Data analysis and machine learning-based modeling for real-time production
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.2.22Keywords:
Machine Learning, Data Analysis, Manufacturing Industry, Real-time data modeling.Dimensions 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 primarily focuses on data analysis and real time data modelling using linear regression and decision tree algorithm that might make revolutionary prediction on production data. Factual time data points include temperature, load, 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 is very much needed in industry as there are recurrent load changes that would create an data drift and in term of maintenance that could impact the production side as need of continues monitoring and control machine learning based approaches would work better on these real time production datasets.Abstract
How to Cite
Downloads
Similar Articles
- Sirajum Munira Priety, Farhan Bin Manjur, AI Driven Approach in Smart Manufacturing in Bangladesh , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- M. Jayakandan, A. Chandrabose, An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- S. Vanaja, Hari Ganesh S, Application of data mining and machine learning approaches in the prediction of heart disease – A literature survey , The Scientific Temper: Vol. 15 No. spl-1 (2024): 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
- Muhammed Jouhar K. K., K. Aravinthan, A bigdata analytics method for social media behavioral analysis , The Scientific Temper: Vol. 15 No. 03 (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
- Sanjeev Kumar, Saurabh Charaya, Rachna Mehta, Multi-Metric Evaluation Framework for Machine Learning-Based Load Prediction in e-Governance Systems , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Hardik Talsania, Kirit Modi, Attention-Enhanced Multi-Modal Machine Learning for Cardiovascular Disease Diagnosis , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Abhishek Pandey, V Ramesh, Puneet Mittal, Suruthi, Muniyandy Elangovan, G.Deepa, Exploring advancements in deep learning for natural language processing tasks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Jhankar Moolchandani, Kulvinder Singh, English language analysis using pattern recognition and machine learning , 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. C. Prabha, P. Sivaraaj, S. Kantha Lakshmi, Data analysis and machine learning-based modeling for real-time production , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
