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Solving Bilevel Programming Problems Using a Neural Network Approach and Its Application to Power System Environment

Shamshul Bahar YAAKOB, Junzo WATADA
2011 SICE Journal of Control Measurement and System Integration  
The combination of a genetic algorithm (GA) and a meta-controlled Boltzmann machine (BM) enables us to formulate a hybrid neural network approach to solving bilevel programming problems.  ...  In this paper, a hybrid neural network approach to solve mixed integer quadratic bilevel programming problems is proposed.  ...  Acknowledgments The first author would like to thank Universiti Malaysia Perlis and the Ministry of Higher Education Malaysia for a study leave at Waseda University under the SLAI-KPT scholarship.  ... 
doi:10.9746/jcmsi.4.387 fatcat:7hoxvt2nyzagteeuqudakeipe4

AI in Finance: Challenges, Techniques and Opportunities [article]

Longbing Cao
2021 arXiv   pre-print
The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance.  ...  AI in finance broadly refers to the applications of AI techniques in financial businesses.  ...  production and decision-making; generating machine learning and data science models and systems that are accurate (can predict well), transparent (can be explained and implemented in human understandable  ... 
arXiv:2107.09051v1 fatcat:g62cz4dqt5dcrbckn4lbveat3u

From Humans and Back: a Survey on Using Machine Learning to both Socially Perceive Humans and Explain to Them Robot Behaviours

Adina M. Panchea, François Ferland
2020 Current Robotics Reports  
To do so, machine learning (ML) is often employed.  ...  Recent Findings The literature has shown a substantial advancement in ML methods with application to social perception and explainable behaviours.  ...  Another study by Zanatto et al. [22] suggests that humans can strategically imitate robots while playing an economic investment game with a robot banker. Furthermore, it is reported by Dragan et al.  ... 
doi:10.1007/s43154-020-00013-6 fatcat:l5dneve33faolgvlvf3ddqkv24

The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data

Leonidas Aristodemou, Frank Tietze
2018 World Patent Information  
This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles.  ...  Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies  ...  investment decisions 59,740 patents Restricted Boltzmann machines, Back propagation Deep belief neural network 49 [85] Prediction of economic growth by extreme learning approach based on  ... 
doi:10.1016/j.wpi.2018.07.002 fatcat:cawnmevwcna2zep7z6ikixwzgu

Sustainable Solutions for Sea Monitoring With Robotic Sailboats: N-Boat and F-Boat Twins

Alvaro P. F. Negreiros, Wanderson S. Correa, André P. D. de Araujo, Davi H. Santos, João M. Vilas-Boas, Daniel H. N. Dias, Esteban W. G. Clua, Luiz M. G. Gonçalves
2022 Frontiers in Robotics and AI  
In this work, we revisit the challenges behind the project design and construction for two fully autonomous sailboats and propose a methodology based on the Restricted Boltzmann Machine (RBM) in order  ...  Strategic management and production of internal energy in autonomous robots is becoming a research topic with growing importance, especially for platforms that target long-endurance missions, with long-range  ...  This management is implemented by way of using a Boltzmann machine (Bu et al., 2015; Passos et al., 2020) .  ... 
doi:10.3389/frobt.2022.788212 pmid:35480088 pmcid:PMC9037383 fatcat:lkrqacjo3zcvzdupz4skg57w3e

Extending the Strada Framework to Design an AI for ORTS [chapter]

Laurent Navarro, Vincent Corruble
2009 Lecture Notes in Computer Science  
This makes indeed the decision and learning algorithms difficult to design and implement.  ...  Strategy games constitute a significant challenge for game AI, as they involve a large number of states, agents and actions.  ...  Three main axes were explored: a decision-making system architecture based on a military hierarchy and a map analysis algorithm, whose goal were to reduce the complexity of the state and action spaces,  ... 
doi:10.1007/978-3-642-04052-8_32 fatcat:3knpqcwipfdubitqmrorbhvojm

Principles and Applications of Science of Information [Scanning the Issue]

Thomas Courtade, Ananth Grama, Michael W. Mahoney, Tsachy Weissman
2017 Proceedings of the IEEE  
In "Decision making with quantized priors leads to discrimination," Varshney et al. introduce an information-based model of signal detection motivated by the question of racial discrimination in decision-making  ...  The channel, which is inspired by a model of protein folding, is called the Boltzmann sequence-structure channel.  ...  editors would like to acknowledge the National Science Foundation Center for Science of Information (http:\\ for providing the intellectual environment and resources to conduct research in  ... 
doi:10.1109/jproc.2016.2646778 fatcat:tn7avmt5enab7p6cqk2b3ziujq

Research on Educational Information Platform Based on Cloud Computing

Ling Fan, Meiyi Xia, Ping Huang, Jianmin Hu, Chi-Hua Chen
2021 Security and Communication Networks  
terminals and virtual machines through the remote desktop terminal architecture.  ...  In order to solve this problem, this paper establishes an educational information platform based on cloud computing.  ...  Acknowledgments is paper was supported by "New Generation Information Technology Innovation Project" sponsored by the Second Batch of Innovation Fund for Chinese Universities in 2019 (no. 2019ITA03001)  ... 
doi:10.1155/2021/3109473 fatcat:jxadw5fxkje3zgbqmozryvskwu

A review of neural networks applied to transport

Mark Dougherty
1995 Transportation Research Part C: Emerging Technologies  
Because the subject is so young, some of the papers appear only in conference proceedings or other less formal publications I make no apology for this; I felt it was important to cover as much of the contemporary  ...  A particular weakness noted in much of the work is the informal approach taken to detailed analysis of the results of the research.  ...  This very interesting paper describes a complex hierarchical hybrid decision making system which contains neural networks similar to Learning Vector Quantisation.  ... 
doi:10.1016/0968-090x(95)00009-8 fatcat:yxk5op4fcvhenkdesvoqqj3lau

Intrusion Detection in the Internet of Things

2020 International Journal of Advanced Trends in Computer Science and Engineering  
The IoT has been booming in recent years and is evolving rapidly, but attacks against it are also continuing to evolve in a worrying way.  ...  In order to take full advantage of these systems, it is worth securing them.  ...  It is used to establish a mathematical model to capture behavior in strategic situations [23] .  ... 
doi:10.30534/ijatcse/2020/0191.52020 fatcat:otprcykv2fgmxhlw7cf5nletwq

Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0

Javier Villalba-Diez, Daniel Schmidt, Roman Gevers, Joaquín Ordieres-Meré, Martin Buchwitz, Wanja Wellbrock
2019 Sensors  
In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface  ...  Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry.  ...  Abbreviations The following abbreviations are used in this manuscript: IIoT Industrial Internet of Things OQC Optical Quality Control DNN Deep Neural Networks GPU Graphic Process Unit RAM Random Access  ... 
doi:10.3390/s19183987 fatcat:4fdsaxujk5dzhismvnd3oexh3m

Big Data in IoT Systems [article]

Fayeem Aziz, Stephan K. Chalup, James Juniper
2019 arXiv   pre-print
Due to rapid progress in Machine Learning and new hardware developments, a dynamic turnaround of methods and technologies can be observed.  ...  Big Data in IoT is a large and fast-developing area where many different methods and techniques can play a role.  ...  SC addressed some aspects of machine learning and data analytics. JJ contributed theoretical and philosophical aspects.  ... 
arXiv:1905.00490v1 fatcat:24xkj2vw5jamjj3wgv6hdfar3a

Deep Learning Based Pain Treatment

Tarun Jaiswal, Sushma Jaiswal
2019 International Journal of Trend in Scientific Research and Development  
Among machine learning methods, a subset has so far been applied to pain research-related problems, SVMs, regression models, deep learning and several kinds of neural networks so far most often revealed  ...  Indeed, the application of machine learning for pain investigationassociated non-imaging problems has been mentioned in publications in scientific journals since 1940-2018.  ...  In a Boltzmann machine, the neurons constitute a recurrent structure, and they operate in a binary manner in that they are either in an "on" state denoted by + 1 or in an "off" state denoted by -1.  ... 
doi:10.31142/ijtsrd23639 fatcat:tqg4u3tkgjhmjpya67g3lnewwu

Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency

Hooman Farzaneh, Ladan Malehmirchegini, Adrian Bejan, Taofeek Afolabi, Alphonce Mulumba, Precious P. Daka
2021 Applied Sciences  
By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation.  ...  This paper is an in-depth review of recent studies on the application of artificial intelligence (AI) technologies in smart buildings through the concept of a building management system (BMS) and demand  ...  By the late 1990s, the reduced uncertainty in decision-making was achieved by statistical learning methods linked to AI.  ... 
doi:10.3390/app11020763 fatcat:3ipak4rmyba67jdrds6fpyuple

Mean-variance Portfolio Optimization with Stock Return Prediction Using XGBoost

This paper studies the performance of a portfolio optimization model when combined with stock return prediction using a machine learning model.  ...  In this study, two portfolio optimization algorithms are proposed.  ...  ACKNOWLEDGMENTS This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(2017R1D1A1B03035543).  ... 
doi:10.24818/18423264/ fatcat:wpop42f235gwnftukpe5ofy6je
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