Dr. Jayasri Santhappan is a Senior Data Scientist at JP Morgan & Co, Houston, TX, USA

Dr. Jayasri Santhappan is currently working as a Senior Data Scientist at JP Morgan & Co. She started her career as a Database Administrator in Hp, IBM, Accenture, Standford in Palo Alto, California, USA. She developed automated tools using softwares to promote revenue for her organization. She handled many industry projects in her jobs. She has 20 years of experience in IT with data analytics and data science skills. She is specialized in data science, Machine Learning, Statistics and all databases such as Oracle, SQL Server, Hana, No SQL databases MongoDB, Cassandra with all professional certification. she has skilled with dynamic databases for analytics and predictive system with robust experiences in various industries. She received her doctorate in Big Data Analytics from Colorado Technical University, Denver, USA with honor of summa cum laude highest academic achievements.

Title of Talk:The Pioneering Role of AI to Predict Market Cap in Profitable Trading System

Abstract: Artificial Intelligence (AI) is a powerful cutting edge techniques from world-class data science which is playing an essential roles in all business. Currently, Artificial Intelligence is pioneering in saving time and money by automating routine processes and tasks. For example, different organizations and people have adopted AI technology to increase productivity and operational efficiencies to make significant and efficient business decisions based on outputs from cognitive technologies. AI helps to avoid mistakes and ‘human error’ which improve the performance in current business. AI is changing our lives in different ways for each year. For example, business people are using it to analyze and forecast for their business. We want to look at some of the roles it’s playing in workplace, from the entryway to your cubicle in the back, especially in the banking sector. This intellectual discussion will explain about how market capitalization influence by the power of social media in financial industries using artificial intelligence.

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Stock Price, Market Share, Social Media

Dr. Angelo A. Beltran Jr., Department of Electronics Engineering at Adamson University, Philippines, Professional Electronics Engineer in the Philippines and a former Research and Development Engineer for Power Electronics and Motor Control in Taiwan

Dr. Angelo A. Beltran Jr. was born in Quezon City, Philippines. He received the B.S. degree in Electronics and Communications Engineering from the AMA Computer University, Philippines, the M.Eng. degree in Electronics and Communications Engineering and the Ph.D. in Electronics Engineering, all in Mapua University in Manila, Philippines. He is currently with the Department of Electronics Engineering at Adamson University, Philippines. From 2015 – 2017, he was a Visiting Scholar in the Department of Electrical Engineering, Chung Yuan Christian University, Taiwan. He is a Professional Electronics Engineer in the Philippines and a former Research and Development Engineer for Power Electronics and Motor Control in Taiwan. He had authored or co-authored over 27 papers in proceedings and international journals and serves as a reviewer for several conferences, engineering and scientific journals. He received the Best Paper Award in Chung Yuan Christian University, First Place Best Department Chair Research Award, and Third Place Best Department Chair Research Award, at the Technological Institute of the Philippines in 2016, 2012, and 2011, respectively. His research interests include renewable energy systems, computational intelligence based control and estimation, embedded systems, applied swarm intelligence algorithms and evolutionary computing techniques, self organizing and adaptive control systems, chaos theoretic concepts and applications.

Title of Talk: Chaos Enhanced PSO with Adaptive Parameters and Its Application to MPPT

Abstract: An enhanced particle swarm optimization (PSO) scheme is presented in this talk to further improve the performance of the standard PSO algorithm. The enhanced PSO is based on the technique of chaos search to overcome the problems of stagnations, being trapped in a local optima, and the risk of premature convergence. A type constriction is incorporated to help strengthen the stability and quality of convergence, and an adaptive learning coefficients is applied to further enhance the exploitation and exploration search characteristics of the algorithm. A several well known benchmark functions are operated to verify the effectiveness of the enhanced PSO. Test performance is compared with the other popular population-based algorithms in the literature. Results show that the enhanced PSO technique exhibits faster rate of convergence, escapes being trapped in a local minima, avoids premature convergence, and stagnation in a high-dimensional problem space. In addition, the validity of the enhanced PSO is further demonstrated by a fuzzy logic-based maximum power point tracking control technique for a stand-alone solar photovoltaic system.

Keywords: Chaos, Fuzzy Logic, Maximum Power Point Tracking, Particle Swarm Optimization, PhotoVoltaic