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. 

To know more about this visit our Conference AI 2020

AI-Virtual The 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.

To know more about this visit our Conference AI 2020

Deep Learning-based automatic detection of Alzheimer’s Disease using Magnetic Resonance Images

Alzheimer’s Disease (AD) is a progressive mental deterioration and an 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.

To know more about this visit our conference AI 2020

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. 

To know more about this visit our Conference AI 2020

Concept Drifting in Big Data

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 in 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 the 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.

To know more about this visit our Conference AI 2020

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 IoT), video summarization or search, self-driving/autonomous vehicles, smart appliances/home/city, biometrics, tracking, and security.

To know more about this visit our conference AI 2020

Health data analytics: Making sense of data in complex healthcare systems

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.

To know more about this visit our conference AI 2020

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 social institutions.

To know more about this visit our Conference AI 2020

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, the classification of salts helps to determine the types of salts which are 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.

To know more about this visit our conference AI 2020

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.

To know more about this visit our Conference AI 2020

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. 

To know more about this visit our conference AI 2020

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 the 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.

 To know more about this visit our conference AI 2020

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 the 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 the 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.

To know more about this visit our conference AI 2020

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 the 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.

 Each theoretical model brings with it certain unique opportunities and limits. This proposed workshop would delineate each of these prevailing theoretical models, detail their unique usefulness, and invite participants to dialogue about their practical application through the use of case studies.

To know more about this visit our Conference AI 2020 

Applications of Artificial Intelligence for analysis of floaters, cataracts, cells of the 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. The human retina has, in general, the following five problems: 1. Floaters, 2.Macular degeneration, 3.Diabetic Eye disease,4.Retinal Detachment,5.Retinitis  Pigmentosa.

To know more about this visit our Conference AI 2020 

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.

To know more about this visit our Conference AI 2020 

Deep Learning through Mobile Edge Commuting in IoT based devices

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 the 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 that 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 problem, and also real-time computing may not be possible. 

To know more about this visit our Conference AI 2020

Building Autonomous Data for the Enterprise with Data Fulcrum

Many years ago, the Data architecture included structured normalized and de-normalized 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 the current state).

However in 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.

To know more about this visit our Conference AI 2020

Estimating the severity of road  traffic accidents using deep learning

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.

To know more about this visit our Conference AI 2020

Suspect Identification in a Crime Scene behind visual backgrounds using Pattern-based Voice Recognition System

According to the latest statistics by the National Crime Records Bureau (NCRB), crime rates in India have been drastically increasing year by year. And some of these crimes may have taken months to prove because of a lack of evidence found on the crime scene. So we have designed a novel architecture that identifies the suspects based on the voice and pitch patterns using deep learning architecture. In our proposal, we have built an architecture based on different feature extraction techniques and build a siamese network for deep learning classification. In the process, we have read the case studies of different researchers and prepared our architecture from the previous drawbacks.

To know more about this visit our conference AI 2020

Machine Learning based Predictive Maintenance of the failure of Industrial equipment using its electrical signature

The utilization of the AI for predictive maintenance of equipment is one of the key propositions in the Industry-4.0 framework. The industrial equipment consumes electricity while performing the production task. Industries are using active power for paying electrical bills and managing the cost. However smart meter deployed in the industry has information about other electrical signatures like power factor and reactive power etc. The values of these parameters in electrical signature depend upon the production load and health of the equipment. This talk will throw light on how the electrical signature can be used to do predictive maintenance using a standard machine learning algorithm. A case study will also be presented in this regard.

To know more about this visit our conference AI 2020

AI in Foreign Language learning

Artificial intelligence (AI) is becoming an integral part of manufacturing, eCommerce, FinTech, education, medicine, electric power, and automotive industries in our daily lives. It is time to implement AI in foreign language learning and pave the way for personalized education. The Institute of Foreign Languages (RUDN University) began to use neural network capabilities and AI in teaching Ph.D. students.

AI is integrated into the learning process. Thanks to using it, the needs of each Ph.D. student can be taken into consideration. Therefore, educators collect data about learners, their abilities, interests, projects, problems, and scientific researches.

To know more about this visit our Conference AI 2020

AI in Foreign Language learning

Artificial intelligence (AI) is becoming an integral part of manufacturing, eCommerce, FinTech, education, medicine, electric power, and automotive industries in our daily lives. It is time to implement AI in foreign language learning and pave the way for personalized education. The Institute of Foreign Languages (RUDN University) began to use neural network capabilities and AI in teaching Ph.D. students.

AI is integrated into the learning process. Thanks to using it, the needs of each Ph.D. student can be taken into consideration. Therefore, educators collect data about learners, their abilities, interests, projects, problems, and scientific researches.

To know more about this visit our Conference AI 2020

Data Science, Bi, Big Data and AI for Corporate growth formula

AI, Data Science, BI Big Data are becoming an essential part of Industries, Business today in this 21st Century. We can Leverage the power of these capabilities to boost Healthcare, Telco, Banking, and Financial Institutions, Here with the help of data science and BI, the signals to growth and reason to loss are analyzed and predicted using a Big data and Data warehouse architectures since, Volume, Velocity and a Variety of Data is growing each fraction of a second. This era is a time of Real time Data drove Decision-making process. Any critical information we miss to capture, we might lose to capture and retention of people surrounded. We will propose an optimized Data Pipelines and Data Science to Social Network Analysis Approached for the real-time business which will impact from cost to adaptation.

To know more about this visit our Conference AI 2020

Estimating and Visualizing the precise position of an aerial swarm

The mechanical devices which are not in contact with the earth's surface physically and located in the air are aerial objects. A large group of such aerial objects is called an aerial swarm. Estimating and visualizing the precise position of an aerial swarm and each individual swarm particle in real-time is one of the most challenging tasks and cannot be achieved by a single framework. This paper addresses this issue by estimating and visualizing the precise position of a swarm. To achieve the desired target, XBEES, LABVIEW, LIDAR, and GPS modules are used for the estimation purpose.

To know more about this visit our Conference AI 2020

Explainable Artificial Kinesthetic Intelligence

Researchers are making progress toward creating machines like humans. Their pursuit is divided into two parts. First is developing Digital Minds so that machines can think and reason like us. Second is focused on developing bodies with Digital Minds so that machines can also act like humans and skillfully carry out physical activities in their environments. Both combined is a machine closer to humans, similar to Sophia, having Artificial Kinesthetic Intelligence. The future is that humans and Machines will live and work together as citizens. But many are concerned about what will humans do in the future, will machines make them redundant or could machines take over human race. Building upon the basics with a roadmap of future, in this talk, we attempt to address the concerns of adversaries, which in our view are mainly borrowed from the science fiction and because behavior and actions of machines remain unexplained, and discuss that a predictable pattern could develop confidence in our co-existence with machines. 

To know more about this visit our Conference AI 2020

Implementation of Self Organizing Map Neural Network clustering to derive Empathy and Adoption Criterion of Compost Sample

The artificial intelligence self-organizing neural network is a supervised or unsupervised kind of attempt to find features and similarities in high dimension data that characterizes the data. SOM is a learning algorithm both supervised and competitive unsupervised, specifying the behavior of a node impact only those nodes and arcs near to it to uncover the hidden patterns in data. Since, compost sample consists of several mineral nutrients consisting of both light and heavy weighted metals. Out of these metals some are beneficial and some may cause terrible harm to the development of crops. Finding prediction range for such critical high dimension sample data like compost is an entail. This paper introduces the implementation process of a self-organizing neural network on the compost sample data to characterize each mineral nutrient according to a desirable value.

To know more about this visit our Conference AI 2020

Implementation of Self Organizing Map Neural Network clustering to derive Empathy and Adoption Criterion of Compost Sample

The artificial intelligence self-organizing neural network is a supervised or unsupervised kind of attempt to find features and similarities in high dimension data that characterizes the data. SOM is a learning algorithm both supervised and competitive unsupervised, specifying the behavior of a node impact only those nodes and arcs near to it to uncover the hidden patterns in data. Since, compost sample consists of several mineral nutrients consisting of both light and heavy weighted metals. Out of these metals some are beneficial and some may cause terrible harm to the development of crops. Finding prediction range for such critical high dimension sample data like compost is an entail. This paper introduces the implementation process of a self-organizing neural network on the compost sample data to characterize each mineral nutrient according to a desirable value.

To know more about this visit our Conference AI 2020