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.

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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.

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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.

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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.

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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.

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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.

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