AI News, June/July 2019 Vol. 33, No. 5 artificial intelligence

Kyriakos G. Vamvoudakis - Publications

Adaptive Control and Differential Games by Reinforcement

Learning Principles, Control Engineering Series, IET Press, 2012.

“Optimal Online Adaptive Controller,”

to appear in IEEE Transactions on Automatic Control, 2020.

“Dynamical Estimation and Optimal Control of Vehicle Active Suspensions,”

“Distributed Output-Feedback Model Predictive Control,”

“Open-Loop Stackelberg Learning Solution for Hierarchical Control Problems,”

“Optimal and Autonomous Control Using Reinforcement Learning: A Survey,”

“Multi-Agent Discrete-Time Graphical Games and

Automatica, vol.

“Online Learning Algorithm for Optimal Control

Nonlinear Control, vol.

“A

Novel Actor-Critic-Identifier Architecture for

Approximate Optimal Control of Uncertain Nonlinear Systems,”

“Reinforcement Learning and Feedback Control:

is consistently listed in the Top 50 Most Accessed Articles, IEEE Control Systems, 2013-present) [45] K.

“Multi-Agent

Differential Graphical Games: Online Adaptive Learning

Solution for Synchronization with Optimality,”

was No.

Elsevier, September 2012) [46] K.

“Online

Neural Network Solution of Nonlinear Two-Player Zero-Sum

Games Using Synchronous Policy Iteration,”

International Journal of Robust and Nonlinear Control,

Control of Mixed-Interlaced forms,”

Backstepping Neural Network Control for Mechanical Pumps,”

2012 Issue, pp.

Vamvoudakis, D.

Adaptive Learning for Team Strategies in Multi-Agent

Systems,”

“Online

Learning Algorithm for Zero-Sum Games with Integral

Reinforcement Learning,”

Intelligence and Soft Computing Research, vol.

“Multi-Player

Non Zero-Sum Games: Online Adaptive Learning Solution of

Coupled Hamilton-Jacobi Equations,”

No.

Elsevier, September 2011) [52] F.

Vamvoudakis,

“Reinforcement

Learning for Partially Observable Dynamic Processes:

Adaptive Dynamic Programming Using Measured Output Data,”

Top Article in Computational Intelligence, BioMedLib,

February 2011-2017) [53] K.

Actor-Critic Algorithm to Solve the Continuous-Time

Infinite Horizon Optimal Control Problem,”

vol.

“Model-Free Reinforcement Learning-Based Control for Continuous-Time Systems,”

“Neuro-Inspired Control,”

“Entropy-Based Proactive and Reactive Cyber-Physical Security,”

“Adaptive H-infinity Tracking Control of Nonlinear Systems Using Reinforcement Learning,”

“Model-Free Learning of Nash Games with Applications to Network Security,”

“Introduction to Complex Systems and Feedback Control,”

“Reinforcement Learning with Applications in Automation Decision and Feedback Control,”

“Past, Present and Future of Control Theory Applied to Autonomous Vehicles/Agents in Severe Conditions,”

“Terrorist Threats, Agile Vehicle Trajectory Deviation and Critical Reposition in Interaction with Environment,”

“New Methods and Techniques in Online Control and Learning,”

“Neural Control and Approximate Dynamic Programming,”

“Neural Networks in Feedback Control Systems,”

Myer Kutz, Chapter 23, John Willey, NY, 2015.

“Online Learning Algorithms for Optimal Control

Learning and Approximate Dynamic Programming for

Feedback Control, eds.

“An Actor-Critic-Identifier Architecture for

Learning and Approximate Dynamic Programming for

Feedback Control, eds.

“Online

Adaptive Learning Solution of Multi-Agent Differential

Graphical Games,”

Control Systems, ed.

INTECH, 2016) [17] K.

“Online

Gaming: Real Time Solution of Nonlinear Two-Player

Zero-Sum Games Using Synchronous Policy Iteration,”

Abdelhamid Mellouk, Chapter 18, INTECH, 2011.

INTECH, 2014) [18] K.

“Online

Synchronous Policy Iteration Method for Optimal Control,”

“Advances in Reinforcement Learning Structures for Continuous-Time Dynamical Systems,”

Security and Sensing, Baltimore, MD, 2013.

(invited paper)

Decision and Control, pp.

and Control, pp.

Multi-Conference on Systems and Control, pp.

Optimal Control Algorithm for Zero-Sum Nash Games with

Integral Reinforcement Learning,”

Guidance, Navigation, and Control Conference,

Iteration Algorithm for Distributed Networks and

Graphical Games,”

Decision and Control, pp.

(invited paper)

Sum Games: Online Learning Solution of Coupled

Hamilton-Jacobi and Coupled Riccati Equations,”

IEEE Multi-Conference on Systems and Control, pp.

171-178, Denver, CO, 2011.

Differential Graphical Games,”

Control Conference, pp.

“Online Learning of Optimal Control Solutions

and Learning Systems, Yale University, 2011.

Adaptive Learning of Optimal Control Solutions Using

Integral Reinforcement Learning,”

Symp.

Backstepping Neural Network Control for Mechanical Pumps,”

Proc.

Conference (CHAOS), pp.

Control of Mixed-Interlaced forms,”

Chaotic Modeling and Simulation International Conference (CHAOS),

Solution of Nonlinear Two-Player Zero-Sum Games Using

Synchronous Policy Iteration,”

Conference on Decision and Control, pp.

Games for Multi-Agent Systems: Games on Communication

Graphs,”

award.) [62] G.

G.

Gaming for Learning Optimal Team Strategies in Real Time,”

Security and Sensing, vol.

Vamvoudakis,

“Optimal

Adaptive Control for Unknown Systems Using Output

8th IEEE International Conference on Control &

Automation, pp.

Atlanta, GA, 2009.

Optimal Controllers Based on Generalized policy

iteration in a Continuous-Time Framework,”

IEEE Mediterranean Conference on Control and Automation, pp.

Thessaloniki, Greece, 2009.

Vamvoudakis,

Backstepping Control for MAPK Cascade Models Using RBF

Neural Networks,”

Automation, Athens, Greece, 2007.

Vamvoudakis, M.

Interlaced and Mixed Interlaced Adaptive Nonlinear

Control for Biological Models,”

Intelligent Systems and Computing: Theory and

Applications Conference (ISYC 06), pp.

Ayia Napa, Cyprus, 2006.

“Chairs Report on the Workshop: Distributed Reinforcement Learning and Reinforcement-Learning Games,”

“Proceedings of the 8th International Conference on the Internet of Things (IoT),”

Vamvoudakis,

Vamvoudakis,

Vamvoudakis,

Artificial intelligence in healthcare

Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data.

What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user.

AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can't adjust itself and only understand what it has been told explicitly, (2) and algorithms are black boxes;

AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care.

to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs.[8]

that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.[9]

During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians.[14]

The ability to interpret imaging results with radiology may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss.

A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.[25]

The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists.[26][27]

Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.[28][29]

In 2018, a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by dermatologists.

On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.[30]

One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline.[citation needed]

To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature.

Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.[40]

Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports.[36][37]

The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility.[43]

A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.[53]

Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.[54]

team associated with the University of Arizona and backed by BPU Holdings began collaborating on a practical tool to monitor anxiety and delirium in hospital patients, particularly those with Dementia.[64]

The AI utilized in the new technology – Senior's Virtual Assistant – goes a step beyond and is programmed to simulate and understand human emotions (artificial emotional intelligence).[65]

Doctors working on the project have suggested that in addition to judging emotional states, the application can be used to provide companionship to patients in the form of small talk, soothing music, and even lighting adjustments to control anxiety.

Virtual nursing assistants are predicted to become more common and these will use AI to answer patient's questions and help reduce unnecessary hospital visits.

Overall, as Quan-Haase (2018) says, technology “extends to the accomplishment of societal goals, including higher levels of security, better means of communication over time and space, improved health care, and increased autonomy” (p. 43).

While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues.

We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.”[75]

As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.[77][78]

November/2019: Dr. Evelio Velis, a professor at Bary University, USA and a long-term ESJ reviewer and senior editor, has been assigned an ESJ associate editor.

For details on the submission process Click here February/2019: ESJ Reviewers of the year 2018:Mary Kathryn Mc VeyFranciscan University of Steubenville, USARania Mohamed HassanUniversity of Montreal, CanadaPaul Waithaka MahingeKenyatta University, KenyaEvelio VelisBarry University, USANirmal Kumar BetchooUniversity of Mascareignes, MauritiusArianna Di VittorioUniversity of Bari “Aldo Moro”, Italy January/2019: 24 January, 6pm (Central European Time): Online meeting of the ESJ editorial office and senior editors.

May/2014: European Scientific Institute, ESI, Promo Videohttps://www.youtube.com/watch?v=Oxn_67BwJfw April/2014: Cooperation with the Middle East University, Jordan March/2014: Cooperation with the University of Almeria, Spain February/2014: Cooperation with the University of Flores, Buenos Aires, Argentina January/2014: United Nations Academic Impact, membership December/2013: Damanhour University, Egypt, partnership November/2013: Memo of Understanding with IBAIS University, Bangladesh November/2013: Partnership established with University of Madeira, Portugal November/2013: Partnership established with UNESCO (World`s Science Day celebration) November/2013: Memo of understanding with Mykolas Romeris University, Lithuania October/2013: Memo of understanding with Cracow University of Economics, Poland October/2013: Memo of understanding with European University of Tirana, Albania September/2013: Memo of understanding with Santiago University, Cape Verde August/2013: Academic Cooperation established with Arab Open University, Lebanon July /2013: Memo of understanding with Botswana Accountancy College, Botswana June /2013: Memo of understanding with the European University of Tirana, Albania May /2013: ESJ`s Index Copernicus Value, ICV, 2011 = 5.09European Scientific Journal - IndexCopernicus™ - Journals Master List http://journals.indexcopernicus.com/passport.php?id=6708 April /2013: 'ESJ`s representative office established at the University of the Azores, Portugal (Coordinator: Jose Noronha Rodrigues) March /2013: '

Call for papers launched, ESJ`s special edition with the Grigol Robakidze University, Tbilisi Georgia' (Project Coordinators: Jovan Shopovski, Nino Kermetelidze) February /2013: 'Call For Papers', ISF International Scientific Forum, December, 2013,  Tirana, Albania (http://isforum.us/) January /2013: 'Call For Papers', ESF, Eurasian Scientific FORUM, 24-26 Octiber, 2013,  Tbilisi, Georgia (http://emforum.eu/) December/2012: Scientific Cooperation with University 'Stefan cel Mare', Suceava, Romania November/2012: Memo of understanding with Vitrina University, Tirana, Albania September/2012: “ESJ” website www.eujournal.org in September 2012 have been visited by more than 8.000 researchers from more than 60 countries worldwide.

Authors can submit papers in French (with English abstract)(Project coordinator: Mr. Bouabre Gnoka Modeste, Professor at the University of Cocody, Abidjan Ivory Cost) May/2012:Cooperation with GALE - A world leader in e-research and educational publishingApril/2012:Memo of understanding with Jazan University, Saudi ArabiaMarch/2012:EURODOC (European Council of doctoral candidates and junior researchers), Annual General Meeting, 21-25 March 2012, Krakow, PolandFebruary/2012:Common project: Joint scientific edition with Univerzity 'Grigol Robakidze', Tbilisi, Georgia 'ESJ' February 2012 /Special/ edition Vol.8 No.2(Project coordinator: Mrs. Nino Kemertelidze, Mr. Jovan Shopovski)December/2011'Student Exchange Agreement' and 'Memo of Understanding' - Signed by European Scientific Institute and University 'Grigol Robakidze', Tbilisi, GeorgiaDecember/2011New Office Of International Relations opened: 55 Park Hill Sw4 9ns Clapham Common, London, United KingdomNovember/2011'Student Exchange Agreement' - Signed by European Scientific Institute and University of the Azores, PortugalNovember/2011Cooperation with 'PI - NET', Hungary (Postgraduate International Network)October/2011'Memo of Understanding' - Signed by European Scientific Institute and University of the Azores, PortugalJuly/2011:Common project with Univerzity 'Grigol Robakidze', Tbilisi, Georgia 'ESJ' July /Special/ edition vol.19 and vol.20(Project coordinator: Mrs. Nino Kemertelidze, Mr. Jovan Shopovski)June/2011:Common Project with University 'Aleksander Moisiu', Faculty of Education, Durres, Albania 'ESJ' June /Special/ edition vol.

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