Name

Tatchanaamoorti Purnshatman

Qualification

Master of Computing- University of Southern Queensland, Australia

Skills / Specialist

SVM, C#, C++, Java, Mobile application android, Matlab(Software), Python

LinkedIn 

Research Interests

Medical Informatics, Image classification and Segmentation, AI Medical Imaging

Achievement, 

Publications, Conferences

Workshop Attended:

Train The Trainer- HRDF CLAIMABLE

Java Programming- SEGI University 2005.

Teaching and Supervision Training- University of Southern Queensland, Toowoomba, Australia 2008

FMT-CT IMALYTICS 2.0 Pre-CLINICAL

Medical Imaging Cardiovascular Training- National Heart Institute Malaysia

Research Committee- Ethical Clearance training

Administrating Windows Server 2012 workshop


Program Committee: 

Representative for Degree & Master in Faculty of Maths & Computing, USQ

Representative for Degree & Master in Teaching & Learning Unit, USQ

Representative for Degree & Master in Faculty of Advising committee, USQ

 Information & Computing System, Brunel University

Council Australia Postgraduate Association CAPA

Bioinformatics Australia Society

Experimental Molecular Imaging, UNIKLINIK RWTH AACHEN GERMANY.


Conference/ Seminar

Medical Cardiac Imaging collaboration workshop National Institute of Heart, Malaysia

Research Methodology presentation at National Institute of Heart, Malaysia

Practical Bioinformatics Workshop 2014 CARIF Cancer Research Initiatives Foundation

High Impact Journal Writing and Evaluation Workshop University Technology Malaysia

Journal Club University Hospital Aachen, Germany

Medical Imaging Seminars at UKM by visiting scholar: Prof Dr David Townsend (co-inventor of PET/CT scanner)


Publication:

Reviewer Paper ID : idtj19-076. Title: Implementation of Deep Convolutional Neural Network for Classification of Multiscaled Remote Sensing Scene. Main Author: Ms.Alegavi. Other authors (if any) : Dr.Raghvendra Sedamkar. Login URL: http://www.idtjournal.prosemanager.com/login.asp Deadline for Submitting Review: 6 Jun 2019


Intelligent Decision Technologies: An International Journal 

Paper: Special Issue: Innovative Trends 

Paper Title: On Lagrangian Twin Parametric-Margin Support Vector Machine 

Paper Details: idtj17-181-(30 Nov 2017)


Books

Call for book chapters (ELSEVIER) by 30th December 2020 Advances in Computational Techniques for Biomedical Image Analysis Methods and Applications

Purnshatman Tatchanaamoorti

Manuscript Chapter submitted

Under review


Published

Tatchanaa, Felix Gremse 2016, Coronary Plaque detection using Novel Imaging techniques, 1ST International Conference on Distruptive Innovation


Infor Med Magazine

Tatchanaa, fMRI detection the growth of Brain Tumor article Info-Med April Issue 2015 pg 16- pg20


Datukun, Sellappan, Tatchanaamoorti 2016, IEEEnigeriaComputConf16, Quality of Internet Service in Plateau University Bokkos


Datukun, Selllappan, Tatchanaamoorti 2016 Corpus ID: 212486056 Proposing Minimum Performance of Proposed Topology in Plateau State University


Peer-reviewed Journal articles

Tatchanaa, Dr.Felix Gremse (2020): Markers Classification for detection of mice tumor 1st Molecular Imaging Conference at Aachen, Germany, 2014, 160-167


Submitted manuscripts without peer review process:

Thatchana, Dr. Felix (2020): CT Coronary plaque classification using SVM algorithm



Bio

My first degree was in Computing at University of Greenwich, London where I became interested in optical imaging and machine learning. I am also interested in learning Artificial Intelligence (AI) models of multivariate time series in order to try and understand the underlying processes generating such data. My current Ph.D entitled "Automated segmentation and image classification in the context of cancer and atherosclerosis imaging. I have completed my 9 Months PhD attachment as a Visiting PhD researcher at University Hospital Aachen, Germany. My projects include extending my work on automated segmentation and image classification using evolutionary computation and support vector machine with various datasets including high dimensional CT coronary plaque data, and FMT-CT mice data from Hospital Aachen.

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