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. Gamal Abd El-Nasser Ahmed Mohamed Said, Department of Computer and Information Technology, PTI, Arab Academy for Science and Technology and Maritime Transport (AASTMT), Egypt.

Dr. Gamal Abd El-Nasser received his Ph.D. in computer science from Faculty of Computer and Information Sciences, Ain Shams University, Egypt. He received M.Sc. in computer science from College of Computing & Information Technology, Arab Academy for Science and Technology and Maritime Transport (AASTMT), Egypt. He received B.Sc from Faculty of Electronic Engineering, Menofia University, Egypt. His work experience as a Researcher, Maritime Researches & Consultancies Center, Egypt. Computer Instructor, College of Technology, Kingdom Of Saudi Arabia and Computer Lecturer, Port Training Institute, (AASTMT), Egypt. His research areas include Artificial Intelligence – Optimization – Modeling & Simulation – Evolutionary Computing – Cloud Computing.

Title of Talk: Big Data Analytics in Cloud Computing Environment

Abstract: Ninety percent of the world’s data has been generated over the last two years. These data are complex and needs to be stored, processed, and analyzed for information that can be used. The commonly model for explaining big data is the multi-V model (variety, volume, velocity, veracity and value). The complexity of this data requires powerful management and technological solutions. Big data analytics applications enable to analyze growing of data. Cloud Computing can be utilized to address big Data challenges. The presentation will view integration of big data analytics and cloud computing to address big Data challenges.

Keywords: Big data, Cloud Computing, Internet of Things

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

Dr. Yousef FARHAOUI, Department of Computer Science, Faculty of sciences and Technic, Moulay Ismail University, Boutalamine, Errachidia, Morocco

Dr. Yousef is Associate professor Specialty: Computer Science (Option: Data Security and Big Data) Theme: “Protection of Computer Systems Against Attacks and Big Data” Faculty of Science and Technology Errachida, Morocco

Title of Talk: Big Data and Internet of Things for Air Quality Prediction

Abstract: The amount of data being generated by connected devices of Internet of Things (IoT) keeps increasing rapidly which brings about an evolving term that can change the world; it’s about Big Data and its serious challenges to deal with highly complex data. we examine the possibility to make a fusion between the two new concepts Big Data and Internet of Things; in the context of predicting environmental issues that face our planet nowadays. Indeed, one of these environmental problems is Air pollution that occurs when harmful substances are introduced into Earth’s atmosphere. In this context, that our project integrates and whose mission is to use the new technologies namely the Internet of objects; Which represents the exchange of information and data from devices present in the real world to the Internet, and the Big data, which means datasets that have become so large and so difficult that they go beyond intuition and Human analytical capabilities and even those of traditional computer-based database or information management tools. This fusion of the two concepts will be carried out in the context of predicting environmental problems such as air pollution in which the concentrations of certain pollutants exceed the levels prescribed by law and affect the health of the population. The collection of data will be carried out by the use of the wireless sensors network (WSN) which will be dispersed within our country in a geographical area that corresponds to the area of interest for the phenomenon captured. These sensors will be able to collect and transmit environmental data in an autonomous way.

Keywords: Big data, Internet of Things (IoT), Air pollution Environmental Issues

Dr. Deepak Garg, Senior Member of IEEE, USA, Senior Member of ACM,UK, Professor & Head, Department of Computer Science and Engineering, Bennett University, Times Group, Greater Noida (UP), India

Dr. Garg is considered as one of the Algorithm Gurus in India. He has done his Ph.D. in the area of efficient algorithm design.  His active research interests are designing efficient machine learning algorithms and knowledge management. He is Senior Member of IEEE, USA, Senior Member of ACM, UK. He has been chair of IEEE Computer Society, India Council. He has been on the Board of governors of IEEE Education Society, USA. He started his career as a Software Engineer in IBM Corporation Southbury, CT, USA. He has taught important core subjects at graduate and undergraduate levels and has guided 10 PhD Students. He has handled research projects of around three crores sponsored by Govt of India. He has successfully organized 19 short-term courses on latest technologies including enablement Programme. Deepak has 100+ publications in various International Journals and conferences. He has 19 years of teaching/research/development experience. He is passionate about transforming the landscape of Indian Engineering Education and a leader in MOOC initiatives of India.

Title of Talk: Deep Learning: Research Directions and Challenges

Abstract: Deep Learning is the revolution that is going to change our world in an unprecedented manner. It is crucial for us to understand the challenges we face as a human race. Also, we need to be smart enough to use it to our advantage and manipulate its infinite power to improve the quality of life on earth and try to solve relevant problems. Many problems that researchers were not able to touch due its sheer size are now very easily solvable with increasing computing power and human-like learning capabilities of the machine. The talk will focus on specific issues, directions and challenges.

Keywords: Machine Learning, Artificial Intelligence, Deep Learning, Data Science, Neural Networks, Transfer Learning

Dr. Vijay Bhaskar Semwal, Member IEEE, Indian institute of Information Technology (IIIT), Dharwad (Hubli), India

Dr. Semwal, currently working as Assistant Professor (CSE) at Indian institute of Information Technology Dharwad (IIIT Dharwad) and before joining here he was serving as faculty at NIT Jamshedpur. He is the main resource person for Computer Science Engineering at IIIT Dharwad. His research includes Biometric Gait Identification, Hybrid and Cellular Automata, Machine & Artificial Intelligence, Sentimental Analysis, Extreme Machine learning & Deep Learning, Operating System, Compiler Design He has publish more then 15 SCI/ESCI/Scopus journals paper including IEEE Transaction. He has published 10 Conference paper on the said field. He obtained his B.Tech. from the College of Engineering Roorkee, Roorkee, in 2008. He received his M.Tech. from IIIT Allahabad in 2010. He has been awarded with Ph.D. in 2017 from IIIT Allahabad (20 June 2017), he worked as a Senior System Engineer (R&D) with Siemens Gurgaon and Bangalore. He has worked for various major organizations, such as Siemens AG and Newgen. Currently he is serving faculty counselor at IEEE student branch of IIIT-Dharwad.

Title of Talk: Data Driven Computational Bipedal Walking Model Using Hybrid Automata And Classification Using Fast Extreme Learning Machine (ELM ) For Clinical Human Walk Data

Abstract:The bipedal walk is one of the complex learning processes. The human being used to learn this behavior using continuous interaction with environment. The walk is very unique biometric identification. This research work is proposing the novel fast ELM for human walk data. The EML is fast compare to neural network neural networks (SLFNs). It is widely using for text data classification. This is the first research article which is using ELM for clinical Human Gait Data Classification. The data is collected for 5 different categories named Healthy gait, Brain Disorder gait, crouch1, crouch2 and crouch3. The dimension of data is for five classes, 500 samples and 6 features. We have received the best accuracy compare to previous work reported by us.

Keywords: Analysis based on statistics, Linear algebra, Dimension reduction of a real world data set, Data smoothing, Curve Fitting, Dimension Reduction Technique for High Dimension data

Dr. Aviral Shrivastava, Associate Professor, Arizona State University, Arizona

Dr. Aviral Shrivastava is Associate Professor in the School of Computing Informatics and Decision Systems Engineering at the Arizona State University, where he has established and heads the Compiler and Microarchitecture Labs (CML) (http://aviral.lab.asu.edu/). He received his Ph.D. and Masters in Information and Computer Science from University of California, Irvine, and bachelors in Computer Science and Engineering from Indian Institute of Technology, Delhi. He is a 2011 NSF CAREER Award Recipient, and recipient of 2012 Outstanding Junior Researcher in CSE at ASU. His 2 students have received the outstanding MS thesis award in CSE at ASU. His papers have been the best paper candidate at DAC 2017, ASPDAC 2008, and won the best student paper award at VLSI 2016. His research lies at the intersection of compilers and architectures of embedded and multi-core systems, with the goal of improving power, predictability, performance, temperature, energy, reliability and robustness. NSF and several industries including Microsoft, Raytheon Missile Systems, Intel, Nvidia, etc fund his research. He serves on organizing and program committees of several premier embedded system conferences, including DAC, ICCAD, ISLPED, CODES+ISSS, EMSOFT, CASES and LCTES, and regularly serves on NSF and DOE review panels. 

Title of Talk: Time in Cyber-Physical Systems

Abstract: Cyber-Physical systems are those that tightly integrate physical and computational systems. One of the big challenges in distributed cyber-physical systems is establishing a common notion of time between the physical world and the computational system. Many modern CPS, especially industrial automation systems, require the actions of different computational systems to be synchronized at much higher rates than is possible through ad hoc designs. Fundamental research is needed in synchronizing clocks of computing systems to a higher degree, and even if the clocks are synchronized, designing CPS nodes so that they can perform actions in a synchronized manner is challenging. We need to find ways to specify distributed CPS applications, ways to specify and verify timing requirements on distributed CPS, confident top-down design methodologies that can ensure the system meets its timing requirements in the first go, dynamically creating and dissolving timing domains using differently build components, and much more. In this talk, I will present some of the work that we have done, and some of the ideas that we want to pursue in order to solve the challenge of confident and simplified CPS design (from the timing perspective). We believe that confident CPS design is possible only when the timing requirements of CPS are specified in the application itself, and not as a separate document. It should not be a list of separate requirements, but must be married to the application specification in as natural way as possible. Second, we need techniques to design the CPS in one-shot. Provably correct by construction is very good, but even design methodologies that improve the confidence in design are also very important. Finally, there should be automated methods to test the CPS.

Keywords: CPS, Adhoc Network