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
- Dileep Pulugu, Shaik K. Ahamed, Senthil Vadivu, Nisarg Gandhewar, U D Prasan, S. Koteswari, Empowering healthcare with NLP-driven deep learning unveiling biomedical materials through text mining , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Raja S, Nagarajan L., Hybridization of bio-inspired algorithms with machine learning models for predicting the risk of type 2 diabetes mellitus , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- V Anitha, Seema Sharma, R. Jayavadivel, Akundi Sai Hanuman, B Gayathri, R. Rajagopal, A network for collaborative detection of intrusions in smart cities using blockchain technology , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Sangeeta Modi, P Usha, Fault analysis in hybrid microgrid for developing a suitable protection scheme , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Kinjal K. Patel, Kiran Amin, Predictive modeling of dropout in MOOCs using machine learning techniques , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Lakshminarayani A, A Shaik Abdul Khadir, A blockchain-integrated smart healthcare framework utilizing dynamic hunting leadership algorithm with deep learning-based disease detection and classification model , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Ravikiran K, Neerav Nishant, M Sreedhar, N.Kavitha, Mathur N Kathiravan, Geetha A, Deep learning methods and integrated digital image processing techniques for detecting and evaluating wheat stripe rust disease , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- D. Padma Prabha, C. Victoria Priscilla, A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- R. Gomathi, Balaji V, Sanjay R. Pawar, Ayesha Siddiqua, M. Dhanalakshmi, Ravi Rastogi, Ensuring ethical integrity and bias reduction in machine learning models , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Temesgen A. Asfaw, Deep learning hyperparameter’s impact on potato disease detection , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
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