the longitudinal dynamics and underlying latent affective structures. This study introduces the Temporal Emotion Dynamics and Latent Regime Framework (TEDLRF) to model the evolution of emotions as a stochastic temporal process. Utilizing a... Understanding the temporal evolution of human emotions is critical for developing empathetic and adaptive Human Robot Interaction (HRI) systems. While existing research predominantly focuses on instantaneous emotion classification, it often overlooks the longitudinal dynamics and underlying latent affective structures. This study introduces the Temporal Emotion Dynamics and Latent Regime Framework (TEDLRF) to model the evolution of emotions as a stochastic temporal process. Utilizing a pre trained Vision Transformer for facial emotion recognition, we analyzed longitudinal sequences of five basic emotions through Markov Chains, Hidden Markov Models (HMM), and Dynamic Bayesian Networks. Our analysis reveals that emotional states exhibit remarkable temporal inertia, with an Emotional Stability Index of 0.991 and self transition probabilities exceeding 97%. Furthermore, HMM state discovery identifies three distinct latent affective regimes: isolated anger, persistent sadness, and a flexible positive neutral subsystem. Survival analysis further demonstrates significant heterogeneity in emotional persistence, where sadness forms highly stable, long duration attractors, whereas surprise remains highly transient. Emotional evolution is characterized by quasi stationary phases punctuated by discrete regime shifts rather than continuous drift. These findings indicate that observable facial expressions are manifestations of deeper, unobservable psychological states. Consequently, we propose that next generation adaptive robots must transition from reactive, mimetic responses to proactive, temporally aware architectures. By inferring latent emotional regimes and anticipating affective transitions via change point detection, robots can deliver context sensitive, empathetic behaviors, ultimately fostering more natural and effective human AI collaboration. Read More Read Less

Abstract Background: Skin cancer remains one of the most prevalent and potentially fatal forms of cancer worldwide, with early detection being critical for improving patient outcomes. Dermoscopic imaging has become an indispensable tool for dermatological assessment; however, manual interpretation is challenging due to substantial variability in lesion appearance, ambiguous boundaries, and common imaging artefacts. Automated computer-aided diagnostic systems have emerged as promising solutions to assist... Background: Skin cancer remains one of the most prevalent and potentially fatal forms of cancer worldwide, with early detection being critical for improving patient outcomes. Dermoscopic imaging has become an indispensable tool for dermatological assessment; however, manual interpretation is challenging due to substantial variability in lesion appearance, ambiguous boundaries, and common imaging artefacts. Automated computer-aided diagnostic systems have emerged as promising solutions to assist clinicians in the early detection and accurate assessment of skin lesions. Objective: This study presents a comprehensive end to end computational framework for automated skin lesion detection and instance segmentation using the Mask R-CNN architecture. The primary objectives are to develop a robust system that simultaneously performs lesion localization, classification, and pixel level segmentation while providing clinically interpretable outputs to support dermatological decision making. Methods: The proposed framework integrates a ResNet-50 backbone with a Feature Pyramid Network (FPN) for multi scale feature extraction, a Region Proposal Network (RPN) for candidate lesion detection, ROI Align for precise spatial feature extraction, and parallel multi task heads for classification, boundingbox regression, and mask prediction. The model was trained and evaluated on a combined dataset of 2,500 dermoscopic images sourced from the International Skin Imaging Collaboration (ISIC) Archive and the PH² dataset, with expert annotated ground truth including bounding boxes and binary segmentation masks. Performance was assessed using Dice coefficient, Intersection over Union (IoU), pixel accuracy, mean Average Precision (mAP), inference time, and training convergence. The framework was systematically compared against the U-Net++ semantic segmentation baseline. Results: The proposed Mask R-CNN framework achieved strong performance on the validation set, with a Dice coefficient of 0.912, an IoU of 0.847, a pixel accuracy of 0.968, and an mAP@0.5 of 0.893. These results represent significant improvements over the U-Net++ baseline, with relative gains of 4.3% in Dice coefficient and 6.9% in IoU. The model demonstrated robust boundary adherence across lesions of varying sizes and morphological characteristics while maintaining clinically acceptable inference speeds of approximately48 ms per image. Training converged efficiently over 15 epochs with a final loss of 0.227. Per class analysis revealed strong performance for both benign (Dice: 0.928) and malignant (Dice: 0.885) lesions. Statistical analysis using paired Wilcoxon signed rank tests confirmed the significance of performance improvements (p = 0.043, effect size d  1.12). Conclusion: The proposed Mask R-CNN framework provides a robust, accurate, and clinically viable solution for automated skin lesion detection and instance segmentation. By simultaneously delivering lesion classification, precise boundary delineation, and instance level segmentation masks, the system offers comprehensive morphological information that directly supports quantitative lesion assessment and computer assisted clinical decision making. While challenges remain in handling severe imaging artefacts and domain generalization, this work establishes Mask R-CNN as a powerful foundation for computer aided dermatological diagnosis, with substantial potential for integration into routine clinical practice to facilitate early melanoma detection and improve patient outcomes. Read More Read Less

Abstract Autoencoders are foundational unsupervised neural networks that learn compact data representations by minimizing reconstruction error. This study presents a comprehensive quantitative and qualitative evaluation of a convolutional autoencoder (CAE) for image reconstruction using the CIFAR-10 dataset. The model, comprising approximately 1.05 million trainable parameters, compresses 32×32×3 RGB images into a 64- dimensional latent space before reconstructing them. A multidimensional evaluation framework integrates pixel-level metrics (MAE,... Autoencoders are foundational unsupervised neural networks that learn compact data representations by minimizing reconstruction error. This study presents a comprehensive quantitative and qualitative evaluation of a convolutional autoencoder (CAE) for image reconstruction using the CIFAR-10 dataset. The model, comprising approximately 1.05 million trainable parameters, compresses 32×32×3 RGB images into a 64- dimensional latent space before reconstructing them. A multidimensional evaluation framework integrates pixel-level metrics (MAE, MSE, RMSE), signal quality (PSNR), and perceptual similarity (SSIM), supplemented by diagnostic visualizations including side by side comparisons, error heatmaps, per-image error distributions, and class wise performance breakdowns. The autoencoder achieved a PSNR of 24.88 dB and SSIM of 0.786, indicating acceptable signal quality and good structural preservation. Statistical validation using one-way ANOVA (F(9,9990) = 17.84, p < 0.001) and Tukey HSD post hoc tests revealed significant class-dependent reconstruction differences, with vehicle categories (automobile, truck, ship) exhibiting lower error than animal classes (cat, dog, bird), reflecting the influence of semantic complexity on compression efficacy. Crucially, latent space analysis through PCA, t-SNE, and UMAP visualizations, alongside clustering metrics (Silhouette Score = 0.74, Calinski Harabasz Index = 4256.9), demonstrated that semantically meaningful class organization emerges without explicit supervision, with confusion heatmaps showing 89- 95% diagonal alignment with ground truth labels. This study contributes a robust, reproducible benchmarking framework for autoencoder evaluation, establishes the relationship between reconstruction fidelity and latent space semantic structure, and positions the CAE as an effective feature extractor for downstream tasks such as unsupervised classification, anomaly detection, and transfer learning in computer vision pipelines. Read More Read Less