Essam D. Profile

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$50 USD / hour

queanbeyan,

australia

$50 USD / hour

It's currently 1:59 AM here

Joined September 24, 2010

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Essam D.

@essamsoliman

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(0 reviews)

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$50 USD / hour

queanbeyan,

australia

$50 USD / hour

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Data Scientist

## BIO Essam Debie received the Ph.D. degree in Computer Science from the University of New South Wales in 2014. ## Area of Expertise Artificial Intelligence, Data Science, Data Management, Data Mining, Machine Learning, Python, R, Matlab, MongoDB, C#, JAVA Relational Database (ORACLE - SQL SERVER - ACCESS), NoSQL Database (MongoDB), Tableau, Weka

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Experience

Saudi Cultural Mission - Canberra - Australia

Oct 2014 - Oct 2015 (1 year)

● Building and administering student databases using MS SQL Server and MS Access ● Analysing educational data to find interesting trends and patterns to support the decision making process. ● Preparing and presenting analytical reports to the manager of the academic advisory department ● Designing software solutions to aid the management of student records and monitoring their progress ● Building user interfaces using C#, and MS Access

University of New South Wales

Nov 2012 - Oct 2015 (2 years, 11 months)

● Working as a member of the Australian defence cyber security research project. The project aims at building an effective and efficient cyber security system for the Australian defence computer networks. ● Worked as a member of the Australian defence force (ADF) operational deployment research project. The project aims at deriving analytical observations about ADF’s historical operational deployments in order to develop preparedness planning tools to improve the decision making process.

Education

Doctor of Philosophy in Computer Science

University of New South Wales,

Australia

2010 - 2014

(4 years)

Publications

Performance analysis of rough set ensemble of learning classifier systems with

Evolutionary Intelligence

A rough set based ensemble of LCS is proposed, which relies on a pre-processed feature partitioning step to train multiple LCS on feature subspaces. Each base classifier in the ensemble is a Michigan style supervised LCS. The traditional genetic algorithm based rule evolution is replaced by a differential evolution based rule discovery, to improve generalisation capabilities of LCS. A voting mechanism is then used to generate output for test instances.

Investigating Multi-Operator Differential Evolution for Feature Selection

Australasian Conference on Artificial Life and Computational Intelligence

In this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. The analysis shows that the proposed algorithm successfully determines efficient feature subsets. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set.

Taxonomy and Evaluation of Feature Selection‐Based Learning Classifier System Ensemble Approaches

Computational Intelligence

In this article, we first propose a conceptual framework that allows us to appropriately categorize ensemble-based methods for fair comparison and highlights the gaps in the corresponding literature. A taxonomy of LCSs-based ensembles is then presented using this framework. The article then focuses on comparing LCS ensembles that use feature selection in the pre-gate stage.

Historical operational data analysis for defence preparedness planning

The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology

This paper presents an analytical study, consisting of both descriptive as well as predictive analysis, of historical defence operational data. The descriptive analysis component of the methodology focuses on identifying useful features in the collected data for building a predictive model. The predictive analysis investigates existing patterns in the data. An artificial neural network based time series forecasting model is developed to predict future operations based on the identified features.