Complex diseases arise from the intricate interactions between multiple genetic factors and environmental influences. Genome-Wide Association Studies (GWAS) are essential for uncovering the genetic underpinnings of complex diseases. GWAS seeks to uncover genotype-phenotype relationships by analyzing the correlations between genetic variations and specific traits in large-scale datasets. Despite its substantial contributions, GWAS struggles to fully capture disease complexity when considering isolated features. Hence, the selection of interactive features has emerged as an innovative strategy to uncover the genetic mechanisms of diseases. This study presents a Cooperative Particle Swarm Optimization method with Random Contrastive Learning (CPSORCL) developed specifically for interactive feature selection. CPSORCL employs three key strategies: an adaptive random contrastive learning strategy, a feature-weight-guided flipping strategy, and a deep search strategy. The adaptive random contrastive learning strategy dynamically adjusts the topological structure according to population evolution, promoting both competition and cooperation among particles to maintain diversity. The feature-weight-guided flipping strategy adjusts feature flipping probabilities dynamically, striking a balance between global exploration and local refinement within the solution space. The deep search strategy accurately identifies relevant features within the candidate pool, determining the final set of interactive features. Experiments are conducted on simulated datasets and an age-related macular degeneration dataset to compare CPSORCL with seven widely used methods. The results demonstrate the potential of CPSORCL for interactive feature selection, highlighting its promise as an alternative to traditional methods. The source code for CPSORCL is publicly available at https://github.com/CDMBlab/CPSORCL.

Small object detection in remote sensing imagery is a challenging task due to the small size of targets, complex background, and low contrast, which makes achieving high precision difficult. To enhance the accuracy of detection, this study proposes a novel oriented object detection model with three significant innovations: Firstly, a lightweight feature extraction network is designed to achieve efficient feature representation at a reduced computational cost, which is particularly effective for the recognition of small targets in remote sensing imagery. Secondly, a Feature-Focused Channel Attention (FFCA) is introduced that enhances the model’s ability to focus on small target areas by combining spatial and channel attention, enhancing the model’s capacity to capture and represent features more effectively. Lastly, an attention-guided multi-scale feature fusion module is proposed to integrate features from different levels, which substantially boosts the model’s ability to accurately detect small-scale objects, especially in remote sensing scenarios with vast fields of view and complex backgrounds. The experimental outcomes validate that our model achieves the best detection performance on two benchmark public datasets for remote sensing imagery, confirming its effectiveness and practicality in remote small object detection tasks.