Abstract: This work discusses the use of two fault-tolerant techniques, duplication with self-checking and triple modular redundancy, for one-hot encoding FSM in SRAM-based techniques. The FSM ...

Many machine learning packages require string characteristics to be translated to numerical representations in order to the proper functioning of models. String characteristics in datasets often ...

One hot encoding is crucial for converting categorical data into a numerical format suitable for machine learning models. Data preprocessing is an essential step before building deep learning models, ...

Abstract: This work discusses the use of two fault-tolerant techniques, duplication with self-checking and triple modular redundancy, for one-hot encoding FSM in SRAM-based techniques. The FSM ...

A command-line utility program for automating the trivial, frequently occurring data preparation tasks: missing value interpolation, outlier removal, and encoding categorical variables. This ...

from OneHotEncode.OneHotEncode import * df,dropped_cols,all_new_cols,new_col_dict = OneHotEncode(df,Categorical_column_list,check_numerical=False,max_var=20) Input ...

Thanks to today’s ultra high definition video and increasing complex demands for video editing, a new video format has risen to the throne, called H.265. This format, popularized by x265 and other ...