Big Data最新文献

{"title":"Advancing Dysarthric Speech-to-Text Recognition with LATTE: A Low-Latency Acoustic Modeling Approach for Real-Time Communication.","authors":"Qurat Ul Ain, Hammad Afzal, Fazli Subhan, Mazliham Mohd Suud, Younhyun Jung","doi":"10.1177/2167647X251411174","DOIUrl":"10.1177/2167647X251411174","url":null,"abstract":"<p><p>Dysarthria, a motor speech disorder characterized by slurred and often unintelligible speech, presents substantial challenges for effective communication. Conventional automatic speech recognition systems frequently underperform on dysarthric speech, particularly in severe cases. To address this gap, we introduce low-latency acoustic transcription and textual encoding (LATTE), an advanced framework designed for real-time dysarthric speech recognition. LATTE integrates preprocessing, acoustic processing, and transcription mapping into a unified pipeline, with its core powered by a hybrid architecture that combines convolutional layers for acoustic feature extraction with bidirectional temporal layers for modeling temporal dependencies. Evaluated on the UA-Speech dataset, LATTE achieves a word error rate of 12.5%, phoneme error rate of 8.3%, and a character error rate of 1%. By enabling accurate, low-latency transcription of impaired speech, LATTE provides a robust foundation for enhancing communication and accessibility in both digital applications and real-time interactive environments.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"122-136"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143844","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}

{"title":"Real-Time Named Entity Recognition from Textual Electronic Clinical Records in Cancer Therapy Using Low-Latency Neural Networks.","authors":"Pir Noman Ahmad, Muhammad Shahid Anwar, Saleha Masood, Atta Ur Rehman, Muhammad Zubair","doi":"10.1177/2167647X251409135","DOIUrl":"10.1177/2167647X251409135","url":null,"abstract":"<p><p>Named entity recognition (NER) is a core task in natural language processing that identifies and classifies entities, such as people, organizations, and locations within text. It has traditionally been applied in areas like text summarization, machine translation, and question answering. In recent years, NER has gained growing importance in health care, where electronic clinical records and online platforms generate large amounts of unstructured medical data. However, applying NER in clinical contexts introduces unique challenges due to the complexity of medical terminology and the need for high accuracy. In this study, we focused on the development of a real-time, low-latency NER system designed for cross-lingual speech-to-text applications, with a particular emphasis on cancer therapy-related clinical records and traditional Chinese medicine (TCM). We explored the integration of deep learning (DL) architectures optimized for low-latency neural processing to extract structured information from multilingual spoken content in medical settings, particularly in multimodal environments. We evaluate DL-based methods and propose a semi-supervised approach that combines TCM-specific corpora with biomedical resources to improve recognition accuracy. The findings provide both a systematic review of current methods and practical insights for building real-time clinical applications that support decision-making and information management in health care.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"137-154"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127360","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}

{"title":"Digital and Intelligent Optimization Mechanism of the Entire Supply Chain Link Based on Generative Artificial Intelligence and Neural Semantic Analysis.","authors":"Minquan Zhao, Can Du, Qing Xu, Hong Luo, Nengpan Wang","doi":"10.1177/2167647X261423120","DOIUrl":"https://doi.org/10.1177/2167647X261423120","url":null,"abstract":"<p><p>In the process of supply chain optimization, data has characteristics such as multi-source heterogeneity and unstructuredness. Traditional supply chain optimization methods that rely on structured data and statistical analysis cannot achieve ideal goals. Therefore, this article studies the digital and intelligent optimization mechanism of the entire supply chain link based on generative artificial intelligence. The entire supply chain link is divided into product design and process links, product raw material procurement links, product production and manufacturing management links, product delivery links, and product retirement and recycling links. For each link, the ChatGPT large language model in generative artificial intelligence adopts a neural semantic analysis method based on an encoding-decoding architecture. For the multi-source heterogeneous general knowledge within the entire supply chain link, general corpus training, expert annotation, and special corpus training are carried out, and the semantic analysis of the general knowledge of the entire supply chain link is realized through in-depth mining and understanding. Based on the semantic analysis results, the generative adversarial network in generative artificial intelligence is used to predict complex patterns or solutions such as product design, transportation routes, and sales methods in each link of the entire supply chain, making the prediction results more accurate and more in line with the actual supply chain business. The experimental results show that this mechanism can accurately analyze the semantics of the general knowledge of the entire supply chain link, improve the accuracy of the prediction of each function of the entire supply chain link, and significantly improve the economic benefits of enterprises.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"2167647X261423120"},"PeriodicalIF":2.6,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576185","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}

{"title":"Editorial Summary of Selected Articles.","authors":"Victor Chang, Péter Kacsuk, Gary Wills, Reinhold Behringer","doi":"10.1177/2167647X251406211","DOIUrl":"10.1177/2167647X251406211","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"2167647X251406211"},"PeriodicalIF":2.0,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835397","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}

{"title":"Unified AI Approach Using Encoding and Generative Large Language Models for Variant Product Matching in e-Commerce.","authors":"Pedro Herrero-Vidal, You-Lin Chen, Cris Liu, Bin Xu, Prithviraj Sen, Lichao Wang","doi":"10.1177/2167647X261423127","DOIUrl":"https://doi.org/10.1177/2167647X261423127","url":null,"abstract":"<p><p>We introduce VARM, <i>va</i>riant <i>r</i>elationship <i>m</i>atcher strategy, to identify pairs of variant products in e-commerce catalogs. Traditional definitions of entity resolution are concerned with whether product mentions refer to the same underlying product. However, this fails to capture product relationships that are critical for e-commerce applications, such as having similar, but not identical, products listed on the same webpage or share reviews. Here, we formulate a new type of entity resolution in <i>variant product</i> relationships to capture these similar e-commerce product links. In contrast with the traditional definition, the new definition requires both identifying if two products are variant matches of each other <i>and</i> what the attributes are that vary between them. To satisfy these two requirements, we developed a strategy that leverages the strengths of both encoding and generative AI models. First, we construct a dataset that captures webpage product links, and therefore variant product relationships, to train an encoding large language model (LLM) to predict variant matches for any given pair of products. Second, we use retrieval-augmented generation-prompted generative LLMs to extract variation and common attributes amongst groups of variant products. To validate our strategy, we evaluated model performance using real data from one of the world's leading e-commerce retailers. The results showed that our strategy outperforms alternative solutions and paves the way to exploiting these new types of product relationships.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"2167647X261423127"},"PeriodicalIF":2.6,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318904","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}

{"title":"Perceived Usefulness, Trust, and Behavioral Intention: A Study on College Student User Adoption Behaviors of Artificial Intelligence Generated News Based on Technology Acceptance Model.","authors":"Xianfeng Gong, Mingyang Mao","doi":"10.1177/2167647X261423109","DOIUrl":"10.1177/2167647X261423109","url":null,"abstract":"<p><p>This study intends to identify the critical factors that shape college students' adoption of AI-generated news, with a specific focus on integrating Big Data methodologies into the Technology Acceptance Model (TAM) framework. Building on TAM, the research incorporates \"trust\" as a core variable to develop a dual-path theoretical model that combines technological cognition (e.g., perceived usefulness, perceived ease of use) and psychological emotions. Unlike traditional TAM-based studies relying solely on questionnaire data, this research enriches its data sources by leveraging Big Data techniques-including the collection and analysis of college students' real-time behavioral data (e.g., AI news reading duration, sharing frequency, source verification clicks) and unstructured text data (e.g., sentiment orientation in comment sections)-to complement the survey data from 300 college students. Through a questionnaire survey of 300 college students and data analysis using the structural equation model, the study found that trust has the strongest direct positive impact on the willingness to use (β = 0.49, <i>p</i> < 0.001), and its influence is significantly greater than perceived usefulness (β = 0.35, <i>p</i> < 0.001). Meanwhile, although perceived ease of use does not directly affect the willingness to use, it has significant indirect effects by enhancing trust and perceived usefulness. The results show that in the AI news context with high-risk perception, trust is a more crucial psychological mechanism than traditional technological cognitive factors. These findings have expanded the explanatory boundaries of the TAM model in new technology fields and provided empirical evidence and practical inspiration for AI developers to optimize system credibility and for educators to conduct algorithmic literacy training.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"56-61"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144007","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}

{"title":"TL-TransUNet: An Improved Lightweight Semantic Segmentation Model of Macular Edema Lesions in Retinal OCT Images.","authors":"Zhijun Gao, Yishuai Yang, Jinhuan Wang, Xin Yue","doi":"10.1177/2167647X261429851","DOIUrl":"10.1177/2167647X261429851","url":null,"abstract":"<p><p>Optical coherence tomography (OCT) offers significant advantages of noncontact operation, high resolution, and real-time imaging, making it particularly suitable for acquiring human retinal images and playing a crucial role in diagnosing and monitoring retinal diseases such as diabetic macular edema (DME). OCT is a key noninvasive imaging modality for retinal diseases such as DME, offering high-resolution visualization of retinal layers and fluid accumulations. However, retinal fluid segmentation faces several challenges including variations in fluid size, location, and shape, as well as complex irregular boundaries. To address these issues, we propose TL-TransUNet, a novel lightweight segmentation model based on TransUNet. The model incorporates a hybrid self-attention mechanism that effectively combines linear self-attention with residual filtered multilayer perceptron modules, reducing both parameter size and computational complexity while capturing global relationships and local details to improve segmentation performance for small lesions. Furthermore, the decoder employs wavelet convolution that utilizes wavelet transform to extract multi-scale features from low- to high-frequency components, enhancing the model's multi-scale learning capability. Experimental results on a public DME dataset demonstrate that our proposed method outperforms several mainstream segmentation approaches, demonstrating superior performance.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"14 1","pages":"29-41"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500742","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}

{"title":"Offline Data-Driven Recommender Systems for Improving Small Business Marketing Strategies.","authors":"Hwijae Son, Sung Woong Cho, Hyung Ju Hwang","doi":"10.1177/2167647X261423114","DOIUrl":"10.1177/2167647X261423114","url":null,"abstract":"<p><p>Recommender systems play a crucial role in enhancing user engagement across domains such as e-commerce, social media, entertainment, and education. Recently, they have also been used in marketing to identify high-value customers and personalize campaigns. However, small businesses often struggle with the high cost per action (CPA) and low conversion rates (CR) associated with online marketing platforms. To address this challenge, we propose a novel recommender system that leverages offline interaction data to identify customers likely to use discount coupons, thereby increasing CRs and reducing marketing costs. We address technical challenges such as cold-start problems and data sparsity by introducing tailored data augmentation techniques. The effectiveness of our approach is validated through experiments using store-level coupon and point log data, evaluated with metrics including CPA, CR, and root mean squared error. Results show that our system significantly outperforms conventional online marketing platforms, emphasizing the value of incorporating offline data with proper augmentation for cost-effective marketing.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"1-12"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168160","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}

{"title":"Does Context Matter? The Role of Fine-Tuned Contextual Augmentation in Online Ad Delivery on Social Media.","authors":"Saifullah Jan, Iftikhar Alam, Inayat Khan","doi":"10.1177/2167647X251398729","DOIUrl":"10.1177/2167647X251398729","url":null,"abstract":"<p><p>This study presents a real-time, context-adaptive advertisement (ad in short) recommendation framework that dynamically updates user context and utilizes a multistage ranking and filtering pipeline to deliver highly relevant and personalized ads. Contextual ads contribute to better conversion rates and play a significant role in e-commerce. In contrast, non-contextual ads engender frustration among advertisers and users: commercialization efforts frequently prove ineffective due to poor user engagement, as evidenced by high ad-skipping rates. The current practices in digital advertising involve non-contextual and irrelevant ads, which result in poor conversion rates. To address this problem, this article explores semantically enriched and context-aware recommender systems, aiming to align ads with user interests. The proposed framework investigates various components, including a user context extractor (UCE), recommender system, ads database, ads ranker, and ads filter. This study also explores how high-quality and relevant content, along with clickable advertising, contributes to improving customer relationships and reducing ad avoidance. During contextual augmentation, ads that become relevant and engaging are projected to have increased click-through rates in a real-world application. Customer engagement and satisfaction would also increase due to a reduction in ad fatigue and the delivery of relevant content. Furthermore, it can curb ad avoidance because users will gladly respond to ads that suit their interests. Businesses make higher conversions because the more relevant recommendation means greater user interaction. The proposed framework combines a UCE, an ad database, a ranking mechanism, and a filtering module to deliver real-time, personalized recommendations. Evaluated using a <i>k</i>-nearest neighbor-based model, the system achieved improved precision (from 0.8275 to 0.9283), recall (from 0.4628 to 0.5201), and normalized discounted cumulative gain (from 0.9906 to 0.9915). These gains demonstrate that integrating fine-grained, dynamic user context substantially enhances recommendation quality and user engagement, offering a scalable foundation for intelligent, adaptive advertising systems. This research contributes toward the future development of an AI-enabled advertising strategy, with an emphasis on dynamic ad targeting that goes hand in hand with personalization and thus improved conversion rate.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"13-28"},"PeriodicalIF":2.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145859048","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}

{"title":"Enhancing NEV Brand Equity Through Big Data Analytics: An LDA-LSTM Approach to Mining Online Consumer Reviews.","authors":"Qiong He, Zhenwei Yang, Yijia Li","doi":"10.1177/2167647X251399169","DOIUrl":"10.1177/2167647X251399169","url":null,"abstract":"<p><p>Enhancing brand value is critical for new energy vehicle (NEV) enterprises amid fierce competition. This study leverages online consumer reviews as core big data to drive brand equity improvement via advanced big data analytics. A large-scale dataset of 5564 reviews for top five best-selling NEVs was collected from \"Dongche Di\" via web scraping, followed by a big data processing pipeline (data cleaning, Jieba segmentation, and stop-word filtering). To mine unstructured text big data, we used word cloud visualization, semantic network analysis, and an Latent Dirichlet Allocation (LDA)-Long Short-Term Memory (LSTM) fusion model: LDA identified key consumer concern dimensions, while LSTM enabled deep sentiment classification. Big data analysis revealed five core NEV brand perception dimensions (range, driving experience, interior space, price, and high-speed performance) and quantified emotions-prominent negativity in driving experience, minimal negativity in interior space, and overall dominant negativity. Guided by the Consumer-Based Brand Equity model, we proposed brand enhancement strategies. This study showcases big data analytics' power in scaling consumer perception understanding, offering a data-centric framework for NEV firms to optimize branding.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"42-55"},"PeriodicalIF":2.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145858991","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}