【标题】Artificial Intelligence-Based Classification of Trusted and Untrusted Sensor Nodes in WBAN Using Multi Layered Stacked Naive Bayes Method for Resilient Infrastructure
【参考中译】基于人工智能的WBAN中可信和不可信传感器节点分类，使用多层堆叠朴素Bayes方法用于弹性基础设施
【类型】 期刊
【关键词】 WBANs; Trusted/Untrusted nodes; Multi_layered_Stacked_Nave_Bayes (MLSNB); Independent component analytic (ICA); Bi_Objective_Genetic algorithms (BGA)
【参考中译】 WBAN;受信任/不受信任的节点; Multi_layered_Nave_Bayes（MLSNB）;独立分量分析（ICA）; Bi_Observer_Genetic算法（LGA）
【作者】 Seba Aziz Sahy; Rajaa Salih Mohammed Hasan; Hala Shaker Mehdy; Israa Ibraheem Al_Barazanchi; Jamal Fadhil Tawfeq; Ravi Sekhar; Pritesh Shah
【摘要】 WBAN Magnetic Sensor Nodes can be classified based on artificial intelligence using the Multi-Layered Stacked Naive Bayes Method for Resilient Infrastructure. As wireless body area networks (WBANs) hold considerable potential for monitoring, identifying, forecasting, and diagnosing disease in humans, this study is significant for the healthcare industry. WBAN data can be inaccurate and unreliable when collected by untrusted sensor nodes, leading to inaccurate diagnoses and treatments. WBAN networks can be improved by identifying untrusted sensor nodes in this study to address this issue. Sensor nodes are categorized using the MLSNB method based on their trust aspects. When compared to other methods currently in use, MLSNB performs better. It is possible, using the proposed methodology, to introduce high-quality, affordable, and easily accessible healthcare systems to the world's growing population, in particular to the elderly and persons living with old-age diseases.
【参考中译】 WBAN磁传感器节点可以使用弹性基础设施的多层堆叠朴素Bayes方法基于人工智能进行分类。由于无线体域网（WBAN）在监测、识别、预测和诊断人类疾病方面具有相当大的潜力，因此这项研究对于医疗保健行业具有重要意义。WBAN数据由不受信任的传感器节点收集时可能不准确且不可靠，导致诊断和治疗不准确。在本研究中，可以通过识别不受信任的传感器节点来改进WBAN网络以解决这个问题。传感器节点根据其信任方面使用MLSNB方法进行分类。与目前使用的其他方法相比，MLSNB的表现更好。使用所提出的方法，有可能为世界上不断增长的人口，特别是老年人和老年病患者引入高质量、负担得起且易于获得的医疗保健系统。
【来源】 Revue d'Intelligence Artificielle 2024, vol.38, no.2
【入库时间】 2024/10/9
【标题】Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning
【参考中译】提升移动机器人技术：人工智能和机器学习的先驱应用
【关键词】 Mobile robot; Artificial intelligence; Machine learning; Robot localization; Artificial neural networks; Convolutional neural networks
【参考中译】 移动机器人;人工智能;机器学习;机器人定位;人工神经网络;卷积神经网络
【作者】 Haider Sahib Nasrallah; Ivan V. Stepanyan; Karrar Sahib Nassrullah; Neder Jair Mendez Florez; Israa M. Abdalameer AL-Khafaji; Abdelrrahmane Mohamed Zidoun; Ravi Sekhar; Pritesh Shah; Sushma Parihar
【摘要】 The present study delves into the utilization of subsumption architecture for the modeling of mobile robot behaviors, particularly those that respond adaptively to environmental dynamics and inaccuracies in sensor measurements. Central to this investigation is the deployment of reactive controller networks, wherein each node-representing a distinct state-is governed by sensor-triggered conditions that dictate state transitions. The methodology adopted comprises a thorough literature review, encompassing sources from IEEE Xplore, ScienceDirect, and the ACM Digital Library, which discuss the integration of subsumption architecture in the realm of mobile robot control. Through this review, the effectiveness of subsumption architecture in crafting reactive robotic behaviors is underscored. It has been established that augmented finite state machines (AFSMs), which are integral to the subsumption architecture and possess internal timing mechanisms, are pivotal in managing the temporal aspects of state transitions. Additionally, the technique of layering-merging multiple simple networks to form intricate behavior patterns-emerges as a significant finding, accentuating the architecture's capability to facilitate complex behavioral constructs. The prime contribution of this body of work lies in identifying and elucidating the strategic role of subsumption architecture in enhancing the adaptability and robustness of mobile robots. The insights gleaned from this study not only advance our understanding of robotic control systems but also hold implications for the amplification of industrial efficiency and effectiveness through the application of sophisticated AI and machine learning techniques in mobile robotics.
【参考中译】 本研究探讨了包含体系结构在移动机器人行为建模中的应用，特别是那些对环境动态和传感器测量误差做出适应性响应的移动机器人行为。这项调查的核心是部署无功控制器网络，其中每个节点-代表不同的状态-由传感器触发的条件控制，这些条件指示状态转换。所采用的方法包括全面的文献综述，包括来自IEEE Xplore、Science Direct和ACM数字图书馆的资料，这些资料讨论了包容体系结构在移动机器人控制领域的集成。通过这篇综述，强调了包容体系结构在制作反应性机器人行为方面的有效性。增广有限状态机(AFSM)是包含体系结构中不可或缺的一部分，具有内部的时序机制，在管理状态转移的时间方面起着关键作用。此外，分层技术--将多个简单网络合并成复杂的行为模式--成为一项重大发现，突显了该体系结构促进复杂行为构建的能力。这项工作的主要贡献在于识别和阐明包容体系结构在增强移动机器人的适应性和健壮性方面的战略作用。从这项研究中获得的见解不仅增进了我们对机器人控制系统的理解，而且对于通过将复杂的人工智能和机器学习技术应用于移动机器人来扩大工业效率和有效性具有重要意义。
【来源】 Revue d'Intelligence Artificielle 2024, vol.38, no.1
【标题】Optimizing Organizational Structures with Artificial Intelligence: Algorithm Design and Application
【参考中译】利用人工智能优化组织结构：算法设计与应用
【关键词】 Organizational structure optimization; Artificial intelligence; Human resource allocation; Internal conflict resolution; Fuzzy cerebellar model articulation controller; Trust network
【参考中译】 组织结构优化;人工智能;人力资源配置;内部冲突的解决;模糊小脑模型关节控制器;信任网络
【作者】 Xiaoran Pang
【摘要】 In the context of globalization and information technology advancement, organizations are confronted with the dual challenges of efficiently allocating resources and promptly addressing internal conflicts. The optimization of organizational structures is identified not only as a strategic measure to enhance competitive advantage but also as a necessary approach to improve decision-making quality and organizational adaptability. This study explores the application of artificial intelligence (AI) technologies in optimizing organizational structures, focusing specifically on the intelligent allocation of human resources and the intelligent identification and resolution mechanisms for internal conflicts. Existing research shows a notable deficiency in resource allocation and conflict resolution, particularly lacking consideration of trust network within organizations and analysis of adaptability to dynamic changes. Addressing these issues, a model based on the fuzzy cerebellar model articulation controller (FCMAC) for the optimization of human resource allocation is proposed. This model is capable of dynamically adjusting strategies in response to the evolving demands of the organization. Concurrently, an intelligent framework for identifying and resolving internal conflicts, which incorporates trust network, has been developed. By quantifying trust relationships, the framework aims to enhance the accuracy of decision-making and the coordination within the organization. Findings suggest that these methodologies significantly improve the efficiency of organizational resource allocation and effectively reduce conflict situations, thereby enhancing overall work efficiency and performance. This research not only offers a new perspective on the role of AI in optimizing organizational decisions but also provides practical solutions for management practices, crucial for aiding organizations to adapt to rapidly changing external environments and enhance their competitiveness.
【参考中译】 在全球化和信息技术进步的背景下，各组织面临着有效分配资源和迅速解决内部冲突的双重挑战。组织结构的优化不仅被认为是增强竞争优势的战略措施，而且是提高决策质量和组织适应性的必要途径。本研究探讨人工智能(AI)技术在组织结构优化中的应用，特别是人力资源的智能配置和内部冲突的智能识别和解决机制。现有的研究表明，在资源分配和冲突解决方面存在着显著的不足，特别是缺乏对组织内部信任网络的考虑和对动态变化的适应性分析。针对这些问题，提出了一种基于模糊小脑模型关节控制器(FCMAC)的人力资源优化配置模型。该模型能够动态调整战略，以响应组织不断变化的需求。同时，还开发了一个智能框架，用于识别和解决内部冲突，其中包括信任网络。通过量化信任关系，该框架旨在提高决策的准确性和组织内部的协调。调查结果表明，这些方法显著提高了组织资源分配的效率，有效地减少了冲突情况，从而提高了整体工作效率和业绩。这项研究不仅为人工智能在优化组织决策中的作用提供了一个新的视角，而且为管理实践提供了实用的解决方案，对于帮助组织适应快速变化的外部环境和提高竞争力至关重要。