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

Breast Cancer Classification Using Concatenated Triple Convolutional Neural Networks Model 基于级联三重卷积神经网络模型的癌症乳腺分类

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-16 DOI: 10.3390/bdcc7030142

M. Alshayeji, Jassim Al-Buloushi

{"title":"Breast Cancer Classification Using Concatenated Triple Convolutional Neural Networks Model","authors":"M. Alshayeji, Jassim Al-Buloushi","doi":"10.3390/bdcc7030142","DOIUrl":"https://doi.org/10.3390/bdcc7030142","url":null,"abstract":"Improved disease prediction accuracy and reliability are the main concerns in the development of models for the medical field. This study examined methods for increasing classification accuracy and proposed a precise and reliable framework for categorizing breast cancers using mammography scans. Concatenated Convolutional Neural Networks (CNN) were developed based on three models: Two by transfer learning and one entirely from scratch. Misclassification of lesions from mammography images can also be reduced using this approach. Bayesian optimization performs hyperparameter tuning of the layers, and data augmentation will refine the model by using more training samples. Analysis of the model’s accuracy revealed that it can accurately predict disease with 97.26% accuracy in binary cases and 99.13% accuracy in multi-classification cases. These findings are in contrast with recent studies on the same issue using the same dataset and demonstrated a 16% increase in multi-classification accuracy. In addition, an accuracy improvement of 6.4% was achieved after hyperparameter modification and augmentation. Thus, the model tested in this study was deemed superior to those presented in the extant literature. Hence, the concatenation of three different CNNs from scratch and transfer learning allows the extraction of distinct and significant features without leaving them out, enabling the model to make exact diagnoses.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42639297","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

Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD) 基于作者的圣训分类数据集(ABCD)

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-14 DOI: 10.3390/bdcc7030141

A. Ramzy, Marwan Torki, Mohamed Abdeen, O. Saif, Mustafa ElNainay, AbdAllah Alshanqiti, E. Nabil

{"title":"Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD)","authors":"A. Ramzy, Marwan Torki, Mohamed Abdeen, O. Saif, Mustafa ElNainay, AbdAllah Alshanqiti, E. Nabil","doi":"10.3390/bdcc7030141","DOIUrl":"https://doi.org/10.3390/bdcc7030141","url":null,"abstract":"Religious studies are a rich land for Natural Language Processing (NLP). The reason is that all religions have their instructions as written texts. In this paper, we apply NLP to Islamic Hadiths, which are the written traditions, sayings, actions, approvals, and discussions of the Prophet Muhammad, his companions, or his followers. A Hadith is composed of two parts: the chain of narrators (Sanad) and the content of the Hadith (Matn). A Hadith is transmitted from its author to a Hadith book author using a chain of narrators. The problem we solve focuses on the classification of Hadiths based on their origin of narration. This is important for several reasons. First, it helps determine the authenticity and reliability of the Hadiths. Second, it helps trace the chain of narration and identify the narrators involved in transmitting Hadiths. Finally, it helps understand the historical and cultural contexts in which Hadiths were transmitted, and the different levels of authority attributed to the narrators. To the best of our knowledge, and based on our literature review, this problem is not solved before using machine/deep learning approaches. To solve this classification problem, we created a novel Author-Based Hadith Classification Dataset (ABCD) collected from classical Hadiths’ books. The ABCD size is 29 K Hadiths and it contains unique 18 K narrators, with all their information. We applied machine learning (ML), and deep learning (DL) approaches. ML was applied on Sanad and Matn separately; then, we did the same with DL. The results revealed that ML performs better than DL using the Matn input data, with a 77% F1-score. DL performed better than ML using the Sanad input data, with a 92% F1-score. We used precision and recall alongside the F1-score; details of the results are explained at the end of the paper. We claim that the ABCD and the reported results will motivate the community to work in this new area. Our dataset and results will represent a baseline for further research on the same problem.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47848493","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 Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty 具有QoS不确定性的Web服务选择智能Bat算法

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-10 DOI: 10.3390/bdcc7030140

Abdelhak Etchiali, Fethallah Hadjila, Amina Bekkouche

引用次数: 1

Executable Digital Process Twins: Towards the Enhancement of Process-Driven Systems 可执行数字过程双胞胎:迈向过程驱动系统的增强

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-08 DOI: 10.3390/bdcc7030139

F. Corradini, Sara Pettinari, B. Re, Lorenzo Rossi, F. Tiezzi

引用次数: 0

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