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检索条件"任意字段=2nd International Conference on Algorithms for Big Data, ICABD 2014"

共 223 条记录，以下是101-110 订阅

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An exploration of live e-commerce paths from an algorithmic research perspective

An exploration of live e-commerce paths from an algorithmic ...

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big data & Artificial Intelligence & Software Engineering (ICBASE), international conference on

作者： Wenxi Zeng Jingying Ding Zehao Jin School of College of Communication Science and Art Chengdu University of Technology Chengdu China School of Journalism and Communication Hong Kong Shue Yan University Hong Kong China School of International and Communication and Education Communication School of Zhejiang Hangzhou China

ISBN: (纸本)9781665427104

Live delivery as a new e-commerce marketing model is in a period of rapid development. It uses algorithms to review content and optimize resource allocation to increase traffic and transaction data. It has become an essential online retail model in China in the era of big data. This article focuses on the evolvement of big data, summarizing the development process and communication characteristics of China's live broadcast e-commerce model and reflects on the ways and algorithmic mechanisms of algorithms participating in China's live broadcast e-commerce model.

关键词： Fans Software algorithms Psychology Production big data Streaming media data models

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Optimizations of Training dataset on House Price Estimation

Optimizations of Training Dataset on House Price Estimation

引用

big data Economy and Information Management (BDEIM), international conference on

作者： Xiangqin Cheri Aquinas International Academy Garden Grove CA

ISBN: (纸本)9781665482899

House price in many cases can strongly reflect local economy, predicting the real estate price can help consumer and the developers. In this way, consumers can inquiry price by giving a series of attributes which describes their ideal house and sellers can get the recommended price by uploading the property description. Thus, a fast, 24-hour responsive auto-inquiry system can greatly improve customers' and sellers' experience. To establish similar system, the dataset which is used to describe the sale of individual residential property in Ames is set as an experiment material. This paper discusses the influence from the data-preprocessing stage on the establishment of later machine learning model using different data groups. Later in the accuracy comparison, controlled experiment is implemented to show the accuracy among different organization of the algorithms. This paper is aimed at how much influence from the model choosing and data preprocessing stage will be made on the prediction accuracy and interpret methods used in preprocessing stage. Furthermore, the experiments show that the single model prediction's accuracy has on average 5% less than the stacked model. In addition, the results represented in this paper have the potential to be integrated into the housing sales system which can be applied to price delivery systems to benefit both consumers and sellers.

关键词： Training Machine learning algorithms Computational modeling data preprocessing Machine learning Predictive models Prediction algorithms

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The Comparison of Some Algorithm Based on Ceemdan

The Comparison of Some Algorithm Based on Ceemdan

引用

big data & Artificial Intelligence & Software Engineering (ICBASE), international conference on

作者： Sijin Wang Yiran Shao Jingwen Qian Shuhan Sun Siyuan Yu School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Affiliated High School of South China Normal University Guangzhou Guangdong China Ningbo Huamao International School Ningbo Zhejiang China Suzhou Foreign Language School Suzhou Jiangsu China Justin-Siena High School Napa Valley California the United States of America

ISBN: (纸本)9781665427104

Stock price prediction is an indispensable part of the investment market and lots of approaches have been proposed. This article implements Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the dataset, because the time series data were usually nonlinear and nonstationary. Meanwhile, our algorithms do not have to decide the basic function. In addition, different algorithms are adopted to process different levels of Intrinsic Mode Function (IMF) in order to capture their different characteristics. In that case, a combined algorithm containing CEEMDAN, MLP-BP, LSTM/GRU and Linear Regression is used. The combined algorithm works well on Tesla’s price series from 2010 to 2020, and it can not only process the data with a high accuracy, but also has a short running time because some of the algorithms in the combination are very simple. Then the combined algorithm is adopted on a more representative dataset called S&P 500 index. It turns out it is still very accurate and fast.

关键词： Runtime Software algorithms Linear regression Time series analysis Prediction algorithms Market research Indexes

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The Choice of Kernel Function for One-Class Support Vector Machine

The Choice of Kernel Function for One-Class Support Vector M...

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big data & Artificial Intelligence & Software Engineering (ICBASE), international conference on

作者： Jiaoyang Li Lingfeng Zuo Tianyu Su Zihan Guo University of Toronto Toronto Ontario Canada Guangdong Country Garden School Shunde Guangdong China Beijing New Oriental Foreign Language School at YangZhou Yangzhou Jiangsu China Shenzhen College of International Education Shenzhen Guangdong China

ISBN: (纸本)9781665427104

Support vector machines (SVM) are mainly used in classification problem, like character recognition, face recognition, pedestrian detection, text classification and other fields. Isolation forest can be used to anomaly detection (outlier detection), which requires relatively small samples from large data sets. SVM has several kernel functions including RBF, linear, polynomial and radial. In this paper, the experiments on different real-world data sets are performed to demonstrate the importance of kernel choice and the corresponding sensitivity of the algorithm. This paper mainly explores the performance of various main kernel functions in different data sets in OCSVM, and compares the final effect of OCSVM and isolation forest in processing the same data set. Furthermore, this paper also compares the OCSVM method using RBF kernel with adaptive boosting.

关键词： Adaptation models Text categorization Software algorithms Support vector machine classification Forestry Boosting Classification algorithms

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Processing and analyzing health data in a big data context: Aspects and implementations 2nd

Processing and analyzing health data in a big data context: ...

引用

2nd international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： Slime, Khaoula Maizate, Abderrahim Hassouni, Larbi ENSEM/UH2 Casablanca Morocco

ISBN: (纸本)9783030366636

Nowadays, the development of machine learning algorithms and big data frameworks make the processing of data a major asset in different sectors. Indeed, this technology allows us to carry out in-depth analyzes of large masses of data and thus generate very useful results for decision-making. Among the sectors where this technology is used is that of health in which analysis and prevention are essential to improve the quality of health care. Our research focuses on improving the application of this technology in the health field. In this paper we will present big data aspects, give some implementations and discuss different big data architectures in healthcare sector. © Springer Nature Switzerland AG 2020.

关键词： Machine learning

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Depth insight for data scientist with RapidMiner « an innovative tool for AI and big data towards medical applications» 20

Depth insight for data scientist with RapidMiner « an innov...

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2nd international conference on Digital Tools and Uses Congress, DTUC 2020

作者： Bjaoui, Mohamed Sakly, Houneida Said, Mourad Kraiem, Naoufel Bouhlel, Mohamed Salim Sfax Tunisia University of Manouba Mannouba Tunisia International Center Carthage Medical Monastir Tunisia

ISBN: (纸本)9781450377539

RapidMiner tool is considered among the advanced analytics and powerful platform services in the field of artificial intelligence besides the big data storage. This solution has emerged towards several industries such as Financial Services, Energy, Logistics, Life Science and Healthcare and has shown a crucial impact for predictive decisions in this area. This work seeks to describe the solution strategy of this tool for data scientist by depicting a depth insight of this concept which contains more 1500 native algorithms, data preparation and data science functions. This features allows professionals to support any machine learning libraries and integrate python and R codes. RapidMiner offers three different modalities to access to their products which are the main Platform, the automated data science and the AI cloud. © 2020 Owner/Author.

关键词： Medical applications

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Application of Face Recognition in Smart Hotels 2

Application of Face Recognition in Smart Hotels

引用

2nd IEEE Eurasia conference on IOT, Communication and Engineering, ECICE 2020

作者： Chen, Zhang-Bin Liu, Yang Fuzhou University of International Studies and Trade Changle Fuzhou Fujian China Fujian Zhongrui Network Co. Ltd. Fuzhou Fujian China

ISBN: (纸本)9781728180601

With the development of AI technology, face recognition technology is more accurate and faster than before. The application of the technology in smart hotels fully integrates the management and services for a guest room system or leisure of entertainment purposes, which is realized by "face". The face recognition system provides relevant technical support for smart hotels and allows visitors to pass quickly, facilitate the management, and enjoy the convenience of high technology. The article introduces the concept of a smart hotel, artificial intelligence technology, face recognition technology and key algorithms, comprehensive application of cloud computing, big data, AI (artificial intelligence), Internet of Things, and other emerging technologies. From the perspective of application to the smart hotel, research on human faces is performed for the application of recognition technology. It provides a technical reference for the construction of smart hotels. © 2020 IEEE.

关键词： Face recognition

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Comparison Analysis for Pruning algorithms of Neural Networks

Comparison Analysis for Pruning Algorithms of Neural Network...

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Computer Engineering and Intelligent Control (ICCEIC) international conference on

作者： Xi Chen Jincheng Mao Jian Xie International School Beijing University of Posts and Telecommunications Beijing China School of Engineering and Informatics University of Sussex Brighton United Kingdom School of Information Engineering Nanchang Institute of Technology Nanchang China

ISBN: (纸本)9781665402132

Deep learning has been widely applied in many fields, such as big data analysis, natural language processing, and image processing, etc. However, the number of parameters in the deep learning model is often too large that the model would be difficult to apply portable devices. Therefore, the researches about pruning algorithms for optimizing the deep learning model has great significance. This paper conducts an in-depth analysis of the pruning algorithm in the model compression method. Besides, we classify and summarize the structure, scheduling, and scoring of the pruning algorithms. And then, the compression effects of some mainstream pruning algorithms on some deep neural networks including accuracy are compared and analyzed. Finally, the future development of the pruning algorithm is promising. Regardless of the different pruning approaches, the mainstream pruning algorithms can all effectively compress the size of the network model while maintaining basic accuracy. However, one pruning algorithm has a different performance when it is applied to different network models and datasets. The future pruning algorithm can be designed in terms of network sparsity structure, scheduling, and parameter ranking criteria. Moreover, the datasets and models are also required to be considered when the corresponding high-performance pruning strategies are used.

关键词： Deep learning Analytical models Image coding Neural networks Graphics processing units Hardware Scheduling

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Variants and Applications of Generative Adversarial Networks

Variants and Applications of Generative Adversarial Networks

引用

big data & Artificial Intelligence & Software Engineering (ICBASE), international conference on

作者： Gaohe Cai Yumeng Sun Yiwen Zhou School of Majestic international college Foshan China School of Cambridge International Beijing China College of Letters & Science University of California Berkeley Berkeley CA USA

ISBN: (纸本)9781665427104

Generative adversarial network (GAN) has been extensively applied in many fields since it was first proposed in 2014. There have been ongoing attempts at improving the model and developing its extensions. The basic idea behind GAN stems from the two-player zero-sum game. Composed of a generator and a discriminator, GAN is trained through a “battle” between the two networks, where the generator tries to fool the discriminator. In contrast, the discriminator tries not to be deceived. The purpose is to estimate the potential distribution of data samples and generate new data samples. GAN is currently being studied in computer vision, language processing, and information security and is still far from reaching its potential. Since there are limited existing resources on GAN reviews, this paper provides a comprehensive review of a few of the most important GAN variants. We divided the variants into three categories: foundation, extension, and advanced, gave an overview of each architecture, and analyzed their advantages and disadvantages. We also summarized some of the most popular applications of GAN, and finally, we brought up the problems of GAN-based architectures that have yet to be solved and pointed out the future direction of GAN research and development.

关键词： Three-dimensional displays Computer architecture Medical services Machine learning Generative adversarial networks Market research Generators

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2nd international conference on Computer, Communication, and Signal Processing: Special Focus on Technology and Innovation for Smart Environment, ICCCSP 2018

2nd International Conference on Computer, Communication, and...

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2nd international conference on Computer, Communication, and Signal Processing, ICCCSP 2018

ISBN: (纸本)9781538611418

The proceedings contain 41 papers. The topics discussed include: design and development of flexible on-board diagnostics and mobile communication for internet of vehicles;analysis of brain wave due to stimulus using EEG;design analysis of multi band FSS antenna in C and KU band;robust dwt-SVD based blind watermarking for DIBR 3D video;analysis of supervised and unsupervised learning in content based multimedia retrieval;word sign recognition of invariant images based on surf with Laplacian eigenmaps;a survey on IOT technologies, evolution and architecture;smart bus rerouting;an approach to detect replay attack in automatic speaker verification system;SIW flower shaped fractal antenna backed with cavity for 60GHz frequency applications;bilingual speech recognition system for isolated words using deep neural network;analysis of single and dual band microstrip FSS annular ring antenna for KU band;automatic code evaluation system;multi-level smart parking system;and a feature based comparison study of big data scheduling algorithms.

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