Machine Learning Techniques Integrated in Robotics, Electrical and Electronics Engineering, and IoT Devices.

• Author(s): Poram Tarun Prakash ; P. Satyanarayana Rao
• Paper ID: 1706277
• Page: 158-163
• Published Date: 12-09-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 3 September-2024

Abstract

This paper aims to find the best machine-learning technique used for integration in robotics, electrical and electronics engineering, and IoT devices. We investigated different recurrent neural networks such as LSTM, BiLSTM, and GRU for combination with Convolution neural networks. We experimented with three different categories related to robotic arms, electrical circuits, and IoT-based smart agriculture. Models are evaluated based on Precision, recall, and F1 score. Finally, we conclude combination of a convolution neural network and a bi-directional long short-term memory model performs well for different predictions.

Keywords

Deep Learning, Electrical Systems, Electronics, IoT, Machine Learning, Robotics

Citations

IRE Journals:
Poram Tarun Prakash , P. Satyanarayana Rao "Machine Learning Techniques Integrated in Robotics, Electrical and Electronics Engineering, and IoT Devices." Iconic Research And Engineering Journals Volume 8 Issue 3 2024 Page 158-163

IEEE:
Poram Tarun Prakash , P. Satyanarayana Rao "Machine Learning Techniques Integrated in Robotics, Electrical and Electronics Engineering, and IoT Devices." Iconic Research And Engineering Journals, 8(3)