An Intelligent System For Arabic Text Categorization

Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. In this paper, an intelligent Arabic text categorization system is presented. Machine learning algorithms are used in this system. Many algorithms for stemming and feature selection are tried. Moreover, the document is represented using several term weighting schemes and finally the k-nearest neighbor and Rocchio classifiers are used for classification process. Experiments are performed over self collected data corpus and the results show that the suggested hybrid method of statistical and light stemmers is the most suitable stemming algorithm for Arabic language. The results also show that a hybrid approach of document frequency and information gain is the preferable feature selection criterion and normalized-tfidf is the best weighting scheme. Finally, Rocchio classifier has the advantage over k-nearest neighbor classifier in the classification process. The experimental results illustrate that the proposed model is an efficient method and gives generalization accuracy of about 98%

Generalized Fast-Convolution-based Filtered-OFDM: Techniques and Application to 5G New Radio

This paper proposes a generalized model and methods for fast-convolution (FC)-based waveform generation and processing with specific applications to fifth generation new radio (5G-NR). Following the progress of 5G-NR standardization in 3rd generation partnership project (3GPP), the main focus is on subband-filtered cyclic prefix (CP) orthogonal frequency-division multiplexing (OFDM) processing with specific emphasis on spectrally well localized transmitter processing. Subband filtering is able to suppress the interference leakage between adjacent subbands, thus supporting different numerologies for so-called bandwidth parts as well as asynchronous multiple access. The proposed generalized FC scheme effectively combines overlapped block processing with time- and frequency-domain windowing to provide highly selective subband filtering with very low intrinsic interference level. Jointly optimized multi-window designs with different allocation sizes and design parameters are compared in terms of interference levels and implementation complexity. The proposed methods are shown to clearly outperform the existing state-of-the-art windowing and filtering-based methods.Comment: To appear in IEEE Transactions on Signal Processin