Smart Grid Stability Prediction Using Adaptive Aquila Optimizer and Ensemble Stacked BiLSTM

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103261 - 103261

Published: Oct. 1, 2024

Language: Английский

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Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 4, 2025

Colorectal cancer (CRC) is the second popular in females and third males, with an increased number of cases. Pathology diagnoses complemented predictive prognostic biomarker information first step for personalized treatment. Histopathological image (HI) analysis benchmark pathologists to rank colorectal various kinds. However, pathologists' are highly subjective susceptible inaccurate diagnoses. The improved diagnosis load pathology laboratory, incorporated reported intra- inter-variability assessment, has prompted quest consistent machine-based techniques be integrated into routine practice. In healthcare field, artificial intelligence (AI) achieved extraordinary achievements applications. Lately, computer-aided (CAD) based on HI progressed rapidly increase machine learning (ML) deep (DL) models. This study introduces a novel Cancer Diagnosis using Optimal Deep Feature Fusion Approach Biomedical Images (CCD-ODFFBI) method. primary objective CCD-ODFFBI technique examine biomedical images identify (CRC). technique, median filtering (MF) approach initially utilized noise elimination. utilizes fusion three DL models, MobileNet, SqueezeNet, SE-ResNet, feature extraction. Moreover, models' hyperparameter selection performed Osprey optimization algorithm (OOA). Finally, belief network (DBN) model employed classify CRC. A series simulations accomplished highlight significant results method under Warwick-QU dataset. comparison showed superior accuracy value 99.39% over existing techniques.

Language: Английский

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The Role of Advanced Machine Learning in COVID-19 Medical Imaging: A Technical Review

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105154 - 105154

Published: May 1, 2025

Language: Английский

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Weighted Fusion Transformer for Dual PET/CT Head and Neck Tumor Segmentation

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 110905 - 110919

Published: Jan. 1, 2024

Accurate tumor segmentation in PET/CT imaging is essential for the diagnosis and treatment of cancer, impacting therapeutic outcomes patient management. Our study introduces a new approach integrating Weighted Fusion Transformer Network to enhance volumes. This method synergizes PET CT modalities through FormerU-Net architecture that employs convolutional neural networks alongside transformer blocks, aiming leverage unique advantages each modality. We evaluated proposed using multi-institutional dataset, applying key performance metrics such as Dice Similarity Coefficient aggregate, Jaccard Index, Volume Correlation, Average Surface Distance assess precision. The results indicate CT/PET/Fusion strategy significantly improves delineation, outperforming traditional methods. main findings suggest this integrative could potentially redefine standard clinical practice. Lastly, offers promising direction enhancing accuracy oncological imaging, with implications improvement patient-specific strategies.

Language: Английский

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A Review of Enhancing Sine Cosine Algorithm: Common Approaches for Improved Metaheuristic Algorithms

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

Language: Английский

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