Merging Future Knowledgebase System of Systems with Artificial Intelligence/Machine Learning Engines to Maximize Reliability and Availability for Decision Support

Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

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AI for augmenting human judgement in Business Processes Management

Scientific Journal of the Military University of Land Forces ◽

10.5604/01.3001.0015.3404 ◽

2021 ◽

Vol 201 (3) ◽

pp. 507-518

Author(s):

Łukasz Osuszek ◽

Stanisław Stanek

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Support ◽

Business Processes ◽

Workflow Management ◽

Organizational Memory ◽

Machine Learning Algorithms ◽

Analytical Models ◽

Case Based Reasoning ◽

Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

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Unlocking the Power of Artificial Intelligence and Big Data in Medicine

Journal of Medical Internet Research ◽

10.2196/16607 ◽

2019 ◽

Vol 21 (11) ◽

pp. e16607 ◽

Cited By ~ 8

Author(s):

Christian Lovis

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Decision Support ◽

Data Science ◽

Global Environment ◽

Data Driven ◽

Cross Linking ◽

Science And Society ◽

Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

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Artificial Intelligence Based Architecture to Support Dementia Patients

Volume 2B: Advanced Manufacturing ◽

10.1115/imece2019-10985 ◽

2019 ◽

Author(s):

Fernando Luís-Ferreira ◽

João Sarraipa ◽

Jorge Calado ◽

Joana Andrade ◽

Daniel Rodrigues ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Support ◽

Intelligent Systems ◽

User Interaction ◽

Special Kind ◽

Target Population ◽

Interaction Data ◽

Dementia Patients ◽

Physiological Assessment

Abstract Artificial Intelligence is driving a revolution in the most diverse domains of computational services and user interaction. Data collected in large quantities is becoming useful for feeding intelligent systems that analyse, learn and provide insights and help decision support systems. Machine learning and the usage of algorithms are of most importance to extract features, reason over collected data so it becomes useful and preventive, exposing discoveries augmenting knowledge about systems and processes. Human driven applications, as those related with physiological assessment and user experience, are possible especially in the health domain and especially in supporting patients and the community. The work hereby described refers to different aspects where the Artificial Intelligence can help citizens and wraps a series devices and services that where developed and tested for the benefit of a special kind of citizens. The target population are those under some kind of Dementia, but the proposed solutions are also applicable to other elder citizens or even children that need to be assisted and prevented from risks.

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Unlocking the Power of Artificial Intelligence and Big Data in Medicine (Preprint)

10.2196/preprints.16607 ◽

2019 ◽

Author(s):

Christian Lovis

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Decision Support ◽

Data Science ◽

Global Environment ◽

Data Driven ◽

Cross Linking ◽

Science And Society ◽

Slowing Down

UNSTRUCTURED Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound, with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

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AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/957 ◽

2019 ◽

Author(s):

Tiago Pinto ◽

Zita Vale

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Support ◽

Electricity Markets ◽

Evolutionary Computing ◽

Multi Agent System ◽

Agent System ◽

Agent Based ◽

Multi Agent ◽

Competitive Electricity Markets

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

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Autonomous Collision Avoidance at Sea: A Survey

Frontiers in Robotics and AI ◽

10.3389/frobt.2021.739013 ◽

2021 ◽

Vol 8 ◽

Author(s):

Hans-Christoph Burmeister ◽

Manfred Constapel

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Support ◽

Collision Avoidance ◽

Data Science ◽

Autonomous Systems ◽

Data Driven ◽

Comprehensive Overview ◽

Planning Methods ◽

Survey Results

In this survey, results from an investigation on collision avoidance and path planning methods developed in recent research are provided. In particular, existing methods based on Artificial Intelligence, data-driven methods based on Machine Learning, and other Data Science approaches are investigated to provide a comprehensive overview of maritime collision avoidance techniques applicable to Maritime Autonomous Surface Ships. Relevant aspects of those methods and approaches are summarized and put into suitable perspectives. As autonomous systems are expected to operate alongside or in place of conventionally manned vessels, they must comply with the COLREGs for robust decision-support/-making. Thus, the survey specifically covers how COLREGs are addressed by the investigated methods and approaches. A conclusion regarding their utilization in industrial implementations is drawn.

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