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The term “Internet of things” (IoT) refers to a network of disparate devices. With the use of this technology, network devices can offer higher-quality services in the form of smart homes, cities, factories, etc. A subfield of computer science known as “machine learning” deals with a system's capacity to draw lessons from its past and use those lessons to predict future events. This study also includes a comparison of two machine learning methods for user authentication and prediction. By applying two different machine learning algorithms on a single dataset, the intended method was demonstrated. Using the Movie Star dataset, Convolutional Neural Networks (CNN) and Conv2D were implemented and evaluated. The application of various algorithms to enhance or assess the precision and speed of new or existing models will be the main focus of the research.

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Assessing the Effectiveness of Machine Learning Models in IoT Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Mahfuzul Huda,
  • Abdullah

摘要

The term “Internet of things” (IoT) refers to a network of disparate devices. With the use of this technology, network devices can offer higher-quality services in the form of smart homes, cities, factories, etc. A subfield of computer science known as “machine learning” deals with a system's capacity to draw lessons from its past and use those lessons to predict future events. This study also includes a comparison of two machine learning methods for user authentication and prediction. By applying two different machine learning algorithms on a single dataset, the intended method was demonstrated. Using the Movie Star dataset, Convolutional Neural Networks (CNN) and Conv2D were implemented and evaluated. The application of various algorithms to enhance or assess the precision and speed of new or existing models will be the main focus of the research.