【标题】Artificial Intelligence-Based Classification of Trusted and Untrusted Sensor Nodes in WBAN Using Multi Layered Stacked Naive Bayes Method for Resilient Infrastructure
【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)
【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.
【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
【关键词】 Mobile robot; Artificial intelligence; Machine learning; Robot localization; Artificial neural networks; Convolutional neural networks
【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.
【来源】 Revue d'Intelligence Artificielle 2024, vol.38, no.1
【标题】Optimizing Organizational Structures with Artificial Intelligence: Algorithm Design and Application
【关键词】 Organizational structure optimization; Artificial intelligence; Human resource allocation; Internal conflict resolution; Fuzzy cerebellar model articulation controller; Trust network
【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.