From Pixels to Predictions: a Deep Dive into the Synergy of Machine Learning and IoT

EasyChair Preprint 11913

Abstract

The integration of Machine Learning (ML) and the Internet of Things (IoT) has transformed the landscape of data analytics, enabling the extraction of valuable insights from the vast streams of data generated by interconnected devices. This paper explores the intricate synergy between ML and IoT, delving into the transition from raw pixels to predictive models. Through a comprehensive examination of methodologies, results, and discussions, this research aims to illuminate the advancements, challenges, and potential treatments in this exciting intersection of technologies.

Keyphrases: Data Analytics, Edge Computing, Internet of Things, IoT sensors, connectivity, deep learning, machine learning, predictive models, smart devices, synergy

@booklet{EasyChair:11913,
  author    = {William Jack and Rohit Donni},
  title     = {From Pixels to Predictions: a Deep Dive into the Synergy of Machine Learning and IoT},
  howpublished = {EasyChair Preprint 11913},
  year      = {EasyChair, 2024}}