Breast cancer is among the most prevalent cancers affecting women worldwide, and early detection through mammography is critical to reducing mortality rates. Convolutional neural networks (CNNs) have demonstrated notable effectiveness in classifying mammograms. However, they are constrained in their ability to capture long-range contextual dependencies. On the other hand, transformer-based models excel at handling global relationships; however, they often require large datasets and substantial computing power, which limits their direct application in medical imaging. To overcome these limitations, we propose Swin-DAMFN, a novel dual-branch hybrid architecture for breast cancer classification from mammograms. The first branch utilizes a Swin Transformer to model global dependencies through shifted window self-attention, while the second branch employs a CNN-based Dual-Attention Multi-scale Fusion Network (DAMFN) to capture fine-grained local features, such as microcalcifications and structural distortions. The CNN branch incorporates two custom modules, Multi Separable Attention (MSA) and Tri-Shuffle Convolution Attention (TSCA), for multi-scale discriminative feature extraction. An attention-guided fusion mechanism integrates global and local features into a unified representation. To address dataset limitations, we adopt an advanced augmentation strategy that combines Generative Adversarial Networks (GANs) to synthesize realistic mammograms and photometric augmentation to introduce appearance variability, thereby mitigating class imbalance and enhancing model generalization. A lightweight classification head based on global average pooling and fully connected layers ensures both efficiency and diagnostic accuracy. Extensive experiments on the MIAS and CBIS-DDSM datasets demonstrate that Swin-DAMFN achieves superior results, reaching 99.30% accuracy, 99.14% sensitivity, and 99.15% F1-score on CBIS-DDSM, while maintaining 98.75% accuracy, 98.37% sensitivity, and 98.42% F1-score on MIAS.

We computed the eigenvalues and eigenfunctions via the Cornell potential, approximately, using Numerov approach. The experimental data for the quantum states ³ S ₁ were utilized to fit the mass spectra. Results were compared with observed data and the results obtained from other potential functions in the existing literature for the heavy ( c c ¯ , b b ¯ and b c ¯ ) as well as heavy-light (B, Bs, D and Ds) meson states. The yielded meson spectra based on this potential are matching well with the available experimental data.

Lung cancer’s high mortality rate underscores the critical need for early and accurate diagnosis, as late-stage diagnoses often lead to 5-year survival rates as low as 5% compared to 56% for early detection, imposing significant economic burdens on healthcare systems and diminishing patient quality of life. While deep learning models offer promising tools for analyzing Computed Tomography (CT) scans, they often suffer from limitations in generalizability, interpretability, and sensitivity to imbalanced data. This paper introduces SE-FusionEAOO Ensemble, a new robust framework for lung cancer classification. Our approach leverages the strengths of multiple deep learning architectures through a sophisticated two-stage process. First, we construct three powerful feature fusion models by strategically pairing diverse pre-trained networks (DenseNet201/EfficientNetB6, Inception v3/MobileNetV2, DenseNet121/ResNet50), each integrated with Squeeze-and-Excitation (SE) blocks for adaptive feature recalibration. Second, we amalgamate the predictions of these expert models using an intelligently weighted aggregation scheme. The key innovation of our framework is the deployment of a new metaheuristic, the Enhanced Animated Oat Optimization algorithm with Genetic Operators (EAOO-GA), to precisely optimize these ensemble weights, ensuring optimal contribution from each model. To address class imbalance in the IQ-OTH/NCCD lung cancer dataset, we employ the Synthetic Minority Over-sampling Technique (SMOTE), significantly improving the model’s sensitivity to minority classes. Extensive experimental results demonstrate that our framework achieves a state-of-the-art accuracy of 99.40%, with 99.2% precision, 99.5% recall, and 99.3% F1-score, outperforming individual models, conventional ensemble methods, and other metaheuristic optimizers. Additionally, the model was externally validated on the LIDC-IDRI dataset, achieving 97.9% accuracy and 97.8% F1-score, confirming its strong generalization capability across independent clinical domains. The proposed framework provides a highly accurate, reliable, and interpretable tool for automated lung cancer detection.

In the current study, we developed an adaptive algorithm that can predict oil mobilization in a porous medium on the basis of optical data. Associated mechanisms based on tuning the electromagnetic response of magnetic and dielectric nanoparticles are also discussed. This technique is a promising method in rational magnetophoresis toward fluid mobility via fiber Bragg grating (FBG). The obtained wavelength shift due to Fe₃O₄ injection was 75% higher than that of dielectric materials. This use of FBG magneto-optic sensors could be a remarkable breakthrough for fluid-flow tracking in oil reservoirs. Our computational algorithm, based on piecewise linear polynomials, was evaluated with an analytical technique for homogeneous cases and achieved 99.45% accuracy. Theoretical values obtained via coupled-mode theory agreed with our FBG experiment data of at a level of 95.23% accuracy.