IDEAS home Printed from https://ideas.repec.org/a/dbk/rlatia/v2y2024ip9id1062486latia20249.html
   My bibliography  Save this article

Enhancing IoT Data Analysis with Machine Learning: A Comprehensive Overview

Author

Listed:
  • Amit Kumar Dinkar
  • Md Alimul Haque
  • Ajay Kumar Choudhary

Abstract

Machine learning techniques are essential for processing the vast volume of IoT data efficiently, improving performance, and managing IoT applications effectively. Machine learning algorithms play a crucial role in detecting malicious attacks and anomalies in real-time IoT data analysis, thereby enhancing the security of IoT devices. The integration of big data analytics methods with machine learning techniques can further enhance IoT data analysis, improving the performance of IoT applications and overcoming related challenges. Real-time data collection using sensors like DHT11 and Gas level sensors, coupled with machine learning algorithms, enables efficient analysis of IoT data, aiding in the identification of anomalies and attacks. The comprehensive overview of enhancing IoT data analysis with machine learning provides insights for future research, including exploring advanced machine learning algorithms and optimizing data preprocessing techniques to enhance IoT data analysis capabilities.

Suggested Citation

Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:9:id:1062486latia20249
DOI: 10.62486/latia20249
as

Download full text from publisher

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a
for a similarly titled item that would be available.

More about this item

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:9:id:1062486latia20249. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Javier Gonzalez-Argote (email available below). General contact details of provider: https://latia.ageditor.uy/ .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.