Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective treatment. This study presents the relationalbi-level Aggrega...
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Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective treatment. This study presents the relational bi-level aggregation graph convolutional network with Dynamic graph Learning and Puzzle Optimization for Alzheimer's Classification (RBAGCN-DGL-PO-AC), using denoised T1-weighted Magnetic Resonance Images (MRIs) collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. Addressing the impact of noise in medical imaging, the method employs advanced denoising techniques includes: the Modified Spline-Kernelled Chirplet Transform (MSKCT), Jump Gain Integral Recurrent Neural network (JGIRNN), and Newton Time Extracting Wavelet Transform (NTEWT), to enhance the image quality. Key brain regions, crucial for classification such as hippocampal, lateral ventricle and posterior cingulate cortex are segmented using Attention Guided Generalized Intuitionistic Fuzzy C-Means Clustering ( AG-GIFCMC ) . Feature extraction and classification using segmented outputs are performed with RBAGCN-DGL and puzzle optimization, categorize input images into Healthy Controls (HC), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD). To assess the effectiveness of the proposed method, we systematically examined the structural modifications to the RBAGCN-DGL-PO-AC model through extensive ablation studies. Experimental findings highlight that RBAGCN-DGL-PO-AC state-of-the art performance, with 99.25 % accuracy, outperforming existing methods including MSFFGCN_ADC, CNN_CAD_DBMRI, and FCNN_ADC, while reducing training time by 28.5 % and increasing inference speed by 32.7 %. Hence, the RBAGCN-DGL-PO-AC method enhances AD classification by integrating denoising, segmentation, and dynamic graph-based feature extraction, achieving superior accuracy and making it a valuable tool for clinical applications, ultimately improving patient out
The integration of solar photovoltaic (SPV) systems with modular multiport converters (MMPC) enables efficient energy conversion and distribution, enhancing the overall performance and reliability of renewable energy ...
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The integration of solar photovoltaic (SPV) systems with modular multiport converters (MMPC) enables efficient energy conversion and distribution, enhancing the overall performance and reliability of renewable energy systems (RES). However, the complexity of the control algorithms and potential issues related to the dynamic response can pose challenges in achieving optimal performance and stability in varying operating conditions. This paper proposes a hybrid method for integrating SPV systems with MMPC to achieve efficient power management in modern renewable energy grids. The proposed hybrid method is the combined execution of the Osprey Optimization Algorithm (OOA) and relational bi-level aggregation graph convolutional network (RBAGCN). Hence it is named as OOA-RBAGCN technique. The aim is to ensure optimal power transfer, minimize total harmonic distortion (THD), maintain voltage stability under dynamic operating conditions, and ultimately improve the overall energy efficiency, reliability, and performance of SPV-based RES within smart grid applications. The OOA is used to optimize the control parameter of the proportional-integral (PI) controller. The RBAGCN is used to predict these optimized parameters. By then, the proposed approach is used on the MATLAB platform and compared with other approaches such as Starling Murmuration Optimization (SMO), Dung Beetle Optimizer (DBO), Improved Harris Hawks Optimization (IHHO), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). The proposed method achieves a high efficiency of 98.1%, and a reduced THD of 2.9% significantly surpassing all existing methods.
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