Dr Subrata Chakraborty is a Senior Lecturer in the School of Information, Systems & Modelling within the Faculty of Engineering & IT, University of Technology Sydney (UTS), Australia. He received his PhD in Decision Support Systems from Monash University, Australia. Previously Dr Chakraborty worked as an academic with University of Southern Queensland, Charles Sturt University, Queensland University of Technology, and Monash University.
Dr Chakraborty's current research interests include Optimisation Models, Data Analytics, Machine Learning, and Image Processing with decision support applications in diverse domains including Business, Agriculture, Transport, Health, and Education.
Can supervise: YES
Decision Support Modelling
Visual Attention Modelling
Chakraborty, S, Paul, M, Murshed, M & Ali, M 2017, 'Adaptive weighted non-parametric background model for efficient video coding', NEUROCOMPUTING, vol. 226, pp. 35-45.View/Download from: Publisher's site
Chakraborty, S, Mengersen, K, Fidge, C, Ma, L & Lassen, D 2016, 'A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport', Decision Analytics, vol. 3, no. 1.View/Download from: Publisher's site
Paul, M, Haque, SME & Chakraborty, S 2013, 'Human detection in surveillance videos and its applications - a review', EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING.View/Download from: Publisher's site
Chakraborty, S & Yeh, C-H 2007, 'Consistency comparison of normalization procedures in multiattribute decision making', WSEAS Transactions on Systems and Control, vol. 2, no. 2, pp. 193-200.
Syed, A, Chakraborty, S, Hafeez-Baig, A & Mandal, A 2019, 'An Exploratory Study to Understand the Phenomena of Eye-Tracking Technology: A Case of the Education Environment' in Dann, CE & O'Neill, S (eds), Technology-Enhanced Formative Assessment Practices in Higher Education, IGI.View/Download from: Publisher's site
Technology has played a pivotal role in revolutionizing the formative aspects of learning and teaching in the current digital age. Due to technology, there is an expectation of having customized medicine, customized interaction, and customized formative communication instead of traditional mass reporting approaches. Formative assessment within higher education teaching and learning environments are no exception to such an approach in the 21st century digital environment. Eye-tracking technology in recent years has provided an insight to understand the human eye movements and concentration patterns, which has application in education. Eye-tracking can be used to examine the processes of individuals in their learning to establish how learning contents are delivered and perceived by all involved (e.g., teaching staff, students, and markers). This chapter proposes that critical and specific information from eye-tracking software can lead to tailored educational content to accommodate, customize, and optimize the unique learning methods for an individual student as per their learning habits. This chapter describes the available eye-tracking technologies and their application in educational processes.
Chakraborty, S, Fidge, C, Ma, L & Sun, Y 2014, 'M-ary trees for combinatorial asset management decision problems' in Lecture Notes in Mechanical Engineering, pp. 107-127.View/Download from: Publisher's site
© Springer-Verlag London 2014. A novel m-ary tree-based approach is presented to solve asset management decisions which are combinatorial in nature. The approach introduces a new dynamic constraint-based control mechanism which is capable of excluding infeasible solutions from the solution space. The approach also provides a solution to the challenges with ordering of assets decisions.
Chowdhury, A, Hafeez-Baig, A, Gururajan, R & Chakraborty, S 2019, 'Conceptual framework for telehealth adoption in Indian healthcare', 24th Annual Conference of the Asia Pacific Decision Sciences Institute: Full papers, Asia-Pacific Decision Sciences Institute (APDSI), pp. 230-239.
Chakraborty, S & Mandal, A 2018, 'A Novel TOPSIS based Consensus Technique for Multiattribute Group Decision Making', ISCIT 2018 - 18th International Symposium on Communication and Information Technology, pp. 110-115.View/Download from: Publisher's site
© 2018 IEEE. Consensus in a group decision setting is a challenging task. We propose a new group consensus technique utilizing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based preference aggregation approach. The technique applies ranking outcome similarity based selection approach in order to select the most preferred consensus technique. We then present a numerical example for better illustrating the new developments along with simulation based comparative results with traditional approach. The new technique provides better overall decision similarity to the group members' individual decisions and produces lower tied rankings.
Chakraborty, S & Mandal, A 2018, 'A Novel TOPSIS based Consensus Technique for Multiattribute Group Decision Making', 2018 18TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES (ISCIT), 18th International Symposium on Communications and Information Technologies (ISCIT), IEEE, Bangkok, THAILAND, pp. 322-326.
Krishnan, D, Zhou, X, Chakraborty, S, Gururajan, R & Gururajan, R 2016, 'Software Development for Managing Nutrition Intake for Type II Diabetes Mellitus', PROCEEDINGS OF 2016 10TH INTERNATIONAL CONFERENCE ON SOFTWARE, KNOWLEDGE, INFORMATION MANAGEMENT & APPLICATIONS (SKIMA), 10th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), IEEE, Chengdu, PEOPLES R CHINA, pp. 215-219.
Chakraborty, S, Zhou, X, Hafeez-Baig, A, Gururajan, R, Paul, M, Mandal, A, Chacko, AE & Barua, PD 2016, 'Objective Analysis of Marker Bias in Higher Education', PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON TEACHING, ASSESSMENT, AND LEARNING FOR ENGINEERING (TALE), 5th IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), IEEE, Bangkok, THAILAND, pp. 453-457.
Hafeez-Baig, A, Gururajan, R & Chakraborty, S 2016, 'Assuring reliability in qualitative studies: A health informatics perspective', Pacific Asia Conference on Information Systems, PACIS 2016 - Proceedings.
Assuring the validity and reliability of data is an essential component of data collection. While quantitative studies use certain statistical techniques such as 'Cronbach Alpha' values for a reliability index, in qualitative studies these type of measures are not widely available, and appear to be predominantly subjective. So, many studies, if at all probe this aspect, highlight what is normally termed as 'saturation' and use this as a measure of reliability. While this serves the purpose to some extent, whether researchers can use this as a concrete evidence is questionable. We propose a new approach to assure reliability in qualitative studies, and provide a case study to demonstrate our approach and its benefits. We hope that this serves as a model to many PhD students and other Early Career Researchers who pursue their studies using qualitative approaches.
Chakraborty, S, Debnath, T, Paul, M, Podder, PK, Gururajan, R & Hafeez-Baig, A 2016, 'An experimental analysis of assessor specific bias in a programming assessment in multi-assessor scenarios utilizing an eye tracker', London International Conference on Education, Infonomics Society, UK, pp. 135-141.
It has been experienced and reported by
academic institutions around the globe that marking
of most subject's assessment scripts vary when
different assessors are utilized for a given subject.
To understand the difference, we capture and
analysis cognitive response of assessors through the
visual pattern while they are marking the scripts. For
this, a Java programming assessment from a real life
university examination is marked by independent
assessors. The assessors marked the scanned
assessment scripts on a computer screen in front of
an Eye tracker machine and their eye gaze data were
recorded real time. Data indicate that different
assessors marked the same answer script differently
and their visual pattern are also varied although
they were given the exact same instructions which
demonstrates bias to a degree. For quality marking,
several findings including the number of assessors
needed are also presented in this manuscript.
Paul, M, Chakraborty, S, Murshed, M & Podder, PK 2015, 'Joint Texture and Depth Coding using Cuboid Data Compression', 2015 18TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (ICCIT), 18th International Conference on Computer and Information Technology (ICCIT), IEEE, Dhaka, BANGLADESH, pp. 138-143.
Podder, PK, Paul, M, Murshed, M & Chakraborty, S 2015, 'Fast intermode selection for HEVC video coding using phase correlation', 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014.View/Download from: Publisher's site
© 2014 IEEE. The recent High Efficiency Video Coding (HEVC) Standard demonstrates higher rate-distortion (RD) performance compared to its predecessor H.264/AVC using different new tools especially larger and asymmetric inter-mode variable size motion estimation and compensation. This requires more than 4 times computational time compared to H.264/AVC. As a result it has always been a big concern for the researchers to reduce the amount of time while maintaining the standard quality of the video. The reduction of computational time by smart selection of the appropriate modes in HEVC is our motivation. To accomplish this task in this paper, we use phase correlation to approximate the motion information between current and reference blocks by comparing with a number of different binary pattern templates and then select a subset of motion estimation modes without exhaustively exploring all possible modes. The experimental results exhibit that the proposed HEVC-PC (HEVC with Phase Correlation) scheme outperforms the standard HEVC scheme in terms of computational time while preserving-the same quality of the video sequences. More specifically, around 40% encoding time is reduced compared to the exhaustive mode selection in HEVC.
Chakraborty, S, Paul, M, Murshed, M & Ali, M 2014, 'AN EFFICIENT VIDEO CODING TECHNIQUE USING A NOVEL NON-PARAMETRIC BACKGROUND MODEL', 2014 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO WORKSHOPS (ICMEW), IEEE International Conference on Multimedia and Expo Workshops (ICMEW), IEEE, ELECTRON DEVICES SOC & RELIABILITY GROUP, Chengdu, PEOPLES R CHINA.
Chakraborty, S, Paul, M, Murshed, M & Ali, M 2016, 'A Novel Video Coding Scheme using a Scene Adaptive Non-Parametric Background Model', 2014 IEEE 16TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP), 16th IEEE International Workshop on Multimedia Signal Processing (MMSP), IEEE, Jakarta, INDONESIA.
Chakraborty, S & Yeh, CH 2012, 'Rank similarity based MADM method selection', ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences", pp. 353-358.View/Download from: Publisher's site
Selecting the most suitable Multiple Attribute Decision Making (MADM) method for a given MADM problem is a challenge for the decision maker. When there are several suitable MADM methods available for the problem, the challenge is even greater. We present a novel MADM method selection approach based on the Spearman's rank correlation. The approach will help the decision maker in selecting the most preferred MADM method from a set of suitable and acceptable methods. The most preferred MADM method is the one that produces the most preferred outcome. The most preferred outcome is the one which is closest to all other outcomes. The closeness between the ranking outcomes are measured in terms of the similarity between them. © 2012 IEEE.
Chakraborty, S & Yeh, CH 2012, 'Comparison based group ranking outcome for multiattribute group decisions', Proceedings - 2012 14th International Conference on Modelling and Simulation, UKSim 2012, pp. 324-327.View/Download from: Publisher's site
A novel group consensus methodology for group ranking problems is presented in this paper. The method considers all the possible ranking outcomes for a given set of decision alternatives. Decision makers are given the freedom to provide their own ranking outcomes using their chosen ranking methods. Spearman's rank correlation is then used to calculate the overall similarity for each of the possible ranking outcomes. The overall similarity for each ranking outcome in the solution space is calculated using its similarities to the ranking outcomes given by the decision makers. The ranking outcome in the solution space which is most similar to the decision makers ranking outcomes is the most preferred one by the group. © 2012 IEEE.
Chakraborty, S & Yeh, C-H 2009, 'A Simulation Comparison of Normalization Procedures for TOPSIS', CIE: 2009 INTERNATIONAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING, VOLS 1-3, International Conference on Computers and Industrial Engineering (CIE39), IEEE, Troyes, FRANCE, pp. 1815-1820.View/Download from: Publisher's site
Chakraborty, S & Yeh, C-H 2007, 'A simulation based comparative study of normalization procedures in multiattribute decision making', AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6, World Scientific and Engineering Academy and Society (WSEAS), Corfu Island, Greece, pp. 102-109.
Normalization procedures are required in multiattribute decision making (MADM) to transform performance ratings with different data measurement units in a decision matrix into a compatible unit. MADM methods generally use one particular normalization procedure without considering the suitability of other available procedures. This study compares four commonly known normalization procedures in terms of their ranking consistency and overall preference value consistency when used with the most widely used simple additive weight method. To achieve this, new performance measure indices are introduced and new simulation settings are devised for dealing with various measurement settings. A wide range of MADM problems with various measurement scales are generated by simulation for the comparison study. The experiment result shows that vector normalization and linear scale transformation (the max method) outperforms other normalization procedures when used with SAW.
Chakraborty, S & Yeh, CH 2007, 'Comparing normalization procedures in multiattribute decision making under various problem settings', Proceedings of CITA'07 : The fifth international conference on information technology in Asia 2007, Fifth International Conference on Information Technology in Asia, Universiti Malaysia sarawak, Malaysia, pp. 36-42.
Bhotta, D, Baig, AH, Gururajan, R, Chakraborty, S, Kavuri, SP & Krishnan, D EasyChair 2019, An Investigation into Usability of Big Data Analytics in the Management of Type 2 Diabetes Mellitus.
Gururajan, R, Little, A, Hafeez-Baig, A, Chakraborty, S & Wickramasignhe, N 2015, 'Mobile telehealth technology assessment: Queensland case study'.