WEB USAGE MINING-A Study of Web data pattern detecting methodologies and its applications in Data Mining

Shukla, Rajesh; Sharma, Prachi; Samaiya, Noopur; Kherajani, Monika

doi:10.1109/idea49133.2020.9170690

Cited by 2 publications

(1 citation statement)

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“…It allows users to analyze data from multiple perspectives, categorize information, and draw conclusions. The process involves searching for correlations between different fields in a database to extract valuable insights and information [15]- [17]. Machine learning enables the categorization of data processing methods in data mining into distinct categories such as classification, regression analysis, association rules, and clustering.…”

Section: A Artificial Intelligence and Data Miningmentioning

confidence: 99%

Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review

Hashfi,

Raharjo

2023

IJACSA

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This paper presents a systematic literature review (SLR) investigating the challenges and impacts of implementing artificial intelligence (AI) in project management, specifically mapping them into the process groups defined in the Project Management Body of Knowledge (PMBOK). The study aims to contribute to the understanding of integrating AI in project management and provides insights into the challenges and impacts within each process group. The SLR methodology was applied, and a total of 34 scientific articles were analyzed. The results and analysis reveal the specific challenges and impacts within each process group. In the Initiating Process Group, AI tools and analysis techniques address challenges in risk assessment, cost prediction, and decision-making. The Planning process group benefits from various tools and methodologies that improve risk assessment, project selection, cost estimation, resource allocation, and decision-making. The Execution process group emphasizes the importance of advanced tools and techniques in enhancing productivity, resource utilization, cost reduction, and decision-making. The Monitoring and Controlling process group demonstrates the potential of advanced tools in achieving efficiency, cost reduction, improved quality, and informed decision-making. Lastly, the Closing process group emphasizes the importance of utilizing advanced tools to minimize waste, optimize resource utilization, reduce costs, improve quality, and project closure success. Overall, this research provides valuable insights and strategies for organizations seeking to implement AI in project management, thereby enhancing the potential for success within the PMBOK Process Group.

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Section: A Artificial Intelligence and Data Miningmentioning

confidence: 99%