Comparative analysis of text classification algorithms for automated labelling of quranic verses

The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as “Shahadah” (the first pillar of Islam) or “Pray” (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses

On pattern classification algorithms - Introduction and survey

Pattern recognition algorithms, and mathematical techniques of estimation, decision making, and optimization theor

Packet Classification Algorithms

V úvodu této bakalářské práce je rozebrána problematika filtrování provozu v síti a podstata klasifikace paketů. Dále se zabývá studiem vlastností několika základních klasifikačních algoritmů. Ve druhé části je detailně představen algoritmus Distributed Crossproducting of Field Labels. V závěru práce je popsán způsob implementace v jazyce Python a vyhodnocení činnosti programu v závislosti na různých množinách vstupních filtračních pravidel.In the beginning of this bachelor's thesis, the packet filtering and a base of the packet classification are discussed. It studies several basic classification algorithms. In the second half of this work, the Distributed Crossproducting of Field Labels algorithm is shown in detail. Finally, the software implementation in Python is described and the evaluation containing the memory dependence on various rule sets is presented.

Packet Classification Algorithms

Tato práce se zabývá algoritmy pro klasifikaci paketů, které jsou určené pro filtrování provozu v počítačových sítích. Pojednává o různých oblastech využití klasifikace paketů. Popisuje množství algoritmů včetně paměťových a rychlostních charakteristik. Dále práce popisuje implementaci dvou vybraných algoritmů založených na bitovém paralelismu a bitových vektorech, které byly integrovány do Netbench, experimentálního frameworku pro testování síťových algoritmů. Jsou popsány paměťové požadavky obou algoritmů, které byly doloženy testováním na různých sadách pravidel. Tyto požadavky jsou porovnány s dalšími algoritmy v Netbench.This work deals with the packet classification algorithms for traffic filtering in computer networks. It contains summary of different areas where packet classification is used. It describes various algorithms and their memory and speed characteristics. Then this work describes implementation of two chosen algorithms based on bit paralelism and bit vectors which were integrated into Netbench, framework for evaluation and experiments with packet processing algorithms. There are described memory requirements of both these algorithms which were tested for different sets of rules. These requirements are compared with other algorithms in Netbench.