Dr. Kalpana Chauhan (M’13-SM’17) ) graduated in Electrical and Electronics Engineering from The College of Engineering Roorkee, India. She received her M. Tech degree in Control and Instrumentation Engineering from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India and PhD in Electrical Engineering from the Indian Institute of Technology Mandi, India. Presently She is working as Associate Professor at the Galgotias College of Engineering and Technology Greater Noida. Dr. Chauhan worked as Dean (Research) at SIRDA Group of Institutions Sundernagar, India (2013-2017). She has also worked as Visiting Scientist in the Center for Electromechnics at the University of Texas at Austin, US. Her special field of interest included DC Micro-grid, Building Automation System, Industrial Automation and Control. She is also the associate member of Institution of Engineers (India), IAENG Hong Kong and ICASIT Singapore. Rajeev Kumar Chauhan (M’10-SM’15) graduated in Electrical Engineering from The Institutions of Engineers (India).He received his M. Tech degree in Control and Instrumentation Engineering from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India and PhD from School of Computing and Electrical Engineering at Indian Institute of Technology Mandi, India. Presently he is working as Assistant Professor (Senior Grade) at the Galgotias College of Engineering and Technology Greater Noida, India. Dr. Chauhan received POSOCO Power system Award (PPSA-2017) and Best PhD Thesis Award to recognize and reward innovative technical research excellence in power system. He has also received 2nd prize (Category Energy) in IEEE IAS CMD Humanitarian project contest 2017 for his project DC Micro-grid for Residential Buildings. He also received Bhaskara Advanced Solar Energy (BASE-2014) Award, for his research proposal in the area of DC microgrid from Department of Science and Technology, Indo-US Science and Technology Forum.

• 1. Cardiac diseases and their diagnosis methods2. Cardiac multimodal image registration using machine learning techniques3. Anatomical photo representations for cardiac imaging training4. Cardiac function review by machine learning approaches5. Despeckling in echocardiographic images using a hybrid fuzzy filter6. Impetus to machine learning in cardiac disease diagnosis7. Wavelet transform for cardiac image retrieval8. AI-based diagnosis techniques for cardiac disease analysis and predictions9. An improved regularization and fitting-based segmentation method for echocardiographic images10. Identification of heart failure from cine-MRI images using pattern-based features11. Medical image fusion methods: Review and application in cardiac diagnosis