The challenge to discover knowledge from environmental data that has led to usage of methods and techniques such as data mining tools, can bridge the knowledge gap between the biological experts and organisms. This research aimed to assess relationships between the diatoms and the indicators of the environment with Naïve Bayes method. Diatoms are ideal indicators of certain physical-chemical pa...

In this paper we address the problem of offline Arabic handwriting word recognition. Offline recognition of handwritten words is a difficult task due to the high variability and uncertainty of human writing. The majority of the recent systems are constrained by the size of the lexicon to deal with and the number of writers. In this paper, we propose an approach for multi-writers Arabic handwrit...

The paper presents three machine learning based keyphrase extraction methods that respectively use Decision Trees, Naïve Bayes, and Artificial Neural Networks for keyphrase extraction. We consider keyphrases as being phrases that consist of one or more words and as representing the important concepts in a text document. The three machine learning based keyphrase extraction methods that we use f...

In this paper, we compare the performance of three classifiers for Arabic text categorization. In particular, the naïve Bayes, k-nearest-neighbors (knn), and distance-based classifiers were used. Unclassified documents were preprocessed by removing punctuation marks and stopwords. Each document is then represented as a vector of words (or of words and their frequencies as in the case of the naï...

Exploratory data analysis over foreign language text presents virtually untapped opportunity. This work incorporates Naïve Bayes classifier with Case-Based Reasoning in order to classify and analyze Arabic texts related to fanaticism. The Arabic vocabularies are converted to equivalent English words using conceptual hierarchy structure. The understanding process operates at two phases. At the f...

Cost-sensitive decision tree and cost-sensitive naïve Bayes are both new cost-sensitive learning models proposed recently to minimize the total cost of test and misclassifications. Each of them has its advantages and disadvantages. In this paper, we propose a novel cost-sensitive learning model, a hybrid cost-sensitive decision tree, called DTNB, to reduce the minimum total cost, which integrat...

the present work was designed to classify and differentiate between the dehalogenase enzyme to non–dehalogenases (other hydrolases) by taking the amino acid propensity at the core, surface and both the parts. the data sets were made on an individual basis by selecting the 3d structures of protein available in the pdb (protein data bank). the prediction of the core amino acid were predicted by i...

IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been used in ...

Text Categorization’s significance is on a continuous acceleration due to the present mammoth escalation in textual data thrusting the importance of analysing and examining the methods for handling textual data. This paper discusses and compares six algorithms for Text categorization, such as: Naïve Bayes, Support Vector Machine, NGrams, K-Nearest Neighbourhood, Back Propagation Network and Gen...

Data mining and classification are most research idea that used in many topics by researchers. This study presents the comparison of three algorithms for classifications such as (Decision Tree, Naïve Bayes Support Vector Machine), applying social media usage dataset NYC, to get best result algorithm can classify instances according platforms. The final this refer Machine returned among these te...