AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems


Keywords:
Internet of Things, nonlinear optimization, data mining, pattern analysis, artificial intelligence, knowledge extractionAbstract
In this paper, systematic exploration of the application of AI-based nonlinear optimization to elicit knowledge and pattern study in the IoT-enabled data mining takes place. The paper explains the complexity and heterogeneity of IoT data on scale by declaring and resolving nonlinear optimization problems using the latest AI methods, including genetic algorithms and neural networks. On smart grid, city traffic, and industrial data, a modular computing framework is constructed consisting of fog computing over edges, cloud storage and embedded AI engines. The result of the experiment is that nonlinear optimization algorithms are better than the classical linear and clustering algorithms in accuracy and effectiveness, which tells of the presence of multi-layered latent structures that are significant in the analytics of IoT. The paper lists the advantages of the nonlinear complexities of determining the actionable patterns then outlines the outlooks of the future development of multi-objective modeling, federated learning, and privacy-respectful analytics in dynamic IoT environments.
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