AI-powered predictive analytics has emerged as a critical tool for modern organizations, enabling data-driven decision-making and strategic planning through the analysis of large-scale structured and unstructured data. By integrating machine learning, deep learning, natural language processing, and optimization techniques, predictive analytics frameworks can identify patterns, forecast future outcomes, and reduce organizational risks. This study presents a comprehensive framework that includes data acquisition, preprocessing, feature engineering, predictive modeling, evaluation, and decision support. The framework emphasizes data quality, computational efficiency, algorithm selection, and model interpretability. Applications across finance, healthcare, manufacturing, retail, and supply chain management demonstrate the effectiveness of AI in improving forecasting accuracy and operational performance. Comparative analysis shows that AI-based models outperform traditional statistical methods in accuracy, adaptability, scalability, and decision support. The study also highlights the role of explainable AI in enhancing transparency and trust, concluding that predictive analytics is a key enabler of intelligent enterprises and future data-driven innovation.
Pooja Agarwal, Rakesh Chandra· International Journal of Mac...· 0 citations
Hybrid AI architectures are emerging as a powerful solution for complex decision support systems (DSS) that must handle uncertainty, heterogeneous data, and large-scale integration. Traditional AI methods are limited in addressing real-world multidimensional challenges. This paper analyzes hybrid AI systems that combine symbolic approaches (rule-based reasoning, interpretability) with sub-symbolic methods (machine learning, neural networks) to improve flexibility and robustness. A modular framework is proposed, consisting of data preprocessing, knowledge representation, inference, and learning components, enabling both offline training and real-time decision-making. The study highlights key challenges such as scalability, knowledge integration, and computational efficiency. Experimental results demonstrate that hybrid models outperform standalone AI techniques in accuracy, precision, recall, and efficiency, especially in dynamic and uncertain environments. The paper concludes by suggesting future directions, including explainable AI and scalable distributed architectures.
Riyaz Mohammed, Pooja Agarwal· International Journal of Art...· 0 citations
An approach based on multi-layered which involves combining real-time data collection, AI-enhanced predictive analytics, and automated decision-making to improve disaster preparedness and response is suggested to improve disaster preparedness and response.
Pooja Agarwal· International Journal of Mod...· 0 citations