کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6866174 | 679096 | 2015 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
FPGA-based hardware accelerator for the prediction of protein secondary class via fuzzy K-nearest neighbors with Lempel-Ziv complexity based distance measure
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: FPGA-based hardware accelerator for the prediction of protein secondary class via fuzzy K-nearest neighbors with Lempel-Ziv complexity based distance measure FPGA-based hardware accelerator for the prediction of protein secondary class via fuzzy K-nearest neighbors with Lempel-Ziv complexity based distance measure](/preview/png/6866174.png)
چکیده انگلیسی
Correct prediction of protein secondary structural classes is vital for the prediction of tertiary structures and understanding of their function. Most of the prediction algorithms require lengthy computation time. Nearest neighbor - complexity distance measure (NN-CDM) algorithm was one of the significant prediction algorithms using Lempel-Ziv (LZ) complexity-based distance measure, but it is slow and ineffective in handling uncertainties. To solve the problems, we propose fuzzy NN-CDM (FKNN-CDM) algorithm that incorporates the confidence level of prediction results and enhance the prediction process by designing hardware architecture that implements the proposed algorithm in an FPGA board. Highest average prediction accuracies for Z277 and 25PDB datasets using proposed algorithm are 84.12% and 47.81% respectively, with 15 times faster computation using an Altera DE2-115 FPGA board.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 148, 19 January 2015, Pages 409-419
Journal: Neurocomputing - Volume 148, 19 January 2015, Pages 409-419
نویسندگان
Yong Tat Tan, Bakhtiar Affendi Rosdi,