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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4179 条记录，以下是3661-3670 订阅

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learning IoT: Basic Experiments of Home Automation using ESP8266, Arduino and XBee 4

Learning IoT: Basic Experiments of Home Automation using ESP...

引用

4th IEEE international conference on Smart Internet of things, SmartIoT 2020

作者： Sarah, Annisa Ghozali, theresia Giano, Geraldo Mulyadi, Melisa Octaviani, Sandra Hikmaturokhman, Alfin Atma Jaya Catholic University of Indonesia Electrical Engineering Jakarta Indonesia Institut Teknologi Telkom Purwokerto Telecommunication Engineering Purwokerto Indonesia

ISBN: (纸本)9781728165141

Extensive implementation of Internet-of-things (IoT) systems increases the needs of IoT engineers. However, some available IoT-Trainer modules, which needed to equipped engineers with IoT skills, are quite expensive and exclusive for private institutions. this research proposes an IoT-Trainer design that offers seven simple experiments to understand IoT concepts, which classified into three aspects: IoT devices, connectivity, and cloud or application system. All devices are available and purchasable from the market, low-price, and easy to configure: ESP8266 (NodeMCU-12E, and ESP-01), Arduino Uno, and Xbee. Moreover, the platforms that we use to tunnel a network and processing data are also open source: Ngrok, thingSpeak, and 000webhost. By exploiting this simple, inexpensive IoT-Trainer design and experimenting with those seven scenarios, learners can study the basic concept of IoT as infrastructure. © 2020 IEEE.

关键词： Internet of things

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Review of Research Progress on Small Sample learning 4

Review of Research Progress on Small Sample Learning

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4th international conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Zhao Zhihao Hong Kong Baptist Univ Comp Sci Hong Kong 999077 Peoples R China

ISBN: (纸本)9781728146898

the neural network model has achieved very good results in the field of computer vision, but the requirements for the amount of data are directly proportional to performance. Data hungry is not only the cause of the high performance of the neural network model, but also a hindrance to its further development. there is not a lot of available data in real-world application scenarios, so how to obtain better performance and more robust models in a small sample environment becomes the goal of less sample learning research. this paper reviews the past small sample learning methods, sorts out its performance and theoretical principles, and points out the existing deficiencies and proposes possible future research directions.

关键词： Few shot learning Meta-learning machine learning computer Vision Data Science

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35th international conference on High Performance Computing , ISC High Performance 2020

35th International Conference on High Performance Computing ...

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35th international conference on High Performance Computing , ISC High Performance 2020

ISBN: (纸本)9783030598501

the proceedings contain 25 papers. the special focus in this conference is on High Performance Computing. the topics include: Characterizing I/O Optimization Effect through Holistic Log Data Analysis of Parallel File Systems and Interconnects;the Importance of Temporal Behavior When Classifying Job IO Patterns Using machine learning Techniques;GOPHER, an HPC Framework for Large Scale Graph Exploration and Inference;ensembles of Networks Produced from Neural Architecture Search;smartPred: Unsupervised Hard Disk Failure Detection;application IO Analysis with Lustre Monitoring Using LASSi for ARCHER;AI-Driven Holistic Approach to Energy Efficient HPC;Characterizing HPC Performance Variation with Monitoring and Unsupervised learning;Service Function Chaining Based on Segment Routing Using P4 and SR-IOV (P4-SFC);static Analysis to Enhance Programmability and Performance in OmpSs-2;Seamlessly Managing HPC Workloads through Kubernetes;interference-Aware Orchestration in Kubernetes;rustyHermit: A Scalable, Rust-Based Virtual Execution Environment;Rootless Containers with Podman for HPC;bioinformatics application with Kubeflow for Batch Processing in Clouds;Converging HPC, Big Data and Cloud Technologies for Precision Agriculture Data Analytics on Supercomputers;Automatic Detection of MPI Assertions;Automatic Code Motion to Extend MPI Nonblocking Overlap Window;complete Deep computer-Vision Methodology for Investigating Hydrodynamic Instabilities;prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep learning;unsupervised learning of Particle Image Velocimetry;reduced Order Modeling of Dynamical Systems Using Artificial Neural Networks Applied to Water Circulation;Parameter Identification of RANS Turbulence Model Using Physics-Embedded Neural Network.

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An Ensemble Interpretable machine learning Scheme for Securing Data Quality at the Edge 4th

An Ensemble Interpretable Machine Learning Scheme for Securi...

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4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 international Cross-Domain conference for machine learning and Knowledge Extraction, CD-MAKE 2020

作者： Karanika, Anna Oikonomou, Panagiotis Kolomvatsos, Kostas Anagnostopoulos, Christos Department of Informatics and Telecommunications University of Thessaly Volos Greece School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9783030573201

Data quality is a significant research subject for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of things (IoT) where numerous devices can interact to exchange and process data. IoT devices are connected to Edge Computing (EC) nodes to report the collected data, thus, we have to secure data quality not only at the IoT infrastructure but also at the edge of the network. In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based on for any data processing activity. Our aim is to secure data quality for those features, at least, that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining ones in every dataset, thus, they can be adopted for dimensionality reduction. We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. Our scheme is capable of timely retrieving the final result and efficiently selecting the appropriate features. We evaluate our model through extensive simulations and present numerical results. Our aim is to reveal its performance under various experimental scenarios that we create varying a set of parameters adopted in our mechanism. © 2020, IFIP international Federation for Information Processing.

关键词： Internet of things

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Bank card number recognition system based on deep learning 20

Bank card number recognition system based on deep learning

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proceedings of the 2020 4th international conference on Electronic Information Technology and computer Engineering

作者： Gege Sun Fucheng You College of Information Engineering Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9781450387811

With the widespread application of mobile payment, identifying the bank card number can help us bind the bank card quickly. the angle of bank card shooting, the light and shade of light, and the background colour of cards from different companies will affect card number recognition accuracy. therefore, this paper designs a bank card number recognition system, which mainly includes three parts: card number location, character recognition and GUI interface design. Firstly, the improved EAST algorithm is proposed to locate the card number, and then the neural network training and character recognition are carried out. In this process, the CRNN-BLSTM-CTC architecture of the mainstream OCR algorithm is mainly used. Finally, the GUI is used to demonstrate the results. the experimental results show that the system significantly improves the accuracy and efficiency of bank card number recognition.

关键词： Image processing Deep learning CNN

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proceedings - 2019 4th international conference on Mechanical, Control and computer Engineering, ICMCCE 2019

Proceedings - 2019 4th International Conference on Mechanica...

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4th international conference on Mechanical, Control and computer Engineering, ICMCCE 2019

ISBN: (纸本)9781728146898

the proceedings contain 232 papers. the topics discussed include: optimal design of ultra-thin seats based on ergonomics;research on coverless text steganography based on multi-rule language models alternation;research on wood species recognition method based on hyperspectral image texture features;optimization of public traffic network considering demand satisfaction rate;optimization planning of distribution network structure in Benxi area based on ant colony algorithm;identity-based aggregate signature scheme in vehicle ad-hoc network;tool wear volume and residual life prediction based on extreme learning machine;and application of improved optics algorithm in intelligent lighting system.

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A Model Selection Method for machine learning by Differential Evolution 19

A Model Selection Method for Machine Learning by Differentia...

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4th international conference on Big Data and Computing (ICBDC)

作者： Chiu, Yi-Chuan Lin, Hsing-Hung Jou, Yung-Tsan Chung Yuan Christian Univ Dep Ind & Syst Engn Taoyuan Taiwan Chunghwa Telecom Co Ltd Big Data Lab Telecom Lab Taoyuan Taiwan

ISBN: (纸本)9781450362788

While the application of big data attracts more and more attention, machine learning algorithms are developing with each passing day, and the models produced by machine learning are increasingly diversified. the focus of big data applications has gradually shifted from model training to the prediction and inference. Choosing the most suitable model for enterprise application scenarios among many machine learning models has become a topic of research that has attracted much attention. though ensemble methods have been proposed to discover best model by multiple training phase, studies of finding best combination within multiple modes are still few. Configuring different machine learning models with appropriate parameters and looking for parameters is an NP-hard problem, which requires an optimization algorithm. this study proposes a differential evolution algorithm to integrate multiple trained machine learning models into an appropriate model. In this paper, the regression model is taken as an example and the differential evolution algorithm is compared with the genetic algorithm. three benchmark datasets are used to examine, and the results show that the differential evolution algorithm outperforms genetic algorithm.

关键词： machine learning big data differential evolution

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Deep learning application for Negative Sequence Electromagnetic Loss and Temperature Rise Calculation 20

Deep Learning Application for Negative Sequence Electromagne...

引用

proceedings of the 2020 4th international conference on Electronic Information Technology and computer Engineering

作者： Wu Guo Jian Guo Zi-yi Liu College of Communication and Electronic Engineering Qiqihar University Qiqihar China College of Architecture and Civil Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9781450387811

During the operation of large generators, asymmetric operation is inevitable, and negative sequence current appears in the three-phase armature winding of the generator. the damage of negative sequence current to generators occurs frequently. With the increase of single generator capacity in recent years, the damage of negative sequence current to generator also rises. Fast and accurate calculation can reduce the damage of negative sequence current to generator rotor and improve the capacity of bearing asymmetric load. It is very important to study the negative sequence loss and temperature rise of generator when it is running asymmetrically. To carry out researches, it is necessary to conduct simulation calculation, analysis and processing of massive data. therefore, this paper adopts the method of parallel computing to boost in-depth research on the negative sequence study of generators, which greatly improves the computing speed, and the research results are accurate and reliable.

关键词： Electromagnetic loss Deep learning Parallel computing Negative sequence

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Breast Cancer Pathological image auto-classification using weighted GLCM-SVM 4

Breast Cancer Pathological image auto-classification using w...

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4th international conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Ding, Shuhan Univ Elect Sci & Technol China Glasgow Coll Chengdu Peoples R China

ISBN: (纸本)9781728146898

At present, when radiologists screen the breast cancer, they always miss about 10-30% tumors for pathological edge blurring and asthenopia after the diagnosis for a long time. For this reason, setting up the computer Aided Diagnosis (CAD) system is of great clinical application value. this thesis proposes the method of weighted GLCM to extract the texture features of mammogram and classify abnormal regions according to the evaluation of the performance of each individual by using support vector machines (SVM). the experiment shows our system is effective for breast cancer pathological image.

关键词： Breast cancer Digtial mammography machine learning Weighted Gray-level co-occurrence matrix Support vector machine

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Active learning for Auditory Hierarchy 4th

Active Learning for Auditory Hierarchy

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4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 international Cross-Domain conference for machine learning and Knowledge Extraction, CD-MAKE 2020

作者： Coleman, William Cullen, Charlie Yan, Ming Delany, Sarah Jane School of Computer Science TU Dublin Kevin Street DublinD08 X622 Ireland Xperi Corporation BangorBT19 7QT United Kingdom

ISBN: (纸本)9783030573201

Much audio content today is rendered as a static stereo mix: fundamentally a fixed single entity. Object-based audio envisages the delivery of sound content using a collection of individual sound ‘objects’ controlled by accompanying metadata. this offers potential for audio to be delivered in a dynamic manner providing enhanced audio for consumers. One example of such treatment is the concept of applying varying levels of data compression to sound objects thereby reducing the volume of data to be transmitted in limited bandwidth situations. this application motivates the ability to accurately classify objects in terms of their ‘hierarchy’. that is, whether or not an object is a foreground sound, which should be reproduced at full quality if possible, or a background sound, which can be heavily compressed without causing a deterioration in the listening experience. Lack of suitably labelled data is an acknowledged problem in the domain. Active learning is a method that can greatly reduce the manual effort required to label a large corpus by identifying the most effective instances to train a model to high accuracy levels. this paper compares a number of Active learning methods to investigate which is most effective in the context of a hierarchical labelling task on an audio dataset. Results show that the number of manual labels required can be reduced to 1.7% of the total dataset while still retaining high prediction accuracy. © 2020, IFIP international Federation for Information Processing.

关键词： Support vector machines

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