Name
Tatchanaamoorti Purnshatman
Qualification
Master of Computing- University of Southern Queensland, Australia
Skills / Specialist
SVM, C#, C++, Java, Mobile application android, Matlab(Software), Python
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: 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.