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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是331-340 订阅

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Research on Radiation Damage Characteristics of Optical Fiber Materials Based on data mining and machine learning 3rd

Research on Radiation Damage Characteristics of Optical Fibe...

引用

3rd European-Alliance-for-Innovation (EAI) international conference on Advanced Hybrid Information Processing (ADHIP)

作者： Li, Ang Wang, Tian-hui Jilin Univ Zhuhai Coll Zhuhai Peoples R China

ISBN: (纸本)9783030364021;9783030364014

In order to better analyze the damage characteristics of fiber materials under radiation environment, combined with data mining algorithm to calculate the degree of damage of material structure damage. Combine with machine learning method to analyze the calculation results, obtain the damage range of fiber material structure, standardize material damage characteristics and Grade, accurately determine the damage of material structure, and finally improve the radiation damage characteristics of fiber materials. Experiments show that the research on radiation damage characteristics of fiber materials based on data mining and machine learning is accurate and reasonable.

关键词： data mining machine learning Fiber material Radiation damage characteristics

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Application of Deep learning in Imaging diagnosis of Brain diseases 3

Application of Deep Learning in Imaging diagnosis of Brain d...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Liang, Junchen Wang, Zhaoze Ye, Xiangyang Xi An Jiao Tong Univ Sch Math & Stat Xian Peoples R China Wenzhou Kean Univ Dept Comp Sci Wenzhou Zhejiang Peoples R China Jilin Univ Coll Automot Engn Changchun Peoples R China

ISBN: (纸本)9781665417907

The brain is one of the most important parts of the human body, and the diagnosis of its diseases is of great significance to the treatment of diseases. With the rapid development of deep learning in recent years, its automatically extracted image features have significant advantages compared to traditional artificially extracted features. Therefore, more and more recognition methods based on deep learning are widely used in medical image recognition tasks (such as CT, MRI, PET-CT.). This paper will introduce the application of traditional methods and deep learning methods in various brain diseases. These methods are compared, analyzed, and summarized, then we explored their development status and future development trends.

关键词： brain disease medical image computer-assisted diagnosis deep learning

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Research on Electricity Information Acquisition System Based on Sample data mining Model 3

Research on Electricity Information Acquisition System Based...

引用

3rd international conference on Computer Information Science and Application Technology (CISAT)

作者： Dang Zhongkui Fu Lei State Grid NingXia Informat & Telecommun Co Guyuan 756000 Peoples R China

In power user information collection and detection, power companies generally have a variety of different detection needs, or need to solve the problem while having additional requirements for certain aspects. Therefore, the SVM classification technology is used in the paper to carry out more detailed pattern recognition of power consumption characteristics for small-scale users or users with major suspicions. Moreover, given the imbalance of the abnormal electricity detection data set, a comprehensive processing model of unbalanced samples is constructed. Meanwhile, the differential evolution algorithm is applied to complete the SVM parameter optimization, which not only solves the problem that the SVM classification performance is significantly affected by the parameters, but also ensures the operating efficiency of the integrated classification model.

关键词： pattern recognition

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data Driven GNSS Based Interference Signal Detection Technology 3

Data Driven GNSS Based Interference Signal Detection Technol...

引用

3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Chen, Yiming Zhuo, Shulong Yihan Yongwu Sanya Aviation and Tourism College Electromechanic Engineering Institude Hainan China Civil Aviation Flight University of China Aviation Engineering Institute Sichuan China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Most of the traditional GNSS interference detection algorithms have disadvantages such as low detection accuracy and complicated algorithms when detecting interference signals. In this regard, a GNSS interference signal detection algorithm based on convolutional neural network is proposed in this paper. Firstly, various types of interference signals are simulated and generated by MATLAB software. The simulated signal RF output is performed by controlling the vector signal generator (VSG) and data acquisition is performed in the microwave laboratory. Subsequently, the received baseband signal data is simulated over the channel, which is used to construct training data sets for various JNR scenarios. Finally, the neural network designed in this paper is trained using the sample data, and the conventional interference detection algorithm is compared with the proposed neural network algorithm in this paper on the same sample data set. The experimental results confirm that the proposed neural network algorithm has better detection performance compared with the existing traditional detection algorithms. © 2022 IEEE.

关键词： Signal interference

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The Application of Deep learning in Micro-Expression recognition 3

The Application of Deep Learning in Micro-Expression Recogni...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Zhao, Xinyuan Sun, Furong Shen, Xunbing Jiangxi Univ Chinese Med Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665417907

With the advent of the era of big data, the application of artificial intelligence and big data technology has led to widespread interest in facial expression recognition. In addition to the common macro-expressions in daily life, facial expressions also have an imperceptible subtle expression called micro-expressions. Micro-expression is a very fast expression, lasting only 1/25 seconds to 1/5 seconds, expressing the real emotions that people try to suppress and hide. Micro-expressions have important applications in public safety, judicial criminal investigation, clinical medicine, etc. Therefore, the research on micro-expression recognition has been increasing in recent years. In the year of 2006, Hinton's proposal of deep learning in an article in "Science" made it appear in front of the world as a new field of machine learning, which set off a wave of deep learning. Deep learning is currently one of the hottest research directions in artificial intelligence and machine learning, and it is widely used in speech recognition, natural language processing, image recognition and other fields. Because of the characteristics of micro-expressions such as short time and subtle changes, traditional machine learning algorithms have poor robustness. Therefore, this paper summarizes the research on microexpression recognition based on deep learning methods, mainly including some mainstream algorithms such as DBN, CNN, and discusses the development problems and trends of deep learning on micro-expression recognition, hoping to provide reference for the subsequent research in this field.

关键词： artificial intelligence micro-expression machine learning deep learning Deep Belief Network(DBN) Convolutional Neural Network(CNN)

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Privacy Preserving in Distributed SVM data mining on Vertical Partitioned data 3

Privacy Preserving in Distributed SVM Data Mining on Vertica...

引用

3rd international conference on Soft Computing and machine Intelligence (ISCMI)

作者： Omer, Mohammed Z. Gao, Hui Sayed, Faisal UESTC Sch Comp Sci & Engn Chengdu 611731 Peoples R China Arab Supplier Gen Trading POB 2894 Dubai U Arab Emirates

ISBN: (纸本)9781509036967

data mining algorithms tacitly quite access to the data either at centralized or distributed form. Distributed data becomes a big challenge and cannot handle by a classical analytic tool. Cloud Computing can solve the issues of processing, storing, and analyzing the data at distributing locations within the cloud. However, a significant problem that is preventing free sharing of data is privacy and security issues, therefore obstructing data mining schemes. Lately, there is increasingly hard to find a solution to these problems. Due to the existing knowledge in a more distributed data and better for data mining issues. An important task of data mining and machine learning is classification, a widely used in classification is support vector machine (SVM) algorithms applicable in many various domains. In this paper, we proposes a privacy-preserving solution for SVM classification. Our workaround constructing a global SVM classification model from vertically partitioned distributed data at multi-parties based on Gram matrix, without revealing a party's data. We proposed an efficient and preserve privacy protocol for SVM classification on vertical partitioned data. Our experimental results, the accuracy of distributed SVM using Gram matrix up to 90% and the privacy not compromised.

关键词： Classification Privacy Preserving Homomorphic Encryption Gram Matrix

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Human Safety Devices using IoT and machine learning: A Review 3

Human Safety Devices using IoT and Machine Learning: A Revie...

引用

3rd international conference for Convergence in Technology (I2CT)

作者： Sharma, Kritika Londhe, Deepali D. Pune Inst Comp Technol Dept Informat Technol Pune Maharashtra India

ISBN: (纸本)9781538642733

Human safety has become one of the most targeted field for the researchers, owning it to its grave importance and the increased competition in the market for human safety gadgets. Hundreds and thousands of human safety devices (HSD) are being developed because of the rapid advancement in the field of Internet of things (IoT) that involve sensing technologies, embedded systems, wireless communication technologies, variety of sensors etc. An essential function of these devices is human activity recognition (HAR).Present human safety devices continuously track human activities with the help of sensors and track down any unusual activity by performing sensor data analysis (SDA)using machine learning (ML) algorithms. This paper aims at reviewing the latest reported systems for human safety and listing down the various sensors that can be used in human safety devices to detect unusual activities along with the machine learning algorithms that are used for the sensor data analysis.

关键词： human activity recognition human safety devices loT machine learning sensors sensor data analysis

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Two approaches to machine learning classification of time series based on recurrence plots 3

Two approaches to machine learning classification of time se...

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3rd IEEE international conference on data Stream mining and Processing, DSMP 2020

作者： Kirichenko, Lyudmyla Radivilova, Tamara Bulakh, Vitalii Zinchenko, Petro Saif Alghawli, Abed Kharkiv National University of Radio Electronics Department of Applied Mathematics Kharkiv Ukraine Kharkiv National University of Radio Electronics V.V. Popovskyy Dept. of Infocommunication Engineering Kharkiv Ukraine Prince Sattam Bin Abdulaziz University Department of Computer Science Aflaj Saudi Arabia

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132143

The article considers the task of classifying fractal time series based on the construction of their recurrence plots. Short realizations of EEG signals were used as input data. Two classification machine learning methods were considered: in the first case, quantitative fractal and recurrent characteristics of the time series were classification features, in the second case, image recognition of recurrence plots was carried out. The results showed fairly high classification quality for both methods, and relative advantage of the image classification method. © 2020 IEEE.

关键词： Time series

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Toward Projection learning between Sensor data and Semantic Word Vector for Zero-shot learning 8

Toward Projection Learning between Sensor Data and Semantic ...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior Computing (ABC)

作者： Matsuki, Moe Inoue, Sow Kyushu Inst Technol Grad Sch Engn Dept Appl Sci Integrated Syst Engn Kitakyushu Fukuoka Japan Kyushu Inst Technol Fac Engn Grad Sch Biol Engn Kitakyushu Fukuoka Japan

ISBN: (纸本)9781728107882

In this paper, we compare 4 learning projection models between sensor domain and text domain for Zero-shot learning (ZSL). In traditional activity recognition with sensor data, the task of collecting training dataset is too tough and costly to apply for social. Our challenge is making the task efficient. The Zero-shot learning's purpose is to recognize the unknown activity which is activity class out of training dataset. In our previous research, we propose the Zero-shot learning method using the word vectors made from Wikipedia corpus for recognizing the human living activities like breakfast, watching TV, etc. We found that this method success to recognize unknown activities and need to improve the projection function for performance. In this paper, we construct 4 learning models for projection and evaluate them with accelerometer sensor data annotated simple activities. As a result, we realize that (1) the learning method with twice projection is useful for performance. (2) It is difficult to identify the two unknown activities whose distance from known activities is closer than that between other combination of two unknown activities and the known ones.

关键词： feature learning activity recognition accelerometer

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machine learning Approaches for Marketing Campaign in Portuguese Banks 3

Machine Learning Approaches for Marketing Campaign in Portug...

引用

3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Alexandra, Jennifer Sinaga, Kristina Pestaria Bina Nusantara Univ Dept Master Informat Syst Management Jakarta Indonesia

ISBN: (纸本)9781665425803

Bank is a type of business that deals with saving, circulation of money, deposits and others. The number of services provided by banks is very diverse, this depends on the capabilities of each bank. The more capable and better the bank is, the more services it will offer. Introducing the product directly has been commonly used for various industries, one of them is the banking industry. In directly introducing products, banks can conduct market analysis by utilizing the information technology space that can assist in making decisions. By analyzing bank marketing data, it can be used to select the type of marketing to do. Marketing campaigns can be carried out via email, telephone, and direct email to prospective customers that allow potential customers to decide whether to take the product offered or not. With increasing time, the amount of incoming data continues to grow. With this increasing data, one of the bank institutions found it difficult to predict whether their clients would subscribe to a term deposit or not. Therefore, in this paper, the data mining process will be carried out using classification (Decision Tree, Wye Bayes, and Random Forest) and clustering (K-Means, K-Medoids, and DBSCAN) methods to predict if the client will subscribe a term deposit.

关键词： component machine learning data mining Classification Clustering

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