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Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography. 高血压对 2 型糖尿病患者冠状动脉斑块和 FFR-CT 的影响:利用人工智能处理冠状动脉计算机断层扫描血管造影进行评估。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1446640
Yan Xi, Yi Xu, Zheng Shu

Objective: This study utilized artificial intelligence (AI) to quantify coronary computed tomography angiography (CCTA) images, aiming to compare plaque characteristics and CT-derived fractional flow reserve (FFR-CT) in type 2 diabetes mellitus (T2DM) patients with or without hypertension (HTN).

Methods: A retrospective analysis was conducted on 1,151 patients with suspected coronary artery disease who underwent CCTA at a single center. Patients were grouped into T2DM (n = 133), HTN (n = 442), T2DM (HTN+) (n = 256), and control (n = 320). AI assessed various CCTA parameters, including plaque components, high-risk plaques (HRPs), FFR-CT, severity of coronary stenosis using Coronary Artery Disease Reporting and Data System 2.0 (CAD-RADS 2.0), segment involvement score (SIS), and segment stenosis score (SSS). Statistical analysis compared these parameters among groups.

Results: The T2DM (HTN+) group had the highest plaque volume and length, SIS, SSS, and CAD-RADS 2.0 classification. In the T2DM group, 54.0% of the plaque volume was noncalcified and 46.0% was calcified, while in the HTN group, these values were 24.0 and 76.0%, respectively. The T2DM (HTN+) group had more calcified plaques (35.7% noncalcified, 64.3% calcified) than the T2DM group. The average necrotic core volume was 4.25 mm3 in the T2DM group and 5.23 mm3 in the T2DM (HTN+) group, with no significant difference (p > 0.05). HRPs were more prevalent in both T2DM and T2DM (HTN+) compared to HTN and control groups (p < 0.05). The T2DM (HTN+) group had a higher likelihood (26.1%) of FFR-CT ≤0.75 compared to the T2DM group (13.8%). FFR-CT ≤0.75 correlated with CAD-RADS 2.0 (OR = 7.986, 95% CI = 5.466-11.667, cutoff = 3, p < 0.001) and noncalcified plaque volume (OR = 1.006, 95% CI = 1.003-1.009, cutoff = 29.65 mm3, p < 0.001). HRPs were associated with HbA1c levels (OR = 1.631, 95% CI = 1.387-1.918).

Conclusion: AI analysis of CCTA identifies patterns in quantitative plaque characteristics and FFR-CT values. Comorbid HTN exacerbates partially calcified plaques, leading to more severe coronary artery stenosis in patients with T2DM. T2DM is associated with partially noncalcified plaques, whereas HTN is linked to partially calcified plaques.

研究目的本研究利用人工智能(AI)量化冠状动脉计算机断层扫描(CCTA)图像,旨在比较有或无高血压(HTN)的 2 型糖尿病(T2DM)患者的斑块特征和 CT 导出的分数血流储备(FFR-CT):我们对在一个中心接受 CCTA 检查的 1,151 名疑似冠状动脉疾病患者进行了回顾性分析。患者被分为 T2DM(n = 133)、HTN(n = 442)、T2DM(HTN+)(n = 256)和对照组(n = 320)。AI 评估了各种 CCTA 参数,包括斑块成分、高危斑块 (HRP)、FFR-CT、使用冠状动脉疾病报告和数据系统 2.0 (CAD-RADS 2.0) 的冠状动脉狭窄严重程度、节段受累评分 (SIS) 和节段狭窄评分 (SSS)。统计分析比较了各组的这些参数:结果:T2DM(高血压+)组的斑块体积和长度、SIS、SSS 和 CAD-RADS 2.0 分级最高。在 T2DM 组中,54.0% 的斑块体积为非钙化,46.0% 为钙化,而在 HTN 组中,这两个数值分别为 24.0% 和 76.0%。T2DM(HTN+)组的钙化斑块(35.7%为非钙化,64.3%为钙化)多于T2DM组。T2DM 组的平均坏死核心体积为 4.25 立方毫米,T2DM(HTN+)组为 5.23 立方毫米,两者无显著差异(P > 0.05)。与 HTN 组和对照组相比,HRP 在 T2DM 组和 T2DM(HTN+)组中更为普遍(P P 3,P 结论:对 CCTA 的 AI 分析确定了定量斑块特征和 FFR-CT 值的模式。合并高血压会加重部分钙化斑块,导致 T2DM 患者冠状动脉狭窄更加严重。T2DM与部分非钙化斑块有关,而高血压与部分钙化斑块有关。
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引用次数: 0
Using large language models to support pre-service teachers mathematical reasoning-an exploratory study on ChatGPT as an instrument for creating mathematical proofs in geometry. 使用大型语言模型支持职前教师进行数学推理--以 ChatGPT 为工具创建几何数学证明的探索性研究。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1460337
Frederik Dilling, Marc Herrmann

In this exploratory study, the potential of large language models (LLMs), specifically ChatGPT to support pre-service primary education mathematics teachers in constructing mathematical proofs in geometry is investigated. Utilizing the theoretical framework of instrumental genesis, the prior experiences of students with LLMs, their beliefs about the operating principle and their interactions with the chatbot are analyzed. Using qualitative content analysis, inductive categories for these aspects are formed. Results indicate that students had limited prior experiences with LLMs and used them predominantly for applications that are not mathematics specific. Regarding their beliefs, most show only superficial knowledge about the technology and misconceptions are common. The analysis of interactions showed multiple types of in parts mathematics-specific prompts and patterns on three different levels from single prompts to whole chat interactions.

在这项探索性研究中,我们调查了大型语言模型(LLM),特别是 ChatGPT 在支持小学数学教师职前几何数学证明建构方面的潜力。利用工具性创生理论框架,分析了学生使用 LLMs 的先前经验、他们对操作原理的信念以及他们与聊天机器人的互动。通过定性内容分析,对这些方面进行了归纳分类。结果表明,学生以前使用 LLM 的经验有限,而且主要用于非数学应用。至于他们的观念,大多数人对该技术的了解都很肤浅,误解也很普遍。对互动的分析表明,在从单一提示到整个聊天互动的三个不同层面上,存在多种类型的数学提示和模式。
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引用次数: 0
Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing. 利用水库计算从时间序列中无监督提取缓慢时变的系统参数动态,预测未观察到的分岔。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-22 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1451926
Keita Tokuda, Yuichi Katori

Introduction: Nonlinear and non-stationary processes are prevalent in various natural and physical phenomena, where system dynamics can change qualitatively due to bifurcation phenomena. Machine learning methods have advanced our ability to learn and predict such systems from observed time series data. However, predicting the behavior of systems with temporal parameter variations without knowledge of true parameter values remains a significant challenge.

Methods: This study uses reservoir computing framework to address this problem by unsupervised extraction of slowly varying system parameters from time series data. We propose a model architecture consisting of a slow reservoir with long timescale internal dynamics and a fast reservoir with short timescale dynamics. The slow reservoir extracts the temporal variation of system parameters, which are then used to predict unknown bifurcations in the fast dynamics.

Results: Through experiments on chaotic dynamical systems, our proposed model successfully extracted slowly varying system parameters and predicted bifurcations that were not included in the training data. The model demonstrated robust predictive performance, showing that the reservoir computing framework can handle nonlinear, non-stationary systems without prior knowledge of the system's true parameters.

Discussion: Our approach shows potential for applications in fields such as neuroscience, material science, and weather prediction, where slow dynamics influencing qualitative changes are often unobservable.

引言非线性和非稳态过程普遍存在于各种自然和物理现象中,系统动态会因分岔现象而发生质的变化。机器学习方法提高了我们从观测到的时间序列数据中学习和预测此类系统的能力。然而,在不知道真实参数值的情况下预测具有时间参数变化的系统行为仍然是一项重大挑战:本研究利用水库计算框架来解决这一问题,即从时间序列数据中无监督地提取缓慢变化的系统参数。我们提出了一种模型架构,包括一个具有长时间尺度内部动态变化的慢水库和一个具有短时间尺度动态变化的快水库。慢速库提取系统参数的时间变化,然后用于预测快速动态中的未知分岔:结果:通过对混沌动力学系统的实验,我们提出的模型成功提取了缓慢变化的系统参数,并预测了训练数据中未包含的分岔。该模型表现出稳健的预测性能,表明水库计算框架可以在不预先知道系统真实参数的情况下处理非线性、非稳态系统:我们的方法显示了在神经科学、材料科学和天气预测等领域的应用潜力,在这些领域,影响质变的缓慢动态变化往往是不可观测的。
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引用次数: 0
Heuristic machine learning approaches for identifying phishing threats across web and email platforms. 在网络和电子邮件平台上识别网络钓鱼威胁的启发式机器学习方法。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1414122
Ramprasath Jayaprakash, Krishnaraj Natarajan, J Alfred Daniel, Chandru Vignesh Chinnappan, Jayant Giri, Hong Qin, Saurav Mallik

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. The sole intention is to collect personal information through malicious program-based attempts embedded in URLs, emails, and website-based attempts. Notably, this proposed technique detects URL, email, and website-based phishing attacks, which will be beneficial and secure us from scam attempts. Subsequently, the data are pre-processed to identify phishing attacks using data cleaning, attribute selection, and attacks detected using machine learning techniques. Furthermore, the proposed techniques use heuristic-based machine learning to identify phishing attacks. Admittedly, 56 features are used to analyze URL phishing findings, and experimental results show that the proposed technique has a better accuracy of 97.2%. Above all, the proposed techniques for email phishing detection obtain a higher accuracy of 97.4%. In addition, the proposed technique for website phishing detection has a better accuracy of 98.1%, and 48 features are used for analysis.

在这个竞争残酷的世界里,先进技术时代让生活变得更加舒适。然而,也有一些新兴的有害技术构成了威胁。毫无疑问,网络钓鱼是日益受到关注的问题之一,它通过通信劫持技术从任何目标节点窃取密码、安全代码和个人数据等重要信息。此外,网络钓鱼攻击还包括发送源自可信来源的虚假信息。此外,网络钓鱼攻击的目的是让受害者运行恶意程序并泄露机密数据,如银行凭证、一次性密码和用户登录凭证。其唯一目的就是通过嵌入在 URL、电子邮件和网站中的恶意程序来收集个人信息。值得注意的是,这项建议的技术可以检测到基于 URL、电子邮件和网站的网络钓鱼攻击,这将使我们受益匪浅,并确保我们免受诈骗企图的侵害。随后,利用数据清理、属性选择和机器学习技术检测攻击,对数据进行预处理以识别网络钓鱼攻击。此外,所提出的技术使用基于启发式的机器学习来识别网络钓鱼攻击。实验结果表明,拟议技术的准确率高达 97.2%。此外,针对电子邮件网络钓鱼检测提出的技术获得了 97.4% 的较高准确率。此外,针对网站网络钓鱼检测的建议技术的准确率为 98.1%,其中使用了 48 个特征进行分析。
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引用次数: 0
Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost. 酶催化效率预测:采用卷积神经网络和 XGBoost。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1446063
Meshari Alazmi

Introduction: In the intricate realm of enzymology, the precise quantification of enzyme efficiency, epitomized by the turnover number (k cat), is a paramount yet elusive objective. Existing methodologies, though sophisticated, often grapple with the inherent stochasticity and multifaceted nature of enzymatic reactions. Thus, there arises a necessity to explore avant-garde computational paradigms.

Methods: In this context, we introduce "enzyme catalytic efficiency prediction (ECEP)," leveraging advanced deep learning techniques to enhance the previous implementation, TurNuP, for predicting the enzyme catalase k cat. Our approach significantly outperforms prior methodologies, incorporating new features derived from enzyme sequences and chemical reaction dynamics. Through ECEP, we unravel the intricate enzyme-substrate interactions, capturing the nuanced interplay of molecular determinants.

Results: Preliminary assessments, compared against established models like TurNuP and DLKcat, underscore the superior predictive capabilities of ECEP, marking a pivotal shift in silico enzymatic turnover number estimation. This study enriches the computational toolkit available to enzymologists and lays the groundwork for future explorations in the burgeoning field of bioinformatics. This paper suggested a multi-feature ensemble deep learning-based approach to predict enzyme kinetic parameters using an ensemble convolution neural network and XGBoost by calculating weighted-average of each feature-based model's output to outperform traditional machine learning methods. The proposed "ECEP" model significantly outperformed existing methodologies, achieving a mean squared error (MSE) reduction of 0.35 from 0.81 to 0.46 and R-squared score from 0.44 to 0.54, thereby demonstrating its superior accuracy and effectiveness in enzyme catalytic efficiency prediction.

Discussion: This improvement underscores the model's potential to enhance the field of bioinformatics, setting a new benchmark for performance.

导言:在错综复杂的酶学领域,精确量化酶的效率(以周转数(k cat)为缩影)是一个至关重要但又难以实现的目标。现有的方法虽然复杂,但往往难以解决酶促反应固有的随机性和多面性问题。因此,有必要探索前卫的计算范式:在此背景下,我们引入了 "酶催化效率预测(ECEP)",利用先进的深度学习技术来增强之前用于预测过氧化氢酶 k cat 的实现 TurNuP。我们的方法结合了从酶序列和化学反应动力学中获得的新特征,大大优于之前的方法。通过 ECEP,我们揭示了酶与底物之间错综复杂的相互作用,捕捉到了分子决定因素之间微妙的相互作用:初步评估结果显示,与 TurNuP 和 DLKcat 等成熟模型相比,ECEP 的预测能力更胜一筹,标志着硅酶周转次数估算的关键转变。这项研究丰富了酶学家可用的计算工具包,为今后在蓬勃发展的生物信息学领域进行探索奠定了基础。本文提出了一种基于多特征集合深度学习的方法,利用集合卷积神经网络和 XGBoost,通过计算每个基于特征的模型输出的加权平均值来预测酶动力学参数,从而超越传统的机器学习方法。所提出的 "ECEP "模型明显优于现有方法,其均方误差(MSE)从 0.81 降至 0.46,降低了 0.35,R 方从 0.44 升至 0.54,从而证明了其在酶催化效率预测方面的卓越准确性和有效性:讨论:这一改进凸显了该模型在提高生物信息学领域的潜力,为其性能设定了新的基准。
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引用次数: 0
Human-centered evaluation of explainable AI applications: a systematic review. 以人为本的可解释人工智能应用评估:系统回顾。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-17 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1456486
Jenia Kim, Henry Maathuis, Danielle Sent

Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there's been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation "good" from a user's perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human-AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.

可解释的人工智能(XAI)旨在为人工智能系统的内部运作和输出提供见解。最近,越来越多的人认识到,可解释性本质上是以人为本的,与人们如何看待解释息息相关。尽管如此,研究界对于用户评估在 XAI 中是否至关重要,以及如果是的话,究竟需要评估什么以及如何评估还没有达成共识。本系统性文献综述详细概述了以人为本的 XAI 评估的现状,从而弥补了这一空白。我们回顾了与用户一起评估 XAI 的 73 篇不同领域的论文。这些研究从用户的角度评估了怎样的解释才是 "好 "解释,即怎样的解释对人工智能系统的用户才是有意义的。我们确定了 30 个有意义解释的组成部分,并将其归类为以人为本的 XAI 评估分类法,其依据是:(a) 解释的情境质量,(b) 解释对人机交互的贡献,以及 (c) 解释对人机交互性能的贡献。我们的分析还显示,XAI用户研究中应用的方法缺乏标准化,73篇论文中只有19篇应用了样本中至少一项其他研究使用的评估框架。这些不一致性阻碍了跨研究比较和更广泛的见解。我们的研究结果有助于理解是什么让解释对用户有意义,以及如何衡量这一点,从而引导 XAI 社区在以人为本的可解释性方面采用更加统一的方法。
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引用次数: 0
The impact of pedagogical beliefs on the adoption of generative AI in higher education: predictive model from UTAUT2. 教学信念对高等教育中采用生成式人工智能的影响:UTAUT2 的预测模型。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-17 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1497705
Julio Cabero-Almenara, Antonio Palacios-Rodríguez, María Isabel Loaiza-Aguirre, Paola Salomé Andrade-Abarca

Artificial Intelligence in Education (AIEd) offers advanced tools that can personalize learning experiences and enhance teachers' research capabilities. This paper explores the beliefs of 425 university teachers regarding the integration of generative AI in educational settings, utilizing the UTAUT2 model to predict their acceptance and usage patterns through the Partial Least Squares (PLS) method. The findings indicate that performance expectations, effort expectancy, social influence, facilitating conditions, and hedonic motivation all positively impact the intention and behavior related to the use of AIEd. Notably, the study reveals that teachers with constructivist pedagogical beliefs are more inclined to adopt AIEd, underscoring the significance of considering teachers' attitudes and motivations for the effective integration of technology in education. This research provides valuable insights into the factors influencing teachers' decisions to embrace AIEd, thereby contributing to a deeper understanding of technology integration in educational contexts. Moreover, the study's results emphasize the critical role of teachers' pedagogical orientations in their acceptance and utilization of AI technologies. Constructivist educators, who emphasize student-centered learning and active engagement, are shown to be more receptive to incorporating AIEd tools compared to their transmissive counterparts, who focus on direct instruction and information dissemination. This distinction highlights the need for tailored professional development programs that address the specific beliefs and needs of different teaching philosophies. Furthermore, the study's comprehensive approach, considering various dimensions of the UTAUT2 model, offers a robust framework for analyzing technology acceptance in education.

教育领域的人工智能(AIEd)提供了先进的工具,可以个性化学习体验,提高教师的研究能力。本文利用UTAUT2模型,通过偏最小二乘法(PLS)预测了425名大学教师对将生成式人工智能融入教育环境的看法。研究结果表明,绩效预期、努力预期、社会影响、便利条件和享乐动机都会对使用人工智能教育的意向和行为产生积极影响。值得注意的是,研究显示,具有建构主义教学信念的教师更倾向于采用人工智能教育技术,这突出了考虑教师的态度和动机对有效整合教育技术的重要意义。这项研究对影响教师决定采用人工智能教育的因素提供了宝贵的见解,从而有助于加深对教育环境中技术整合的理解。此外,研究结果还强调了教师的教学取向对他们接受和使用人工智能技术的关键作用。建构主义教育者强调以学生为中心的学习和主动参与,与注重直接教学和信息传播的传授型教育者相比,他们更容易接受人工智能教育工具。这种区别凸显了针对不同教学理念的具体信念和需求而量身定制专业发展计划的必要性。此外,本研究的综合方法考虑了UTAUT2模型的各个维度,为分析教育领域的技术接受度提供了一个强有力的框架。
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引用次数: 0
The potential of Logistics 4.0 technologies: a case study through business intelligence framing by applying the Delphi method. 物流 4.0 技术的潜力:应用德尔菲法通过商业智能框架进行的案例研究。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-17 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1469958
Joaquim Jorge Vicente, Lurdes Neves, Inês Bernardo

Introduction: The growing competitiveness and the importance of data availability for organizations have created a demand for intelligent information systems capable of analyzing data to support strategy and decision-making. Organizations are generating more and more data due to new technologies associated with Industry 4.0 and Logistics 4.0, making it essential to transform this data into relevant information to streamline decision-making processes. This paper examines the influence of these technologies on gaining a competitive advantage, specifically in a logistics company, which is scarce in the literature.

Methods: A case study was conducted in a Portuguese company using the Delphi method with 61 participants-employees who use the company's integrated BI tool daily. The participants were presented with a questionnaire via the online platform Welphi, requiring qualitative responses to various statements based on the literature review and the results of semi-structured meetings with the company.

Results: The study aimed to identify areas where employees believe more investment/ development is needed to optimize processes and improve the use of the BI tool in the future. The results indicate that BI is a crucial technology when aligned with a company's objectives and needs, highlighting the necessity of top management's involvement in optimizing the BI tool. Encouraging employees to use the BI tool emerged as a significant factor, underscoring the importance of leadership in innovative projects to achieve greater competitive advantage for the company.

Discussion: This study aims to understand the importance of Business Intelligence (BI) and how its functionalities should be adapted according to a company's strategy and objectives to optimize decision-making processes. Thereby, the discussion focused on the essential role of BI technologies in leveraging the company's competitive advantage.

导言:企业的竞争力不断增强,数据可用性对企业的重要性日益凸显,这就对能够分析数据以支持战略和决策的智能信息系统产生了需求。由于与工业 4.0 和物流 4.0 相关的新技术的出现,企业正在生成越来越多的数据,因此将这些数据转化为相关信息以简化决策流程至关重要。本文研究了这些技术对获得竞争优势的影响,特别是对物流公司的影响,这在文献中很少见:方法:采用德尔菲法在一家葡萄牙公司进行了案例研究,共有 61 名参与者,他们都是每天使用公司综合商业智能工具的员工。参与者通过 Welphi 在线平台接受问卷调查,要求对基于文献综述和公司半结构化会议结果的各种陈述做出定性回答:研究旨在确定员工认为今后需要在哪些领域进行更多投资/开发,以优化流程并改进商业智能工具的使用。研究结果表明,如果与公司的目标和需求相一致,商业智能是一项至关重要的技术,这凸显了高层管理人员参与优化商业智能工具的必要性。鼓励员工使用商业智能工具也是一个重要因素,这凸显了领导力在创新项目中的重要性,从而为公司带来更大的竞争优势:本研究旨在了解商业智能(BI)的重要性,以及应如何根据公司战略和目标调整其功能,以优化决策过程。因此,讨论的重点是商业智能技术在利用公司竞争优势方面的重要作用。
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引用次数: 0
Intrinsic motivation in cognitive architecture: intellectual curiosity originated from pattern discovery. 认知结构的内在动力:源于模式发现的求知欲。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-17 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1397860
Kazuma Nagashima, Junya Morita, Yugo Takeuchi

Studies on reinforcement learning have developed the representation of curiosity, which is a type of intrinsic motivation that leads to high performance in a certain type of tasks. However, these studies have not thoroughly examined the internal cognitive mechanisms leading to this performance. In contrast to this previous framework, we propose a mechanism of intrinsic motivation focused on pattern discovery from the perspective of human cognition. This study deals with intellectual curiosity as a type of intrinsic motivation, which finds novel compressible patterns in the data. We represented the process of continuation and boredom of tasks driven by intellectual curiosity using "pattern matching," "utility," and "production compilation," which are general functions of the adaptive control of thought-rational (ACT-R) architecture. We implemented three ACT-R models with different levels of thinking to navigate multiple mazes of different sizes in simulations, manipulating the intensity of intellectual curiosity. The results indicate that intellectual curiosity negatively affects task completion rates in models with lower levels of thinking, while positively impacting models with higher levels of thinking. In addition, comparisons with a model developed by a conventional framework of reinforcement learning (intrinsic curiosity module: ICM) indicate the advantage of representing the agent's intention toward a goal in the proposed mechanism. In summary, the reported models, developed using functions linked to a general cognitive architecture, can contribute to our understanding of intrinsic motivation within the broader context of human innovation driven by pattern discovery.

关于强化学习的研究已经发展了好奇心的表征,好奇心是一种内在动机,会导致在某类任务中取得优异成绩。然而,这些研究并没有深入研究导致这种表现的内在认知机制。与以往的研究框架不同,我们从人类认知的角度出发,提出了一种以模式发现为核心的内在动机机制。本研究将求知欲作为内在动机的一种,它能从数据中发现新颖的可压缩模式。我们用 "模式匹配"、"效用 "和 "生产编译 "来表示求知欲驱动的任务的持续和厌倦过程,这些都是思维理性自适应控制(ACT-R)架构的一般功能。我们在模拟中实施了三种具有不同思维水平的 ACT-R 模型,通过操纵求知欲的强度来浏览多个不同大小的迷宫。结果表明,在思维水平较低的模型中,求知欲会对任务完成率产生负面影响,而在思维水平较高的模型中,求知欲则会对任务完成率产生正面影响。此外,与传统强化学习框架(内在好奇心模块:ICM)所开发的模型进行的比较表明,在所提出的机制中代表代理对目标的意图具有优势。总之,所报告的模型是利用与一般认知架构相关联的功能开发的,有助于我们在由模式发现驱动的人类创新的大背景下理解内在动机。
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引用次数: 0
A review on the efficacy of artificial intelligence for managing anxiety disorders. 人工智能管理焦虑症疗效综述。
IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-16 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1435895
K P Das, P Gavade

Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.

焦虑症是一种精神疾病,其特点是患者在面对各种事件或情况时会产生长期和普遍的焦虑。目前,焦虑症被认为是全球最普遍的精神疾病。在临床实践中,药物治疗和不同类型的心理治疗是治疗焦虑症的主要方法。然而,众所周知,将这两种方法结合起来会比单独使用药物治疗产生更显著的疗效。然而,欠发达地区资源匮乏,可供选择的心理疗法有限。心理治疗方法包括放松技巧、控制呼吸练习、可视化练习、控制暴露练习和认知干预,如挑战消极想法。这些方法对焦虑症的治疗至关重要,但要熟练地执行这些方法却要求很高。此外,焦虑症患者会被处方药物,在某些情况下可能会出现戒断症状。此外,面对面的心理治疗不够普及,在初期阶段预测和监测焦虑症患者的健康、行为和环境方面的能力也受到限制。近年来,在开发和利用基于人工智能(AI)的应用程序和环境以提高诊断和治疗各类焦虑症的精确度和灵敏度方面取得了显著进展。因此,本研究旨在确定人工智能环境在应对现有焦虑症管理挑战方面的功效,减少对药物治疗的依赖,并调查整合人工智能辅助焦虑症医疗保健和实现个性化治疗的潜在优势、问题和机遇。
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引用次数: 0
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Frontiers in Artificial Intelligence
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