Job Experiences

Education

Honors, Awards & Grants

  • 2015 - 2017
    DAAD Research Grants – Scholarship
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    Scholarship in the program “Research Grants - Bi-nationally Supervised Doctoral Degrees, 2015/16” for doing the double-degree joint doctoral program between The National University of Malaysia (UKM) and University of Duisburg-Essen (Germany). more info...
  • July 2014
    Samsung Gear App Challenge
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    Runner-up in the Samsung Gear (Smart Watch) App Challenge based on Tizen OS for Wearables. My Puzzle, Mobile Shield & Gear Dictionary were nominated in the top 200 Apps in Round 1. more info...
  • 2013 - 2014
    Award of Excellence - Computer & Technical Advisor
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    Iranian Students Association at Universiti Kebangsaan Malaysia (ISAUKM)
    Awarded due to the Computer and Technical Advising at ISAUKM during the 8th year of association.
  • 2013 - 2015
    UKM Research Fellowship
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    Faculty of Engineering and Built Environment, The National University of Malaysia (UKM)
    Scholarship Conducted by Centre for Graduate Management (PPS) until Aug. 2014. Since Sep. 2014 by Research & Innovation Center (CRIM), UKM
  • 2010
    RoboSoccer Small Size Competition
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    Faculty of Technology & Information Sciences, The National University of Malaysia
    Team Name: Zeus
    1st Round Ranking: 1st
    Final Round Ranking: 4th
  • 2008, 06, 05
    Distinguished Researcher at Islamic Azad University, Tafresh
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    Awarded as the top research student at Islamic Azad University, Tafresh during my bachelor studies for 3 years of 2005, 2006 & 2008.
    This award was due to the student research activities and publications.
  • 2007
    Distinguished Researcher at Islamic Azad University, Iran
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    3rd Festival of the Distinguished Researchers in Islamic Azad University, Karaj.
    Awarded as a top research student among all branches of Islamic Azad University in Iran during my bachelor studies in 2007.
    The ceremony was conducted in Islamic Azad University, Karaj by IAU Chancellor.
    This award was due to the student research activities and publications.

Research Collaboration

Assoc. Prof. Dr. Hafizah Husain

The National University of Malaysia

Profile

Prof. Dr-Ing. Jürgen Ziegler

University of Duisburg-Essen, Germany

Profile

Mona Taghavi

PhD Candidate at Concordia University, Canada

Website

Ehsan Golkar

PhD Candidate at UKM, Malaysia

Website

Dr. Arya Ahmadi-Ardakani

PhD. Electrical Engineering at UCLA, USA

Dr. Masoud Shakiba

Monash University, Malaysia

Website

Great Research Group!

I enjoy a great professional collaboration with researchers across the world. We have a very professional, friendly, sympathetic and enthusiastic research members in a variety of research domains, countries and business or academic positions. We also welcome whoever is willing to share and promote the knowledge by joining our research group.

Research Projects

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    Adaptive Computational Advertising

    Effective Online Advertisements Based on the User’s Data

    Abstract

    To be completed...

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    Respiratory Signal Extraction from IR 3D-Depth Sensors

    Effective Reduction of Noise and Error in Contactless Respiratory Signal Extraction from an IR Depth Sensor

    Abstract

    The human respiratory signal is one of the biological features which can be used in many research areas from medicine, psychology to computer science. This paper explains how a reliable contactless respiration signal can be extracted via an IR Depth Sensor. The raw depth data is very sensitive to minor and tiny depth changes. Moreover, human movements also provide additional noise in the IR depth values. Because of the existing noises, the previous studies on contactless respiratory were concentrated on signal extraction from a lying subject on a static surface in order to minimize the possible noises and errors. In this research, we have proposed a solution to improve the accuracy of the signal while the subject is interacting with the system. Firstly, the extracted depth signal has been filtered and smoothed. Secondly, a self-correction technique based on the greedy algorithm was applied. Thirdly, the respiration characteristics were measured. Lastly, the processed signal has been validated with a simultaneous signal from a commercial respiratory belt. The calculated correlation coefficient value between the extracted signals and the respiratory belt shows a high reliability and high positive correlation.

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    Affective Computing in Adaptive Recommender System

    Integration of Human Emotions and User's Interaction into the Recommender Systems.

    Abstract

    In this research, we propose a novel adaptive recommender system on computational advertising based on emotional and cultural intelligence. It processes the users’ interactions with the common input devices such as mouse, keyboard, touch-screen monitors. The system recommends the relevant advertisements to the network users based on their favorites and emotional states. In addition it adapts to the users requirements and needs psychologically and culturally responsive. A metaphor semantic ontology is also developed to retrieve the users’ cultures based on the metaphorical meaning of the context. This ontology can be different with the traditional semantic and direct meanings of the words. For evaluation of the system, despite the traditional Precision/Recall methods, emotional intelligence system and fuzzy/probabilistic calculations are proposed to measure the performance and accuracy of the resulted recommendation. The available hypothesis say in theory that the suggested system should be working in a reliable state and efficiency.

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    Affective Computing by Common Input Devices

    Intelligent system of human emotions recognition by using computer mouse, keyboard and touch-screen.

    Abstract

    Nowadays, emotions play an important role among human interactions. These emotions can be integrated into the computer system to make more effective interactions with users. Human Emotions Recognition (HER) is an innovative software-based method for detecting user’s emotions to determine proper responses in human-computer interaction. There are several methods to recognize the emotions like processing the facial expressions, human voice, body gestures and Electroencephalography (EEG) machines. However, there are still some challenges such as the accuracy of recognition, the number of supported emotions by the system, real-time processing, and recognition of emotions without any direct interaction between the user and computer. This thesis research aims to provide two solutions to increase the accuracy of recognition and to detect the emotion without direct interaction between human and computer. It is conducted in two phases. The first phase provides a solution based on HCI. It performs analysis on collected data of the users’ emotions and interactions with various input devices such as the mouse, keyboard and touch-screen. The system is trained in a supervised mode by Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. Further analysis is applied to find the collaboration of various emotions, which may happen at a same time with different emotion scales. Moreover, the data are plotted on a fuzzy model since each emotion data are collected in diverse scales with overlapping crisp spectrums. The second phase, proposes a solution to recognize the emotion when the user is not interacting with the machine. This solution is based on HCI studies and un-concentrated recognition by processing the environment and surrounding area. For this purpose, it is suggested that environment features can be collected, tagged and trained for the system to recognize the users’ emotions at the same environment. The result of the first solution shows an increase of around 5% in accuracy by comparing with the existing methods. Furthermore, this result also shows that the cultural and language backgrounds can make different emotional expressions for the same emotion. The result of the second solution proves the recognition possibility, but with less accuracy. These results are significant contributions to show new directions of future research in this topical area of emotion recognition in computer.

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    Intrusion Detection & Prevention System

    A security system to detect and prevent network intrusions on Smart Grids and Cloud Computing.

    Abstract

    The distributed and open structure of cloud computing and services becomes an attractive target for potential cyber-attacks by intruders. The traditional Intrusion Detection and Prevention Systems (IDPS) are deemed largely inefficient to be deployed in cloud computing environments due to their openness, dynamicity and virtualization in offered services. This research surveys and explores the possible solutions to detect and prevent intrusions in cloud computing systems by providing a comprehensive taxonomy of existing IDPS. It discusses the key features of IDPS that are challenging and crucial for choosing the right security measures for designing an IDPS. The research further reviews the current state of the art of developed IDPSs for cloud computing which uses advanced techniques in overcoming the challenges imposed by cloud computing requirements for more resilient, effective and efficient IDPSs, abbreviated as CIPDS.

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    24xe

    An autonomous system on online money and stock exchange.

    Abstract

    The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This research presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.

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A Comprehensive Comparison of Educational Growth within Four Different Developing Countries between 1990 and 2012

Masoud Shakiba, Nader Ale Ebrahim, Mahmoud Danaee, Kaveh Bakhtiyari, Elankovan Sundararajan
Journal Revista de Gestão e Secretariado, Apr. 2016
e-ISSN: 2178-9010, Volume 6, Issue 3, Pages 152 - 174

Abstract

Educational growth is a fundamental infrastructure factor required to achieve sustainable development. Therefore, evaluation and measurement of educational growth is essential for establishing a development road map. Because of this, there are many organizations and databases that work to capture academic trends and provide the general view of institute achievements. Web of Science and Scopus are the two most popular and scientific. In this paper, we define the important effective factors in educational growth and discuss them; we then compare these defined factors across four different developing countries: Brazil, Iran, Malaysia, and Turkey. As well as the comparisons, this paper uses the Pearson product moment correlation coefficient method to analyze the factors and the strong or weak relationship between the factors are discussed.

Hybrid Affective Computing - Keyboard, Mouse and Touch Screen: From Review to Experiment

Kaveh Bakhtiyari, Mona Taghavi, Hafizah Husain
Journal Journal of Neural Computing and Applications, Aug. 2015
Springer, ISSN 0941-0643, Volume 26, Issue 6, Pages 1277 - 1296

Abstract

Emotions play an important role in human interactions. They can be integrated into the computer system to make human-computer interaction more effective. Affective Computing is an innovative computational modelling and detecting user’s emotions to optimize system responses in Human-Computer Interaction (HCI). However, there is a trade-off between recognition accuracy and real-time performance in some of the methods such as processing the facial expressions, human voice and body gestures. Other methods lack efficiency and usability in real world applications such as Natural Language Processing (NLP) and Electroencephalography (EEG) signals. To accomplish a reliable, usable and high performance system, this paper proposes an intelligent hybrid approach to recognize users’ emotions by using easily accessible and low computational cost input devices including keyboard, mouse (touch-pad: single touch) and touch screen display (single touch). Using the proposed approach, the system is developed and trained in a supervised mode by Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. The result shows an increase of accuracy of 6% (93.20%) by SVM in comparison to the currently existing methods. It is a significant contribution to show new directions of future research in emotion recognition, user modelling and emotional intelligence.

Do we have privacy in digital world?

Kaveh Bakhtiyari
Note Published Online as a Note
Last Update: 10 December 2014 (Peer-Reviewed) - DOI: 10.13140/RG.2.1.2492.5203/1

Abstract

Not really.

Planning for Sustainable Development in the Emerging Information Societies

Mona Taghavi, Kaveh Bakhtiyari, Hamed Taghavi, Vahhab Olyaee, Aini Hussain
Journal Journal of Science and Technology Policy Management, Nov. 2014
Emerald, ISSN 2053-4620, Volume 5, Issue 3, Pages 178-211

Abstract

Purpose – This research work investigates the recent status of the information and communication technology services industry in Iran. It proposes a systemic applicable approach at policy making level and appropriate strategic planning steps to enlighten developing countries toward achieving their target objectives of an Information Society.

Design/methodology/approach – Largely based on existing literature and usage statistics in ICT services, global technology trends, and results from a survey to obtain consistent and up-to-date information about current issues of ICT services in the public and private sectors in Iran.

Findings – This study elaborates on all issues, points, and best practices relevant to the ICT services industry in Iran which is addressed by recommending some documented policies.

Research limitations/implications - Majority of the experts who attended the workshop and responded to the questionnaire were service consumers rather than service providers.

Practical implications - This paper discusses some of the implications for the development of this ICT services strategy and provides policy recommendations.

Originality/value – This study provides a government refining process policy to address the common gaps in the ICT services industry in these developing countries and emphasizes a formidable policy foundation before implementing and monitoring of the Flagship ICT projects.

Ethical and Unethical Methods of Plagiarism Prevention in Academic Writing

Kaveh Bakhtiyari, Hadi Salehi, Mohamed Amin Embi, Masoud Shakiba, Azam Zavvari, Masoomeh Shahbazi-Moghadam, Nader Ale Ebrahim, Marjan Mohammadjafari
Journal Journal of International Education Studies, Jul. 2014
ISSN 1913-9020, Volume 7, No. 7, Pages 52-62

Abstract

This paper discusses plagiarism origins, and the ethical solutions to prevent it. It also reviews some unethical approaches, which may be used to decrease the plagiarism rate in academic writings. We propose eight ethical techniques to avoid unconscious and accidental plagiarism in manuscripts without using online systems such as Turnitin and/or iThenticate for cross checking and plagiarism detection. The efficiency of the proposed techniques is evaluated on five different texts using students individually. After application of the techniques on the texts, they were checked by Turnitin to produce the plagiarism and similarity report. At the end, the “effective factor” of each method has been compared with each other; and the best result went to a hybrid combination of all techniques to avoid plagiarism. The hybrid of ethical methods decreased the plagiarism rate reported by Turnitin from nearly 100% to the average of 8.4% on 5 manuscripts.

Fuzzy Model of Dominance Emotions in Affective Computing

Kaveh Bakhtiyari, Hafizah Husain
Journal Journal of Neural Computing and Applications, Jun. 2014
Springer, ISSN 0941-0643, Volume 25, Issue 6, Pages 1467-1477

Abstract

To date, most of the human emotion recognition systems are intended to sense the emotions and their dominance individually. This paper discusses a fuzzy model for multilevel affective computing based on the dominance dimensional model of emotions. This model can detect any other possible emotions simultaneously at the time of recognition. One hundred and thirty volunteers from various countries with different cultural backgrounds were selected to record their emotional states. These volunteers have been selected from various races and different geographical locations. Twenty-seven different emotions with their strengths in a scale of 5 were questioned through a survey. Recorded emotions were analyzed with the other possible emotions and their levels of dominance to build the fuzzy model. Then this model was integrated into a fuzzy emotion recognition system using three input devices of mouse, keyboard and the touch screen display. Support vector machine classifier detected the other possible emotions of the users along with the directly sensed emotion. The binary system (non-fuzzy) sensed emotions with an incredible accuracy of 93%. However, it only could sense limited emotions. By integrating this model, the system was able to detect more possible emotions at a time with slightly lower recognition accuracy of 86 %. The recorded false positive rates of this model for four emotions were measured at 16.7 %. The resulted accuracy and its false positive rate are among the top three accurate human emotion recognition (affective computing) systems.

Implementation of Emotional-Aware Computer Systems Using Typical Input Devices

Kaveh Bakhtiyari, Mona Taghavi, Hafizah Husain
Book Chapter The 6th Asian Conference on Intelligent Information and Database Systems (ACIIDS), Bangkok, Thailand, 7-9 Apr. 2014
Springer Lecture Notes in Computer Science (LNCS), ISBN 978-3-319-05476-6, Volume 8397, Pages 364-374

Abstract

Emotions play an important role in human interactions. Human Emotions Recognition (HER - Affective Computing) is an innovative method for detecting user’s emotions to determine proper responses and recommendations in Human-Computer Interaction (HCI). This paper discusses an intelligent approach to recognize human emotions by using the usual input devices such as keyboard, mouse and touch screen displays. This research is compared with the other usual methods like processing the facial expressions, human voice, body gestures and digital signal processing in Electroencephalography (EEG) machines for an emotional-aware system. The Emotional Intelligence system is trained in a supervised mode by Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. The result shows 93.20% in accuracy which is around 5% more than the existing methods. It is a significant contribution to show new directions of future research in this topical area of emotion recognition, which is useful in recommender systems.

Emotional and Cultural Intelligence in Recommender Systems

Kaveh Bakhtiyari, Hafizah Husain, Jürgen Ziegler
Conference The International Conference on Engineering and Built Environment (ICEBE) 2013, UKM, Malaysia, 19-20 Nov. 2013

Abstract

In this paper, we propose a novel adaptive recommender system based on emotional and cultural intelligence by processing the users’ interactions with the common input devices such as mouse, keyboard, touch-screen monitors and EEG. This system can be employed in a computational advertising system. A metaphor semantic ontology should also be developed to retrieve the users’ cultures based on the metaphor meaning of the context which can be different with the traditional semantic and direct meanings of the words. For evaluation of the system, despite the traditional Precision/Recall methods, emotional intelligence system and fuzzy/probabilistic calculations are proposed to measure the performance and accuracy of the resulted recommendation.

Agent-Based Computational Investing Recommender System

Mona Taghavi, Kaveh Bakhtiyari, Edgar Scavino
Conference 7th ACM Conference Series on Recommender Systems (RecSys) 2013, HK City University, Hong Kong, 12 – 16 Oct. 2013
ACM, ISBN ACM 978-1-4503-2409-0/13/10, Pages 455-458

Abstract

The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This paper presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.

Fuzzy Model on Human Emotions Recognition

Kaveh Bakhtiyari, Hafizah Husain
Conference 12th WSEAS International Conference on Applications of Computer Engineering (ACE '13), Cambridge, MA, USA, 30 Jan. – 1 Feb. 2013
ISBN 978-1-61804-156-2, Pages 77-82

Abstract

This paper discusses a fuzzy model for multi-level human emotions recognition by computer systems through keyboard keystrokes, mouse and touch-screen interactions. This model can also be used to detect the other possible emotions at the time of recognition. Accuracy measurements of human emotions by the fuzzy model are discussed through two methods; the first is accuracy analysis and the second is false positive rate analysis. This fuzzy model detects more emotions, but on the other hand, for some of emotions, a lower accuracy was obtained with the comparison with the non-fuzzy human emotions detection methods. This system was trained and tested by Support Vector Machine (SVM) to recognize the users’ emotions. Overall, this model represents a closer similarity between human brain detection of emotions and computer systems.

Intrusion Detection and Prevention System in Cloud Computing:A Systematic Review

Ahmed Patel, Mona Taghavi, Kaveh Bakhtiyari, Joaquim Celestino Júnior
Journal Journal of Network and Computer Applications
Elsevier, ISSN 1084-8045, Volume 36, Issue 1, Jan. 2013, Pages 25-41

Abstract

The distributed and open structure of cloud computing and services becomes an attractive target for potential cyber-attacks by intruders. The traditional Intrusion Detection and Prevention Systems (IDPS) are largely inefficient to be deployed in cloud computing environments due to their openness and specific essence. This paper surveys, explores and informs researchers about the latest developed IDPSs and alarm management techniques by providing a comprehensive taxonomy and investigating possible solutions to detect and prevent intrusions in cloud computing systems. Considering the desired characteristics of IDPS and cloud computing systems, a list of germane requirements is identified and four concepts of autonomic computing self-management, ontology, risk management, and fuzzy theory are leveraged to satisfy these requirements.

Taxonomy and Proposed Architecture of Intrusion Detection and Prevention Systems for Cloud Computing

Ahmed Patel, Mona Taghavi, Kaveh Bakhtiyari, Joaquim Celestino Júnior
Book Chapter The 4th International Symposium on Cyberspace Safety and Security (CSS 2012), Deakin University Melbourne Burwood Campus, Melbourne, Australia, Dec. 2012
Springer Lecture Notes in Computer Science, ISBN 978-3-642-35362-8, Volume 7672, Pages 441-458

Abstract

The distributed and open structure of cloud computing and services becomes an attractive target for potential cyber-attacks by intruders. The traditional Intrusion Detection and Prevention Systems (IDPS) are deemed largely inefficient to be deployed in cloud computing environments due to their openness, dynamicity and virtualization in offered services. This paper surveys and explores the possible solutions to detect and prevent intrusions in cloud computing systems by providing a comprehensive taxonomy of existing IDPS. It discusses the key features of IDPS that are challenging and crucial for choosing the right security measures for designing an IDPS. The paper further reviews the current state of the art of developed IDPSs for cloud computing which uses advanced techniques in overcoming the challenges imposed by cloud computing requirements for more resilient, effective and efficient IDPSs, abbreviated as CIPDS.

Evaluation of Cheating Detection Methods in Academic Writings

Ahmed Patel, Kaveh Bakhtiyari, Mona Taghavi
Journal Journal of Library Hi Tech, Nov. 2011
Emerald, ISSN 0737-8831, Volume 29, Issue 4, Pages 623-640

Abstract

Purpose – This paper aims to focus on plagiarism and the consequences of anti-plagiarism services such as Turnitin.com, iThenticate, and PlagiarismDetect.com in detecting the most recent cheatings in academic and other writings.

Design/methodology/approach – The most important approach is plagiarism prevention and finding proper solutions for detecting more complex kinds of plagiarism through natural language processing and artificial intelligence self-learning techniques.

Findings – The research shows that most of the anti-plagiarism services can be cracked through different methods and artificial intelligence techniques can help to improve the performance of the detection procedure.

Research limitations/implications – Accessing entire data and plagiarism algorithms is not possible completely, so comparing is just based on the outputs from detection services. They may produce different results on the same inputs.

Practical implications – Academic papers and web pages are increasing over time, and it is very difficult to capture and compare documents with all available data on the network in an up to date manner.

Originality/value – As many students and researchers use the plagiarism techniques (e.g. PDF locking, ghost-writers, dot replacement, online translators, previous works, fake bibliography) to cheat in academic writing, this paper is intended to prevent plagiarism and find suitable solutions for detecting more complex kinds of plagiarism. This should also be of grave concern to teachers and librarians to provide up to date/standard anti-plagiarism services. The paper proposes some new solutions to overcome these problems and to create more resilient and intelligent future systems.

Encryption Systems (in Persian Language)

Kaveh Bakhtiyari
Journal Web Magazine, Aug. 2004
ISSN 1735-1820, Volume 50, Page 16-19

Chips Speed and Technology Improvement Rate in the Future (in Persian Language)

Kaveh Bakhtiyari
Journal Web Magazine, Jun. 2004
ISSN 1735-1820, Volume 48, Page 24-25

Talks & Presentations

  • 2016 11 Jan.

    Recommender Systems in Future Intelligence

    University of Concordia, Montreal, CANADA
    Faculty of Engineering & Computer Science
    Organized by Assoc. Prof. Dr. Bentahar, & Assoc. Prof. Dr. Yu

  • 2014 10 Oct.

    Emotional Intelligence in Adaptive Recommender Systems

    University of Duisburg – Essen, Duisburg, GERMANY
    Interactive Systems, Faculty of Engineering
    Organized by Prof. Dr-Ing. Jürgen Ziegler

  • 2013 22 Aug.

    Cultural & Emotional Intelligence in Adaptive Recommender Systems

    Hotel Equatorial Bangi, Serdang, MALAYSIA
    Annual Seminar of DSP Research Group, Faculty of Engineering, The National University of Malaysia

  • 2013 30 Mar.

    Workshop on Social Engineering: The art of human hacking

    Swiss Garden Hotel, Cherating Beach, Kuantan, MALAYSIA
    Organized by ISAUKM

  • 2011 2 Nov.

    Human Emotions Recognition in HCI

    University of Duisburg – Essen, Duisburg, GERMANY
    Department of Psychology-Media and Communication, Faculty of Engineering
    Organized by Prof. Dr. Nicole Krämer

Software Development

Codes & Scripts

At University of Duisburg-Essen

You can find me at my office located at the University of Duisburg-Essen at the following address:

Interactive Systems
University of Duisburg-Essen (Campus Duisburg)
Forsthausweg 2 (Building LF)
47057 Duisburg, North Rhine-Westphalia (NRW)
GERMANY

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At My Work

For more information and details, please visit the MUSINGWAY Ltd. website at musingway.com .

At The National University of Malaysia (UKM)

At the moment, I am not always available in campus.

You can find me at my office located at the National University of Malaysia at the following address:

Digital Signal Processing (DSP) Lab
Level 2, Faculty of Engineering & Built Environment
Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor
MALAYSIA

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