Gea Rahman

Charles Sturt University · School of Computing, Mathematics and Engineering

VIDEO: https://www.youtube.com/watch?v=CBgf0X_v5YI&t=51s. Natural data sets often have missing values in them. An accurate missing value imputation is crucial to increase the usability of a data set for statistical analyses and data mining tasks. In this paper we present a novel missing value imputation technique using a data set’s existing pattern...

Data pre-processing and cleansing play a vital role in data mining by ensuring good quality of data. Data cleansing tasks include imputation of missing values, identification of outliers, and identification and correction of noisy data. In this paper, we present a novel technique called A Fuzzy Expectation Maximization and Fuzzy Clustering based Mi...

In this study we present a novel framework that uses two layers/steps of imputation namely the Early-Imputation step and the Advanced-Imputation step. In the early imputation step we first impute the missing values (both numerical and categorical) using existing techniques. The main goal of this step is to carry out an initial imputation and thereb...

VIDEO: https://www.youtube.com/watch?v=7oa2uCT-PpM&t=14s. We present two novel techniques for the imputation of both categorical and numerical missing values. The techniques use decision trees and forests to identify horizontal segments of a data set where the records belonging to a segment have higher similarity and attribute cor-relations. Using...

Data pre-processing and cleansing play a vital role in data mining for ensuring good quality of data. Data cleansing tasks include imputation of missing values, and identification and correction of incorrect/noisy data. In this paper, we present a novel approach called Co-appearance based Analysis for Incorrect Records and Attribute-values Detectio...

Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target doma...

In this study, we present an incremental machine learning framework called Adaptive Decision Forest (ADF), which produces a decision forest to classify new records. Based on our two novel theorems, we introduce a new splitting strategy called iSAT, which allows ADF to classify new records even if they are associated with previously unseen classes....

Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target doma...

In this study, we present an incremental machine learning framework called Adaptive Decision Forest (ADF), which produces a decision forest to classify new records. Based on our two novel theorems, we introduce a new splitting strategy called iSAT, which allows ADF to classify new records even if they are associated with previously unseen classes....

Discretization is the process of converting numerical values into categorical values. There are many existing techniques for discretization. However, the existing techniques have various limitations such as the requirement of a user input on the number of categories and number of records in each category. Therefore, we propose a new discretization...

Quality of data in Wireless Sensor Networks (WSNs) is one of the major concerns for many applications. The data quality may drop due to various reasons including the existence of missing values and incorrect values (also known as noisy or corrupt values) that can be caused by factors such as interference and machine malfunctioning. A drop in data q...

In this paper we present a novel technique called iDMI that imputes missing values of a data set by combining a decision tree algorithm (DT) and an expectation-maximization (EMI) algorithm. We first divide a data set into horizontal segments through applying a DT algorithm such as C4.5, and then apply an EMI algorithm on each segment in order to im...

Imputation of missing values is an important data mining task for improving the quality of data mining results. The imputation based on similar records is generally more accurate than the imputation based on all records of a data set. Therefore, in this paper we present a novel algorithm called kDMI that employs two levels of horizontal partitionin...

Having missing values in a data set is very common due to various reasons including human error, misunderstanding and equipment malfunctioning. Therefore, imputation of missing values is important to improve the quality of a data set. In our previous study we presented an imputation technique called DMI, which we then found better than an existing...

Data pre-processing plays a vital role in data mining for ensuring good quality of data. In general data preprocessing tasks include imputation of missing values, identification of outliers, smoothening out of noisy data and correction of inconsistent data. In this paper, we present an efficient missing value imputation technique called DMI, which...

Data pre-processing plays a vital role in data mining for ensuring good quality of data. In general data preprocessing tasks include imputation of missing values, identification of outliers, smoothening out of noisy data and correction of inconsistent data. In this paper, we present an efficient missing value imputation technique called DMI, which...

This study presents a novel framework for developing an agent-mediated E-commerce environment for the mobile shopper. Intelligent agents represent both shoppers and the store to negotiate for desired products based on shopper preferences. In this system, buyer and seller agents are created to simulate the e-market. Seller agents advertise his produ...

This study presents an application of mobile agent technology to solve the problem of web-based face recognition. Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult. The current face recognition models, which suffer from slow performance and platform dependence...

Governments all over the world are trying to implement e-Governance for strengthening interfaces with citizens. The complexities involved in the implementation of e-Governance projects and low success rates of such projects suggest that e-Governance is more of a managerial issue than the technological one. This paper focuses on management of contin...

Intelligent Software agent is one of the most active and exciting research areas in the computer science today. In this paper, we present a novel approach to implement an intelligent software agent as a personal communication assistant on the Internet. This study was conducted at the software laboratory, Computer Science and Mathematics department,...

Conversion from a text image to text is very essential in the context of huge data entry from paper based document to computer file system. The documents that are scanned are in image format, generally, take huge amount of memory space. But if we can change the image to text then it will take less space and will be efficient. In this paper, we pres...

This paper presents a new approach of developing a complete Hospital Management System using Natural Language Technology (English query). In this system, we have used English Query that helps users to find database information using plain English instead of a formal query language. The English Query application uses English commands, statements and...

The objective of this study was to develop an artificial neural network (ANN) to identify the rice disease in the laboratory of Bangladesh Agricultural University, Mymensingh, during the period from July 2006 to March 2007. Rice leaves of different stages from different fields were collected, scanned and saved into the computer as red, green, blue...

This paper presents the implementation of gender identification system from a front view facial image using artificial neural network. In this system the facial images from different persons were taken as its input. At first, face images were projected onto a feature space that span the significant variations among face images. The features of the...

The objectives of this study were to assess the extents and to estimate the areas, and to generate a layer of programming practices in the design and development technique of a Bangla Programming Environment with its compiler. At first, we have developed a new High-Level Programming Language (HLPL) named Bangla Language (BL) using Bangla character...

This paper presents a novel approach of developing a decision support system using on-line analytical proc-essing (OLAP). This application is optimal for data queries that do not change data. The system database is designed to promote: heavy indexing to improve query performance, denormalization of the database to satisfy common query requirements...

This paper presents a novel approach to verify signatures using Back-Propagation Neural Network (BPNN) and Hidden Markov Model (HMM) individually. To train the signature for each person, Back-Propagation algorithm has been used. In order to get exact weights and threshold values we have changed learning rates and spread factors as well as learning...