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arXiv 2019年

作者： Ahmed, Bestoun S. Gambardella, Luca M. Zamli, Kamal Z. Manno-Lugano 6928 Switzerland Salahaddin University - Erbil Faculty of Computer Systems and Software Engineering University Malaysia Pahang Gambang Malaysia

Owing to the significance of combinatorial search strategies both for academia and industry, the introduction of new techniques is a fast growing research field these days. These strategies have really taken different forms ranging from simple to complex strategies in order to solve all forms of combinatorial problems. Nonetheless, despite the kind of problem these approaches solve, they are prone to heavy computation with the number of combinations and growing search space dimensions. This paper presents a new approach to speed up the generation and search processes using a combination of stack and hash table data structures. This approach could be put to practice for the combinatorial approaches to speed up the generation of combinations and search process in the search space. Furthermore, this new approach proved its performance in diverse stages better than other known strategies. Copyright © 2019, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Health index determination of aged high voltage substation in distribution system 16

Health index determination of aged high voltage substation i...

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16th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2019

作者： Somboonchaiwong, Chaiyaboon Suwanasri, Thanapong Suwanasri, Cattareeya University of Technology North Bangkok Electrical and Software Systems Engineering Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's 1518 Pracharat 1 Rd. Bangsue Bangkok Thailand King Mongkut's University of Technology North Bangkok Electrical and Computer Engineering Faculty of Engineering 1518 Pracharat 1 Rd. Bangsue Bangkok Thailand

ISBN: (纸本)9781728133614

This paper presents condition assessment of high voltage equipment and substation as health index. Major equipment of high voltage substation consists of circuit breaker, disconnecting switch, current transformer, voltage transformer and surge arrester. The condition of equipment is assessed by visual inspection and electrical test. The weighting and scoring method is applied throughout the process of health index calculation. The Analytic Hierarchy Process is used to calculate the weight for each criterion. The conditional score and its weight of each testing are used to calculate the weighting average score. Then, the obtained weighting average score is adjusted by visual inspection, maintenance completeness and failure rate of equipment. The adjusted average score of all equipment in a bay is integrated into bay health index and subsequently adjusted by age and operational stress of bay. Finally, the health index of fifteen substations can be determined. © 2019 IEEE.

关键词： Electric circuit breakers

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Hybrid Filter for Attributes Reduction in Soft Set 2nd

Hybrid Filter for Attributes Reduction in Soft Set

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Mohammed, Mohammed Adam Taheir Mohd, Wan Maseri Wan Arshah, Ruzaini Abdullah Mungad, M. Sutoyo, Edi Chiroma, Haruna Faculty of Computer System & Software Engineering Universiti Malaysia Pahang Lebuhraya TunRazak KuantanPahang Malaysia Department of Information Systems University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Department of Artificial Intelligence University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Gombe Nigeria

ISBN: (纸本)9789811317972

The purpose of this research is to overcome parameterization reduction limitation that focuses only on individual parameter reduction, whereas in some cases the individual parameter reduction is not sufficient even implies reduction. It was found that the dimensions sometimes are not able to reduce the number of data in the case of big data;hence, for this reason it became necessary to look for an alternative technique that can significantly reduce the parameters. This paper proposed an alternative decision partition order method based on rough set indiscernibility to select attributes reductions in soft set using decompositions. For significant candidates, the method decomposition partition order used R supp checking to confirm the correctness of the reduction. Comparison of the reduction methods shows that the proposed method provides better result than the parameterization reduction in enhancing reduction. The false candidates were filtered in the huge candidate reduction by the Min supp. The proposed method can be used to maintain object before attribute reduction as well as to reduce parameter size drastically while maintaining consistency in decision making. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Decision making

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Explaining predictions of deep neural classifier via activation analysis

arXiv

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arXiv 2020年

作者： Stano, Martin Benesova, Wanda Marták, Lukáš Samuel Department of Informatics Informational Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Department of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Institute of Computational Perception Johannes Kepler University Linz4040 Austria LIT Artificial Intelligence Lab Johannes Kepler University Linz4040 Austria

In many practical applications, deep neural networks have been typically deployed to operate as a black box predictors. Despite high amount of work on interpretability and high demand on reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of a decision-making process to a human expert operating a deep learning system based on Convolutional Neural Network (CNN). By modeling activation statistics on selected layers of a trained CNN via Gaussian Mixture Models (GMM), we develop a novel perceptual code in binary vector space that describes how input sample is processed by the CNN. By measuring distances between pairs of samples in this perceptual encoding space, for any new input sample, we can now retrieve a set of most perceptually similar and dissimilar samples from an existing atlas of labeled samples, to support and clarify the decision made by the CNN model. Possible uses of this approach include for example computer-Aided Diagnosis (CAD) systems working with medical imaging data, such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We demonstrate the viability of our method in the domain of medical imaging for patient condition diagnosis, as proposed decision explanation method via similar ground truth domain examples (e.g. from existing diagnosis archives) will be interpretable by the operating medical personnel. Our results indicate that our method is capable of detecting distinct prediction strategies which enables to identify most similar predictions from an Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A New Support Value Method Filtering Based on Object Support Partition for Soft Reduction 2nd

A New Support Value Method Filtering Based on Object Support...

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Mohammed, Mohammed Adam Taheir Mohd, Wan Maseri Wan Arshah, Ruzaini Abdullah Mungad, M. Sutoyo, Edi Chiroma, Haruna Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak KuantanPahang Malaysia Department of Information System University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Department of Artificial Intelligence University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Gombe Nigeria

ISBN: (纸本)9789811317972

Soft set time complexity is become really a problem when the numbers of parameters are increased. In order to solve time complexity problem, it necessary to reduce the boundary of optimal soft set growth and due to this the time cost can be enhanced. Several soft set methods are determining the soft set reduction but in performing the reduction it spends more time to produce the result and this happens because the false candidate sets are a part of solution. So, if the boundary of candidate reduction is narrowed then the reduction process will speed up. In this paper, we proposed a new method which reducing the boundary of candidate reduction using Lipschitz constant and wavelet discrete transform to eliminate large false sets from the solution. In Lipschitz constant function the value of candidate implies are determined, where based on wavelet WDT the false sets which is not in the form of implies also can be determined. The proposed method remove an inconsistency noise from the soft set in a pre-processing filtering based on if then method which help to classify further reduction in short time. It found that by using Lipschitz constant function and wavelet discrete transform the reduction time can be enhanced several times comparing to previous reduction methods. The result indicates that Lipschitz constant function and wavelet WDT algorithm. It complements each other to determine candidate soft set reduction. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Set theory

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IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II)

IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive...

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IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Haibin Zhu George Baciu Guiming Luo Fernando Rubio Jie Sui Runhe Huang Hironori Hiraishi Jun Peng Liang Chen FI2CICC Dept of ECE Univ. of Manitoba Winnipeg Canada FI2CICC Nipissing University ON Canada FI2CICC Dept. of Computing Hong Kong Polytechnic University Hong Kong FI2CICC Software Institute Tsinghua University Beijing China FI2CICC Dept. of Computer Systems &#x0026 Computation Complutense Univ. of Madrid Spain FI2CICC Aberdeen University Aberdeen UK FI2CICC Faculty of Computer and Information Sciences Hosei University Japan Faculty of Engineering Ashikaga University Tochigi Japan FI2CICC School of AI Chongqing Univ. of Science and Technology Chongqing China Dept. of Computer Science Univ. of Northern British Columbia BC Canada

ISBN: (纸本)9781665421201

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-and-mission-critical systems.

关键词： Knowledge engineering Intelligent vehicles Autonomous systems Conferences Cognitive informatics Cognitive robotics Cognitive systems

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series

Optimization of Neural Network Parameters Based on a Genetic...

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International Multi-Conference on Industrial engineering and Modern Technologies (FarEastCon)

作者： V.S. Tormozov A.L. Zolkin K.A. Vasilenko Senior lecturer of the Software engineering for computers and computer-based systems sub-faculty Pacific National University Khabarovsk Tikhookeanskaya St. 136 Ph.D. in Engineering Science Povolzhskiy State University of Telecommunications and Informatics Samara Highest category lecturer Service and design college of the Vladivostok State University of Economics and Service Service and design college of the Vladivostok State University of Economics and Service Vladivostok

ISBN: (数字)9781728169514

ISBN: (纸本)9781728169521

The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values.

关键词： Genetic algorithms Sociology Neurons Biological cells Artificial neural networks Time series analysis Optimization

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Modeling and Simulation of Emergency Evacuation from Industry Buildings under Toxic Cloud Release 2

Modeling and Simulation of Emergency Evacuation from Industr...

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2nd Borneo International Conference on Applied Mathematics and engineering, BICAME 2018

作者： Benseghir, Hocine Mohd Idris, Mohd Fadzil Bin Ibrahim, Abdullah Kabir, Muhammad Nomani University Malaysia Pahang Faculty of Engineering Technology Gambang Kuantan26300 Malaysia University Malaysia Pahang Faculty of Computer Systems and Software Engineering Gambang Kuantan26300 Malaysia

ISBN: (纸本)9781538677254

Toxic cloud caused by accidents in the industries is extremely dangerous for the occupants. Thus, in case of such accidents, the occupants must be immediately evacuated from industry buildings. In this paper, we explore modeling and simulation of occupant-evacuation during a toxic cloud release. The proposed model evaluates the toxic cloud concentrations in terms of probability values and then the exit route of each occupant is computed such that the toxic effect on the occupants is minimized. We integrated A∗ search algorithm with the toxic cloud model to find the safest routes for occupants to exit through the doors from the current positions of the occupants. To find the best route, the model considers the distance to egress, the obstacles and the probability of toxic concentration. We presented the simulation results of the model to demonstrate the plausibility of the model. This model can be used by evacuation management to guide the industry occupants to exit the industry building through safest routes during an accident scenario. © 2018 IEEE.

关键词： Accidents

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Application of Enhanced Extended Observer in Station-Keeping of a Quadrotor with Unmeasurable Pitch and Roll Angles ⁎

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IFAC-PapersOnLine 2019年第16期52卷 837-842页

作者： Oleg I. Borisov Anton A. Pyrkin Alberto Isidori ITMO University Faculty of Control Systems and Robotics St. Petersburg Russia Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia University of Rome “La Sapienza” Department of Computer Control and Management Engineering “A. Ruberti” Rome Italy

In this paper we provide an example of application of the design of a control law that makes use of an enhanced version of the so-called extended observer. The enhancement in question reposes on the extension of a classical Lemma due to Dayawansa to the case of nonlinear systems whose normal form includes time-varying (and measurable) gains. The presented example concerns a station-keeping problem of a quadrotor with unmeasurable pitch and roll angles.

关键词： Dayawansa’s lemma Robust observers Nonlinear systems High-gain observers Quadrotors

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