Frontiers in Big Data最新文献_第10页

Modeling and analyzing the action process of monoamine hormones in depression: a Petri nets-based intelligent approach. 抑郁症单胺类激素作用过程的建模与分析：一种基于Petri网的智能方法。

IF 3.1

Frontiers in Big Data Pub Date : 2023-09-25 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1268503

Xuyue Wang, Wangyang Yu, Chao Zhang, Jia Wang, Fei Hao, Jin Li, Jing Zhang

{"title":"Modeling and analyzing the action process of monoamine hormones in depression: a Petri nets-based intelligent approach.","authors":"Xuyue Wang, Wangyang Yu, Chao Zhang, Jia Wang, Fei Hao, Jin Li, Jing Zhang","doi":"10.3389/fdata.2023.1268503","DOIUrl":"10.3389/fdata.2023.1268503","url":null,"abstract":"In contemporary society, the incidence of depression is increasing significantly around the world. At present, most of the treatment methods for depression are psychological counseling and drug therapy. However, this approach does not allow patients to visualize the logic of hormones at the pathological level. In order to better apply intelligence computing methods to the medical field, and to more easily analyze the relationship between norepinephrine and dopamine in depression, it is necessary to build an interpretable graphical model to analyze this relationship which is of great significance to help discover new treatment ideas and potential drug targets. Petri net (PN) is a mathematical and graphic tool used to simulate and study complex system processes. This article utilizes PN to study the relationship between norepinephrine and dopamine in depression. We use PN to model the relationship between the norepinephrine and dopamine, and then use the invariant method of PN to verify and analyze it. The mathematical model proposed in this article can explain the complex pathogenesis of depression and visualize the process of intracellular hormone-induced state changes. Finally, the experiment result suggests that our method provides some possible research directions and approaches for the development of antidepressant drugs.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1268503"},"PeriodicalIF":3.1,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41219958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking open source and paid services for speech to text: an analysis of quality and input variety. 对语音到文本的开源和付费服务进行基准测试：对质量和输入多样性的分析。

IF 3.1

Frontiers in Big Data Pub Date : 2023-09-20 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1210559

Antonino Ferraro, Antonio Galli, Valerio La Gatta, Marco Postiglione

{"title":"Benchmarking open source and paid services for speech to text: an analysis of quality and input variety.","authors":"Antonino Ferraro, Antonio Galli, Valerio La Gatta, Marco Postiglione","doi":"10.3389/fdata.2023.1210559","DOIUrl":"10.3389/fdata.2023.1210559","url":null,"abstract":"Introduction: Speech to text (STT) technology has seen increased usage in recent years for automating transcription of spoken language. To choose the most suitable tool for a given task, it is essential to evaluate the performance and quality of both open source and paid STT services.Methods: In this paper, we conduct a benchmarking study of open source and paid STT services, with a specific focus on assessing their performance concerning the variety of input text. We utilizes ix datasets obtained from diverse sources, including interviews, lectures, and speeches, as input for the STT tools. The evaluation of the instruments employs the Word Error Rate (WER), a standard metric for STT evaluation.Results: Our analysis of the results demonstrates significant variations in the performance of the STT tools based on the input text. Certain tools exhibit superior performance on specific types of audio samples compared to others. Our study provides insights into STT tool performance when handling substantial data volumes, as well as the challenges and opportunities posed by the multimedia nature of the data.Discussion: Although paid services generally demonstrate better accuracy and speed compared to open source alternatives, their performance remains dependent on the input text. The study highlights the need for considering specific requirements and characteristics of the audio samples when selecting an appropriate STT tool.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1210559"},"PeriodicalIF":3.1,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41157619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An intelligent telemonitoring application for coronavirus patients: reCOVeryaID. 用于冠状病毒患者的智能远程监测应用程序：reCOVeryaID。

IF 3.1

Frontiers in Big Data Pub Date : 2023-09-18 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1205766

Daniela D'Auria, Raffaele Russo, Alfonso Fedele, Federica Addabbo, Diego Calvanese

{"title":"An intelligent telemonitoring application for coronavirus patients: reCOVeryaID.","authors":"Daniela D'Auria, Raffaele Russo, Alfonso Fedele, Federica Addabbo, Diego Calvanese","doi":"10.3389/fdata.2023.1205766","DOIUrl":"https://doi.org/10.3389/fdata.2023.1205766","url":null,"abstract":"The COVID-19 emergency underscored the importance of resolving crucial issues of territorial health monitoring, such as overloaded phone lines, doctors exposed to infection, chronically ill patients unable to access hospitals, etc. In fact, it often happened that people would call doctors/hospitals just out of anxiety, not realizing that they were clogging up communications, thus causing problems for those who needed them most; such people, often elderly, have often felt lonely and abandoned by the health care system because of poor telemedicine. In addition, doctors were unable to follow up on the most serious cases or make sure that others did not worsen. Thus, uring the first pandemic wave we had the idea to design a system that could help people alleviate their fears and be constantly monitored by doctors both in hospitals and at home; consequently, we developed reCOVeryaID, a telemonitoring application for coronavirus patients. It is an autonomous application supported by a knowledge base that can react promptly and inform medical doctors if dangerous trends in the patient's short- and long-term vital signs are detected. In this paper, we also validate the knowledge-base rules in real-world settings by testing them on data from real patients infected with COVID-19.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1205766"},"PeriodicalIF":3.1,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41159201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Authentication, access, and monitoring system for critical areas with the use of artificial intelligence integrated into perimeter security in a data center. 关键区域的身份验证、访问和监控系统，将人工智能集成到数据中心的外围安全中。

IF 3.1

Frontiers in Big Data Pub Date : 2023-08-31 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1200390

William Villegas-Ch, Joselin García-Ortiz

{"title":"Authentication, access, and monitoring system for critical areas with the use of artificial intelligence integrated into perimeter security in a data center.","authors":"William Villegas-Ch, Joselin García-Ortiz","doi":"10.3389/fdata.2023.1200390","DOIUrl":"10.3389/fdata.2023.1200390","url":null,"abstract":"Perimeter security in data centers helps protect systems and the data they store by preventing unauthorized access and protecting critical resources from potential threats. According to the report of the information security company SonicWall, in 2021, there was a 66% increase in the number of ransomware attacks. In addition, the message from the same company indicates that the total number of cyber threats detected in 2021 increased by 24% compared to 2019. Among these attacks, the infrastructure of data centers was compromised; for this reason, organizations include elements Physical such as security cameras, movement detection systems, authentication systems, etc., as an additional measure that contributes to perimeter security. This work proposes using artificial intelligence in the perimeter security of data centers. It allows the automation and optimization of security processes, which translates into greater efficiency and reliability in the operations that prevent intrusions through authentication, permit verification, and monitoring critical areas. It is crucial to ensure that AI-based perimeter security systems are designed to protect and respect user privacy. In addition, it is essential to regularly monitor the effectiveness and integrity of these systems to ensure that they function correctly and meet security standards.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1200390"},"PeriodicalIF":3.1,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10289348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of methods for assigning causes of death from verbal autopsies in India. 印度死因鉴定方法的评价。

IF 3.1

Frontiers in Big Data Pub Date : 2023-08-24 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1197471

Sudhir K Benara, Saurabh Sharma, Atul Juneja, Saritha Nair, B K Gulati, Kh Jitenkumar Singh, Lucky Singh, Ved Prakash Yadav, Chalapati Rao, M Vishnu Vardhana Rao

{"title":"Evaluation of methods for assigning causes of death from verbal autopsies in India.","authors":"Sudhir K Benara, Saurabh Sharma, Atul Juneja, Saritha Nair, B K Gulati, Kh Jitenkumar Singh, Lucky Singh, Ved Prakash Yadav, Chalapati Rao, M Vishnu Vardhana Rao","doi":"10.3389/fdata.2023.1197471","DOIUrl":"10.3389/fdata.2023.1197471","url":null,"abstract":"Background: Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (COD) in countries where medical certification of death is low. Computer-coded verbal autopsy (CCVA), an alternative method to PCVA for assigning the COD is considered to be efficient and cost-effective. However, the performance of CCVA as compared to PCVA is yet to be established in the Indian context.Methods: We evaluated the performance of PCVA and three CCVA methods i.e., InterVA 5, InSilico, and Tariff 2.0 on verbal autopsies done using the WHO 2016 VA tool on 2,120 reference standard cases developed from five tertiary care hospitals of Delhi. PCVA methodology involved dual independent review with adjudication, where required. Metrics to assess performance were Cause Specific Mortality Fraction (CSMF), sensitivity, positive predictive value (PPV), CSMF Accuracy, and Kappa statistic.Results: In terms of the measures of the overall performance of COD assignment methods, for CSMF Accuracy, the PCVA method achieved the highest score of 0.79, followed by 0.67 for Tariff_2.0, 0.66 for Inter-VA and 0.62 for InSilicoVA. The PCVA method also achieved the highest agreement (57%) and Kappa scores (0.54). The PCVA method showed the highest sensitivity for 15 out of 20 causes of death.Conclusion: Our study found that the PCVA method had the best performance out of all the four COD assignment methods that were tested in our study sample. In order to improve the performance of CCVA methods, multicentric studies with larger sample sizes need to be conducted using the WHO VA tool.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1197471"},"PeriodicalIF":3.1,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10225201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Do you hear the people sing? Comparison of synchronized URL and narrative themes in 2020 and 2023 French protests. 你听到人民在歌唱吗？2020 年和 2023 年法国抗议活动中同步 URL 和叙事主题的比较。

IF 3.1

Frontiers in Big Data Pub Date : 2023-08-24 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1221744

Lynnette Hui Xian Ng, Kathleen M Carley

引用次数: 0

Universal skepticism of ChatGPT: a review of early literature on chat generative pre-trained transformer. 对 ChatGPT 的普遍怀疑：聊天生成预训练转换器早期文献综述。

IF 2.4

Frontiers in Big Data Pub Date : 2023-08-23 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1224976

Casey Watters, Michal K Lemanski

引用次数: 0

Nonstationary time series forecasting using optimized-EVDHM-ARIMA for COVID-19. 针对 COVID-19 使用优化-EVDHM-ARIMA 进行非平稳时间序列预测。

IF 2.4

Frontiers in Big Data Pub Date : 2023-06-14 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1081639

Suraj Singh Nagvanshi, Inderjeet Kaur, Charu Agarwal, Ashish Sharma

{"title":"Nonstationary time series forecasting using optimized-EVDHM-ARIMA for COVID-19.","authors":"Suraj Singh Nagvanshi, Inderjeet Kaur, Charu Agarwal, Ashish Sharma","doi":"10.3389/fdata.2023.1081639","DOIUrl":"10.3389/fdata.2023.1081639","url":null,"abstract":"The Coronavirus (COVID-19) outbreak swept the world, infected millions of people, and caused many deaths. Multiple COVID-19 variations have been discovered since the initial case in December 2019, indicating that COVID-19 is highly mutable. COVID-19 variation \"XE\" is the most current of all COVID-19 variants found in January 2022. It is vital to detect the virus transmission rate and forecast instances of infection to be prepared for all scenarios, prepare healthcare services, and avoid deaths. Time-series forecasting helps predict future infected cases and determine the virus transmission rate to make timely decisions. A forecasting model for nonstationary time series has been created in this paper. The model comprises an optimized EigenValue Decomposition of Hankel Matrix (EVDHM) and an optimized AutoRegressive Integrated Moving Average (ARIMA). The Phillips Perron Test (PPT) has been used to determine whether a time series is nonstationary. A time series has been decomposed into components using EVDHM, and each component has been forecasted using ARIMA. The final forecasts have been formed by combining the predicted values of each component. A Genetic Algorithm (GA) to select ARIMA parameters resulting in the lowest Akaike Information Criterion (AIC) values has been used to discover the best ARIMA parameters. Another genetic algorithm has been used to optimize the decomposition results of EVDHM that ensures the minimum nonstationarity and maximal utilization of eigenvalues for each decomposed component.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1081639"},"PeriodicalIF":2.4,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10114998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Crime, inequality and public health: a survey of emerging trends in urban data science. 犯罪、不平等与公共卫生：城市数据科学新趋势调查。

IF 2.4

Frontiers in Big Data Pub Date : 2023-05-25 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1124526

Massimiliano Luca, Gian Maria Campedelli, Simone Centellegher, Michele Tizzoni, Bruno Lepri

引用次数: 0

Fused multi-modal similarity network as prior in guiding brain imaging genetic association. 融合多模态相似性网络作为指导脑成像基因关联的先验网络。

IF 3.1

Frontiers in Big Data Pub Date : 2023-05-05 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1151893

Bing He, Linhui Xie, Pradeep Varathan, Kwangsik Nho, Shannon L Risacher, Andrew J Saykin, Jingwen Yan

{"title":"Fused multi-modal similarity network as prior in guiding brain imaging genetic association.","authors":"Bing He, Linhui Xie, Pradeep Varathan, Kwangsik Nho, Shannon L Risacher, Andrew J Saykin, Jingwen Yan","doi":"10.3389/fdata.2023.1151893","DOIUrl":"10.3389/fdata.2023.1151893","url":null,"abstract":"Introduction: Brain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that the incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable.Methods: In this study, we explore a new data-driven prior knowledge that captures the subject-level similarity by fusing multi-modal similarity networks. It was incorporated into the sparse canonical correlation analysis (SCCA) model, which is aimed to identify a small set of brain imaging and genetic markers that explain the similarity matrix supported by both modalities. It was applied to amyloid and tau imaging data of the ADNI cohort, respectively.Results: Fused similarity matrix across imaging and genetic data was found to improve the association performance better or similarly well as diagnosis information, and therefore would be a potential substitute prior when the diagnosis information is not available (i.e., studies focused on healthy controls).Discussion: Our result confirmed the value of all types of prior knowledge in improving association identification. In addition, the fused network representing the subject relationship supported by multi-modal data showed consistently the best or equally best performance compared to the diagnosis network and the co-expression network.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"6 ","pages":"1151893"},"PeriodicalIF":3.1,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10036800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0