Search

Parkinson's disease (PD) is an irreversible neurodegenerative disease that has serious impacts on patients' lives. To provide timely accurate treatment and delay the deterioration of the disease, the accurate early diagnosis and clinical score prediction of PD are extremely important. Differing from previous studies on PD, we propose a network combining feature selection method with feature learning to obtain the most discriminative feature representation for longitudinal early diagnosis and clinical score prediction. Specifically, we first preprocess the multi-modal neuroimaging data at multiple time points to extract original longitudinal multi-modal features. Then, the feature selection method is performed to preliminary reduce the feature dimensions. Finally, the stacked sparse nonnegative autoencoder (SSNAE) is employed to obtain more discriminative longitudinal multi-modal features to improve the accuracy of early diagnosis and clinical score prediction at multiple time points. To verify our proposed network, diverse and extensive experiments are performed on the Parkinson's Progression Markers Initiative dataset, which aims to identify biological markers of PD risk, onset and progression. The results demonstrate that our proposed method is more efficient and achieves promising performance on both longitudinal early diagnosis and clinical scores prediction compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multipurpose intelligent image watermarking scheme for both content authentication and copyright protection. To achieve this, we first utilize integer discrete wavelet transform (IDWT) for watermark insertion and detection. The low frequency component of IDWT is used to insert a robust watermark (e.g., the copyright information) for copyright protection, whereas the high frequency component of IDWT is used to embed a fragile watermark (e.g., the logo data) for content authentication. To achieve a good tradeoff among the watermark conflicting requirements (e.g., robustness, fidelity and capacity), we develop an artificial bee colony (ABC) algorithm for optimal parameter selection. Experimental results demonstrate that the proposed scheme achieves promising performance in both copyright protection and content authentication simultaneously, which confirm the superiority of our proposed algorithm as compared to existing methods. [ABSTRACT FROM AUTHOR]