oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2019 ( 60 )

2018 ( 72 )

2017 ( 56 )

2016 ( 63 )

Custom range...

Search Results: 1 - 10 of 4711 matches for " Chowdhury Mofizur Rahman "
All listed articles are free for downloading (OA Articles)
Page 1 /4711
Display every page Item
Association Rule Mining in Dynamic Database using the Concept of Border Sets
Ferdous Ahmed Sohel,Chowdhury Mofizur Rahman
Asian Journal of Information Technology , 2012,
Abstract: Mining of association rules is one of the important tasks in Data Mining. Association Rules find the influence of one set of items on another set of items. There are many influential algorithms to determine association rules (Agarwal et al., 1996a; Agarwal et al., 1994; Mannila et al., 1994; Savasere et al., 1995). Most of the algorithms assume a static database. There are a few algorithms, which find association rules for dynamic database. Border algorithms (Feldman et al., 1997 and Das and Bhattacharyya, 2003) are such algorithms, which use the concept of Border set for incremental database. But there are such situations where data are to be upgraded or deleted from the database. This paper presents an algorithm, which uses the concept of border algorithm for diminishing database.
Text Categorization using Association Rule and Na?ve Bayes Classifier
S. M. Kamruzzaman,Chowdhury Mofizur Rahman
Asian Journal of Information Technology , 2012,
Abstract: As the amount of online text increases, the demand for text categorization to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic categorization of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. Text categorization using Association Rule and Na?ve Bayes Classifier is proposed here. Instead of using words word relation i.e association rules from these words is used to derive feature set from pre-classified text documents. Na?ve Bayes Classifier is then used on derived features for final categorization.
Assigning Weights to Training Instances Increases Classification Accuracy
Dr. Dewan Md. Farid,Chowdhury Mofizur Rahman
International Journal of Data Mining & Knowledge Management Process , 2013,
Abstract: The decision tree (DT) approach is most useful in classification problem. In conventional decision tree learning the weights of every training instances are set to one or equal value, which contradicts general intuition. In this paper, we proposed a new decision tree learning algorithm by assigning appropriate weights to each training instance in the training data that increases classification accuracy of the decision tree model. The main advantage of this proposed approach is to set appropriate weights to training instances using na ve Bayesian classifier before trying to construct the decision tree. In our approach the training instances are assigned to weight values based on the posterior probability. The training instances having less weight values are either noisy or posses unique characteristics compared to other traininginstances. The experimental results manifest that the proposed approach for decision tree construction can achieve high classification accuracy with compare to traditional decision tree algorithms on different types of benchmark datasets from UCI machine learning repository.
Text Categorization using Association Rule and Naive Bayes Classifier
S M Kamruzzaman,Chowdhury Mofizur Rahman
Computer Science , 2010, DOI: 10.3923/ajit.2004.657.665
Abstract: As the amount of online text increases, the demand for text categorization to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic categorization of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. Text categorization using Association Rule and Na\"ive Bayes Classifier is proposed here. Instead of using words word relation i.e association rules from these words is used to derive feature set from pre-classified text documents. Naive Bayes Classifier is then used on derived features for final categorization.
Prediction of State of Wireless Network Using Markov and Hidden Markov Model
MD. Osman Gani,Hasan Sarwar,Chowdhury Mofizur Rahman
Journal of Networks , 2009, DOI: 10.4304/jnw.4.10.976-984
Abstract: Optimal resource allocation and higher quality of service is a much needed requirement in case of wireless networks. In order to improve the above factors, intelligent prediction of network behavior plays a very important role. Markov Model (MM) and Hidden Markov Model (HMM) are proven prediction techniques used in many fields. In this paper, we have used Markov and Hidden Markov prediction tools to predict the number of wireless devices that are connected to a specific Access Point (AP) at a specific instant of time. Prediction has been performed in two stages. In the first stage, we have found state sequence of wireless access points (AP) in a wireless network by observing the traffic load sequence in time. It is found that a particular choice of data may lead to 91% accuracy in predicting the real scenario. In the second stage, we have used Markov Model to find out the future state sequence of the previously found sequence from first stage. The prediction of next state of an AP performed by Markov Tool shows 88.71% accuracy. It is found that Markov Model can predict with an accuracy of 95.55% if initial transition matrix is calculated directly. We have also shown that O(1) Markov Model gives slightly better accuracy in prediction compared to O(2) MM for predicting far future.
A Hybrid Classifier using Boosting, Clustering, and Na ve Bayesian Classifier
A. J. M. Abu Afza,Dewan Md. Farid,Chowdhury Mofizur Rahman
World of Computer Science and Information Technology Journal , 2011,
Abstract: a new classifier based on boosting, clustering, and na ve Bayesian classifier is introduced in this paper, which considers the misclassification error produced by each training example and update the weights of training examples in training dataset associated to the probability of each attribute of that example. The proposed classifier clusters the training examples based on the similarity of attribute values and then generates the probability set for each cluster using na ve Bayesian classifier. Boosting trains a series of classifiers for a number of rounds that emphasis to the misclassification rate in each round. The proposed classifier addresses the problem of classifying the large data set and it has been successfully tested on a number of benchmark problems from the UCI repository, which achieved high classification rate.
Text Classification using the Concept of Association Rule of Data Mining
Chowdhury Mofizur Rahman,Ferdous Ahmed Sohel,Parvez Naushad,S. M. Kamruzzaman
Computer Science , 2010,
Abstract: As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper we will discuss a procedure of classifying text using the concept of association rule of data mining. Association rule mining technique has been used to derive feature set from pre-classified text documents. Naive Bayes classifier is then used on derived features for final classification.
DEVELOPMENT AND IN-VITRO EVALUATION OF SUSTAINED RELEASE MATRIX TABLETS OF SALBUTAMOL SULPHATE USING METHOCEL K100M CR POLYMER
Md. Mofizur Rahman
International Journal of Pharmaceutical Research and Development , 2011,
Abstract: In the present study, an attempt has been made to evaluate the effect of hydrophilic polymers on the release profile of drug from matrix system. Salbutamol sulphate, an anti-asthmatic agent, was used as a model drug to evaluate its release characteristics from different matrices. Matrix tablets of salbutamol sulphate were prepared by direct compression process using methocel K100M CR polymer. Release kinetics of salbutamol sulphate from these sustained release matrices in distilled water using USP paddle method with sinker for 8 hours were studied. Statistically significant differences were found among the drug release profile from different formulations. Higher polymer content (70%) in the matrix decreased the rate of the drug due to increased tortuosity and decreased porosity. At lower polymeric level (30%), the rate of drug release was elevated. The release mechanism was explored and explained with zero order, first order, Higuchi and Korsmeyer equations. The results generated in this study showed that the profile and kinetics of drug release were functions of polymer type, polymer level and physico-chemical properties of the drug.
EVALUATION OF ANTIBACTERIAL ACTIVITY OF STUDY OF LEAVES OF TABERNAEMONTANA DIVARICATA (L)
Rahman Md. Ashikur,Md. Hasanuzzaman,Rahman Md,Rahman Md. Mofizur
International Research Journal of Pharmacy , 2011,
Abstract: Phytochemical analysis of the dried leaves of Tabernaemontana divaricata (L). (Apocynaceae) indicated the presence of a steroids,tannins, saponins, gums and reducing sugar. The pharmacological interest of these compounds, coupled with the use of this plant in traditional medicine prompted the authors to check for its possible antibacterial activity. The extracts (ethanol, petroleum ether, diethyl ether, methanol and aqueous) were found to possess maximum potency against infectious pathogens Staphylococcus saprophyticus, Staphylococcus aureus, Enterococcus facealis, Staphylococcus pyogenes, Streptococcus agalactae, Salmonella typhi, Escherichia coli, Shigella boydii, Shigella dysenteriae and Pseudomonas aeruginosa. The zone of inhibition was observed with almost all bacteria with some exceptions. Minimum inhibitory concentrations of the extracts were found to be significant. The obtained results provide a support for the use of this plant in traditional medicine and its further investigation.
Performance Based Maintenance of Road Infrastructure by Contracting—A Challenge for Developing Countries  [PDF]
Masuda Sultana, Anisur Rahman, Sanaul Chowdhury
Journal of Service Science and Management (JSSM) , 2012, DOI: 10.4236/jssm.2012.52015
Abstract: Road authorities always strive to reduce the maintenance costs of road infrastructure systems using various traditional methods of contract. Contracting out road maintenance to the private sector based on performance measures is an alternative solution to maintain road infrastructure in a cost-effective way and is named as Performance Based Maintenance Contracting (PBMC). Many countries have succeeded in minimizing road infrastructure maintenance costs using performance-based maintenance contracts over the last two decades. However, implementation of PBMC is still a challenge for many developing countries because of resource and skill limitations, corruption and poor management systems. This paper discusses and analyses the problems and difficulties in the successful implementation of PBMC in developing countries.
Page 1 /4711
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.