Convolutional neural networks (CNNs) are widely used in medical image classification, yet their performance is often hindered by data imbalance, limited adaptability, and overfitting. This study aims to improve CNN robustness by introducing a reinforcement learning (RL) framework based on Deep Deterministic Policy Gradients (DDPG) to automatically optimize data augmentation parameters. We propose an RL-driven optimization framework in which a DDPG agent autonomously adjusts data augmentation parameters to improve classification performance. The framework is evaluated across several benchmark datasets, including breast cancer, brain tumor, and COVID-19 X-ray images. Seven publicly available CNN architectures and a transformer model are used for comparison. Explainable artificial intelligence (XAI) using Grad-CAM is employed to validate model attention and prediction reliability. The proposed RL-based approach enables CNNs to rapidly identify optimal augmentation parameters, yielding superior performance compared to seven state-of-the-art CNN baselines and existing RL-integrated models. The DDPG agent demonstrates significant capability in automatically adapting augmentation strategies, consistently improving classification accuracy across medical. Grad-CAM visualizations further confirm improved feature focus and enhanced prediction consistency. The RL-driven DDPG framework effectively enhances image classification performance by autonomously optimizing augmentation parameters and improving CNN adaptability. Validation on multiple datasets and architectures—including transformers—shows that RL contributes to varying degrees of performance gains, establishing the proposed method as a promising direction for robust medical image analysis. The code is accessible at https://github.com/AIPMLab/DDPG.
3D morphable models (3DMMs) reconstruct the structure and appearance of 3D objects from 2D images, particularly human faces and bodies. Conventional 3DMM pipelines combine identity and expression blendshapes with a template mesh and are controlled by parameters such as shape, texture, illumination, and camera pose. However, many 3D human reconstruction methods focus on global facial geometry and make limited use of semantic facial cues such as age, gender, and landmark structure, characterising facial boundaries, curves, dips, and wrinkles. In order to adapt these high-level facial characteristics, this paper presents SARS, a shape- and appearance- aware modular reconstruction system that extracts face and body information from a single image and reconstructs a full body 3D human model. SARS improves the accuracy of semantic surface editing to capture expression-driven facial muscle deformation and appearance changes for different genders, while maintaining identity. SARS has three transformation modules. The first module estimates high-level facial attributes, including age, gender, and facial landmarks. The second module combines 3DMM expression coefficients, displacement maps, signed distance fields, and semantic facial features in a latent space to generate an identity-aware 3D face mesh. The third module uses the Skinned Multi-Person Linear model (SMPL) to extract high-level descriptors of the full body and based on these parameters, generates a 3D body model. A mesh fusion module then integrates the reconstructed face with the body mesh. Experiments on MICC, 3DPW, and EHF indicate that SARS improves facial detail and face-body continuity while maintaining competitive full-body reconstruction accuracy. Our code is available at https://github.com/gulraizk94/SARS_Integration.
This paper investigates the fuzzy distributed reinforcement learning (RL) optimal resilient control problem for multiple unmanned surface vehicle (multi-USV) systems under denial-of-service (DoS) attacks. Firstly, the considered multi-USV systems are modeled by Takagi-Sugeno (T-S) fuzzy systems. Then, a resilient distributed formation observer is developed to estimate the leader’s states. A fuzzy optimal resilient formation control scheme is proposed by using the proposed resilient observer and game theory. Considering that the analytical solution of the optimized control is transformed into the game algebraic Riccati equations (GAREs), which are difficult to obtain analytically, a value iteration (VI) learning algorithm is designed to learn the approximate solutions of GAREs under a mild initial excitation (IE) condition. It is proved that the designed controller guarantees that multi-USV systems achieve the desired formation shape with DoS attacks. In addition, the proposed learning algorithm is demonstrated to be convergent and the persistence of excitation (PE) condition in the existing RL algorithms is relaxed. Finally, the simulation and comparison results confirm the feasibility of the proposed optimized control approach.
This paper introduces a family of FMAVT-Sort models based on a fuzzy extension of the Multi-Criteria Decision Analysis (MCDA) method MAVT, alongside a framework for Uncertainty and Distinction Quantification (UQ and DQ) of the model outputs. The models differ in approaches to evaluating functions of fuzzy numbers (FNs), employing both precise and approximate techniques, and utilize four fuzzy ranking methods: defuzzification-based approaches (Centroid Index and Integral of Means), and two methods designed for ranking dependent FNs. The models are applied to a case study on university teacher evaluation. Assessment of local (within the case study) UQ and DQ and global (across a space of independent multicriteria problems) DQ is performed using algorithms that process output values from a large number of fuzzy multicriteria sorting problems simulated via Monte Carlo techniques in a fuzzy environment. A set of UQ indices, combined with a pre-uncertainty analysis stage, is implemented in a General Algorithm for Multicriteria Sorting, designed to support the decision-making process. The results demonstrate the effectiveness of UQ indices for decision support in multicriteria sorting and reveal significant distinctions among FMAVT-Sort models, underscoring the impact of model selection on final sorting outcomes. These findings hold scientific, methodological, and practical significance.
