Bhubaneswar, Oct 25 (LocalWire): Issues like smart operations, digital supply chains and sustainability figured prominently at the two-day International Conference on Applied Machine Learning (ICAML-2019) which began at the SOA Deemed to be University here today.
‘Recent dynamic events around the world have provided frequent reminders that we live in an unpredictable and changing world and it has become imperative for channel entities to incorporate risk management tools in the management of their supply chains,’ Dr Vipul Jain, a senior faculty in Operations and Supply Chain Management at Victoria Business School, Victoria University, Wellington, said while delivering the key note address at the conference.
Experts attending the conference, organised by the departments of Computer Science and Information Technology and Computer Application at the Institute of Technical Education and Research (ITER), SOA’s faculty of engineering, are deliberating on recent research and application of algorithms, tools and platforms to solve problems existing in organizing and analyzing massive and noisy data sets.
Noisy data is data with a large amount of additional meaningless information in it called ‘noise’.
This includes data corruption and the term is often used as a synonym for corrupt data besides data that a user system cannot understand and interpret correctly.
Dr Jain said smart operations, digital supply chains and sustainability could be considered from different points of view which implied an integration of industrial engineering, business informatics, management and operations research competencies.
He spoke on new emerging research methodologies which were geared towards providing and developing effective solutions for performance and risk analysis of integrated supply chain networks, making them smarter.
Prof Amit Banerjee, vice-Chancellor of SOA, presided over the inaugural session which was also addressed by Prof Srikanta Pattnaik, SOA’s director, International Relations and Publications and Prof Ajit Kumar Nayak, Programme Chair, ICAML-2019.
Machine Learning is a multidisciplinary intersectional area which includes field of computer science, artificial intelligence, statistics and operation research.
Its objective is to understand the structure of the data and fit that data into models which could be understood and utilized by people.
Machine Learning facilitates computers in building models from sample data in order to automate decision making processes based on data inputs.
Machine Learning techniques have currently become very helpful in research work to improve efficiency, speed and accuracy of outcomes in the presence of large scale data.