Data analysis and machine learning-based modeling for real-time production
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.11Keywords:
Data analysis, Machine learning, Fault detectionDimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This article focuses on data analysis and real-time data modeling using linear regression and decision tree algorithms that might make revolutionary predictions on production data. Factual time data points, including temperature, load, and warning on all the presented axis, are the dependent parameters which be contingent on the changes in the autonomous paraments like load. Monitoring and innovative prediction are very much needed in industry as there are recurrent load changes that would create a data drift and, in terms of maintenance, that could impact the production side, the need for continuous monitoring and control. Machine learning-based approaches would work better on these real-time production datasetsAbstract
How to Cite
Downloads
Similar Articles
- R. Selvakumar, A. Manimaran, Janani G, K.R. Shanthy, Design and development of artificial intelligence assisted railway gate controlling system using internet of things , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Shobhit Shukla, Suman Mishra, Gaurav Goel, River flow modeling for flood prediction using machine learning techniques in Godavari river, India , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- K. Kalaiselvi, M. Kasthuri, Tuning VGG19 hyperparameters for improved pneumonia classification , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Sachin V. Chaudhari, Jayamangala Sristi, R. Gopal, M. Amutha, V. Akshaya, Vijayalakshmi P, Optimizing biocompatible materials for personalized medical implants using reinforcement learning and Bayesian strategies , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- D. Prabakar, Santhosh Kumar D.R., R.S. Kumar, Chitra M., Somasundaram K., S.D.P. Ragavendiran, Narayan K. Vyas, Task offloading and trajectory control techniques in unmanned aerial vehicles with Internet of Things – An exhaustive review , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Thilagavathi K, Thankamani K., P. Shunmugapriya, D. Prema, Navigating fake reviews in online marketing: Innovative strategies for authenticity and trust in the digital age , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Krishna P. Kalyanathaya, Krishna Prasad K, A framework for generating explanations of machine learning models in Fintech industry , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Amol Garge, Monika Tripathi, Navigating the virtual frontier: Best practices for ERP implementation in the digital age , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Heikham G. Chanu, Sudha A. Raddi, Anita Dalal, Sangeeta N. Kharde, Shivani Tendulkar, Association between the socio-demographic variables of women admitted for delivery to a Tertiary Care Hospital and their maternal and neonatal outcome - A cross-sectional study , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Somalee Mahapatra, Manoranjan Dash, Subhashis Mohanty, Adoption of artificial intelligence and the internet of things in dental biomedical waste management , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
<< < 10 11 12 13 14 15 16 17 18 19 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- S ChandraPrabha, S. Kantha Lakshmi, P. Sivaraaj, Data analysis and machine learning-based modeling for real-time production , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
