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IT Infrastructure Management: Optimizing Performance, Scalability, and Reliability IT 基础设施管理:优化性能、可扩展性和可靠性
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.30
Jugendra Singh, Nisha Sharma, S. Saini
: This abstract examines the essential elements of IT infrastructure management, focusing on increased performance, scalability, and reliability. In today’s dynamic business environment, organizations are increasingly reliant on their IT systems for productivity and efficiency. Maintaining these systems requires a strategic approach while adjusting scalability and maintaining reliability. This abstract explores the key elements of infrastructure efficiency, including proactive inspections, robust security measures, easy integration of new technologies, and efficient resource allocation It emphasizes the importance of flexibility in order to adapt to the evolving patterns of business needs It can lay the foundation for the flexible, adaptable and efficient technology ecosystems needed to enable modern businesses it has been successful.
:本摘要探讨了 IT 基础设施管理的基本要素,重点是提高性能、可扩展性和可靠性。在当今充满活力的商业环境中,企业越来越依赖信息技术系统来提高生产力和效率。维护这些系统需要采取战略性方法,同时调整可扩展性并保持可靠性。本摘要探讨了基础设施效率的关键要素,包括主动检查、强大的安全措施、新技术的轻松集成以及高效的资源分配。 它强调了灵活性的重要性,以适应不断变化的业务需求模式。它可以为现代企业所需的灵活、适应性强且高效的技术生态系统奠定基础。
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引用次数: 0
GENERATIVE ADVERSARIAL NETWORKS APPLICATIONS 生成式对抗网络的应用
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v10i1.13
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引用次数: 0
IoT Device Management and Security 物联网设备管理与安全
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v10i1.17
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引用次数: 0
IoT Security in Smart Homes 智能家居中的物联网安全
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.31
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引用次数: 0
Food waste reduction technologies 减少食物垃圾技术
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.25
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引用次数: 0
Responsive Web Design with HTML and CSS 使用 HTML 和 CSS 进行响应式网页设计
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.22
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引用次数: 0
Distributed Computing with Dask and Apache Spark: A Comparative Study 使用 Dask 和 Apache Spark 的分布式计算:比较研究
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.21
Ankita Jain, Devendra Singh Sendar, Sarita Mahajan
In the unexpectedly expanding landscape of dispensed computing, the choice of frameworks profoundly affects the efficiency and scalability of records processing workflows. This comparative take a look at delves into the architectures, overall performance metrics, and consumer reports of main allotted computing frameworks: Dask and Apache Spark. Both frameworks have won prominence for his or her ability to handle huge-scale records processing, yet they diverge of their essential tactics. Dask embraces a flexible mission graph paradigm, even as Apache Spark is predicated on a resilient allotted dataset (RDD) abstraction. This summary presents an outline of our exploration into their ancient development, benchmarking analyses, and adaptableness to numerous computing environments. By evaluating their strengths and boundaries, this observe gives insights vital for practitioners and organizations navigating the dynamic landscape of distributed records processing. As the extent and complexity of information continue to grow exponentially, disbursed computing frameworks have turn out to be instrumental in addressing the computational challenges posed by means of large datasets. Dask and Apache Spark have emerged as powerful gear, every presenting unique solutions for disbursed statistics processing. This comparative take a look at pursuits to offer a nuanced understanding in their architectures, performance traits, and value, supporting practitioners in making knowledgeable selections whilst choosing a framework for distributed computing duties.Understanding the ancient improvement and layout principles of Dask and Apache Spark
在出人意料地不断扩展的分配计算领域,框架的选择对记录处理工作流的效率和可扩展性影响深远。本比较报告深入探讨了主要分配计算框架的架构、总体性能指标和用户报告:Dask 和 Apache Spark。这两个框架都因其处理大规模记录的能力而备受瞩目,但它们的基本策略却各不相同。Dask 采用灵活的任务图范式,而 Apache Spark 则基于弹性配给数据集 (RDD) 抽象。本摘要概述了我们对它们的古代开发、基准分析以及对众多计算环境的适应性的探索。通过评估它们的优势和局限性,本报告为从业人员和组织机构在分布式记录处理的动态环境中导航提供了至关重要的见解。随着信息的范围和复杂性不断呈指数级增长,分布式计算框架已成为应对大型数据集带来的计算挑战的重要工具。Dask 和 Apache Spark 已成为强大的工具,它们都为分散式统计处理提供了独特的解决方案。这本比较研究旨在提供对它们的架构、性能特征和价值的细致了解,帮助从业人员在为分布式计算任务选择框架时做出明智的选择。 了解 Dask 和 Apache Spark 的古老改进和布局原理
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引用次数: 0
Explainable AI (XAI): Bridging the Gap between Machine Learning and Human Understanding 可解释的人工智能(XAI):缩小机器学习与人类理解之间的差距
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v10i1.19
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引用次数: 0
Introduction to Data Visualization Techniques: A Comparative Analysis 数据可视化技术简介:比较分析
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.32
Yogesh Bhomia
: Data visualization is a effective tool for expertise and speaking complicated information. This studies paper gives an introductory exploration of diverse records visualization techniques, presenting a comparative analysis of their strengths, weaknesses, and applications. In an technology characterized via facts abundance, selecting the proper visualization technique is vital for effective information analysis. This paper examines a wide range of visualization techniques, from simple charts to advanced visualizations, to help researchers and practitioners in choosing the most suitable technique for their precise records analysis desires. The comparative analysis considers elements consisting of visual effectiveness, use instances, scalability, interactivity, equipment, and ethical issues. Through case research and first-rate practices, this paper aims to beautify the know-how of statistics visualization and its role in extracting meaningful insights from statistics.
:数据可视化是一种有效的工具,可用于专业知识和讲述复杂的信息。本研究论文介绍了各种记录可视化技术,并对其优缺点和应用进行了比较分析。在以事实丰富为特征的技术中,选择适当的可视化技术对于有效的信息分析至关重要。本文研究了从简单图表到高级可视化的各种可视化技术,以帮助研究人员和从业人员选择最适合其精确记录分析需求的技术。比较分析考虑的要素包括视觉效果、使用实例、可扩展性、交互性、设备和道德问题。通过案例研究和一流实践,本文旨在美化统计可视化技术及其在从统计数据中提取有意义见解方面的作用。
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引用次数: 0
Cracking the Code: Enhancing Trust in AI through Explainable Models 破解密码:通过可解释模型增强对人工智能的信任
Pub Date : 2024-03-01 DOI: 10.48047/resmil.v10i1.20
Vipin Gupta, Shailendra Shukla, Kumari Nikita
In this paper, we explore the critical challenges of building trust in artificial intelligence (AI) systems, particularly those characterized by black box models. The proliferation of complex and opaque AI models has raised concerns about a lack of interpretability, hindering users’ understanding and confidence in these systems Significant problem solved in this review addresses the importance of increasing the reliability of AI through semantic AI (XAI) approaches . clarify the complexity of the model To address this issue, our approach is a comprehensive review of the existing literature on XAI, black-box models, and their implications for reliability We thoroughly analyze various XAI methods, such as local interpretive model-agnostic explanations (LIME), SHapley explanatory agnostic explanations (SHAP). and reflection methods, in addition to clarifying their efforts aimed at making AI models transparent, we examine real-world case studies in which the use of XAI has enhanced trustworthiness of AI systems have improved in various sectors. The main findings of our study highlight the important role of XAI in reducing the uncertainty associated with black-box models. We highlight examples where the adoption of interpretable approaches not only increased the interpretability of AI systems but also enhanced user confidence. By providing transparent insights into decision-making processes, XAI is proving to help remove complex models and establish a foundation of trust between users and AI systems The implications of our research apply to a range of industries that rely on AI, including healthcare, finance and autonomous systems. While opening up the benefits of XAI for building trust, we recommend its inclusion in AI development practices and highlight possible future developments in this area. However, our study acknowledges the existing
在本文中,我们探讨了在人工智能(AI)系统中建立信任的关键挑战,尤其是那些以黑盒模型为特征的系统。复杂而不透明的人工智能模型的激增引发了人们对缺乏可解释性的担忧,阻碍了用户对这些系统的理解和信心。 本综述解决的重要问题是通过语义人工智能(XAI)方法提高人工智能可靠性的重要性。我们深入分析了各种 XAI 方法,如本地解释性模型不可知论解释(LIME)、SHapley 解释性不可知论解释(SHAP)和反思方法,除了阐明它们旨在使人工智能模型透明化的努力之外,我们还研究了现实世界中的案例研究,在这些案例研究中,XAI 的使用提高了各行业人工智能系统的可信度。我们研究的主要发现强调了 XAI 在减少与黑箱模型相关的不确定性方面的重要作用。我们着重举例说明,采用可解释方法不仅提高了人工智能系统的可解释性,还增强了用户信心。事实证明,通过提供对决策过程的透明见解,XAI 有助于消除复杂的模型,并在用户和人工智能系统之间建立信任基础。 我们研究的意义适用于一系列依赖人工智能的行业,包括医疗保健、金融和自主系统。在开放 XAI 对建立信任的益处的同时,我们建议将其纳入人工智能开发实践,并强调了该领域未来可能的发展。不过,我们的研究也承认现有的
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引用次数: 0
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