The geometry of soft actuators significantly impacts their performance, including force generation, range of motion, and adaptability. Optimizing actuator geometry and material properties under specific constraints is crucial for achieving desired performance. This paper presents an optimization workflow employing metaheuristic algorithms in synergy with SolidWorks and Sorotoki, a newly developed MATLAB toolkit for soft robotics. The workflow optimizes actuator geometry to maximize bending while minimizing actuating pressure. A metaheuristic algorithm iteratively modifies the actuator’s design in SolidWorks, according to finite element analysis conducted using Sorotoki. To ensure accurate simulations, a uniaxial tensile test is performed on Thermoplastic Polyurethane (TPU), with curve fitting based on metaheuristic algorithms for precise hyperelastic modeling. The Ogden and Yeoh models are compared, with results indicating the Ogden model best represents TPU behavior. Four metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm, Simulated Annealing, and Moth Flame Optimization (MFO)—are evaluated. PSO outperforms others in material modeling, while MFO yields the most effective actuator geometry. This workflow enables the design of more efficient and adaptable soft actuators for applications in robotics, prosthetics, and biomedical devices.

We propose a novel hybrid deep learning framework that synergistically integrates Convolutional Neural Networks (CNNs), Spiking Neural Networks (SNNs), and Generative Adversarial Networks (GANs) for robust and accurate classification of high-resolution frontal and sagittal human gait video sequences—capturing both lower-limb kinematics and upper-body posture—from subjects with Knee Osteoarthritis (KOA), Parkinson’s Disease (PD), and healthy Normal (NM) controls, classified into three disease-type categories. Our approach first employs a tailored CNN backbone to extract rich spatial features from fixed-length clips (e.g., 16 frames resized to 128 × 128 px), which are then temporally encoded and processed by an SNN layer to capture dynamic gait patterns. To address class imbalance and enhance generalization, a conditional GAN augments rare severity classes with realistic synthetic gait sequences. Evaluated on the controlled, marker-based KOA-PD-NM laboratory public dataset, our model achieves an overall accuracy of 99.47%, a sensitivity of 98.4%, a specificity of 99.0%, and an F1-score of 98.6%, outperforming baseline CNN, SNN, and CNN–SNN configurations by over 2.5% in accuracy and 3.1% in F1-score. Ablation studies confirm that GAN-based augmentation yields a 1.9% accuracy gain, while the SNN layer provides critical temporal robustness. Our findings demonstrate that this CNN–SNN–GAN paradigm offers a powerful, computationally efficient solution for high-precision, gait-based disease classification, achieving a 48.4% reduction in FLOPs (1.82 GFLOPs to 0.94 GFLOPs) and 9.2% lower average power consumption (68.4 W to 62.1 W) on Kaggle P100 GPU compared to CNN-only baselines. The hybrid model demonstrates significant potential for energy savings on neuromorphic hardware, with an estimated 13.2% reduction in energy per inference based on FLOP-based analysis, positioning it favorably for deployment in resource-constrained clinical environments and edge computing scenarios.

Automated pneumonia screening from chest X-rays (CXRs) faces two coupled difficulties. Annotated radiographs are scarce. The disease also presents in two forms at once: focal lobar consolidation and diffuse interstitial infiltration. Purely spatial encoders resolve compact, high-contrast consolidations well. They under-exploit, however, the multi-scale frequency texture that carries most of the discriminative energy of diffuse infiltrates. We introduce WaveCAF, a dual-branch network that couples a ConvNeXt-Tiny spatial encoder with a frequency branch built on a single-level 2D discrete wavelet transform (DWT). The four Haar sub-bands (LL/LH/HL/HH) are re-weighted by an energy-driven learnable sub-band attention, encoded into tokens, and merged with the spatial tokens through a bidirectional Cross-Attention Fusion (CAF) block whose outputs are combined by a learned scalar gate; training couples a class-balanced focal loss with a cross-branch spectral-consistency regularizer, so class imbalance is handled analytically rather than by synthetic oversampling. On the Kaggle Chest X-ray Pneumonia benchmark, the released reference implementation of WaveCAF attains 97.9% (±0.4) accuracy, 98.5% F1, 98.3% recall and an AUC of 0.998 under stratified 5-fold cross-validation, statistically indistinguishable from the ConvNeXt-Tiny spatial baseline trained under the identical protocol (98.1% ± 0.2) and at the level that our harmonized reproductions identify as the ceiling of this benchmark. We further report (i) an ablation that isolates the sub-band attention, the cross-attention fusion and the gate against element-wise-summation and channel-concatenation fusion controls, (ii) a two-model data-efficiency sweep from 10% to 100% of the labels, (iii) a computational-efficiency budget covering parameters, FLOPs and single-image GPU latency, (iv) a robustness evaluation under thirteen image-degradation settings, and (v) an external evaluation on 26,684 adult films of the multi-institutional RSNA cohort, in which both models fall to ~0.87 AUC and the fused model detects more pneumonias at equal AUC (recall 69.6% vs. 62.7%, balanced accuracy 77.0% vs. 75.4%), together with a qualitative Grad-CAM and sub-band saliency analysis. On clean, fully supervised data the fused model does not surpass the spatial baseline; all ablated configurations remain within the noise floor of this saturated task. Once its gate and frequency encoder are properly initialized, however, WaveCAF is consistently at or above the baseline at every labeled-data fraction. It is also markedly more robust to degradations that suppress spatial amplitude: up to +7.8 accuracy points under severe contrast reduction, +1.9 under blur, and a higher AUC in all thirteen corruption settings. The cost is an increased sensitivity to additive noise, which contaminates the detail sub-bands directly. We report both directions of this trade-off in full, and we delimit where an explicit frequency prior pays.