Artificial Intelligence-Based Predictive Models for Complex Social and Industrial Challenges

Pamela Ugwudike

Artificial Intelligence-Based Predictive Models for Complex Social and Industrial Challenges

Author(s)	Pamela Ugwudike
Country	United States
Abstract	The increasing complexity of modern social and industrial environments has created a strong demand for advanced analytical approaches capable of predicting future patterns, identifying risks, and supporting effective decision-making. Traditional analytical methods often face limitations when dealing with large-scale, dynamic, and multidimensional datasets generated through contemporary digital ecosystems. Artificial Intelligence (AI)-based predictive models have emerged as powerful approaches that utilize machine learning, deep learning, data analytics, and intelligent algorithms to analyse complex information and generate accurate forecasts. These predictive systems are increasingly applied in areas such as industrial optimization, social behaviour analysis, resource management, risk assessment, and strategic planning. This study examines artificial intelligence-based predictive models designed to address complex social and industrial challenges by improving forecasting capability, decision accuracy, and adaptive intelligence. The research explores how AI-driven prediction frameworks integrate data processing, pattern recognition, and learning mechanisms to support evidence-based decision-making. A simulation-based analytical framework is developed to compare traditional predictive approaches with AI-based models using evaluation indicators including prediction accuracy, adaptability, decision-support effectiveness, and risk identification capability. The findings demonstrate that AI-based predictive models provide significant improvements in managing complex environments by identifying hidden patterns, processing diverse information sources, and generating timely insights. The study highlights that successful AI prediction requires not only advanced algorithms but also responsible data management, ethical considerations, and human-centered implementation strategies. The research contributes to the development of intelligent predictive frameworks capable of supporting future social and industrial decision-making processes through improved analytical capability and adaptive learning.
Keywords	Artificial Intelligence, Predictive Models, Machine Learning, Social Analytics, Industrial Intelligence, Decision Support Systems, Intelligent Forecasting.
Field	Engineering
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-01-31

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Artificial Intelligence-Based Predictive Models for Complex Social and Industrial Challenges

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