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
- Anita M, Shakila S, Stochastic kernelized discriminant extreme learning machine classifier for big data predictive analytics , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- V Vijayaraj, M. Balamurugan, Monisha Oberai, Machine learning approaches to identify the data types in big data environment: An overview , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Shaik Khaleel Ahamed, Neerav Nishant, Ayyakkannu Selvaraj, Nisarg Gandhewar, Srithar A, K.K.Baseer, Investigating privacy-preserving machine learning for healthcare data sharing through federated learning , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- V. Babydeepa, K. Sindhu, Piecewise adaptive weighted smoothing-based multivariate rosenthal correlative target projection for lung and uterus cancer prediction with big data , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- P S Renjeni, B Senthilkumaran, Ramalingam Sugumar, L. Jaya Singh Dhas, Gaussian kernelized transformer learning model for brain tumor risk factor identification and disease diagnosis , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Pravin P. Adivarekar1, Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, Ravi Rastogi, Automated machine learning and neural architecture optimization , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Balaji V, Purnendu Bikash Acharjee, Muniyandy Elangovan, Gauri Kalnoor, Ravi Rastogi, Vishnu Patidar, Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Thangatharani T, M. Subalakshmi, Development of an adaptive machine learning framework for real-time anomaly detection in cybersecurity , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Raja Selvaraj, Manikandasaran S. Sundari, EAM: Enhanced authentication method to ensure the authenticity and integrity of the data in VM migration to the cloud environment , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- V. Umadevi, S. Ranganathan, IoT based energy aware local approximated MapReduce fuzzy clustering for smart healthcare data transmission , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
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
