Big Data最新文献

Cloud Resource Scheduling Using Multi-Strategy Fused Honey Badger Algorithm.

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-02-01 DOI: 10.1089/big.2023.0146

Haitao Xie, Chengkai Li, Zhiwei Ye, Tao Zhao, Hui Xu, Jiangyi Du, Wanfang Bai

{"title":"Cloud Resource Scheduling Using Multi-Strategy Fused Honey Badger Algorithm.","authors":"Haitao Xie, Chengkai Li, Zhiwei Ye, Tao Zhao, Hui Xu, Jiangyi Du, Wanfang Bai","doi":"10.1089/big.2023.0146","DOIUrl":"https://doi.org/10.1089/big.2023.0146","url":null,"abstract":"Cloud resource scheduling is one of the most significant tasks in the field of big data, which is a combinatorial optimization problem in essence. Scheduling strategies based on meta-heuristic algorithms (MAs) are often chosen to deal with this topic. However, MAs are prone to falling into local optima leading to decreasing quality of the allocation scheme. Algorithms with good global search ability are needed to map available cloud resources to the requirements of the task. Honey Badger Algorithm (HBA) is a newly proposed algorithm with strong search ability. In order to further improve scheduling performance, an Improved Honey Badger Algorithm (IHBA), which combines two local search strategies and a new fitness function, is proposed in this article. IHBA is compared with 6 MAs in four scale load tasks. The comparative simulation results obtained reveal that the proposed algorithm performs better than other algorithms involved in the article. IHBA enhances the diversity of algorithm populations, expands the individual's random search range, and prevents the algorithm from falling into local optima while effectively achieving resource load balancing.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"13 1","pages":"59-72"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

gtfs2net: Extraction of General Transit Feed Specification Data Sets to Abstract Networks and Their Analysis. gtfs2net:抽象网络中通用传输馈电规范数据集的提取及其分析。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-02-01 Epub Date: 2023-04-24 DOI: 10.1089/big.2022.0269

Gergely Kocsis, Imre Varga

引用次数: 0

Generic User Behavior: A User Behavior Similarity-Based Recommendation Method. 通用用户行为:基于用户行为相似度的推荐方法。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-02-01 Epub Date: 2023-04-19 DOI: 10.1089/big.2022.0260

Zhengyang Hu, Weiwei Lin, Xiaoying Ye, Haojun Xu, Haocheng Zhong, Huikang Huang, Xinyang Wang

{"title":"Generic User Behavior: A User Behavior Similarity-Based Recommendation Method.","authors":"Zhengyang Hu, Weiwei Lin, Xiaoying Ye, Haojun Xu, Haocheng Zhong, Huikang Huang, Xinyang Wang","doi":"10.1089/big.2022.0260","DOIUrl":"10.1089/big.2022.0260","url":null,"abstract":"Recommender system (RS) plays an important role in Big Data research. Its main idea is to handle huge amounts of data to accurately recommend items to users. The recommendation method is the core research content of the whole RS. However, the existing recommendation methods still have the following two shortcomings: (1) Most recommendation methods use only one kind of information about the user's interaction with items (such as Browse or Purchase), which makes it difficult to model complete user preference. (2) Most mainstream recommendation methods only consider the final consistency of recommendation (e.g., user preferences) but ignore the process consistency (e.g., user behavior), which leads to the biased final result. In this article, we propose a recommendation method based on the Entity Interaction Knowledge Graph (EIKG), which draws on the idea of collaborative filtering and innovatively uses the similarity of user behaviors to recommend items. The method first extracts fact triples containing interaction relations from relevant data sets to generate the EIKG; then embeds the entities and relations in the EIKG; finally, uses link prediction techniques to recommend items for users. The proposed method is compared with other recommendation methods on two publicly available data sets, Scholat and Lizhi, and the experimental result shows that it exceeds the state of the art in most metrics, verifying the effectiveness of the proposed method.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"3-15"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9477294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pneumonia Detection Using Enhanced Convolutional Neural Network Model on Chest X-Ray Images. 基于增强卷积神经网络模型的胸部x线图像肺炎检测。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-02-01 Epub Date: 2023-04-17 DOI: 10.1089/big.2022.0261

Shadi A Aljawarneh, Romesaa Al-Quraan

{"title":"Pneumonia Detection Using Enhanced Convolutional Neural Network Model on Chest X-Ray Images.","authors":"Shadi A Aljawarneh, Romesaa Al-Quraan","doi":"10.1089/big.2022.0261","DOIUrl":"10.1089/big.2022.0261","url":null,"abstract":"Pneumonia, caused by microorganisms, is a severely contagious disease that damages one or both the lungs of the patients. Early detection and treatment are typically favored to recover infected patients since untreated pneumonia can lead to major complications in the elderly (>65 years) and children (<5 years). The objectives of this work are to develop several models to evaluate big X-ray images (XRIs) of the chest, to determine whether the images show/do not show signs of pneumonia, and to compare the models based on their accuracy, precision, recall, loss, and receiver operating characteristic area under the ROC curve scores. Enhanced convolutional neural network (CNN), VGG-19, ResNet-50, and ResNet-50 with fine-tuning are some of the deep learning (DL) algorithms employed in this study. By training the transfer learning model and enhanced CNN model using a big data set, these techniques are used to identify pneumonia. The data set for the study was obtained from Kaggle. It should be noted that the data set has been expanded to include further records. This data set included 5863 chest XRIs, which were categorized into 3 different folders (i.e., train, val, test). These data are produced every day from personnel records and Internet of Medical Things devices. According to the experimental findings, the ResNet-50 model showed the lowest accuracy, that is, 82.8%, while the enhanced CNN model showed the highest accuracy of 92.4%. Owing to its high accuracy, enhanced CNN was regarded as the best model in this study. The techniques developed in this study outperformed the popular ensemble techniques, and the models showed better results than those generated by cutting-edge methods. Our study implication is that a DL models can detect the progression of pneumonia, which improves the general diagnostic accuracy and gives patients new hope for speedy treatment. Since enhanced CNN and ResNet-50 showed the highest accuracy compared with other algorithms, it was concluded that these techniques could be effectively used to identify pneumonia after performing fine-tuning.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"16-29"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9737399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Data-Driven Analysis Method for the Trajectory of Power Carbon Emission in the Urban Area. 城市电力碳排放轨迹的数据驱动分析方法。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-02-01 Epub Date: 2023-06-16 DOI: 10.1089/big.2022.0299

Yi Gao, Dawei Yan, Xiangyu Kong, Ning Liu, Zhiyu Zou, Bixuan Gao, Yang Wang, Yue Chen, Shuai Luo

引用次数: 0

Introduction to the Special Issue on Big Data and the Internet of Things in Complex Information Systems.

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-02-01 DOI: 10.1089/big.2024.0132

Victor Chang, Péter Kacsuk, Gary Wills, Reinhold Behringer

引用次数: 0

Enhancing Real-Time Patient Monitoring in Intensive Care Units with Deep Learning and the Internet of Things. 利用深度学习和物联网加强重症监护病房患者实时监测。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-01-17 DOI: 10.1089/big.2024.0113

Yiting Bai, Baiqian Gu, Chao Tang

{"title":"Enhancing Real-Time Patient Monitoring in Intensive Care Units with Deep Learning and the Internet of Things.","authors":"Yiting Bai, Baiqian Gu, Chao Tang","doi":"10.1089/big.2024.0113","DOIUrl":"https://doi.org/10.1089/big.2024.0113","url":null,"abstract":"The demand for intensive care units (ICUs) is steadily increasing, yet there is a relative shortage of medical staff to meet this need. Intensive care work is inherently heavy and stressful, highlighting the importance of optimizing these units' working conditions and processes. Such optimization is crucial for enhancing work efficiency and elevating the level of diagnosis and treatment provided in ICUs. The intelligent ICU concept represents a novel ward management model that has emerged through advancements in modern science and technology. This includes communication technology, the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data analytics. By leveraging these technologies, the intelligent ICU aims to significantly reduce potential risks associated with human error and improve patient monitoring and treatment outcomes. Deep learning (DL) and IoT technologies have huge potential to revolutionize the surveillance of patients in the ICUs due to the critical and complex nature of their conditions. This article provides an overview of the most recent research and applications of linical data for critically ill patients, with a focus on the execution of AI. In the ICU, seamless and continuous monitoring is critical, as even little delays in patient care decision-making can result in irreparable repercussions or death. This article looks at how modern technologies like DL and the IoT can improve patient monitoring, clinical results, and ICU processes. Furthermore, it investigates the function of wearable and advanced health sensors coupled with IoT networking systems, which enable the secure connection and analysis of various forms of patient data for predictive and remote analysis by medical professionals. By assessing existing patient monitoring systems, outlining the roles of DL and IoT, and analyzing the benefits and limitations of their integration, this study hopes to shed light on the future of ICU patient care and identify opportunities for further research.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Impact of Cloaking Digital Footprints on User Privacy and Personalization. 隐藏数字足迹对用户隐私和个性化的影响。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-01-10 DOI: 10.1089/big.2024.0036

Sofie Goethals, Sandra Matz, Foster Provost, David Martens, Yanou Ramon

{"title":"The Impact of Cloaking Digital Footprints on User Privacy and Personalization.","authors":"Sofie Goethals, Sandra Matz, Foster Provost, David Martens, Yanou Ramon","doi":"10.1089/big.2024.0036","DOIUrl":"https://doi.org/10.1089/big.2024.0036","url":null,"abstract":"Our online lives generate a wealth of behavioral records-digital footprints-which are stored and leveraged by technology platforms. These data can be used to create value for users by personalizing services. At the same time, however, it also poses a threat to people's privacy by offering a highly intimate window into their private traits (e.g., their personality, political ideology, sexual orientation). We explore the concept of cloaking: allowing users to hide parts of their digital footprints from predictive algorithms, to prevent unwanted inferences. This article addresses two open questions: (i) can cloaking be effective in the longer term, as users continue to generate new digital footprints? And (ii) what is the potential impact of cloaking on the accuracy of desirable inferences? We introduce a novel strategy focused on cloaking \"metafeatures\" and compare its efficacy against just cloaking the raw footprints. The main findings are (i) while cloaking effectiveness does indeed diminish over time, using metafeatures slows the degradation; (ii) there is a tradeoff between privacy and personalization: cloaking undesired inferences also can inhibit desirable inferences. Furthermore, the metafeature strategy-which yields more stable cloaking-also incurs a larger reduction in desirable inferences.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Sports Injury Rehabilitation Detection Based on IoT Models for Digital Health Care. 基于物联网模型的数字医疗运动损伤康复检测研究。

IF 2.6 4区计算机科学

Big Data Pub Date : 2024-12-17 DOI: 10.1089/big.2023.0134

Zhiyong Wu, Zhida Huang, Nianhua Tang, Kai Wang, Chuanjie Bian, Dandan Li, Vumika Kuraki, Felix Schmid

{"title":"Research on Sports Injury Rehabilitation Detection Based on IoT Models for Digital Health Care.","authors":"Zhiyong Wu, Zhida Huang, Nianhua Tang, Kai Wang, Chuanjie Bian, Dandan Li, Vumika Kuraki, Felix Schmid","doi":"10.1089/big.2023.0134","DOIUrl":"https://doi.org/10.1089/big.2023.0134","url":null,"abstract":"Physical therapists specializing in sports rehabilitation detection help injured athletes recover from their wounds and avoid further harm. Sports rehabilitators treat not just commonplace sports injuries but also work-related musculoskeletal injuries, discomfort, and disorders. Sensor-equipped Internet of Things (IoT) monitors the real-time location of medical equipment such as scooters, cardioverters, nebulizer treatments, oxygenation pumps, or other monitor gear. Analysis of medicine deployment across sites is possible in real time. Health care delivery based on digital technology to improve access, affordability, and sustainability of medical treatment is known as digital health care. The challenging characteristics of such sports injury rehabilitation for digital health care are playing position, game strategies, and cybersecurity. Hence, in this research, health care IoT-enabled body area networks (HIoT-BAN) have been designed to improve sports injury rehabilitation detection for digital health care. The health care sector may benefit significantly from IoT adoption since it allows for enhanced patient safety; health care investment management includes controlling the hospital's pharmaceutical stock and monitoring the heat and humidity levels. Digital health describes a group of programmers made to aid health care delivery, whether by assisting with clinical decision-making or streamlining back-end operations in health care institutions. A HIoT-BAN effectively predicts the rise in sports injury rehabilitation detection with faster digital health care based on IoT. The research concludes that the HIoT-BAN effectively indicates sports injury rehabilitation detection for digital health care. The experimental analysis of HIoT-BAN outperforms the IoT method in terms of performance, accuracy, prediction ratio, and mean square error rate.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prognostic Modeling for Liver Cirrhosis Mortality Prediction and Real-Time Health Monitoring from Electronic Health Data. 基于电子健康数据的肝硬化死亡率预测和实时健康监测的预后建模。

IF 2.6 4区计算机科学

Big Data Pub Date : 2024-12-09 DOI: 10.1089/big.2024.0071

Chengping Zhang, Muhammad Faisal Buland Iqbal, Imran Iqbal, Minghao Cheng, Nadia Sarhan, Emad Mahrous Awwad, Yazeed Yasin Ghadi

{"title":"Prognostic Modeling for Liver Cirrhosis Mortality Prediction and Real-Time Health Monitoring from Electronic Health Data.","authors":"Chengping Zhang, Muhammad Faisal Buland Iqbal, Imran Iqbal, Minghao Cheng, Nadia Sarhan, Emad Mahrous Awwad, Yazeed Yasin Ghadi","doi":"10.1089/big.2024.0071","DOIUrl":"https://doi.org/10.1089/big.2024.0071","url":null,"abstract":"Liver cirrhosis stands as a prominent contributor to mortality, impacting millions across the United States. Enabling health care providers to predict early mortality among patients with cirrhosis holds the potential to enhance treatment efficacy significantly. Our hypothesis centers on the correlation between mortality and laboratory test results along with relevant diagnoses in this patient cohort. Additionally, we posit that a deep learning model could surpass the predictive capabilities of the existing Model for End-Stage Liver Disease score. This research seeks to advance prognostic accuracy and refine approaches to address the critical challenges posed by cirrhosis-related mortality. This study evaluates the performance of an artificial neural network model for liver disease classification using various training dataset sizes. Through meticulous experimentation, three distinct training proportions were analyzed: 70%, 80%, and 90%. The model's efficacy was assessed using precision, recall, F1-score, accuracy, and support metrics, alongside receiver operating characteristic (ROC) and precision-recall (PR) curves. The ROC curves were quantified using the area under the curve (AUC) metric. Results indicated that the model's performance improved with an increased size of the training dataset. Specifically, the 80% training data model achieved the highest AUC, suggesting superior classification ability over the models trained with 70% and 90% data. PR analysis revealed a steep trade-off between precision and recall across all datasets, with 80% training data again demonstrating a slightly better balance. This is indicative of the challenges faced in achieving high precision with a concurrently high recall, a common issue in imbalanced datasets such as those found in medical diagnostics.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0