: 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。
{"title":"IT Infrastructure Management: Optimizing Performance, Scalability, and Reliability","authors":"Jugendra Singh, Nisha Sharma, S. Saini","doi":"10.48047/resmil.v9i1.30","DOIUrl":"https://doi.org/10.48047/resmil.v9i1.30","url":null,"abstract":": 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.","PeriodicalId":517991,"journal":{"name":"resmilitaris","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140399933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Responsive Web Design with HTML and CSS","authors":"","doi":"10.48047/resmil.v9i1.22","DOIUrl":"https://doi.org/10.48047/resmil.v9i1.22","url":null,"abstract":"","PeriodicalId":517991,"journal":{"name":"resmilitaris","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140399004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"Distributed Computing with Dask and Apache Spark: A Comparative Study","authors":"Ankita Jain, Devendra Singh Sendar, Sarita Mahajan","doi":"10.48047/resmil.v9i1.21","DOIUrl":"https://doi.org/10.48047/resmil.v9i1.21","url":null,"abstract":"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","PeriodicalId":517991,"journal":{"name":"resmilitaris","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.48047/resmil.v10i1.19
{"title":"Explainable AI (XAI): Bridging the Gap between Machine Learning and Human Understanding","authors":"","doi":"10.48047/resmil.v10i1.19","DOIUrl":"https://doi.org/10.48047/resmil.v10i1.19","url":null,"abstract":"","PeriodicalId":517991,"journal":{"name":"resmilitaris","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: 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.
{"title":"Introduction to Data Visualization Techniques: A Comparative Analysis","authors":"Yogesh Bhomia","doi":"10.48047/resmil.v9i1.32","DOIUrl":"https://doi.org/10.48047/resmil.v9i1.32","url":null,"abstract":": 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.","PeriodicalId":517991,"journal":{"name":"resmilitaris","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140403762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 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
{"title":"Cracking the Code: Enhancing Trust in AI through Explainable Models","authors":"Vipin Gupta, Shailendra Shukla, Kumari Nikita","doi":"10.48047/resmil.v10i1.20","DOIUrl":"https://doi.org/10.48047/resmil.v10i1.20","url":null,"abstract":"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","PeriodicalId":517991,"journal":{"name":"resmilitaris","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140405791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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