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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AI-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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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 MRI dataset of normal and diseased subjects. It classified the database of 111 subjects into Mild Cognitive Impairment (MCI), Alzheimer’s Disease (AD), and Normal classes.

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David 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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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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The 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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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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Influence 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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Sensor 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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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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Improving 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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ChemSuite: 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.

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Big 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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Medical Artificial Intelligence Ethics 101: Choosing Between Utilitarianism, Moral Absolutism, and Virtue

 Artificial Intelligence (AI) continues to gain traction as the next zenith of technological development in the medical field. As medical AI grows in scope of application and complexity questions about its ethical application likewise grow in scope complexity. There are currently three predominant competing theories about the proper ethical discourse surrounding the application of AI in medicine; utilitarianism, moral absolutism, and virtue ethics.

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Applications 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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Investigating 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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The 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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Many 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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Road 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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