M
Mengyu Qiao
Researcher at South Dakota School of Mines and Technology
Publications - 32
Citations - 972
Mengyu Qiao is an academic researcher from South Dakota School of Mines and Technology. The author has contributed to research in topics: Steganalysis & Steganography. The author has an hindex of 18, co-authored 32 publications receiving 891 citations. Previous affiliations of Mengyu Qiao include New Mexico Institute of Mining and Technology.
Papers
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Journal ArticleDOI
Temporal Derivative-Based Spectrum and Mel-Cepstrum Audio Steganalysis
TL;DR: Experimental results show that proposed derivative-based and wavelet-based approaches remarkably improve the detection accuracy.
Journal ArticleDOI
Gene selection and classification for cancer microarray data based on machine learning and similarity measures
Qingzhong Liu,Andrew H. Sung,Zhongxue Chen,Jianzhong Liu,Lei Chen,Mengyu Qiao,Zhaohui Wang,Xudong Huang,Youping Deng,Youping Deng +9 more
TL;DR: Recursive Feature Addition with Lagging prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF, and showed that Lagging Prediction Peepholes Optimization is superior to random strategy.
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Comparison of feature selection and classification for MALDI-MS data.
Qingzhong Liu,Andrew H. Sung,Mengyu Qiao,Zhongxue Chen,Jack Y. Yang,Mary Qu Yang,Xudong Huang,Youping Deng +7 more
TL;DR: The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data.
Journal ArticleDOI
Detection of Double MP3 Compression
TL;DR: To detect double MP3 compression, this paper extracts the statistical features on the modified discrete cosine transform and applies a support vector machine to the extracted features for classification and shows that the designed method is highly effective for detecting faked MP3 files.
Journal ArticleDOI
Neighboring joint density-based JPEG steganalysis
TL;DR: A new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images is proposed and prominently outperforms the well-known Markov-process based approach.