Titlebook: Computational Vision and Bio-Inspired Computing; Proceedings of ICCVB S. Smys,João Manuel R. S. Tavares,Fuqian Shi Conference proceedings 2

GAVEL

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Tan Kiat Shi,Willi-Hans Steeb,Yorick Hardyn of house number is the noise which is predominant. Hence, this work concentrates on the preprocessing of the data which is done twice to eliminate noise. Further XGBoosting and deep random forest approaches are utilised to improve the accuracy in recognition which utilizes less memory. Results sho

Anguish

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关节炎

Early Detection of ColoRectal Cancer Using Patch-Based Hybrid Model and Transfer Learning, and low-level features are examined for early prediction. The dataset used for analysis of this model is taken from the hospital comprising the samples of 100,000 images where 54,000 images are the ColoRectal cancer affected patients and 46,000 images are non-cancerous. This method is performing th

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Biomaterials Analysis and Deep Learning Outperforms Clinical Experts in Diagnosis and Classificatioorked. Also, the proposed system has yielded 99.54% accuracy with the help of 5-fold validation while classifying both healthy and fractured bones. Both algorithms gave better accuracy results than comparable existing algorithms in terms of performance.

Odyssey

Classification of Epileptic Seizures Using EEMD with Multi-entropy Features Integrating Different Tseizures with accuracy of 95.33%, specificity of 97.6%, and sensitivity of 95.3%. Thus, the current method would be an efficient for automated prediction and detection of epileptic seizures in real-time.

Jejune

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