International Journal of Information Management Data Insights最新文献_第2页

Examining the moderating effect of perceived government support on e-business adoption among Omani SMEs 考察感知到的政府支持对阿曼中小企业采用电子商务的调节作用

International Journal of Information Management Data Insights

Pub Date : 2025-10-17 DOI: 10.1016/j.jjimei.2025.100378

Mahfooz Ahmed , Salim Nasser Al-Riyami , Wan Rohaida Binti Wan Husain , Nurita Binti Juhdi , Fatima Al-Shuwaikh , Ali Haji

This study examines the moderating effect of Perceived Government Support (PGS) on the adoption of e-business among Small and Medium Enterprises (SMEs) in Oman, drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT). While prior research has examined the direct influence of UTAUT constructs, fewer studies have considered institutional support as a contextual moderator in developing economies. To address this gap, the present study integrates PGS into the UTAUT model to explore its role in shaping e-business adoption in Oman. A quantitative, cross-sectional survey was conducted with 641 SME owners, managers, and decision-makers who had benefited from government support programs. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the hypothesised relationships. The results show that Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions significantly influence e-business adoption. More importantly, PGS was found to positively moderate the relationship between Facilitating Conditions and e-business adoption, underscoring the critical role of perceived institutional support in amplifying the enabling environment for digital transformation. The study’s novelty lies in extending UTAUT by incorporating a government-related contextual moderator that reflects the realities of emerging economies. Practically, the findings provide policymakers and SME development agencies with evidence-based insights to design targeted interventions such as infrastructure investment, training, and financial incentives that are especially vital for micro and small enterprises operating under resource constraints.

本研究利用技术接受和使用统一理论（UTAUT），考察了感知到的政府支持（PGS）对阿曼中小企业（SMEs）采用电子商务的调节作用。虽然先前的研究已经检查了UTAUT结构的直接影响，但很少有研究认为制度支持是发展中经济体的语境调节因素。为了解决这一差距，本研究将PGS整合到UTAUT模型中，以探索其在塑造阿曼电子商务采用方面的作用。我们对641名受益于政府支持计划的中小企业老板、经理和决策者进行了一项定量的横断面调查。使用偏最小二乘结构方程模型（PLS-SEM）来检验假设的关系。结果表明，绩效期望、努力期望、社会影响和促进条件显著影响电子商务的采用。更重要的是，我们发现PGS对便利条件和电子商务采用之间的关系具有正向调节作用，强调了在扩大数字化转型的有利环境方面，感知到的制度支持的关键作用。这项研究的新颖之处在于，它通过纳入反映新兴经济体现实的与政府相关的语境调节来扩展UTAUT。实际上，研究结果为政策制定者和中小企业发展机构提供了基于证据的见解，以设计有针对性的干预措施，如基础设施投资、培训和财政激励措施，这些措施对在资源限制下运营的微型和小型企业尤其重要。

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引用次数: 0

The role of technology readiness motivators, positive and negative impact toward smart farming technology adoption: Insight from Thai farmers 技术准备激励因素的作用，对智能农业技术采用的积极和消极影响：来自泰国农民的见解

International Journal of Information Management Data Insights

Pub Date : 2025-10-17 DOI: 10.1016/j.jjimei.2025.100380

Saowakhon Nookhao , Supaporn Kiattisin , Vipa Thananant

Smart farming technology (SFT) supports agriculture 4.0, driving the achievement of sustainable development goals to ensure food security while combating climate change. This study examines the role of motivators and its direct and indirect impacts on SFT acceptance in Thailand. Data were collected from 400 Thai farmers using a questionnaire and analyzes by Structural Equation Model. The study found that farmers with positive attitudes and an innovative mindset are more likely to accept SFT, with an indirect influence through perceived ease of use and perceived usefulness, which act as mediating variables. In terms of direct influence, perceived usefulness has the highest positive direct influence, while perceived cost has the highest negative direct influence on the adoption of SFT by Thai farmers. The results will be beneficial for government agencies and relevant organizations involved in the development of sustainable smart agriculture, particularly for developing countries with agricultural contexts similar to Thailand.

智慧农业技术（SFT）支持农业4.0，推动实现可持续发展目标，在应对气候变化的同时确保粮食安全。本研究探讨动机的作用及其直接和间接影响在泰国的SFT接受。采用问卷调查法对400名泰国农民进行数据收集，并采用结构方程模型进行分析。研究发现，具有积极态度和创新思维的农民更容易接受SFT，并通过感知易用性和感知有用性作为中介变量间接影响SFT。在直接影响方面，感知有用性对泰国农民采用SFT具有最高的正面直接影响，而感知成本对泰国农民采用SFT具有最高的负面直接影响。研究结果将有利于参与可持续智能农业发展的政府机构和相关组织，特别是具有类似泰国农业背景的发展中国家。

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引用次数: 0

Exploring the bibliometric impact of artificial intelligence in radiology: An analytical approach 探索人工智能在放射学中的文献计量学影响：一种分析方法

International Journal of Information Management Data Insights

Pub Date : 2025-10-02 DOI: 10.1016/j.jjimei.2025.100376

Bharti Chogtu , Ritheesh V , Ashwath K Naik , Shubhra Dutta , Santhosh KV

Background

Artificial intelligence (AI) is revolutionizing operations worldwide and is particularly transforming radiology. AI has a key role in enhancing diagnostic accuracy, workflow efficiency, and research output in radiology. This article presents a comprehensive bibliometric analysis of the influence of AI on radiology over five years (2018–2022).

Methodology

Reports published between 2018 and 2022 were identified through the Scopus database and categorized based on the AI methodologies employed. The study presents the volume and distribution of studies on AI, identifies publication patterns by country, and measures the impact of studies in terms of citation counts and field-weighted citation indices (FWCIs). Field-weighted view impact (FWVI), a field-normalized view metric that estimates the visibility of studies and the accessibility and engagement of AI studies in radiology, is used in this study.

Results

Compared with non-AI studies, the United States leads radiology-related AI publications, with AI-based articles having higher citation indices. The findings reveal a strong increasing trend for AI-related studies over the duration of the study. Moreover, open-access AI publications are found to have higher FWVI scores than subscription-based articles with greater visibility and higher readership.

Conclusion

This paper highlights the growing dominance of AI in radiology and how it is influencing trends in clinical development and research. Through publication increase, citation impact, and study availability, this paper provides informative insight into how AI radiology studies are developing.

人工智能（AI）正在改变世界范围内的操作，特别是改变放射学。人工智能在提高放射学诊断准确性、工作流程效率和研究成果方面发挥着关键作用。本文对人工智能在五年内（2018-2022年）对放射学的影响进行了全面的文献计量分析。方法通过Scopus数据库确定2018年至2022年发布的报告，并根据所采用的人工智能方法进行分类。该研究展示了人工智能研究的数量和分布，按国家确定了出版模式，并根据引用计数和领域加权引用指数（fwci）衡量了研究的影响。本研究使用了场加权视图影响（FWVI），这是一种场标准化视图度量，用于估计研究的可见性以及人工智能研究在放射学中的可及性和参与度。结果与非人工智能研究相比，美国在放射学相关的人工智能出版物中处于领先地位，基于人工智能的文章具有更高的引用指数。研究结果显示，在研究期间，与人工智能相关的研究呈强劲增长趋势。此外，发现开放获取的人工智能出版物比基于订阅的文章具有更高的FWVI分数，具有更高的知名度和更高的读者群。结论人工智能在放射学中的主导地位日益增强，并影响着临床发展和研究的趋势。通过出版物增加、引用影响和研究可用性，本文提供了有关人工智能放射学研究如何发展的信息见解。

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引用次数: 0

Understanding sustainable technologies use: The role of empowerment and cultural dimensions 理解可持续技术的使用：授权和文化维度的作用

International Journal of Information Management Data Insights

Pub Date : 2025-09-28 DOI: 10.1016/j.jjimei.2025.100375

Catarina Neves , Tiago Oliveira , Stylianos Karatzas

Given today’s paradigm of environmental crises, studies on the role of technology for sustainable purposes are now more relevant than ever. Therefore, this work analyses sustainable technology use behaviours from a social perspective, evaluating the impact of empowerment and culture-specific dimensions: context and time perception. For that, a research model is created and tested with a sample of 400 responses using structural equation modelling. This study reveals a strong positive impact of empowerment on deep structure use and cognitive absorption. Additionally, time perception is found to be a positive moderator between empowerment and user behaviors. These findings are relevant for theory - exploring the explanatory power of empowerment in a sustainable context, as well as the importance of cultural dimensions in understanding user behaviors – and practice – better understanding strategies to increase sustainable technologies use.

鉴于今天的环境危机范例，研究技术对可持续目的的作用现在比以往任何时候都更有意义。因此，本研究从社会角度分析了可持续的技术使用行为，评估了授权和文化特定维度的影响：背景和时间感知。为此，我们创建了一个研究模型，并使用结构方程模型对400个响应样本进行了测试。本研究揭示了授权对深层结构使用和认知吸收的积极影响。此外，时间感知是授权和用户行为之间的正向调节因子。这些发现与理论相关-探索在可持续背景下授权的解释力，以及文化维度在理解用户行为方面的重要性-以及实践-更好地理解增加可持续技术使用的策略。

引用次数: 0

Determinants of artificial intelligence adoption in the financial services industry: Understanding employees’ perspectives 金融服务业采用人工智能的决定因素：了解员工的观点

International Journal of Information Management Data Insights

Pub Date : 2025-09-03 DOI: 10.1016/j.jjimei.2025.100371

Ahyar Yuniawan , Hersugondo Hersugondo , Fuad Mas'ud , Hengky Latan , Douglas W.S. Renwick

This study examines the factors influencing AI adoption in Indonesia’s financial services sector, focusing on knowledge and awareness levels, perceived risks and benefits, self-confidence, and the moderating role of managerial support. Grounded in innovation diffusion theory (IDT), protection motivation theory (PMT), and self-determination theory (SDT), the study analyzes data from 489 employees using structural equation modeling with SmartPLS 4 software to test the hypotheses. The findings reveal that higher levels of knowledge and awareness, along with self-confidence, positively influence AI adoption intentions, while perceived risks and benefits exert a negative effect. Furthermore, managerial support moderates these relationships by enhancing the positive effects of knowledge and awareness levels and self-confidence, while mitigating the negative impact of perceived risks. These results emphasize the critical role of managerial support in promoting AI adoption and highlight the necessity of cultivating a supportive organizational culture and leadership to ensure successful AI integration.

本研究考察了影响印度尼西亚金融服务部门采用人工智能的因素，重点关注知识和意识水平、感知风险和收益、自信以及管理支持的调节作用。本研究以创新扩散理论（IDT）、保护动机理论（PMT）和自我决定理论（SDT）为基础，利用SmartPLS 4软件的结构方程模型对489名员工的数据进行分析，验证假设。研究结果显示，更高水平的知识和意识，以及自信，对人工智能的采用意图产生积极影响，而感知到的风险和收益则产生负面影响。此外，管理人员的支持通过增强知识和意识水平以及自信的积极作用来调节这些关系，同时减轻感知风险的消极影响。这些结果强调了管理支持在促进人工智能采用方面的关键作用，并强调了培养支持性组织文化和领导力以确保人工智能成功整合的必要性。

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引用次数: 0

Strengthening the UK regulatory framework: Enhancing cybersecurity in supply chains 加强英国监管框架：加强供应链的网络安全

International Journal of Information Management Data Insights

Pub Date : 2025-09-02 DOI: 10.1016/j.jjimei.2025.100370

Betul Gokkaya , Konstantina Spanaki , Erisa Karafili

The increasing risks associated with cybersecurity in global supply chains present a significant problem, threatening the operational integrity and security of organisations on a global scale. The UK’s Network and Information Systems (NIS) Framework, although fundamental in cybersecurity regulation, has significant gaps in effectively addressing the complexities of contemporary global supply chain architectures entangled with quickly advancing cyber threats. In this work, we analyse the UK NIS framework, identify key gaps, and propose solutions drawn from other existing frameworks, e.g., US NIST, EU NIS2. We base this analysis on a comparative evaluation using defined criteria related to supply chain coverage, adaptability, and risk management specificity. We enhanced the cybersecurity in supply chains by proposing novel security requirements plans for each risk profile. Furthermore, we examined various solutions for risk assessments and self-risk assessments for supply chain security. We analysed practical risk assessment approaches, including self-assessment strategies, particularly suited for SMEs. Moreover, we investigated the contracting between supply chains in the context of data and information sharing.

全球供应链中与网络安全相关的风险日益增加，这是一个重大问题，威胁着全球范围内组织的运营完整性和安全性。英国的网络和信息系统（NIS）框架虽然是网络安全监管的基础，但在有效解决与快速发展的网络威胁纠缠在一起的当代全球供应链架构的复杂性方面存在重大差距。在这项工作中，我们分析了英国NIS框架，确定了关键差距，并从其他现有框架（如美国NIST，欧盟NIS2）中提出了解决方案。我们使用与供应链覆盖范围、适应性和风险管理特异性相关的定义标准进行比较评估，并以此为基础进行分析。我们通过为每个风险概况提出新颖的安全需求计划来增强供应链中的网络安全。此外，我们还研究了供应链安全风险评估和自我风险评估的各种解决方案。我们分析了实际的风险评估方法，包括特别适合中小企业的自我评估策略。此外，我们还研究了数据和信息共享背景下供应链之间的契约。

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引用次数: 0

Retrieving and discovering new knowledge from documents' abstracts in scientific databases: Proposing a query-based abstractive summarization model 从科学数据库的文献摘要中检索和发现新知识：提出一种基于查询的抽象摘要模型

International Journal of Information Management Data Insights

Pub Date : 2025-09-02 DOI: 10.1016/j.jjimei.2025.100366

Neda Abbasi Dashtaki , Mehrdad CheshmehSohrabi , Mitra Pashootanizadeh , Hamidreza Baradaran Kashani

Current search engines for Knowledge Retrieval (KR) and Knowledge Discovery (KD) do not effectively utilize scientifically validated documents, especially those indexed in scientific databases. Scientific databases e.g., Scopus primarily consist of document-based content and provide documents' abstract. Their Information Retrieval (IR) system only perform document searches and lack the capability to extract and discover new knowledge from documents' abstract in these databases and responding to users’ queries. The aim is to introduce a model that can efficiently perform these tasks. The statistical population for this study encompasses all scientific databases, with a particular emphasis on Scopus. To clarify the process of KR and KD as we define it, we employed a systematic review and meta-analysis framework using 33 queries. We conducted the identification, screening, eligibility, and inclusion steps following the PRISMA protocol. Next, we performed extraction, labeling, grouping, analysis, and inference. The outcome of these processes provided us with novel insights, which contribute to our exploratory knowledge. To automate these processes, we have proposed a conceptual model from query-based indirect abstractive summarization approach. The outcomes of this research offer fresh insights to database designers, administrators, and researchers, enabling the development of tools for KR and KD within these invaluable knowledge repositories. The integration of such tools into scientific databases will enhance user access to scientific knowledge to meet their informational and research needs.

目前的知识检索（KR）和知识发现（KD）搜索引擎不能有效地利用科学验证的文献，特别是那些在科学数据库中索引的文献。科学数据库，例如Scopus，主要由基于文档的内容组成，并提供文档摘要。它们的信息检索（Information Retrieval， IR）系统只进行文档搜索，缺乏从这些数据库中的文档摘要中提取和发现新知识和响应用户查询的能力。目的是引入一个能够有效执行这些任务的模型。本研究的统计人口包括所有科学数据库，特别强调Scopus。为了澄清我们定义的KR和KD的过程，我们采用了一个系统的回顾和荟萃分析框架，使用了33个查询。我们按照PRISMA方案进行了鉴定、筛选、入选和纳入步骤。接下来，我们进行了提取、标记、分组、分析和推理。这些过程的结果为我们提供了新的见解，有助于我们的探索性知识。为了实现这些过程的自动化，我们提出了一个基于查询的间接抽象摘要方法的概念模型。这项研究的结果为数据库设计人员、管理员和研究人员提供了新的见解，从而能够在这些宝贵的知识库中开发用于KR和KD的工具。将这些工具纳入科学数据库将增加用户获取科学知识的机会，以满足他们的信息和研究需要。

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引用次数: 0

BAR-analytics: A web-based platform for analyzing information spreading barriers in news: Comparative analysis across multiple barriers and events BAR-analytics：一个基于web的平台，用于分析新闻中的信息传播障碍：跨多个障碍和事件的比较分析

International Journal of Information Management Data Insights

Pub Date : 2025-09-02 DOI: 10.1016/j.jjimei.2025.100368

Abdul Sittar, Dunja Mladenić, Alenka Guček, Marko Grobelnik

This paper presents BAR-Analytics, a web-based, open-source platform designed to analyze news dissemination across geographical, economic, political, and cultural boundaries. Using the Russian–Ukrainian and Israeli–Palestinian conflicts as case studies, the platform integrates four analytical methods: propagation analysis, trend analysis, sentiment analysis, and temporal topic modeling. Over 350,000 articles were collected and analyzed, with a focus on economic disparities and geographical influences using metadata enrichment. We evaluate the case studies using coherence, sentiment polarity, topic frequency, and trend shifts as key metrics. Our results show distinct patterns in news coverage: the Israeli–Palestinian conflict tends to have more negative sentiment with a focus on human rights, while the Russia–Ukraine conflict is more positive, emphasizing election interference. These findings highlight the influence of political, economic, and regional factors in shaping media narratives across different conflicts.

本文介绍了BAR-Analytics，这是一个基于网络的开源平台，旨在分析跨越地理、经济、政治和文化边界的新闻传播。该平台以俄乌冲突和巴以冲突为例，集成了四种分析方法：传播分析、趋势分析、情感分析和时间主题建模。收集和分析了超过35万篇文章，重点是利用元数据富集研究经济差异和地理影响。我们使用连贯性、情感极性、主题频率和趋势变化作为关键指标来评估案例研究。我们的研究结果显示了新闻报道的不同模式：巴以冲突往往有更多的负面情绪，关注人权，而俄乌冲突更积极，强调选举干预。这些发现强调了政治、经济和区域因素在不同冲突中塑造媒体叙事的影响。

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引用次数: 0

The metaverse: Privacy and information security risks 虚拟世界：隐私和信息安全风险

International Journal of Information Management Data Insights

Pub Date : 2025-08-30 DOI: 10.1016/j.jjimei.2025.100373

Héctor Laiz-Ibanez , Cristina Mendaña-Cuervo , Juan Luis Carus Candas

The advent of the metaverse—a convergence of physical and virtual realities catalyzed by a spectrum of emerging technologies—heralds a new epoch in the digital era. As the metaverse unfolds its immense potential, it simultaneously reveals unprecedented privacy and information security risks. Understanding these risks is paramount, as the pose significant implications for user safety, data integrity, and the overall trustworthiness of the metaverse. Consequently, this paper conducts a Systematic Literature Review (SLR) to meticulously analyze these emerging risks. Utilizing the Population, Intervention, Comparison, Outcomes, Context (PICOC) method, the review examines 735 articles from four databases, distilling essential insights from 35 key studies. The review identifies major challenges, including vulnerabilities in AI and IoT integration, threats from surveillance capitalism, and insufficient user education on privacy risks. To address these issues, the study proposes strategies such as holistic security frameworks, privacy-first design principles, and multi-stakeholder collaboration. These findings provide actionable insights for navigating the intricate dynamics of the metaverse, fostering a secure and privacy-conscious digital ecosystem. The study’s contributions aim to guide academic discourse, inform industry practices, and influence future policy development. The contributions from this research are intended to stimulate further academic discourse and influence future practices and policy in the context of the metaverse.

超现实的出现——由一系列新兴技术催化的物理和虚拟现实的融合——预示着数字时代的新纪元。在虚拟世界展现其巨大潜力的同时，它也暴露出前所未有的隐私和信息安全风险。了解这些风险是至关重要的，因为它们对用户安全、数据完整性和元环境的整体可信度产生了重大影响。因此，本文通过系统文献综述（SLR）对这些新出现的风险进行了细致的分析。利用人口、干预、比较、结果、背景（PICOC）方法，该综述检查了来自四个数据库的735篇文章，从35项关键研究中提炼出重要见解。该审查确定了主要挑战，包括人工智能和物联网集成中的漏洞、监控资本主义的威胁以及用户对隐私风险的教育不足。为了解决这些问题，该研究提出了诸如整体安全框架、隐私优先设计原则和多方利益相关者协作等策略。这些发现为引导虚拟世界的复杂动态，培养安全和注重隐私的数字生态系统提供了可行的见解。该研究的贡献旨在指导学术论述，为行业实践提供信息，并影响未来的政策制定。本研究的贡献旨在促进进一步的学术论述，并影响未来在元宇宙背景下的实践和政策。

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引用次数: 0

A lightweight transfer learning based ensemble approach for diabetic retinopathy detection 基于轻量级迁移学习的集成方法在糖尿病视网膜病变检测中的应用

International Journal of Information Management Data Insights

Pub Date : 2025-08-26 DOI: 10.1016/j.jjimei.2025.100372

S JAHANGEER SIDIQ, T BENIL

Diabetic retinopathy (DR) is a fatal and irreversible eye disease that affects millions of people worldwide. It occurs due to high blood sugar level in the body of a diabetic patient, so it requires immediate attention which goes beyond the clinical solutions. With the advancements in deep learning and computer vision there are maximum possibilities of predicting this disease at early stages. Based on the severity of disease, different labels have been assigned to different classes of this disease as follows: 4 for proliferative DR, 3 for severe DR, 2 for moderate DR, 1 for mild DR and 0 for No DR. In this paper we proposed a deep learning-based ensemble approach using pre-trained and customized bi-class (CNN) base-learners like MobileNet, InceptionV3and DenseNet121 which were identified during initial investigation. These deep learning models were used as the base learner because of their promising performance in ensembles compared to the other deep learning base learners. All the work in the literature has studied this as a single complex multi-class problem or a bi-class problem where earlier stages are grouped together (0 to 3) and treated as one class and 4 as separate another class. Our work breaks this multi-class problem into multiple simpler two class problems using OVO(One-Versus-One) approach. Several benchmark data sets such as APTOS 2019, IDRiD, Messidor-2 and DDR which are multi-class data sets were used for training and testing our models. Data augmentation techniques were also utilized. Performance metrics such as precision, recall, f1-score, and accuracy were used for evaluation. Our ensemble models showed a remarkable performance with precision, recall, f1-score, and accuracy for most of the datasets used in this study. In addition to this our ensemble models have minimum number of trainable parameters which makes them an ultimate choice.

糖尿病性视网膜病变（DR）是一种致命且不可逆转的眼病，影响着全世界数百万人。它是由于糖尿病患者体内的高血糖引起的，因此需要立即关注，这超出了临床治疗的范围。随着深度学习和计算机视觉的进步，在早期阶段预测这种疾病的可能性最大。根据疾病的严重程度，该疾病的不同类别被分配了不同的标签，如下：增殖性DR为4，严重DR为3，中度DR为2，轻度DR为1，无DR为0。在本文中，我们提出了一种基于深度学习的集成方法，使用预先训练和定制的双类（CNN）基础学习器，如MobileNet， inceptionv3和DenseNet121，这些学习器是在初步调查中确定的。这些深度学习模型被用作基础学习器，因为与其他深度学习基础学习器相比，它们在集成中表现良好。文献中的所有工作都将其作为一个复杂的多类问题或双类问题进行研究，其中早期阶段被分组在一起（0到3），并作为一个类处理，4作为单独的另一个类处理。我们的工作使用OVO（One-Versus-One）方法将这个多类问题分解为多个更简单的两类问题。使用APTOS 2019、IDRiD、Messidor-2和DDR等多类基准数据集对模型进行训练和测试。还利用了数据增强技术。使用精度、召回率、f1评分和准确性等性能指标进行评估。对于本研究中使用的大多数数据集，我们的集成模型在精度、召回率、f1得分和准确性方面表现出色。除此之外，我们的集成模型具有最小数量的可训练参数，这使它们成为最终选择。

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引用次数: 0