Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis

Convolutional neural networks (CNNs) have become increasingly difficult to deploy in resource-constrained environments due to their large memory and computational requirements. Although low-rank compression methods can reduce this burden, most existing approaches compress spatial and channel redundancy independently and therefore do not fully exploit the localised structure within convolutional feature maps. This paper proposes a hierarchical spatio-channel low-rank compression framework for CNNs that exploits redundancy across spatial regions and channel activations. Unlike conventional methods, which apply a uniform decomposition across an entire layer, the proposed approach first partitions feature maps into spatial regions, then groups channels according to their co-activation patterns within each region, and finally applies rank-adaptive SVD to each resulting spatio-channel cluster. The method is evaluated on an AlexNet-based brain tumour MRI classification model and compared with Global SVD and Tucker decomposition under \(3\times\) and \(6\times\) compression budgets. Our method outperforms both baselines, reducing FLOPs from \(8.21\,\mathrm{G}\) to \(1.55\,\mathrm{G}\) (\(81.1\%\) reduction), achieving a \(1.38\times\) inference speed-up, and increasing classification accuracy from \(87.76\%\) to \(89.80\%\). The method also improves the macro \(F_1\)-score and performance on challenging classes such as meningioma. A hyper-parameter trade-off analysis demonstrates that the framework provides Pareto-optimal configurations, enabling control over the balance between compression and predictive performance. Moderate clustering with adaptive rank selection yields strong results. Bootstrap standard errors are reported for all classification metrics.

Hybrid Fuzzy Logic and Shading-Aware Particle Swarm Optimization for Dynamic Photovoltaic Shading Faults Mitigation

Shading faults remain one of the most critical challenges affecting photovoltaic (PV) system efficiency, as they not only reduce power generation but also disturb maximum power point tracking (MPPT). To address this issue, this study introduces a hybrid optimization framework that combines Fuzzy Logic Control (FLC) with a Shading-Aware Particle Swarm Optimization (SA-PSO) method. The proposed scheme is designed to adapt dynamically to both partial shading (20%-80%) and complete shading events, ensuring reliable global maximum power point (GMPP) detection. In this approach, the fuzzy controller provides rapid decision support based on shading patterns, while SA-PSO accelerates the search process and prevents the system from becoming trapped in local minima. A comparative performance assessment with the conventional Perturb and Observe (P\&O) algorithm highlights the advantages of the hybrid model, showing up to an 11.8% improvement in power output and a 62% reduction in tracking time. These results indicate that integrating intelligent control with shading-aware optimization can significantly enhance the resilience and energy yield of PV systems operating under complex real-world conditions.