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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是271-280 订阅

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A data-driven Dynamic Discretization Framework to Solve Combinatorial Problems Using Continuous Metaheuristics 1

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11th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020

作者： Cisternas-Caneo, Felipe Crawford, Broderick Soto, Ricardo de la Fuente-Mella, Hanns Tapia, Diego Lemus-Romani, José Castillo, Mauricio Becerra-Rozas, Marcelo Paredes, Fernando Misra, Sanjay Pontificia Universidad Católica de Valparaíso Valparaíso Chile Universidad Diego Portales Santiago Chile Covenant University Ota Nigeria

ISBN: (数字)9783030736033

ISBN: (纸本)9783030736026

Combinatorial optimization problems are very common in the real world but difficult to solve. Among the promising algorithms that have been successful in solving these problems are metaheuristics. The two basic search behaviors used in metaheuristics are exploration and exploitation, and the success of metaheuristic search largely depends on the balance of these two behaviors. Machine learning techniques have provided considerable support to improve data-driven optimization algorithms. One of the techniques that stands out is Q-Learning, which is a reinforcement learning technique that penalizes or rewards actions according to the consequence it entails. In this work, a general discretization framework is proposed where Q-Learning can adapt a continuous metaheuristic to work in discrete domains. In particular, we use Q-learning so that the algorithm learns an optimal binarization schemEqe selection policy. The policy is dynamically updated based on the performance of the binarization schemes in each iteration. Preliminary experiments using our framework with sine cosine algorithm show that the proposal presents promising results compared to other algorithms. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Combinatorial optimization

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WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT 35th

WHTE: Weighted Hoeffding Tree Ensemble for Network Attack De...

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35th international conference on Industrial, Engineering and Other Applications of Applied intelligent systems (IEA/AIE)

作者： Hameed, Shilan S. Selamat, Ali Latiff, Liza Abdul Razak, Shukor A. Krejcar, Ondrej Univ Teknol Malaysia Malaysia Japan Int Inst Technol MJIIT Kuala Lumpur 54100 Malaysia Koya Univ Directorate Informat Technol Koya 44023 Iraq Univ Teknol Malaysia Fac Engn Sch Comp Johor Baharu 81310 Johor Malaysia Univ Teknol Malaysia Media & Games Ctr Excellence MagicX Skudai 81310 Johor Bahru Malaysia Univ Hradec Kralove Fac Informat & Management Ctr Basic & Appl Res Rokitanskeho 62 Hradec Kralove 50003 Czech Republic Univ Teknol Malaysia Razak Fac Technol & Informat Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9783031085307;9783031085291

The fog-based attack detection systems can surpass cloud-based detection models due to their fast response and closeness to IoT devices. However, current fog-based detection systems are not lightweight to be compatible with ever-increasing IoMT big data and fog devices. To this end, a lightweight fog-based attack detection system is proposed in this study. Initially, a fog-based architecture is proposed for an IoMT system. Then the detection system is proposed which uses incremental ensemble learning, namely Weighted Hoeffding Tree Ensemble (WHTE), to detect multiple attacks in the network traffic of industrial IoMT system. The proposed model is compared to six incremental learning classifiers. Results of binary and multi-class classifications showed that the proposed system is lightweight enough to be used for the edge and fog devices in the IoMT system. The ensemble WHTE took trade-off between high accuracy and low complexity while maintained a high accuracy, low CPU time, and low memory usage.

关键词： Intrusion detection Machine learning Incremental ensemble classifier. Fog-computing Attack detection

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IMQA: A Knowledge Graph driven Question Answering System for Internal Medicine

IMQA: A Knowledge Graph Driven Question Answering System for...

引用

IEEE international conference on Cloud computing and Intelligence systems (CCIS)

作者： Mengzhan Li Haisheng Li Wei Wang Di Wang School of Computer Science and Engineering Beijing Technology and Business University Beijing China

ISBN: (纸本)9781665456579

The prevention and treatment of internal medicine diseases are a focus of public attention. The emergence of various medical websites makes online consultation a popular trend. Knowledge graph can construct the correlation between different knowledge and provide the data support for question answering systems. Therefore, to assist patients with self-diagnosis, a knowledge graph driven question answering system for internal medicine is constructed in this paper. In the implementation, the ontology schema of internal medicine diseases, which is the basis of knowledge graph construction, is designed. Multiple entities and relationships, extracted from the data on professional healthcare websites, are stored in the Neo4j. After the segmentation of the question, the Aho-Corasick automaton algorithm and semantic similarity are applied to entity recognition. The rule-based approach, an accurate way, is used to identify intentions. This intelligent system, which has high value for the treatment of internal medicine diseases, can effectively solve health issues and reduce the cost of the consultation.

关键词： Costs Correlation Semantics Ontologies Market research Question answering (information retrieval) data mining

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OFFICERS: Operational Framework for intelligent Crime-and-Emergency Response Scheduling 32

OFFICERS: Operational Framework for Intelligent Crime-and-Em...

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32nd international conference on Automated Planning and Scheduling, ICAPS 2022

作者： Chase, Jonathan Goh, Siong Thye Tran, Phong Lau, Hoong Chuin Singapore Management University Singapore

ISBN: (纸本)9781577358749

In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was performed with the CPLEX solver. Furthermore, we show that our proposed framework is designed to be readily transferable between use cases, handling a wide range of both criminal and non-criminal incidents, with the use of deep learning and a general-purpose efficient solver, reducing dependence on context-specific details. We demonstrate the value of our approach on a police patrol case study, and discuss both the ethical considerations, and operational requirements, for deployment of a lightweight and responsive planning system. © 2022, Association for the Advancement of Artificial Intelligence.

关键词： Simulated annealing

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Remaining Useful Life for Aircraft Engine Forecasting Based on data-driven

Remaining Useful Life for Aircraft Engine Forecasting Based ...

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2021 international conference on Mechatronics, Automation and intelligent Control, MAIC 2021

作者： Niu, Wei Huang, Wei Wang, Hao Cheng, Juan Wang, Sen Xi'An Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'An North Jierui Opto-electronic Technology CO. LT Xi'an China Xi'An Institute of Applied Optics Xi'an China

As for the low forecasting precision problem about time series in avionics complex system, a time series combined forecasting model of complex systems was established based on GM (grey model is referred to as GM). However, using the traditional GM(1, 1) model to predict complex system failures might result in huge forecasting errors. To solve the drawbacks of the traditional GM(1, 1) model, this work developed the traditional GM(1, 1) model (abbreviated as DGM(1, 1)), which can predict non-geometric sequences more correctly. To reduce such mistakes, a PS-DGM (1, 1) forecast model was created by combining DGM(1, 1) with the particle-swarm optimization process. The DGM(1, 1) model was utilized to generate the forecasting sequence, and then the particle-swarm optimization approach was employed to further alter them, resulting in the degradation trend prediction of avionics complex system characteristic parameters. The experimental results show that the PS-DGM(1, 1) model's validity was confirmed by tracking and forecasting avionics equipment experiment data in a real-world setting, and that the suggested model achieves higher precision than the old one. © Published under licence by IOP Publishing Ltd.

关键词： Time series

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Correlation between human core body temperature fluctuation and sleep efficiency using time series analysis

Correlation between human core body temperature fluctuation ...

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international Symposium on Soft computing and intelligent systems (SCIS)

作者： Hiroaki Sakamoto Yutaka Yoshida Emi Yuda Center for Data-driven Science and Artificial Intelligence Tohoku University Sendai Japan

Core body temperature has relation to sleep. However, unlike body surface temperature, it is difficult to obtain time series data on core body temperature, and few studies have focused on the variability of core body temperature. In this study, we focused on the measurement of core body temperature using a non-contact infrared sensor, measured continuous time-series data of core body temperature during sleep, and investigated the correlation between indices obtained from core body temperature data and sleep indices obtained from a wristband-type wearable sensor. The results showed a negative correlation between the very low frequency component (VLF) calculated from core body temperature fluctuations and deep sleep (r=-0.69). The results suggest that VLF values of core body temperature may be useful as a new sleep evaluation index.

关键词： Temperature measurement Temperature sensors Temperature distribution Time-frequency analysis Fluctuations Correlation Time series analysis

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Hybrid MCDM for Cloud Services: AHP(blocks) & Entropy, TOPSIS & MOORA (methodology Review and advances) 24th

Hybrid MCDM for Cloud Services: AHP(blocks) & Entropy, TOPSI...

引用

24th international conference on Distributed Computer and Communication Networks, DCCN 2021

作者： Petrov, Iliyan Bulgarian Academy of Sciences Institute of information and communication technologies Akad. G. Bonchev str. bl. 2 Sofia1113 Bulgaria

ISBN: (纸本)9783030971090

Cloud services (CS) offer virtual, reliable, and flexible hardware and software resources as part of the distant Cloud computing (CC) concept. The wide variety of products from different providers create the necessity for users to collect data and define criteria for comparing alternatives with reliable evaluation methods and techniques. This paper presents a combined approach based on Analytical Hierarchy Process(AHP), Information Entropy, and Multi-criteria decision making (MCDM) techniques. The set of evaluation criteria is decomposed and logically structured in a reasonable number of blocks which weights are computed more easily and effectively with a reduced number of AHP pair-comparisons. With the integration of Information Entropy, the weights of individual criteria within each block are defined objectively on basis of real primary data for quantitative and quantifiable parameters. This hybrid approach is applied in a case study based on QoS and QoE criteria with popular evaluation techniques like TOPSIS and MOORA. © 2022, Springer Nature Switzerland AG.

关键词： Decision making

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intelligent Fault Detection Method for Distribution Network Based on Logistic Regression

Intelligent Fault Detection Method for Distribution Network ...

引用

international conference on Multi-modal Information Analytics, MMIA 2021

作者： Wu, Zhiqiang Zhang, Lizong Yu, Gang Wang, Ying Huang, Tao Zhou, Yanfang State Grid Shao Xing Company ShaoxingZhejiang China China Ji Liang University HangzhouZhejiang China

ISBN: (纸本)9783030748104

This paper proposes a data-driven fault detection method for distribution network. The optimized logistic regression is used to train electrical data to obtain the optimal classifier. Using the classifier test data, the fault of the distribution network can be accurately detected, and the short-circuit fault can be classified, which verifies the correctness and feasibility of the model. Experiments show that this method has higher accuracy and faster detection speed than support vector machines and logistic regression. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distributed power generation

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Design of intelligent Recommendation System of Smart Library Under Big data Environment and Its Application Research in Applied University 5th

Design of Intelligent Recommendation System of Smart Library...

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5th international conference on intelligent, Interactive systems and Applications, IISA 2020

作者： Huang, Weining Guangdong University of Science and Technology Dongguan523083 China

ISBN: (纸本)9783030637835

With the development of society and the progress of the times, the era of big data has gradually become a new stage of development. "Big data" is a collection of data that is difficult to obtain, manage and analyze in a short time under the calculation of conventional software. The so-called "big data environment" is a large-scale data integration that can be developed by the support of the current network technology and computer technology. The recommendation system of the smart library is to be established and founded under such an environment. The recommendation system of smart library is designed to provide more beneficial help for the education system of application-oriented universities on the basis of perceiving students’ preference in reading and learning. In the context of big data, the intelligent library recommendation system designed in this paper is to collect data on students’ learning and reading needs with the help of big data. Integrate, process and analyze on the basis of collection, and finally send the results presented after this process to students to form a specialized smart library recommendation system suitable for applied universities. The establishment of this system can better meet the needs of students of applied universities, recommend students to adapt to their unique learning resources, and help the data-driven development of applied universities to be carried out better. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Comparative Analysis of Machine Learning Models for Students’ Performance Prediction

Comparative Analysis of Machine Learning Models for Students...

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international conference on advances in Digital Science, ICADS 2021

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Al Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030717810

Machine learning for education is an emerging discipline where a model is developed based on training data to make predictions on students’ performance. The main aim is to identify students who would have difficulty in their learning and to take precautionary measures to help them. In this paper, we conduct a comparative analysis of the most used machine learning classification models in the literature. We evaluate the performance of the models in terms of accuracy, F-measure, and execution time using two real-life education datasets. The performance of the models is data-driven. We give insights into the models’ performance and advise on the best model to use accordingly. We believe the results of this paper will be widely used by education professionals for accurate predictions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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