Software maintainability is a critical quality attribute that directly impacts the long-term cost, reliability, and evolution of software systems. Predicting maintainability early in the development lifecycle enables developers and managers to make informed design decisions, allocate resources efficiently, and reduce technical debt. This paper investigates the use of artificial intelligence (AI) models for predicting software maintainability based on code metrics, historical project data, and architectural characteristics. We explore supervised learning techniques, including regression models, decision trees, and neural networks, as well as ensemble and hybrid approaches, to estimate maintainability scores and identify key factors influencing maintainability. Experiments on open-source and industrial datasets demonstrate the effectiveness of AI-based predictions in improving software quality assessment, providing actionable insights, and supporting proactive maintenance strategies. The study highlights the potential of AI-driven methods to enhance software engineering practices and reduce maintenance effort.
Fatima Noor· International Journal of Mac...· 0 citations
Cities flooding is a major hydrological risk in rapidly urbanizing regions, intensified by climate change, extreme rainfall, and inadequate drainage systems. Traditional hydrological models require extensive calibration and high-resolution data, which are often unavailable for many cities. To address this issue, this study proposes a flood prediction framework that integrates Geographic Information Systems (GIS) with Machine Learning (ML). Multi-source geospatial data such as digital elevation models (DEM), land use–land cover (LULC), soil properties, drainage density, rainfall intensity, and historical flood records are used to derive flood conditioning factors in a GIS environment. These factors are then applied as inputs for supervised ML algorithms including Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting. Model performance is evaluated using RMSE, MAE, R², and ROC–AUC metrics. Results indicate that hybrid GIS-ML models outperform traditional approaches by effectively capturing non-linear relationships between hydrometeorological and urban factors. The framework provides a reliable decision-support tool for urban planners and disaster management authorities to identify flood-prone areas, improve drainage planning, and enhance early warning systems.
Ahmed Hassan, Fatima Noor· International Journal of Mod...· 0 citations
Business Intelligence (BI) systems have become essential tools for strategic financial management by transforming large volumes of financial data into actionable insights for planning, forecasting, budgeting, investment analysis, risk management, and decision-making. Unlike traditional financial management methods that relied on historical reporting and manual analysis, BI technologies automate data collection, integrate information from multiple sources, and provide real-time analytical capabilities. Key BI components such as data warehousing, OLAP, dashboards, reporting tools, data mining, and predictive analytics help organizations monitor performance, identify trends, assess risks, and develop evidence-based financial strategies. BI adoption across industries, including banking, manufacturing, healthcare, and telecommunications, has improved decision quality, reporting efficiency, forecasting accuracy, financial transparency, and organizational competitiveness. This study presents a systematic review of BI systems in strategic financial management before 2019, examining their evolution, architecture, analytical methods, and impact on financial performance. 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. Overall, BI technologies serve as a foundation for modern data-driven financial decision support systems.
Ahmed Hassan, Fatima Noor· International Journal of Com...· 0 citations
Cognitive Data Engineering (CDE) is an advanced paradigm that integrates artificial intelligence, machine learning, and knowledge-based systems into traditional data engineering to enable automated and intelligent data management. 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. Unlike conventional rule-based pipelines, the proposed framework adapts dynamically to data changes, anomalies, and schema evolution through self-learning and context-aware capabilities. The framework employs metadata-driven intelligence, semantic modeling, reinforcement learning, and cognitive agents within a layered architecture comprising perception, reasoning, learning, and execution. It also leverages knowledge graphs and ontologies to enhance semantic interoperability and data discovery. Experimental results demonstrate improved performance, reduced errors, and increased flexibility compared to traditional systems. Overall, the study highlights the potential of CDEFs in enabling efficient, scalable, and autonomous data management, with future scope in edge computing, real-time analytics, and self-governing data ecosystems.
Fatima Noor, Suresh Babu Reddy· International Journal of Dat...· 0 citations