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检索条件"任意字段=6th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2021"

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Proceedings - 2021 ieee/acis 6th international conference on big data, cloud computing, and data science, bcd 2021

Proceedings - 2021 IEEE/ACIS 6th International Conference on...

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

ISBN: (纸本)9781728176819

the proceedings contain 17 papers. the topics discussed include: a cryptocurrency prediction model using LSTM and GRU algorithms;intrusion prediction using long short -term memory deep learning with UNSW-NB15;unsupervised ensemble learning and its application to temporal data mining;a low-illumination image enhancement algorithm based on morphological-Retinex(MR) operator;a streaming data collection and analysis for cryptocurrency price prediction using LSTM;dolphin identification method based on improved pst;classification of breast cancer based on improved PSPNet;a machine learning approach to predict the trend of obesity prevalence at a global level;and study on breast optical spectrum classification based on machine learning.

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Customer Review Analysis: A Systematic Review 6

Customer Review Analysis: A Systematic Review

引用

6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： Davis, Storm Tabrizi, Nasseh East Carolina University Dept. of Computer Science GreenvilleNC United States

ISBN: (纸本)9781728176819

Due to the continuous growth of E-Commerce platforms, more and more data is becoming widely available. Almost every online transaction, from buying products on Amazon to renting vacation homes on Airbnb, allows for users to leave feedback in the form of customer reviews. therefore, there is a need to provide an overview of the status of customer review analysis. this paper systematically reviews research works conducted in the past ten years (2010-2020) to identify how customer reviews are being analyzed and how this analysis can serve the consumers or vendors. We present several commonly used machine learning algorithms, datasets, and numerous applications of customer review analysis to provide insight as well as discuss directions for future research. © 2021 ieee.

关键词： big data

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Dolphin Identification Method Based on Improved PST 6

Dolphin Identification Method Based on Improved PST

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： Wang, Shiyu Cai, Zhanchuan Cao, Wei Yuan, Jieyu Faculty of Information Technology Macau University of Science and Technology Macau China Ministry of Education of China Beijing Institute of Technology Key Laboratory of Photoelectric Imaging Technology and System Zhuhai China

ISBN: (纸本)9781728176819

Dolphin identification is an important task on the environmental protection. In previous studies phase stretch transform (PST) edge detection algorithm was a global algorithm that used the phase contour of the complex amplitude image to detect edges. In this paper, the improved PST algorithm is applied to the actual scene for identifying the Chinese white dolphins. the edges of the detected dolphins are selected by frames and can be displayed in the RGB image. the noise is removed to ensure that the pixel value near the edge will not affect the result of edge detection. the MSE and PSNR metric are applied to evaluate the performance of edge detection operators on several real images. © 2021 ieee.

关键词： Edge detection

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A Streaming data Collection and Analysis for Cryptocurrency Price Prediction using LSTM 6

A Streaming Data Collection and Analysis for Cryptocurrency ...

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： Kim, Jongyeop Wimmer, Hayden Liu, Hong Kim, Seongsoo Georgia Southern University Department of Information Technology StatesboroGA United States Indiana University Kokomo Department of Information Technology KokomoIN United States

ISBN: (纸本)9781728176819

big data analysis for accurate predictions requires adherence to systematic procedures. this study shows an entire data analysis phase from the data collection to model evaluation using the Long Short-Term memory(LSTM) for cryptocurrency price prediction. three different coin prices are directly collected from the CoinMarketCap in nearly real-Time by applying the web scraping technique. the LSTM model trained with this data varying random seed or static seed parameters to find optimal conditions, leading to better accuracy of the LSTM model. Our model evaluated their accuracy in terms of MAE, RMSE, and SMAPE indicators. As a result of this experiment, most of the best candidate parameters are classified at the fixed seed trail in terms of the RMSE for Bit coin, Ethereum, and Lite Coin. © 2021 ieee.

关键词： Long short-term memory

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A Cryptocurrency Prediction Model Using LSTM and GRU Algorithms 6

A Cryptocurrency Prediction Model Using LSTM and GRU Algorit...

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： Kim, Jongyeop Kim, Seongsoo Wimmer, Hayden Liu, Hong Georgia Southern University Department of Information Technology StatesboroGA United States Indiana University Department of Computer Science Kokomo United States

ISBN: (纸本)9781728176819

this study aims to predict cryptocurrency prices using Long Short-Term Memory(LSTM) and Gated Recurrent Unit(GRU) for three different coins: BitCoin, Ethereum, and Litecoin. For the training data for prediction, two data sets with different statistical characteristics in terms of Kurtosis and Skewness are used. LSTM and GRU models are trained and tested on the same hyperparameter configuration while increasing the number of epochs from 1 to 30. the accuracy of each model is measured by Root Mean Square Error (RMSE) and MAE (Mean Absolute Error). As a result of comparing GRU and LSTM, in BTC and Eth, the GRU was more advantageous for the downward stabilization trend, and the LSTM was suitable for the upward stabilization trend. However, in case of low-priced LTC, LSTM and GRU showed the same performance in sample type A, and in the case of type B, GRU was more accurate. © 2021 ieee.

关键词： Long short-term memory

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A Machine Learning Approach to Predict the Trend of Obesity Prevalence at a Global Level 6

A Machine Learning Approach to Predict the Trend of Obesity ...

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： Barzinji, Ala Othman Ma, Chaoying Du, Wencai Ma, Jixin School of Computing and Mathematical Sciences Faculty of Liberal Arts and Sciences University of Greenwich United Kingdom Institute of Data Science City University of Macau China

ISBN: (纸本)9781728176819

Excessive weight is associated with adverse health risks. Understanding the global trends in Obesity, in children and adolescents, is paramount to impede the increasing rate of global Obesity prevalence. Different machine learning models were used to predict Obesity prevalence, in children and adolescents, at a global level. this paper presents a novel approach to predict Obesity beyond 2030 using machine learning. the data was derived from a global population-based survey in 2015. In the main study, we applied machine learning models to predict the exponential rise in Obesity prevalence across the world in 2030, 2040, and 2050. In the second study, we further calculated the Obesity prevalence rates according to Socio-Demographic Index (SDI). We obtained promising results with model prediction accuracies of up to 99% R2 for the main study, and up to 92% R2 for the SDI study. © 2021 ieee.

关键词： Health risks

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Topic Model based Ensemble Learning for Rating Prediction and Its Application 6

Topic Model based Ensemble Learning for Rating Prediction an...

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： Shao, Liwei La, Lei Zhang, En Hua, Hong Lei Zhongshan Research Institute Beijing Institute of Technology Zhongshan China SITM University of International Business and Economics Beijing China Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9781728176819

User rating prediction is an important problem in recommendation system, this task has been plagued by data sparsity for a long time. Especially, the rating prediction based on product features, user behavior and other background data is not only troubled by cold start, but also difficult to find the migration of user interest. In order to solve above problems, this paper proposes an integrated learning method based on topic model. In this method, the interest points of user comments are obtained by topic model questions, and then the score is predicted based on the improved CatBoost. through this strategy, we can use the semantic information of the comment text more effectively for rating prediction. the proposed method is implemented in an online travel data analysis platform, experiment result shows it has ideal performance in rating prediction of online travel community. © 2021 ieee.

关键词： Forecasting

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Performance and Cost Analysis of Sia, a Blockchain-Based Storage Platform 6

Performance and Cost Analysis of Sia, a Blockchain-Based Sto...

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： Austria, Phillipe Park, Chol Hyun Hoffman, Alex Kim, Yoohwan University of Nevada Computer Science Department Las VegasLas Vegas United States

ISBN: (纸本)9781728176819

cloud storage has become a prevalent choice for data storage for both business and personal use due to increasing data use. While cloud storage services, such as Google Drive, provide customers with high levels of data integrity and security, customers however lose privacy and require trust in the service provider to not act maliciously with their data. We explore an alternative to centralized, traditional cloud storage (TCS) using blockchain technology. In this paper, we tested and evaluated the performance and cost of Sia, a decentralized blockchain-based storage platform (BBS) that ensures users' privacy. the platform allows users to rent storage space from other users in exchange for Siacoin, Sia's native currency. Metrics were evaluated through various storage tests and results were compared to Google Drive. Performance tests showed Sia's available time, how long it takes for a file to upload and be download ready, to be 8-12 times slower than Google Drive. In contrast, Sia downloads were 2-3 times faster. Cost test revealed Sia's overall storage spending to be comparable to Google Drive and other popular TCS;however, results raised questions about spending details. Further testing is needed to fully understand the complexities of a BBS platform like Sia. © 2021 ieee.

关键词： Blockchain

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A Low-illumination Image Enhancement Algorithm Based on Morphological-Retinex(MR) Operator 6

A Low-illumination Image Enhancement Algorithm Based on Morp...

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： Hu, Haokun Cao, Wei Yuan, Jieyu Yang, Juqing Ministry of Education of China Beijing Institute of Technology Zhuhai China Key Laboratory of Photoelectric Imaging Techology and System Zhuhai China Macau University of Science and Technology Macao China

ISBN: (纸本)9781728176819

Low-illumination images were often caused by night environments, which are lack contrast and sharpness. Night image recognition, capture, or autonomous-driving systems require meritorious algorithms to enhance the low-illumination images. the Retinex-based method has been used in image enhancement universally, but previous literature had some enhancement limitations. Since the earlier methods show poor robustness, the night recognition and low-illumination image training programs were prone to fail. For solving those problems, a new algorithm called Morphology-Retinex (MR) is proposed to enhance the details and edges of the low illumination images. We also offer a new automatic color restoration method to restore the authentic color of the object. As a result, the scores of PNSR and SSIM are 15% higher than traditional methods, and the clarity is excellent, which is about 50% higher than similar algorithms in recent years. this algorithm can improve the quality of low-illuminance image data sets and reduce the difficulty of image recognition. When training machine learning models using low-illumination image data sets, this method will increase the success rate. © 2021 ieee.

关键词： Image enhancement

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6th ieee/acis international conference on big data, cloud computing, and data science, bcd 2021

作者： La, Lei Shao, Liwei Zhang, En Hua, Honglei SITM University of International Business and Economics Beijing China Zhongshan Research Institute Beijing Institute of Technology Zhongshan China Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9781728176819

the catering industry is one of the industries most seriously affected by Covid-19, it is significant for us to understand the customers' concerns and find the epidemic prevention loopholes in the catering industry by analyzing the pandemic related topics through online reviews. However, topic detection of short texts such as online reviews has always been a difficult problem in this field. In addition, the interference of background topics related to diet makes this study more difficult. this paper proposes a topic detection method based on topic model and feature embedding. the proposed method called ER-BTM solves the long tail problem of online text by embedding and topic estimation uses feature-pairs instead of features to optimize the process of parameter estimation. the experimental results show that the proposed method can effectively identify epidemic related topics on word-of-mouth platforms such as Yelp. Its performance is also better than control methods on public datasets, it is a powerful tool in online topic detection and epidemic prevention. © 2021 ieee.

关键词： Embeddings

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