ICITR 2021

6th International Conference on Information Technology Research
"Digital Resilience & Reinvention"


01 to 03 DECEMBER 2021 - Virtual Event

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Dr. Yogachandran Rahulamathavan

Loughborough University London, United Kingdom


Yogachandran Rahulamathavan is a Senior Lecturer and the Programme Director for MSc Cyber Security and Data Analytics at Loughborough University’s London Campus in the UK. Yoga obtained his PhD degree from Loughborough University in mathematical optimisation techniques for information processing in 2012. His research interest is on developing novel security protocols to advance machine learning techniques to solve complex privacy issues. Currently focussing on post-quantum encryption techniques to develop privacy-preserving machine learning algorithms. Currently, Dr Rahul coordinates a UK-India project between Loughborough University London, IIT Kharagpur, India and City, University of London. He is a Senior Member of IEEE and an Associate Editor for the IEEE Access journal.


Title: "Hide-and-Seek: Machine Learning in Encrypted Domain"


Abstract: Machine Learning models were built using a huge amount of high-quality and application-specific data. Even though the machine learning models can only be trained at places where the data is available, anyone can use the trained model for classification tasks via the Internet. While it sounds revolutionary, the trained ML models are not readily available to users in healthcare, finance, or marketing due to privacy issues. Users do not want to share their sensitive data with service providers due to a lack of trust. Simply encrypting the data only protects them during storage and transmission. Researchers and industries are developing novel techniques known as privacy-preserving techniques to process the data in an encrypted domain to tackle the privacy issue. Homomorphic encryption plays a key role in developing privacy-preserving machine learning algorithms. While homomorphic properties exist in traditional cryptographic schemes such as RSA, this talk will focus on fully homomorphic encryption from lattice-based cryptography. We will also go through the state-of-the-art works, challenges and future trend in this domain.


Dr. Sadeep Jayasumana

Senior Research Scientist at Google Research, New York


Sadeep Jayasumana is a Senior Research Scientist at Google Research, New York. His research interests are in the areas of computer vision, deep learning, and machine learning in general. Before joining Google, Sadeep has held research positions at Five AI, a UK-based self-driving car startup, and Man AHL, a London-based quantitative hedge fund. During his time in academia, Sadeep completed a Postdoc at the University of Oxford, and a PhD at the Australian National University - both in computer vision. He obtained his undergraduate degree from the Department of Electronic and Telecommunication Engineering, University of Moratuwa.


Title: "Advances in Deep Learning"


Abstract: Deep learning has become the key machine learning tool in various AI application areas such as computer vision, natural language processing, and speech recognition. Systems powered by deep learning are already in everyday use; examples are: image and voice recognition software on smartphones, recommendation systems on eCommerce websites, and language translation software. Even more exciting deep-learning-powered systems like self-driving cars are just around the corner.


Professor Jiang Liu

Waseda University, Japan


Jiang Liu is an Associate Professor at Waseda University in Japan. She obtained her Ph .D. degree in information and telecommunications from Waseda University in 2012. After that, She joined Faculty of Science and Engineering at Waseda University as an assistant professor, and since 2017 she has been an associate professor affiliated with the International Center for Science and Engineering Programs. Her research focuses on Near Field Communication, Wireless Network Systems, and their applications in healthcare industry and 6G network design. She is a senior member of IEEE and a committee member of the Institute of Image Electronics Engineers of Japan (IIEEJ). She also serves as the secretary of the Japan Division for the Institution of Engineering and Technology (IET).

Title: "Near Field Communication based Smart Devices and Health Monitoring Systems""


Abstract: Nowadays the spread of the Covid-19 has caused significant changes in society and is triggering many people to develop a new lifestyle. The awareness of vital sign such as blood oxygen saturation concentration (SpO2) has been improved since SpO2 can help monitor and detect breathing issues.. This motivated us to develop some smart devices to obtain useful vital data continually and easily. In this research direction, Near Field Communication (NFC) technology has attracted much attention in developing sensing systems for health care monitoring and high-secure, short-range data exchange. We design and develop contactless smart devices using NFC technology to obtain the vital data such as pulse rate, SpO2, blood pressure, blood glucose and others. We also aim to develop some smart devices to recognize human languages or understand human emotions without traditional input interface. To accomplish this goal, we design and test a sensor-based data acquisition glove for Japanese Sign Language (JSL) hand gesture recognition, and a wearable air-writing system that enables users to write the English alphabet in the three-dimensional space without any writing rules. In summary, this discussion delivers an introduction of the non-contact data sensing technology and smart devices along with our latest research outcome.


Professor Sunny Joseph Kalayathankal

Jyothi Engineering College ,hrissur, Kerala India.


Prof. Sunny Joseph Kalayathankal received the MSc. degree from Kerala University , Kerala, India in 1986, BEd from Calicut University, Kerala in 1987, MPhil Kerala University in 1993 and Ph.D (Mathematics) degree in 2010 from Kerala University, MCA from Indira Gandhi National Open University, New Delhi, India in 2002, M.Tech IT from Karnataka State Open University in 2013 and Ph.D. in Computer Science under Bharathiar University, Coimbatore, India in 2018. He is currently working as a Principal (Professor and Dean of Research) Jyothi Engineering College Affiliated to APJ Abdul Kalam Technological University, Thrissur, Kerala India and has 34 years and 8 months of teaching and 16 years of research experience. He has published more than 84 papers in the areas of Fuzzy modelling and decision making, Graph theory and Applied Mathematics. He has served as Keynote and invited speaker in various National and International conferences. He is the reviewer of Iranian Journal of Fuzzy System, International Journal of Fuzzy system and Journal of Mathematical Modeling and Computer Simulation.

Title: "Fuzzy Modeling and Decision-Making Applications in Engineering Science""


Abstract: The thought process involved in the act of decision making is a complex array of streaming possibilities in which a person selects or discards information made available from diverse sources. In doing so one is led by a meaningful analysis of available information and optimal selection out of several apparently equi-efficient decisions. Since Zadeh (1965) published the fuzzy set theory as an extension of classic set theory, it has been widely used in many fields of application, such as pattern recognition, data analysis, system control, etc. The unique characteristic of this theory, in contrast to classic mathematics, is its operation on various membership functions (MF) instead of the crisp real values of the variables. Molodtsov (1999) initiated the concept of soft set theory as a new mathematical tool for dealing with uncertainties. Pabitra Kumar Maji et al. (2001) introduced fuzzy soft set theory which also deals with uncertainties.
Out of the several higher order fuzzy sets, intuitionistic fuzzy sets by Atanassov (1985) and Ordered intuitionistic fuzzy sets proposed by Kalayathanal et al. (2010) have been found to be highly useful to deal with vagueness. Intuitionistic fuzzy set is described by two functions: a membership function and a non - membership function. We develop and apply similarity measures between ordered intuitionistic fuzzy sets to multiple attribute decision making (MADM) under fuzzy environment