Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 3rd Internal conference on Artificial Intelligence and Robotics San Diego, California, USA.

Day 2 :

Keynote Forum

Mikhail Moshkov

King Abdullah University of Science and Technology (KAUST), Saudi Arabia

Keynote: Extensions of Dynamic Programming for Decision Tree Study

Time : 10:00-10:45

OMICS International Automation and Robotics 2017 International Conference Keynote Speaker Mikhail Moshkov photo

Mikhail Moshkov is a Professor in the CEMSE Division at King Abdullah University of Science and Technology, Saudi Arabia. He has earned his Master’s degree
from Nizhni Novgorod State University, received his Doctorate degree from Saratov State University and Habilitation from Moscow State University. He was with
Nizhni Novgorod State University for a few years, later he worked in Poland in the Institute of Computer Science, University of Silesia and also in the Katowice
Institute of Information Technologies. His main areas of research are complexity of algorithms, combinatorial optimization and machine learning. He is the author
or co-author of fi ve research monographs published by Springer


In the presentation, we consider extensions of dynamic programming approach to the study of decision trees as algorithms
for problem solving, as a way for knowledge extraction and representation and as classifi ers which, for a new object given
by values of conditional attributes, defi ne a value of the decision attribute. Th ese extensions allow us (1) to describe the set of
optimal decision trees, (2) to count the number of these trees, (3) to make sequential optimization of decision trees relative
to diff erent criteria, (4) to fi nd the set of Pareto optimal points for two criteria, and (5) to describe relationships between
two criteria. Th e results include the minimization of average depth for decision trees sorting eight elements (this question
was open since 1968), improvement of upper bounds on the depth of decision trees for diagnosis of 0-1-faults in read-once
combinatorial circuits, existence of totally optimal (with minimum depth and minimum number of nodes) decision trees for
Boolean functions, study of time-memory tradeoff for decision trees for corner point detection, study of relationships between
number and maximum length of decision rules derived from decision trees, study of accuracy-size tradeoff for decision trees
which allows us to construct enough small and accurate decision trees for knowledge representation and decision trees that as
classifi ers, outperform oft en decision trees constructed by CART. Th e end of the presentation is devoted to the introduction

Keynote Forum

Jose B. Cruz Jr

National Academy of Science and Technology, Philippines

Keynote: Interacting Intelligent Computers in a Complex Cyber-physical System

Time : 11:05-11:50

OMICS International Automation and Robotics 2017 International Conference Keynote Speaker Jose B. Cruz Jr photo

Jose B Cruz is an Academician, National Academy of Science and Technology, Philippines and a Member of the US National Academy of Engineering. He is also
a Fellow of the International Federation on Automatic Control and Fellow of IEEE. He has received the Richard E. Bellman Control Heritage Award, AACC and the
IEEE James H Mulligan and Jr Education Medal. He has worked as an Associate Head of ECE, University of Illinois, Urbana-Champaign, Chair of ECE, University
of California, Irvine and Dean of Engineering, the Ohio State University


We focus on the decision-making aspect of artifi cial intelligence in cyber-physical systems. According to NSF, cyberphysical
systems are engineered systems that are built from and depend upon, the seamless integration of computational
algorithms and physical components. Generally, there are multiple decision-making entities (multi-agents) in such systems.
We highlight the presence of multi-agents with diff erent objectives by defi ning these as complex cyber-physical systems
(CCPS). Multi-agents are implemented as intelligent computers. Th ere is relatively less attention on multiple decision-making
entities in the cyber-physical systems and in the artifi cial intelligence literatures. In this presentation we attempt to stimulate
the artifi cial intelligence community to broaden the scope of their research to include work on CCPS. Much of the underlying
basis of CCPS is nonzero-sum dynamic game theory. With very few exceptions, the theory involves simplifying assumptions
that result in problems that are mathematically tractable. In the fi rst part of the presentation, we provide an overview of these
results. In the second part of the presentation we explore trends on how some of the simplifi cations might be relaxed to increase
the scope of applications. One simplifying assumption is that the CCPS has a single mathematical model that is known to all
decision-making entities. In realistic applications, each decision-making entity constructs its own mathematical model, known
only to itself, of the same CCPS. Another simplifying assumption is that each decision-making entity knows the objective
function of each decision-making entity. We consider a parameterization concept to relax these assumptions. We illustrate
parameterization in an electric power network

Keynote Forum

Fuchiang (Rich) Tsui

University of Pittsburgh School of Medicine, USA

Keynote: Predictive Modeling and its Applications in Healthcare

Time : 11:50-12:35

OMICS International Automation and Robotics 2017 International Conference Keynote Speaker Fuchiang (Rich) Tsui photo

Fuchiang Tsui has received his PhD in Electrical Engineering, Premed training and Postdoctoral training in Biomedical Informatics at the University of Pittsburgh,
USA. He is the Director of the Tsui Laboratory. He has published more than 100 peer-reviewed papers and has been working in healthcare analytics for more than
20 years. His research interests include clinical informatics, population informatics, machine learning, data mining, natural language processing, mobile healthcare,
data warehouse and large real-time production systems.


Now, more than ever, Electronic Health Records (EHRs) are generated in large quantities and in diverse contents. Th is
explosion of information has naturally enabled powerful patient data analyses to potentially improve healthcare. Th is talk
will review several on-going research projects focusing on predictive modeling from structured and unstructured EHR data
and real-time production systems deployed at hospitals that we have developed in the Department of Biomedical Informatics,
University of Pittsburgh (Pitt). We have developed Bayesian networks and utilized machine learning methods in conjunction
with natural language processing to predict 30-day hospital readmissions, detect infectious diseases from emergency
department visits, classify the severity of psychiatric reports; we will also report our pilot study on infant mortality predictive
modeling based on various EHR data and non-EHR information. We have developed several production systems deployed at
the University of Pittsburgh Medical Center (UPMC) that provide daily infl uenza surveillance reports, real-time laboratory
reporting and event-driven based 30-day readmission risk prediction; we also developed a national retail data monitor system
at Pitt, that monitors over-the-counter medicine sales on a daily basis from 30,000+ retail stores in the US. I will demonstrate
one of our currently deployed production systems, the System for Hospital Adaptive Readmission Prediction and Management
(SHARP). Th is is integrated into the EHR system in place at the Children's Hospital of Pittsburgh of UPMC and demonstrates
how the research we have developed can translate directly into practice.

Keynote Forum

Ryspek Usubamatov

Kyrgyz Technical University, kyrgyzstan

Keynote: MBGT-Mathematical Basis of Gyroscope Theory

Time : 12:35-13:20

OMICS International Automation and Robotics 2017 International Conference Keynote Speaker Ryspek Usubamatov photo

Ryspek Usubamatov has completed his graduation as Professional Engineer, PhD from Bauman Moscow State Technical University and Doctor of Technical Sciences
from Academy of Sciences of Kyrgyzstan. He has worked as an Engineer-Designer of machine tools at engineering company. He is a Professor at Kyrgyz
State Technical University and worked at universities in Malaysia. He has published more than 300 papers in reputed journals, more than 60 patents of inventions
in engineering and seven books in area of manufacturing engineering. His research interests include gyroscope theory and productivity theory for industrial engineering.


Gyroscope devices are primary units for navigation and control systems in engineering. Th e main property of the gyroscopic
device is maintaining the axis of a spinning rotor for which mathematical models have been formulated on the changes in
the angular momentum. However, known theories for the gyroscope eff ects do not match actual forces and motions underway.
Th e nature of the gyroscope properties is more complex than represented by contemporary theories. Recent investigations
have demonstrated that gyroscope’s spinning rotor with symmetrical location of the supports, have four basic inertial forces
interdependently and simultaneously acting on them around two axes. Th ese forces are generated by the mass elements and
center mass of the spinning rotor and represented by centrifugal, Coriolis and common inertial forces as well as changes
in angular momentum. Th e applied torque generates internal resistance torques that based on the action of centrifugal and
Coriolis forces and the precession torques generated by common inertial forces and by the change in the angular momentum.
Th e new mathematical models for gyroscope eff ects describe clearly and exactly the physics of all known and new gyroscope
properties. New analytical approach for the most unsolvable motions of the gyroscope is validated by practical tests. Formulated
mathematical models for acting torques in the gyroscope represent fundamental principles of gyroscope theory based on the
action of inertial forces of classical mechanics. Mathematical models for gyroscope forces and motions can be solved manually.
Th is new theoretical approach for the gyroscope problems represents new challenge in engineering science.