CASComputing And Software
.01

ABOUT

PERSONAL DETAILS
1280 Main Street West. Hamilton, Ontario L8S 4L8.
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ghaffh1`{`Alpha`}`mcmaster`{`Beta`}`ca
I am a researcher and senior web base software system developer

BIO

ABOUT ME

Habib Ghaffari-Hadigheh is a computer science researcher and a senior software developer. He has been working as a web-based software systems developer since 2005 and researching the area of computer science since 2011. He has a Bs. in applied mathematics with major in the area of operations research, and an MSc. of computer science. After successfully defending his thesis in 2014, since he is eagerly interested in researching more on the application of ML in a different area of computer science, especially computer vision and data mining, he has started to study more about ML specifically “deep-learning” which has a broad application in various realms of science and outstanding progress has recently occurred in this domain. Furthermore, he is continuing his interdisciplinary studies between operations research and computer science. Currently, he is a Ph.D. student in Computer Science under the supervision of Dr. Chrisopher Anand at McMaster University, Hamilton ON, CA.

HOBBIES

INTERESTS

- Sports: I used to be a professional swimmer when I was younger. Later I tried different kinds of sports such as Track and field, Soccer and Volleyball. Now I am an amateur bodybuilder and hiker.
- Film: Not really picky on movie genres. I would watch a movie if the reviews and critics about it are really good.
- Music: Almost all kinds of music. But most of the time it is going to be classic, pop or country.
- Reading: Novels and biographies are my favorites.

FACTS

FACTS ABOUT ME

I am married with no kid. I am the oldest of two children. I have 12 cousins, 12 aunts and uncles, and 5 niblings (nieces and nephews).
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RESUME

EDUCATION
  • 2011
    2014
    Johor Bahru, Malaysia

    Computer Science - Master

    Universiti Teknologi Malaysia (UTM)

ACADEMIC AND PROFESSIONAL POSITIONS
  • 2019
    -
    Hamilton, ON, CA

    Ph.D. Student

    McMaster University

  • 2019
    2014
    Tabriz, Iran

    Team Leader

    Elegance Studio Advertisement Agency

  • 2014
    2011
    Johor Bahru, Malaysia

    Master Student

    Universiti Teknologi Malaysia (UTM)

  • 2010
    2008
    Tabriz, Iran

    Team Leader and senior software developer

    Pouya Andishan IT Co

    Directing and working with a 7-member team to develop a group of web-based systems such as Eshop, Office Automation Systems, and Websites
  • 2008
    2006
    Tabriz, Iran

    IT Manager

    Emrouz Advertising Center

    Working with a 2-member team to develop a local official web site of 8th parliamentary elections of the Islamic Republic of Iran in Tabriz area for governor of Tabriz to publish the latest news of the election, introducing the candidates and showing the latest results of election in the Tabriz city.
  • 2006
    2005
    Tabriz, Iran

    Design, Development and Management of 37Th Annual Mathematic Conference Official Web Site

    Azarbaijan Shahid Madani University

    Working and directing a 10-member team to develop a website for the 37th annual mathematic conference(AMC37), that helps applicants to register in the conference and send their documents and other information to the secretariat of the conference. Publishing the conference's latest news, events and photos are other futures of the project. This web site is developed bilingual (Farsi and English)
  • 2005
    2004
    Tabriz, Iran

    Jr software developer

    Danesh Afzaye Farivarean IT Co

    Part-time working with a 5-member team to develop a web-based comprehensive statistical application that helps cooperation’s offices countrywide to send their statistical information to the Ministry of Cooperatives of Iran.
.03

PUBLICATIONS

PUBLICATIONS LIST
04 Jan 2008

Bi-parametric optimal partition invariancy sensitivity analysis in linear optimization

VANCOUVER - CANADA

n bi-parametric linear optimization (LO), perturbation occurs in both the right-hand-side and the objective function data with different parameters. In this paper, the bi-parametric LO problem is considered and we are interested in identifying the regions where the optimal partitions are invariant. These regions are referred to as invariancy regions. It is proved that invariancy regions are separated by vertical and horizontal lines and generate a mesh-like area. It is proved that the boundaries of these regions can be identified in polynomial time. The behavior of the optimal value function in these regions is investigated too.

Journal Paper Alireza Ghaffari-HadighehEmail authorHabib Ghaffari-Hadigheh, Tamás Terlaky

Bi-parametric optimal partition invariancy sensitivity analysis in linear optimization

Alireza Ghaffari-HadighehEmail authorHabib Ghaffari-Hadigheh, Tamás Terlaky
Journal Paper
About The Publication
n bi-parametric linear optimization (LO), perturbation occurs in both the right-hand-side and the objective function data with different parameters. In this paper, the bi-parametric LO problem is considered and we are interested in identifying the regions where the optimal partitions are invariant. These regions are referred to as invariancy regions. It is proved that invariancy regions are separated by vertical and horizontal lines and generate a mesh-like area. It is proved that the boundaries of these regions can be identified in polynomial time. The behavior of the optimal value function in these regions is investigated too.
01 Aug 2017

Feature Base Fusion for Splicing Forgery Detection Based on Neuro Fuzzy

48-he annual Iranian mathematics conference, Hamedan, Iran, August 2017

Most of the researches on image forensics has been mainly focused on the detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms is not perfect, and accordingly, the provided output might be noisy, inaccurate and only partially correct. Furthermore, a forged image in practical scenarios is often the result of utilizing several tools available by image-processing software systems. Therefore, reliable tamper detection requires developing more powerful tools to deal with various tempering scenarios. The fusion of forgery detection tools based on the Fuzzy Inference System has been used before for addressing this problem. Adjusting the membership functions and defining proper fuzzy rules for attaining better results are time-consuming processes. This can be accounted for as the main disadvantage of fuzzy inference systems. In this paper, a Neuro-Fuzzy inference system for the fusion of forgery detection tools is developed. The neural network characteristic of these systems provides an appropriate tool for automatically adjusting the membership functions. Moreover, the initial fuzzy inference system is generated based on fuzzy clustering techniques. The proposed framework is implemented and validated on a benchmark image splicing data set in which three forgery detection tools are fused based on the adaptive Neuro-Fuzzy inference system. The outcome of the proposed method reveals that applying Neuro-Fuzzy inference systems could be a better approach for the fusion of forgery detection tools.

Conferences Habib Ghaffari-Hadigheh, Ghazali bin sulong

Feature Base Fusion for Splicing Forgery Detection Based on Neuro Fuzzy

Habib Ghaffari-Hadigheh, Ghazali bin sulong
Conferences
About The Publication
Most of the researches on image forensics has been mainly focused on the detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms is not perfect, and accordingly, the provided output might be noisy, inaccurate and only partially correct. Furthermore, a forged image in practical scenarios is often the result of utilizing several tools available by image-processing software systems. Therefore, reliable tamper detection requires developing more powerful tools to deal with various tempering scenarios. The fusion of forgery detection tools based on the Fuzzy Inference System has been used before for addressing this problem. Adjusting the membership functions and defining proper fuzzy rules for attaining better results are time-consuming processes. This can be accounted for as the main disadvantage of fuzzy inference systems. In this paper, a Neuro-Fuzzy inference system for the fusion of forgery detection tools is developed. The neural network characteristic of these systems provides an appropriate tool for automatically adjusting the membership functions. Moreover, the initial fuzzy inference system is generated based on fuzzy clustering techniques. The proposed framework is implemented and validated on a benchmark image splicing data set in which three forgery detection tools are fused based on the adaptive Neuro-Fuzzy inference system. The outcome of the proposed method reveals that applying Neuro-Fuzzy inference systems could be a better approach to the fusion of forgery detection tools.
.04

RESEARCH

My Research

I am interested in two area of researches. First is the interdisciplinary research areas between operations research and computer science, and the second is the application of ML in different areas of computer science.

Recently, Deep Learning (DL), one of the sub domains of ML is on the trend among the researches. The use of the expression “Deep Learning” in the context of Artificial Neural Networks was introduced by Igor Aizenberg and colleagues in 2000. Later in 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov drew additional attention by showing how many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation. The recent progress in the area of cloud computing, distributed systems and have the privilege of using faster and stronger CPU and GPU processors put DL on the trend by the researchers active in the area of ML. There are already many applications of DL introduced by researchers in the different realms of science and lots of research group around the world works on finding either to find a novel application for it or try to use it as a new approach, tackling previously introduced problems. More information about Deep learning could be found in here.

Since I already familiar with the area of ML based on my personal experience on my master thesis, I decided to do more research in the area of ML. Especially, I am interested in the application of ML in the area of Signal Processing,Operations Research (OR), and data-mining.

Research Interests

  • Operations Research
  • Signal Processing
  • Machine Learning
  • Data Mining
Research Experience

My bachelor Project

The project was about a kind of sensitivity analysis in linear programming. We supposed that the optimal partition of the linear problem is known and there are perturbation on the rim data. This project was based on the Chapter 19 of the book “Interior Point Methods for Linear Optimization, by Roos, C., Terlaky, T. and Vial, J.-Ph”. I implemented all of the algorithms in this chapter and realized the differences between the traditional point of view which has been based on having a basic optimal solution.

Cellular Automata and its applications

The project belonged to advance theory of computer science. During the project, cellular automata studied and some of its applications in image processing, route planning and cryptography were introduced. For a better understanding of the project concept some implementation down based on the introduced algorithms.

Application of AI in Reflexology

The project belonged to the advanced Artificial Intelligent course. The aim of the project was to study the relation of reflexology.

Fuzzy web-based image retrieval system

The project belonged to an advanced database course. The project is a web-based image retrieval system that get an image as an input and return similar images based on color and segments similarity. It also lets users improve search results by choosing unrelated images. Information such as color spaces, image histogram, image quantization, image segmentation, edge detection, object extraction, fuzzy logic, image retrieval was studied during the project. Final result presented with a complete report and a web-based image retrieval sample web-site.

Feature Base Fusion for Splicing Forgery Detection Based on Neuro Fuzzy

Master Thesis

Most of the researches on image forensics has been mainly focused on the detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms is not perfect, and accordingly, the provided output might be noisy, inaccurate and only partially correct. Furthermore, a forged image in practical scenarios is often the result of utilizing several tools available by image-processing software systems. Therefore, reliable tamper detection requires developing more powerful tools to deal with various tempering scenarios. The fusion of forgery detection tools based on the Fuzzy Inference System has been used before for addressing this problem. Adjusting the membership functions and defining proper fuzzy rules for attaining better results are time-consuming processes. This can be accounted for as the main disadvantage of fuzzy inference systems. In this paper, a Neuro-Fuzzy inference system for fusion of forgery detection tools is developed. The neural network characteristic of these systems provides an appropriate tool for automatically adjusting the membership functions. Moreover, the initial fuzzy inference system is generated based on fuzzy clustering techniques. The proposed framework is implemented and validated on a benchmark image splicing data set in which three forgery detection tools are fused based on the adaptive Neuro-Fuzzy inference system. The outcome of the proposed method reveals that applying Neuro-Fuzzy inference systems could be a better approach for the fusion of forgery detection tools.
.05

TEACHING

CURRENT
Feature
History
  • Jan
    April
    McMaster Computing and Software (CAS)

    TEACHING ASSISTANT

    McMaster University

    Full Course Code:
    Comp Sci / Sfwr Eng 3FP3
    Winter 2020
    Course Summary:
    Functional programming; lists and algebraic data types, pattern matching, parametric polymorphism, higher-order functions, reasoning about programs; lazy and strict evaluation; programming with monads; domain-specific languages.
    Course Category:
    Undergraduate
    Course Level:
    Level 4
  • Sep
    April
    McMaster Computing and Software (CAS)

    TEACHING ASSISTANT

    McMaster University

    Full Course Code:
    COMPSCI 4ZP6A/B
    Fall/Winter 2019 - 2020
    Course Summary:
    Students, in teams of two to four students, undertake a substantial project in an area of computer science by performing each step of the software life cycle. The lecture component presents an introduction to software management and project management.
    Course Category:
    Undergraduate
    Course Level:
    Level 4
  • 2004
    2005
    Payamenour University

    TEACHING ASSISTANT

    McMaster University

    Work as an instructor for the "Advanced Programming" course’s Computer Lab, helping students to understand the concepts of the course and implement some basic C++ programs themselves.
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SKILLS

Technical Skills
Bibliographic Database >
LEVEL : Professional
JabRef EndNote Zotero
Information management and data analysis >
LEVEL : INTERMEDIATE
Microsoft Excel R Python
Text Processors >
LEVEL : ADVANCED EXPERIENCE : 5 YEARS
Microsoft Word Libre Office
Text Editing Softwares >
LEVEL : ADVANCED EXPERIENCE : 5 YEARS
WinEdt TexMaker TexStudio
Programming Skills
Web Development >
LEVEL : ADVANCED EXPERIENCE : 15 YEARS
ASP.NET .NET Core 2.2 J2EE
Programming Languages >
LEVEL : ADVANCED EXPERIENCE : 15 YEARS
C# JAVA Python Haskell Visual Basic.NET F#
DataBase Management Systems (DBMS) >
LEVEL : ADVANCED EXPERIENCE : 15 YEARS
SQL server MySql
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CONTACT

Interesting in my research, drop me a line

GET IN TOUCH

I would happy to talk more about my research interests. You can contact me throw my accounts in social media. You can also fill the form below and directly send an email to my personal inbox.
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