Frontiers in Big Data最新文献_第2页

Cloud computing convergence: integrating computer applications and information management for enhanced efficiency.

IF 2.4

Frontiers in Big Data Pub Date : 2025-02-19 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1508087

Guo Zhang

引用次数: 0

Deep learning for accurate classification of conifer pollen grains: enhancing species identification in palynology.

IF 2.4

Frontiers in Big Data Pub Date : 2025-02-14 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1507036

Masoud A Rostami, LeMaur Kydd, Behnaz Balmaki, Lee A Dyer, Julie M Allen

{"title":"Deep learning for accurate classification of conifer pollen grains: enhancing species identification in palynology.","authors":"Masoud A Rostami, LeMaur Kydd, Behnaz Balmaki, Lee A Dyer, Julie M Allen","doi":"10.3389/fdata.2025.1507036","DOIUrl":"https://doi.org/10.3389/fdata.2025.1507036","url":null,"abstract":"Accurate identification of pollen grains from Abies (fir), Picea (spruce), and Pinus (pine) is an important method for reconstructing historical environments, past landscapes and understanding human-environment interactions. However, distinguishing between pollen grains of conifer genera poses challenges in palynology due to their morphological similarities. To address this identification challenge, this study leverages advanced deep learning techniques, specifically transfer learning models, which are effective in identifying similarities among detailed features. We evaluated nine different transfer learning architectures: DenseNet201, EfficientNetV2S, InceptionV3, MobileNetV2, ResNet101, ResNet50, VGG16, VGG19, and Xception. Each model was trained and validated on a dataset of images of pollen grains collected from museum specimens, mounted and imaged for training purposes. The models were assessed on various performance metrics, including accuracy, precision, recall, and F1-score across training, validation, and testing phases. Our results indicate that ResNet101 relatively outperformed other models, achieving a test accuracy of 99%, with equally high precision, recall, and F1-score. This study underscores the efficacy of transfer learning to produce models that can aid in identifications of difficult species. These models may aid conifer species classification and enhance pollen grain analysis, critical for ecological research and monitoring environmental changes.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1507036"},"PeriodicalIF":2.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11868112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544467","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

Editorial: Machine learning and immersive technologies for user-centered digital healthcare innovation.

IF 2.4

Frontiers in Big Data Pub Date : 2025-02-14 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1567941

Federico Colecchia, Daniele Giunchi, Rui Qin, Eleonora Ceccaldi, Fang Wang

引用次数: 0

Edge-level multi-constraint graph pattern matching with lung cancer knowledge graph.

IF 2.4

Frontiers in Big Data Pub Date : 2025-02-10 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1546850

Houdie Tu, Lei Li, Zhenchao Tao, Zan Zhang

{"title":"Edge-level multi-constraint graph pattern matching with lung cancer knowledge graph.","authors":"Houdie Tu, Lei Li, Zhenchao Tao, Zan Zhang","doi":"10.3389/fdata.2025.1546850","DOIUrl":"10.3389/fdata.2025.1546850","url":null,"abstract":"Introduction: Traditional Graph Pattern Matching (GPM) research mainly focuses on improving the accuracy and efficiency of complex network analysis and fast subgraph retrieval. Despite their ability to return subgraphs quickly and accurately, these methods are limited to their applications without medical data research.Methods: In order to overcome this limitation, based on the existing research on GPM with the lung cancer knowledge graph, this paper introduces the Monte Carlo method and proposes an edge-level multi-constraint graph pattern matching algorithm TEM with lung cancer knowledge graph. Furthermore, we apply Monte Carlo method to both nodes and edges, and propose a multi-constraint hologram pattern matching algorithm THM with lung cancer knowledge graph.Results: The experiments have verified the effectiveness and efficiency of TEM algorithm.Discussion: This method effectively addresses the complexity of uncertainty in lung cancer knowledge graph, and is significantly better than the existing algorithms on efficiency.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1546850"},"PeriodicalIF":2.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732893","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

Training and onboarding initiatives in high energy physics experiments.

IF 2.4

Frontiers in Big Data Pub Date : 2025-02-10 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1497622

Allison Reinsvold Hall, Nicole Skidmore, Gabriele Benelli, Ben Carlson, Claire David, Jonathan Davies, Wouter Deconinck, David DeMuth, Peter Elmer, Rocky Bala Garg, Stephan Hageböck, Killian Lieret, Valeriia Lukashenko, Sudhir Malik, Andy Morris, Heidi Schellman, Graeme A Stewart, Jason Veatch, Michel Hernandez Villanueva

引用次数: 0

Big data analytics and AI as success factors for online video streaming platforms.

IF 2.4

Frontiers in Big Data Pub Date : 2025-02-06 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1513027

Muhammad Arshad, Choo Wou Onn, Ashfaq Ahmad, Goabaone Mogwe

{"title":"Big data analytics and AI as success factors for online video streaming platforms.","authors":"Muhammad Arshad, Choo Wou Onn, Ashfaq Ahmad, Goabaone Mogwe","doi":"10.3389/fdata.2025.1513027","DOIUrl":"https://doi.org/10.3389/fdata.2025.1513027","url":null,"abstract":"As the trend in the current generation with the use of mobile devices is rapidly increasing, online video streaming has risen to the top in the entertainment industry. These platforms have experienced radical expansion due to the incorporation of Big Data Analytics and Artificial Intelligence which are critical in improving the user interface, improving its functioning, and customization of recommended content. This paper seeks to examine how Big Data Analytics makes it possible to obtain large amounts of data about users and how they view, what they like, or how they behave. While customers benefit from this data by receiving more suitable material, getting better recommendations, and allowing for more efficient content delivery, AI utilizes it. As a result, the study also points to the importance and relevance of such technologies to promote business development, and user interaction and maintain competitiveness in the online video streaming market with examples of their effective application. This work presents a comprehensive investigation of the combined role of Big Data and AI and presents the necessary findings to determine their efficacy as success factors of existing and future video streaming services.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1513027"},"PeriodicalIF":2.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11841674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469954","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

Editorial: Visualizing big culture and history data.

IF 2.4

Frontiers in Big Data Pub Date : 2025-02-04 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1563730

Florian Windhager, Steffen Koch, Sander Münster, Eva Mayr

引用次数: 0

On explaining recommendations with Large Language Models: a review.

IF 2.4

Frontiers in Big Data Pub Date : 2025-01-27 eCollection Date: 2024-01-01 DOI: 10.3389/fdata.2024.1505284

Alan Said

{"title":"On explaining recommendations with Large Language Models: a review.","authors":"Alan Said","doi":"10.3389/fdata.2024.1505284","DOIUrl":"https://doi.org/10.3389/fdata.2024.1505284","url":null,"abstract":"The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging LLMs to generate explanations for recommendations-a critical aspect for fostering transparency and user trust. We conducted a comprehensive search within the ACM Guide to Computing Literature, covering publications from the launch of ChatGPT (November 2022) to the present (November 2024). Our search yielded 232 articles, but after applying inclusion criteria, only six were identified as directly addressing the use of LLMs in explaining recommendations. This scarcity highlights that, despite the rise of LLMs, their application in explainable recommender systems is still in an early stage. We analyze these select studies to understand current methodologies, identify challenges, and suggest directions for future research. Our findings underscore the potential of LLMs improving explanations of recommender systems and encourage the development of more transparent and user-centric recommendation explanation solutions.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"7 ","pages":"1505284"},"PeriodicalIF":2.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11808143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392512","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

Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization.

IF 2.4

Frontiers in Big Data Pub Date : 2025-01-23 eCollection Date: 2024-01-01 DOI: 10.3389/fdata.2024.1419562

Ahsan Shabbir, Abdul Haleem Butt, Taha Khan, Lorenzo Chiari, Ahmad Almadhor, Vincent Karovic

{"title":"Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization.","authors":"Ahsan Shabbir, Abdul Haleem Butt, Taha Khan, Lorenzo Chiari, Ahmad Almadhor, Vincent Karovic","doi":"10.3389/fdata.2024.1419562","DOIUrl":"10.3389/fdata.2024.1419562","url":null,"abstract":"Introduction: Ambient acoustic detection and localization play a vital role in identifying events and their origins from acoustic data. This study aimed to establish a comprehensive framework for classifying activities in home environments to detect emergency events and transmit emergency signals. Localization enhances the detection of the acoustic event's location, thereby improving the effectiveness of emergency services, situational awareness, and response times.Methods: Acoustic data were collected from a home environment using six strategically placed microphones in a bedroom, kitchen, restroom, and corridor. A total of 512 audio samples were recorded from 11 activities. Background noise was eliminated using a filtering technique. State-of-the-art features were extracted from the time domain, frequency domain, time frequency domain, and cepstral domain to develop efficient detection and localization frameworks. Random forest and linear discriminant analysis classifiers were employed for event detection, while the estimation signal parameters through rational-in-variance techniques (ESPRIT) algorithm was used for sound source localization.Results: The study achieved high detection accuracy, with random forest and linear discriminant analysis classifiers attaining 95% and 87%, respectively, for event detection. For sound source localization, the proposed framework demonstrated significant performance, with an error rate of 3.61, a mean squared error (MSE) of 14.98, and a root mean squared error (RMSE) of 3.87.Discussion: The integration of detection and localization models facilitated the identification of emergency activities and the transmission of notifications via electronic mail. The results highlight the potential of the proposed methodology to develop a real-time emergency alert system for domestic environments.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"7 ","pages":"1419562"},"PeriodicalIF":2.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366790","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

Balancing act: Europeans' privacy calculus and security concerns in online CSAM detection.

IF 2.4

Frontiers in Big Data Pub Date : 2025-01-22 eCollection Date: 2025-01-01 DOI: 10.3389/fdata.2025.1477911

Răzvan Rughiniş, Simona-Nicoleta Vulpe, Dinu Ţurcanu, Daniel Rosner

{"title":"Balancing act: Europeans' privacy calculus and security concerns in online CSAM detection.","authors":"Răzvan Rughiniş, Simona-Nicoleta Vulpe, Dinu Ţurcanu, Daniel Rosner","doi":"10.3389/fdata.2025.1477911","DOIUrl":"10.3389/fdata.2025.1477911","url":null,"abstract":"This study examines privacy calculus in online child sexual abuse material (CSAM) detection across Europe, using Flash Eurobarometer 532 data. Drawing on theories of structuration and risk society, we analyze how individual agency and institutional frameworks interact in shaping privacy attitudes in high-stakes digital scenarios. Multinomial regression reveals age as a significant individual-level predictor, with younger individuals prioritizing privacy more. Country-level analysis shows Central and Eastern European nations have higher privacy concerns, reflecting distinct institutional and cultural contexts. Notably, the Digital Economy and Society Index (DESI) shows a positive association with privacy concerns in regression models when controlling for Augmented Human Development Index (AHDI) components, contrasting its negative bivariate correlation. Life expectancy emerges as the strongest country-level predictor, negatively associated with privacy concerns, suggesting deep institutional mechanisms shape privacy attitudes beyond individual factors. This dual approach reveals that both individual factors and national contexts are shaping privacy calculus in CSAM detection. The study contributes to a better understanding of privacy calculus in high-stakes scenarios, with implications for policy development in online child protection.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1477911"},"PeriodicalIF":2.4,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11794313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366792","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