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Experiments demonstrate the effectiveness of AI-based predictions in improving software quality assessment, providing actionable insights, and supporting proactive maintenance strategies in improving software quality assessment and reducing maintenance effort.
A flood prediction framework that integrates Geographic Information Systems (GIS) with Machine Learning (ML) and results indicate that hybrid GIS-ML models outperform traditional approaches by effectively capturing non-linear relationships between hydrometeorological and urban factors.
The findings indicate that BI frameworks significantly enhance financial planning, governance, and strategic decision-making despite challenges related to data integration, organizational resistance, and implementation costs.
This paper presents a Cognitive Data Engineering Framework (CDEF) designed to automate key data lifecycle processes such as ingestion, transformation, integration, quality assurance, and governance through self-learning and context-aware capabilities.
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