Vineela Chandra Dodda

The presence of clutter in ground penetrating radar (GPR) images obscures or distorts the responses of subsurface targets, significantly impacting the accuracy of detecting and identifying targets. Current clutter removal approaches often result in residual clutter or deformation of target responses, especially when confronted with intricate and uneven clutter in real-world GPR signatures. To address this issue in clutter removal under realistic conditions, this research introduces the Spatial Attention-based Convoluted Gazelle Neural Network (SACGNN). SACGNN is trained on large-scale dataset designed to reflect real-time conditions and incorporates a residual block into its spatial attention-based architecture. This integration enhances its ability to suppress clutter and restore target reflections on surfaces like homogeneous, rough, water, and grass surfaces. Tuning the SACGNN using the gazelle optimization algorithm excels in clutter removal by offering faster convergence, maintaining a balanced exploration-exploitation trade-off, and enhancing robustness across various data patterns.

Cholangiocarcinoma, a form of liver bile duct cancer, is challenging to detect due to its critically low 5-year survival rate. Conventional imaging modalities, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are widely used, but recent advancements in Hyperspectral Imaging (HSI) offer a promising, non-invasive alternative for cancer diagnosis. However, supervised learning methods often require large annotated datasets that can be difficult to obtain. To alleviate this limitation, we propose an unsupervised learning strategy using Generative Adversarial Networks (GANs) for cholangiocarcinoma detection. This approach, named Unsupervised Spectral and Spatial Attention-based GAN (USSGAN), employs an unsupervised Spectral-Spatial attention-based GAN to classify and segment cancerous regions without relying on labeled training data. The integration of an adaptive step size into Tasmanian Devil Optimization (TDO) enhances the convergence speed and effectively captures diverse cancerous features. Enhanced Tasmanian Devil Optimization (ETDO) further improves segmentation performance, making the framework robust and computationally efficient. The proposed method was tested on a publicly available multidimensional choledochal cholangiocarcinoma dataset, achieving superior performance compared with existing techniques in the literature. USSGAN demonstrated high accuracy across key metrics such as overall accuracy (OA), average accuracy (AA), and Cohen’s Kappa. Ablation studies confirmed the critical contributions of the proposed enhancements to the overall performance. With an overall accuracy of 98.03%, the USSGAN closely aligns with the assessments of experienced pathologists while maintaining minimal computational requirements. Its lightweight nature ensures real-time deployment, providing results within a minute, making it a practical and effective solution for clinical applications.

In seismic data processing, denoising is one of the essential steps to identifying the earth’s subsurface layer information. The noise present in the seismic data is categorized into two types: random and erratic noise. The random noise is distributed uniformly over the seismic data. The erratic noise attenuation is always challenging due to the unknown distribution of high-amplitude peaks over seismic data. The existing double sparsity dictionary learning (DSDL) method performs with analytical and adaptive transforms; both the transforms include iterative algorithms with K-singular-value decomposition (SVD); it is computationally costly, and the dictionary is initialized with trained data. To address these limitations, we propose a novel method of dictionary learning with double regularization (DLDR) to denoise both random and erratic noise from seismic data. In double regularization, we used with $\ell _{1}$ -norm and nuclear norm. The denoised data is applied to the alternating direction method of multipliers (ADMMs) to improve denoising while preserving the signal features from seismic data while reducing the computational cost. We evaluated the performance of the proposed method using signal-to-noise ratio (SNR), mean squared error (MSE), and local similarity map. The numerical results demonstrated that the proposed method resulted in higher SNR, lower MSE, and less signal leakage from seismic data. The method gives precise interpretation from the denoised seismic data.