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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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A Parallel Segmentation after Classification Algorithm of Multi-Spectral Image of K-Means of Deep learning and Panchromatic Based On Wavelet 11

A Parallel Segmentation after Classification Algorithm of Mu...

引用

11th international conference on Digital Image Processing (ICDIP)

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Song, Donghong Xue, Xiaoyong Xi An Jiao Tong Univ Dept Comp Sci & Technol & AI Xian 710049 Shaanxi Peoples R China Anyang Normal Univ Sch Comp & Informat Engn Anyang 455000 Henan Peoples R China Guangzhou Univ South China Inst software Engn Dept AI & Software Engn Guangzhou 510990 Guangdong Peoples R China Middle Sch Luonan Cty Shangluo 726100 Shaanxi Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

[Purpose]To take the advantages of a variety of remote sensing data, the application of remote sensing image classification is a very important choice. Remote sensing image classification is large in computing capacity and time-consuming, and with the development of modern remote sensing technology, the amount of various remote sensing data obtained is getting larger and larger, the issue of how to fuse remote sensing image quickly and accurately and of getting useful information is becoming more and more urgent especially in some remote sensing applications such as disaster monitoring, prevention and relief, etc. In this paper, in order to fuse remote sensing image quickly and accurately, a parallel classification algorithm of multi-spectral image and panchromatic image based on wavelet transform is proposed.[Methods] In the method, based on parallel computing, the low-frequency components of wavelet decomposition are fused with the classification rule based on the feature matching, and the high-frequency components of wavelet decomposition are fused with the classification rule based on the sub-region variance. then the low-frequency components and the high-frequency components after classification are processed with the inverse wavelet transform, and the fused image is obtained. According to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis, an efficient image segmentation method based on Deep learning Semantic analysis and wavelet transform is proposed to achieve precision of classification.[Results] the experiment results show that the proposed method can get better classification results and faster computing speed for multi-spectral image and panchromatic image. Originality. In the proposed classification algorithm of multi-spectral image and panchromatic image, wavelet transform and different proper classification rules for low-frequency components and high-frequency components of wavelet decomposition are used.

关键词： remote sensing image classification panchromatic image multi-spectral image wavelet transform parallel computing data clustering

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Trust Mechanism of Cloud Manufacturing Service Platform Based on Blockchain

Trust Mechanism of Cloud Manufacturing Service Platform Base...

引用

international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Ruifang Li Tingling Chen Ping Lou Junwei Yan Jiwei Hu School of Information Engineering Wuhan University of Technology (WUT) Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan China

Cloud manufacturing is a new kind of manufacturing paradigm, in which geographically distributed various manufacturing resources belonging to different enterprises are encapsulated as manufacturing services (service providers) for providing users (service consumers) with services and users are able to find, select, and trade with MSs via Networks on a cloud manufacturing platform. To solve the trust issues between service providers and service consumers, a trust mechanism based on blockchain(BC) for services' information was proposed. Based on the decentralized, distributed storage and non-tamperable of BC, using public BC and consortium BC to construct a trusted mechanism for cloud manufacturing service platforms. Public BC is high credible, consortium BC is efficient and has larger capacity. the trusted mechanism can ensure that the source and storage of the service information is trusted, to solve the problem that the cloud manufacturing platform service data is untrustworthy and easily falsified. It can provide trusted choices to service consumers while operating efficiently and at low cost.

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Proactive Caching in Auto Driving Scene via Deep Reinforcement learning

Proactive Caching in Auto Driving Scene via Deep Reinforceme...

引用

international conference on Wireless Communications and Signal Processing (WCSP)

作者： Zihui Zhu Zhengming Zhang Wen Yan Yongming Huang Luxi Yang School of Information Science and Engineering Southeast University Nanjing China

the Internet of Vehicles is a product of the mobile Internet background and an innovative driving force for the industrial upgrading of the automotive industry. the rapid and reliable completion of the vehicle to roadside unit (V2R) communication is an important research direction. In order to improve the communication efficiency between the vehicles and the roadside units (RSUs), we propose a deep reinforcement learning approach for joint proactive caching and automatic vehicle control in the vehicle network. First, we model the autonomous vehicle system and the proactive caching system as separate Markov decision processes, respectively. then, we propose a model-free algorithm based on deep Q-learning to find effective automatic vehicle control policy and proactive caching policy. Finally, the simulation results are given to demonstrate that the proposed solution can effectively maintain high-quality user experience in the V2R communication.

关键词： Machine learning Internet Road transportation Base stations Prediction algorithms data models Aerospace electronics

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Blended learning Approach to Teaching Oral Communication: Video Blogging in ESL Classroom

Blended Learning Approach to Teaching Oral Communication: Vi...

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IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management

作者： Dylyn A. Junio Argel A. Bandala Deren of Aied Linguisics Eecronics nd Counicions Engineering Deren De L Se UniversiyDeren of Aied Linguisics Eecronics nd Counicions Engineering Deren De L Se Universiy

ISBN: (数字)9781728130446

ISBN: (纸本)9781728130453

With the rapid advancement of modern technology, the restructuring of pedagogical teaching is necessitated. Blended learning is one of the recent approaches to teaching which gained massive popularity. In recent years, many studies have been conducted in English teaching instruction;however, research on its application to English as a second language (ESL) oral communication instruction is scarce. the present study reports on the effectiveness of blended learning using video blogs to ESL's oral communication skills. the mixed-methods design was used to collect quantitative and qualitative data. To determine the impact of a blended learning model to ESL's oral communication ability, pretest and posttest were administered to measure improvement. the results of the quantitative data revealed that the implementation of blended learning using video blogs improved students' oral proficiency in terms of pronunciation, fluency, syntax, lexical range and general use of the English language. the qualitative data, on the other hand, indicated the positive attitude of students toward the blended learning experience.

关键词： Video recording Face Blogs Oral communication Education Task analysis Interviews

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Annotation Intent Identification toward Enhancement of Marketing Campaign Performance

Annotation Intent Identification toward Enhancement of Marke...

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international conference on Knowledge and Systems engineering

作者： Sorratat Sirirattanajakarin Boontawee Suntisrivaraporn Data Science Siam commercial bank Bangkok Thailand

An annotation is a short free text associating with each financial transaction made on SCB Easy, one of the leading mobile banking applications in thailand. Identifying the intent(s) hidden within this text is a challenging task in Natural Language Processing (NLP). On the one hand, the availability of labeled data is limited, and on the other hand, an annotation oftentimes carries multiple intents. this paper describes AI~2, an NLP framework for identifying multiple intents from short annotations. the framework is based on long short-term memory architectures. We conduct two sets of empirical experiments: one helps identify the best architecture between MIBC and SMLC;and the other finds the optimal model parameters. Apart from assuring experimental results, AI2 predictions are used in marketing campaigns with promising performance uplifts.

关键词： Computational modeling Natural language processing Predictive models Computer architecture Banking Neural networks Machine learning

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An unsupervised learning model for pattern recognition in routinely collected healthcare data 11

An unsupervised learning model for pattern recognition in ro...

引用

11th international conference on Health Informatics, HEALthINF 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Khalid, Sara Judge, Andrew Pinedo-Villanueva, Rafael Nuffield Dept. of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford United Kingdom

ISBN: (纸本)9789897582813

this study examines a large routinely collected healthcare database containing patient-level self-reported outcomes following knee replacement surgery. A model based on unsupervised machine learning methods, including k-means and hierarchical clustering, is proposed to detect patterns of pain experienced by patients and to derive subgroups of patients with different outcomes based on their pain characteristics. Results showed the presence of between two and four different sub-groups of patients based on their pain characteristics. Challenges associated with unsupervised learning using real-world data are described and an approach for evaluating models in the presence of unlabelled data using internal and external cluster evaluation techniques is presented, that can be extended to other unsupervised learning applications within healthcare and beyond. To our knowledge, this is the first study proposing an unsupervised learning model for characterising pain-based patient subgroups using the UK NHS PROMs database. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： data mining

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Anomaly Detection in Spatial Layer Models of Autonomous Agents 1

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Kiermeier, Marie Feld, Sebastian Phan, thomy Linnhoff-Popien, Claudia Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

For describing the complete state of complex environments with multiple mobile and autonomous agents, spatial layer models (SLM) are popular data structures. these models consist of several planes describing the spatial structure of selected features. those SLM can directly be used for deep reinforcement learning tasks. However, detecting anomalies in such SLM poses two major challenges: the state space explosion in such settings and the spatial relations between the features. In this paper, we present a method for anomaly detection in SLM which solves both challenges by first extracting significant sub-patterns from training data and storing them in a dictionary. Afterwards, the entries of this dictionary are used for reconstructing SLM, which have to be validated. the resulting covering rate is an indicator for the (ab)normality of the given SLM. We show the applicability of our approach for a simple multi-agent scenario, and more complex smart factory scenarios with autonomous agents.

关键词： Anomaly detection Autonomous agents Spatial data

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Instance-Based Stacked Generalization for Transfer learning 19th

Instance-Based Stacked Generalization for Transfer Learning

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Baghoussi, Yassine Mendes-Moreira, Joao LIAAD INESC TEC Porto Portugal Univ Porto Fac Sci Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783030034931;9783030034924

We present a method for improving the prediction accuracy using multiple predictive algorithms. Several techniques have been developed to tackle this issue such as bagging, boosting and stacking. In contrary to the first two that, usually, generate homogeneous ensembles of classifiers, stacking techniques have demonstrated success using heterogeneous ensembles. In our method, we adopt the stacking mechanism. Several models are generated using different learning algorithms. Forward stepwise selection is implemented to link each instance to its appropriate learning model. Experiments with three datasets benchmarked with four learning schemes show that this novel method improves prediction accuracy and can serve as a bridge to transfer knowledge between tasks given the same feature space but different data distributions.

关键词： Ensemble learning Stacking Transfer learning Operational planning

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learning Optimal Q-Function Using Deep Boltzmann Machine for Reliable Trading of Cryptocurrency 19th

Learning Optimal Q-Function Using Deep Boltzmann Machine for...

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Bu, Seok-Jun Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030034931;9783030034924

the explosive price volatility from the end of 2017 to January 2018 shows that bitcoin is a high risk asset. the deep reinforcement algorithm is straightforward idea for directly outputs the market management actions to achieve higher profit instead of higher price-prediction accuracy. However, existing deep reinforcement learning algorithms including Q-learning are also limited to problems caused by enormous searching space. We propose a combination of double Q-network and unsupervised pre-training using Deep Boltzmann Machine (DBM) to generate and enhance the optimal Q-function in cryptocurrency trading. We obtained the profit of 2,686% in simulation, whereas the best conventional model had that of 2,087% for the same period of test. In addition, our model records 24% of profit while market price significantly drops by -64%.

关键词： Deep reinforcement learning Q-network Deep Boltzmann Machine Portfolio management

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Deep learning-Based Approach for the Semantic Segmentation of Bright Retinal Damage 1

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Silva, Cristiana Colomer, Adrian Naranjo, Valery Univ Minho Campus Gualtar P-4710 Braga Portugal Univ Politecn Valencia Inst Invest & Innovac Bioingn I3B Camino Vera S-N Valencia 46022 Spain

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Regular screening for the development of diabetic retinopathy is imperative for an early diagnosis and a timely treatment, thus preventing further progression of the disease. the conventional screening techniques based on manual observation by qualified physicians can be very time consuming and prone to error. In this paper, a novel automated screening model based on deep learning for the semantic segmentation of exudates in color fundus images is proposed with the implementation of an end-to-end convolutional neural network built upon U-Net architecture. this encoder-decoder network is characterized by the combination of a contracting path and a symmetrical expansive path to obtain precise localization with the use of context information. the proposed method was validated on E-OPHthA and DIARETDB1 public databases achieving promising results compared to current state-of-the-art methods.

关键词： Semantic segmentation Deep learning Fundus images Exudates U-Net

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