
PROFESSIONAL EVENT SPEAKERS

Mr. David Tareen
Global AI product Marketing Director Raleigh-Durham, North Carolina Area.Lessons learned from 5 real AI deployments around the world
What happens when you deploy 5 very real and diverse AI projects around the world? This talk will cover the stories of five real use cases including Volvo trucks, Swisscom, large European retailer and others. Each use case will go over the customer, their business problem they were trying to solve with AI, the process of how the solution was developed, the outcome and finally, a lesson learned that can be applied to a broad set of AI projects around the world. The technologies covered in this talk will include AI, machine learning, deep learning, computer vision, natural language processing, Internet of Things (IoT) and forecasting.
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Prof.Christopher
Professor Fresenius University, GermanyAI-Virtual
Reality in Refugee Aid / Government Integration
Program
This overview
is intended to present the previous efforts in the field of AI and virtual
reality in refugee aid on the one hand, and to give an outlook on the upcoming
studies of the working group on the other hand.
AI-VR for the relief of voluntary helpers
With approx. 1.9 million asylum applications since 2010 in the whole of Germany, the federal states have an enormous number of procedures to process and high integration work to perform.
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Mr.MUHAMMED RAEES PC
Department of Electronics, Govt. Model Engineering College, Kochi, India.Deep
Learning based automatic detection of Alzheimer’s Disease using Magnetic
Resonance Images
Alzheimer’s Disease (AD) is a progressive mental deterioration and incurable
neurodegenerative disease that can occur in middle or old age, due to
generalized degeneration of the brain. Because of the irreversible nature of
the progression of Alzheimer’s Disease, the early diagnosis of AD has an
immense clinical, social and economic need. This research output is proposing a
state-of-the-art, easy and early automated deep learning based system to predict AD from a large
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Prof. David J. Gunkel
Distinguished Teaching Professor Department of Communication Northern Illinois University - USADavid J. Gunkel is an award-winning educator
and scholar, specializing in ethics and emerging technology. He is the author
of over 75 scholarly articles and has published twelve books, including Thinking Otherwise: Philosophy, Communication,
Technology (Purdue University Press 2007), The
Machine Question: Critical Perspectives on AI, Robots, and Ethics (MIT
Press 2012), Robot Rights (MIT
Press 2018), and An Introduction to
Communication and Artificial Intelligence (Polity Press 2019). He has lectured
and delivered award-winning papers throughout North and South America and
Europe and is the founding co-editor of the International
Journal of Žižek Studies and the Indiana University Press book series
Digital Game Studies. Dr. Gunkel currently holds the position of Presidential
Teaching Professor in the Department of Communication at Northern Illinois
University, USA.
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Dr. Sarah Kohail
Business data scientist at Bechtle AG.We live in
a dynamic world, where changes
are a part of everyday
life. When there
is a shift in data, the
classification or prediction models need to be adaptive to the changes. In data
mining, the phenomenon of change in data distribution over time is known as
concept drift.
For example,
click-streams of user's navigating news website may reflect the preferences of
reading through the analysis systems. When people's interest of reading change,
however, the old user's behaviour model is not applicable any more than the
drifting of concepts appears. Traditional approaches don’t take into consideration
the fact that user’s interests keep on changing with respect to time and latest
reading behaviour, which reflects the user’s current interest, should be given
more importance over old behaviour.
The same applies to many other non-stationary data like stock marketing, weather and customer preferences.
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Bijan Tadayon, PhD, JD
CEO Z Advanced Computing, Inc. (ZAC), Potomac, MD, USAThe first 3D image recognition and search platform, based on Explainable-AI (called ZAC)
ZAC is the first
3D image recognition & search platform, based on ZAC Explainable-AI. The
platform mimics how humans discover, recognize, and learn. It requires only a
few training samples, with reusable, modular, cumulative and scalable learning,
enabling the technology to detect fine details in images taken at various
camera angles (3D).
There are many vertical applications, e.g., for medical imaging/diagnosis, e-commerce/retail, search engine, referral and ad networks, social networks, learning machines, data mining, knowledge extraction & analytics, big data (e.g., generated from sensors, cameras, wearables, or IoTs), video summarization or search, self-driving/autonomous vehicles, smart appliances/home/city, biometrics, tracking, and security.
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conference AI 2020

Dr.Lalit Garg,
Faculty of Information and Communication Technology, University of Malta.Healthcare systems involve many stakeholders, interfaces institutions and data collection sources. A good understanding of complex healthcare systems dynamics and underlying factors affecting it is important for health managers and Policymakers to develop policies to ensure the quality of care delivery and optimum utilization of scarce health resources. There are numerous internal and external factors that can affect the health care systems.
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Dr. Shubh Gupta
Ph.D, CSSP, JNUInfluence of Good Artificial Intelligence in shaping the
Social Order
Every society is threaded by some norms, values, and beliefs which checks an individual’s action in society. These shared values collectively shape the “Social Order” (Frank, 1944) which connects an individual with the society by the process of socialization and internalization. In every era of industrial revolution technology has influenced the existing social order and arranged the social institutions.
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Dr. Lina Rose
Assistant Professor, Karunya Institute of Technology and Sciences, Tamil Nadu, IndiaSensor Data Classification using Machine Learning Algorithm
In water different types of salts are present which may be healthy or poisonous. Salts such as sodium in drinking water are an essential mineral in our diet. On the other hand, salts such as arsenic are very harmful. In this work, classification of salts helps to determine the types of salts which is present in the water. Therefore this helps in the desalination of harmful salts. The application of the classification of salts will be useful in industrial purposes, household uses.
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Dr. Adeola Lawal
Ladoke Akintola University of Technology Ogbomoso, Oyo State, Nigeria.BREAST CANCER DETECTION AND CLASSIFICATION USING THE SVM MACHINE LEARNING ALGORITHM.
Breast cancer is the most frequent cancer among women, impacting 2.1 million women each year, and also causes the greatest number of cancer-related deaths among women. There is a total estimate of 627,000(15%) women who died from breast cancer in the year 2018. Mammography screening which is one of the most effective and efficient ways of breast cancer detection provided by the medical field is not totally reliable as it has been reported to have possibilities of leading to inconclusive test results.
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Ms.Andjela Todorovic
Faculty of Sciences and Mathematics,University of Nis, Department of Computer Science, Nis, SerbiaImproving Advanced Object Detection Algorithms
in Automatic Tag Suggestion and Content Categorization Using Content-Based Image
Retrieval
Deep
learning is a division of artificial intelligence where networks of simple
interconnected units are utilized to extract patterns from data to solve
complex problems. Deep learning algorithms have shown groundbreaking
performance in a variety of sophisticated tasks, especially those related to
computer vision.
Object detection algorithms focus on classifying different images but also try to precisely estimate the locations of objects contained in each image, either by region or bounding box. In the field of automatic content categorization and image tagging, it is customary to fine-tune the existing object detection algorithms (YOLO, R-CNN) for the given dataset and derive the detected class names as possible tags.
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Dr. Sujit R. Tangadpalliwar
National Institute of Pharmaceutical Education and Research (NIPER), Mohali, IndiaChemSuite: A package for
chemoinformatics calculations and machine learning
Prediction of
biological and toxicological properties of small molecules using in silico
approaches have become a wide practice in pharmaceutical research to lessen the
cost and enhance productivity. The development of a tool “ChemSuite,” a
stand-alone application for chemoinformatics calculations and machine learning
model development, is reported. The availability of multi-functional features makes
it widely accepted in various fields. Force field such as UFF is incorporated
in tool for optimization of molecules. Packages like RDKit, PyDPI, and PaDEL
help to calculate 1D, 2D and 3D descriptors and more than 10 types of
fingerprints. MinMax Scaler and Z-Score algorithms are available to normalize
descriptor values. Varied descriptor selection and machine learning algorithms
are available for model development.

Dr. Yousef Farhaoui
Department of Computer Science | Faculty of sciences and Technic | Moulay Ismail UniversityBig
Data and Internet of Things for Air quality prediction:
Today,
the climate change and its harmful effects on the environment represent a major
concern for world. Indeed, this is clearly manifested in the organization of
the international conference COP22 on the environment which took place from 7
to 18 November in Marrakech.
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.
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Dr. D. Doyle-Burke
University of Denver, Daniels School of Business, Denver, USAMedical Artificial Intelligence Ethics 101: Choosing Between Utilitarianism, Moral Absolutism, and Virtue
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Dr. Sujoy Seal
Department of Computer Science and Engineering, Institute of Engineering & Management, KolkataApplications of Artificial Intelligence for analysis of floaters, cataracts, cells of non-responsive retina: A proposal
The following paper presents a pantheon of proposed solutions to eye problems using a hybrid combination of Opthalmological[1] sciences along with engineering technologies like swarm intelligence,robotics,artificial and convolutional neural networks.In this paper we claim that these must provide optimal and more accurate results than recorded in our present statistics.This would be particularly helpful when surgery has risks.Human retina has in general the following five problems: 1. Floaters, 2.Macular degeneration, 3.Diabetic Eye disease,4.Retinal Detachment,5.Retinitis Pigmentosa.
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Dr. M.H. Anjom SHoa
University Of Vali-e-Asr of rafsanjan, Department of Mathemathics, Rafsanjan, IraanInvestigating the Hidden Operations Management Components in Universities Considering Organizational Architecture for Hybrid Structures
Given the shift in approach to creating, managing or controlling crises that have turned from one-dimensional to hybrid and hybrid. Organizational architecture, with a holistic and comprehensive description of the parameters of hidden operations in universities, can play a major role in controlling and countering hidden operations in universities as one of the most important parts of society. Therefore, according to the research findings, the components of the Zuckman table for hidden operation parameters in universities have been identified and it has been shown in another section that a single pattern and one parameter cannot be assumed at the time of occurrence or execution of one of the hidden operation parameters in the university.
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Dr. A. Sufian
University of Gour Banga, West Bengal, IndiaThe recent success of Artificial Intelligence (AI) especially deep learning and computer vision inspire many researchers to works on many real world’ case studies for humankind. In near future a huge number of IoT based Unmanned Aerial Vehicle (UAV) or drones will be used for many purposes. Therefore, smart AI computing systems need to be developed to efficiently use and augment the applications of those IoT based UAV for humankind. Most of the UAV shall have less computing power as well as less memory, so cloud commuting shall be required. But for visual commuting, a huge number of image or video data need to be transferred to a cloud machine for commuting. So bandwidth scarcity shall be a huge problems, and also real time computing may not be possible.
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Dr. Niranjana Mangaleswaran
BI & Advanced Analytics Practice LeadMany years ago, the Data architecture included structured
normalised and de-normalised data, in a semantic layer, being queried by a BI
layer on top of it, providing descriptive analytics (Visualisation of as-is
state for business to understand the past/existing state) and sometimes
diagnostic analysis (Root cause identification on why the business is in
current state).
However in the recent years, the Enterprise data landscape
is becoming more and more complex and the Data (Structured, semi-structured and
unstructured) is increasing exponentially, due to its explosion from scattered,
isolated, unrelated, heterogeneous data sources, leading to the risk of
information overload.
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Mr. R. Ranjan
IIIT Senapati, Department of Computer Science and Engineering, Manipur, IndiaRoad accidents have severe impact on society which cause lots of fatalities
and injuries. The road accidents are major source of deaths, property damage
and casualties throughout the globe every year. According to National Crime
Records Bureau (NCRB) 2016 report states that there 464,674 collisions which
caused 148,707 deaths in India. Deep learning has great potential in learning
patterns and in dealing with real life data related to road traffic accidents. To address this significant issue we
took a stab at evaluating the severity of road traffic accidents using deep
learning. Through this undertaking, we additionally attempted to visualize a
legitimate perception to evaluate the underlying root cause of the road traffic
accidents.
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