Big Data and Cognitive Computing最新文献_第6页

Cumulative and Rolling Horizon Prediction of Overall Equipment Effectiveness (OEE) with Machine Learning 基于机器学习的总体装备效能累积滚动预测

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-02 DOI: 10.3390/bdcc7030138

Péter Dobra, J. Jósvai

引用次数: 0

An Approach Based on Recurrent Neural Networks and Interactive Visualization to Improve Explainability in AI Systems 基于递归神经网络和交互式可视化的人工智能系统可解释性改进方法

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-31 DOI: 10.3390/bdcc7030136

W. Villegas-Ch., J. Garcia-Ortiz, Ángel Jaramillo-Alcázar

引用次数: 0

EnviroStream: A Stream Reasoning Benchmark for Environmental and Climate Monitoring 环境和气候监测的流推理基准

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-31 DOI: 10.3390/bdcc7030135

Elena Mastria, Francesco Pacenza, J. Zangari, Francesco Calimeri, S. Perri, G. Terracina

引用次数: 0

Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting 使用情绪丰富的时间序列预测预测比特币价格

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-31 DOI: 10.3390/bdcc7030137

Markus Frohmann, Manuel Karner, Said Khudoyan, Robert Wagner, M. Schedl

{"title":"Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting","authors":"Markus Frohmann, Manuel Karner, Said Khudoyan, Robert Wagner, M. Schedl","doi":"10.3390/bdcc7030137","DOIUrl":"https://doi.org/10.3390/bdcc7030137","url":null,"abstract":"Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the future price of Bitcoin, which is currently the most popular cryptocurrency. More precisely, we propose a hybrid approach, combining time series forecasting and sentiment prediction from microblogs, to predict the intraday price of Bitcoin. Moreover, in addition to standard sentiment analysis methods, we are the first to employ a fine-tuned BERT model for this task. We also introduce a novel weighting scheme in which the weight of the sentiment of each tweet depends on the number of its creator’s followers. For evaluation, we consider periods with strongly varying ranges of Bitcoin prices. This enables us to assess the models w.r.t. robustness and generalization to varied market conditions. Our experiments demonstrate that BERT-based sentiment analysis and the proposed weighting scheme improve upon previous methods. Specifically, our hybrid models that use linear regression as the underlying forecasting algorithm perform best in terms of the mean absolute error (MAE of 2.67) and root mean squared error (RMSE of 3.28). However, more complicated models, particularly long short-term memory networks and temporal convolutional networks, tend to have generalization and overfitting issues, resulting in considerably higher MAE and RMSE scores.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49452726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Driving Excellence in Official Statistics: Unleashing the Potential of Comprehensive Digital Data Governance 推动官方统计的卓越发展：释放数字数据综合治理的潜力

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-29 DOI: 10.3390/bdcc7030134

Hossein Hassani, S. MacFeely

引用次数: 0

Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning 基于对比学习的电动汽车充电站运行评价方法

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-24 DOI: 10.3390/bdcc7030133

Ze-Yang Tang, Qi-Biao Hu, Yibo Cui, Lei Hu, Yi-Wen Li, Yu-Jie Li

{"title":"Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning","authors":"Ze-Yang Tang, Qi-Biao Hu, Yibo Cui, Lei Hu, Yi-Wen Li, Yu-Jie Li","doi":"10.3390/bdcc7030133","DOIUrl":"https://doi.org/10.3390/bdcc7030133","url":null,"abstract":"This paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To overcome this limitation, this paper proposes an evaluation method based on natural language models for assessing the operation of charging stations. By utilizing the proposed SimCSEBERT model, this study analyzes the operational data, user charging data, and basic information of charging stations to predict the operational status and identify influential factors. Additionally, this study compared the evaluation accuracy and impact factor analysis accuracy of the baseline and the proposed model. The experimental results demonstrate that our model achieves a higher evaluation accuracy (operation evaluation accuracy = 0.9464; impact factor analysis accuracy = 0.9492) and effectively assesses the operation of EVCSs. Compared with traditional evaluation methods, this approach exhibits improved universality and a higher level of intelligence. It provides insights into the operation of EVCSs and user demands, allowing for the resolution of supply–demand contradictions that are caused by power supply constraints and the uneven distribution of charging demands. Furthermore, it offers guidance for more efficient and targeted strategies for the operation of charging stations.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49283617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Development of a Kazakh Speech Recognition Model Using a Convolutional Neural Network with Fixed Character Level Filters 基于固定字符级滤波器的卷积神经网络哈萨克语语音识别模型的开发

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-20 DOI: 10.3390/bdcc7030132

N. Kadyrbek, M. Mansurova, A. Shomanov, G. Makharova

{"title":"The Development of a Kazakh Speech Recognition Model Using a Convolutional Neural Network with Fixed Character Level Filters","authors":"N. Kadyrbek, M. Mansurova, A. Shomanov, G. Makharova","doi":"10.3390/bdcc7030132","DOIUrl":"https://doi.org/10.3390/bdcc7030132","url":null,"abstract":"This study is devoted to the transcription of human speech in the Kazakh language in dynamically changing conditions. It discusses key aspects related to the phonetic structure of the Kazakh language, technical considerations in collecting the transcribed audio corpus, and the use of deep neural networks for speech modeling. A high-quality decoded audio corpus was collected, containing 554 h of data, giving an idea of the frequencies of letters and syllables, as well as demographic parameters such as the gender, age, and region of residence of native speakers. The corpus contains a universal vocabulary and serves as a valuable resource for the development of modules related to speech. Machine learning experiments were conducted using the DeepSpeech2 model, which includes a sequence-to-sequence architecture with an encoder, decoder, and attention mechanism. To increase the reliability of the model, filters initialized with symbol-level embeddings were introduced to reduce the dependence on accurate positioning on object maps. The training process included simultaneous preparation of convolutional filters for spectrograms and symbolic objects. The proposed approach, using a combination of supervised and unsupervised learning methods, resulted in a 66.7% reduction in the weight of the model while maintaining relative accuracy. The evaluation on the test sample showed a 7.6% lower character error rate (CER) compared to existing models, demonstrating its most modern characteristics. The proposed architecture provides deployment on platforms with limited resources. Overall, this study presents a high-quality audio corpus, an improved speech recognition model, and promising results applicable to speech-related applications and languages beyond Kazakh.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43245154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data 基于时间大数据驱动的轻型信息器的实时车速预测方法

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-15 DOI: 10.3390/bdcc7030131

Xinyu Tian, Qinghe Zheng, Zhiguo Yu, Mingqiang Yang, Yao Ding, Abdussalam Elhanashi, S. Saponara, K. Kpalma

{"title":"A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data","authors":"Xinyu Tian, Qinghe Zheng, Zhiguo Yu, Mingqiang Yang, Yao Ding, Abdussalam Elhanashi, S. Saponara, K. Kpalma","doi":"10.3390/bdcc7030131","DOIUrl":"https://doi.org/10.3390/bdcc7030131","url":null,"abstract":"At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45047438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption 节点能耗计算与监测数据收集指南

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-11 DOI: 10.3390/bdcc7030130

A. del Río, Giuseppe Conti, S. Castaño-Solis, Javier Serrano, David Jiménez, J. Fraile-Ardanuy

{"title":"A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption","authors":"A. del Río, Giuseppe Conti, S. Castaño-Solis, Javier Serrano, David Jiménez, J. Fraile-Ardanuy","doi":"10.3390/bdcc7030130","DOIUrl":"https://doi.org/10.3390/bdcc7030130","url":null,"abstract":"The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45666839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos 第一人称行车记录仪视频中的端到端在线交通风险事件预测

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-07-06 DOI: 10.3390/bdcc7030129

Hilmil Pradana

{"title":"An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos","authors":"Hilmil Pradana","doi":"10.3390/bdcc7030129","DOIUrl":"https://doi.org/10.3390/bdcc7030129","url":null,"abstract":"Predicting traffic risk incidents in first-person helps to ensure a safety reaction can occur before the incident happens for a wide range of driving scenarios and conditions. One challenge to building advanced driver assistance systems is to create an early warning system for the driver to react safely and accurately while perceiving the diversity of traffic-risk predictions in real-world applications. In this paper, we aim to bridge the gap by investigating two key research questions regarding the driver’s current status of driving through online videos and the types of other moving objects that lead to dangerous situations. To address these problems, we proposed an end-to-end two-stage architecture: in the first stage, unsupervised learning is applied to collect all suspicious events on actual driving; in the second stage, supervised learning is used to classify all suspicious event results from the first stage to a common event type. To enrich the classification type, the metadata from the result of the first stage is sent to the second stage to handle the data limitation while training our classification model. Through the online situation, our method runs 9.60 fps on average with 1.44 fps on standard deviation. Our quantitative evaluation shows that our method reaches 81.87% and 73.43% for the average F1-score on labeled data of CST-S3D and real driving datasets, respectively. Furthermore, the proposed method has the potential to assist distribution companies in evaluating the driving performance of their driver by automatically monitoring near-miss events and analyzing driving patterns for training programs to reduce future accidents.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44738453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0