Research Article
Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms
Rushikesh Muley*,
Shanti Priya
Issue:
Volume 9, Issue 1, June 2024
Pages:
1-9
Received:
18 October 2023
Accepted:
13 November 2023
Published:
11 January 2024
Abstract: Producing machine learning models to capture and comprehend the relationship between variables and mechanical properties of hot-rolled steels was the goal of this effort. Mechanical Property are impacted by a variety of variables throughout the process. The innovation that has been presented would significantly alter this process. Metrics of accuracy that are utilised in the process of evaluating the effectiveness of machine learning models. To deal with complexity and uncertainty, a number of machine learning models have been created for the ultimate tensile strength, yield strength, and elongation as functions of chemical elements and thermo-mechanical variables. Machine learning techniques such as multiple linear regression, random forest model, gradient boosting model and XGBoost are used to predict mechanical properties of hot rolled steel by specifying processing parameters such as chemical composition and various thermo mechanical variables. By changing one variable while holding the other variables constant, the models were utilised to interpret trends. Spearheaded a high-impact initiative at JSW Steel to create a cutting-edge property prediction model for their hot strip mill, enhancing operational efficiency and product quality.
Abstract: Producing machine learning models to capture and comprehend the relationship between variables and mechanical properties of hot-rolled steels was the goal of this effort. Mechanical Property are impacted by a variety of variables throughout the process. The innovation that has been presented would significantly alter this process. Metrics of accura...
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Research Article
Deep Learning Model for COVID-19 Classification Using Fine Tuned ResNet50 on Chest X-Ray Images
Issue:
Volume 9, Issue 1, June 2024
Pages:
10-25
Received:
10 April 2024
Accepted:
27 April 2024
Published:
10 May 2024
Abstract: Amid the COVID-19 pandemic, extensive research has focused on deep learning methodologies for accurately diagnosing the virus from chest X-ray images. Various models, including Convolutional Neural Networks (CNNs) and pre-trained models, have achieved accuracies ranging from 85.20% to 99.66%. However, the proposed Fine-Tuned ResNet50 model consistently outperforms others with an impressive accuracy of 98.20%. By leveraging on transfer learning and careful architectural design the proposed model demonstrates superior performance compared to previous studies using DarkNet, ResNet50, and pre-trained models. Graphical comparisons highlight its competitive edge, emphasizing its effectiveness in COVID-19 classification tasks. The ResNet50 architecture, known for its deep residual layers and skip connections, facilitates robust feature extraction and classification, especially in medical imaging. Data pre-processing techniques, like noise reduction and contrast enhancement, ensure input data quality and reliability, enhancing the model's predictive abilities. Training results reveal the model's steady accuracy improvement and loss reduction over 20 epochs, aligning closely with validation metrics. Evaluation on a test set of COVID-19 chest X-ray images confirms exceptional accuracy (98.20%), precision (99.00%), recall (98.82%), and F1-score (98.91%), highlighting its proficiency in identifying COVID-19 cases while minimizing false positives and negatives. Comparative analyses against prior studies further validate its superior performance, establishing the Fine-Tuned ResNet50 model as a reliable tool for COVID-19 diagnosis. Future research should focus on exploring ensemble learning techniques, interpretability methods, and stakeholder collaboration to ensure safe AI deployment in clinical settings. Moreover, larger and diverse datasets are crucial for validating model performance and improving generalization, ultimately enhancing patient care and public health outcomes in the mitigating COVID-19 and future pandemics.
Abstract: Amid the COVID-19 pandemic, extensive research has focused on deep learning methodologies for accurately diagnosing the virus from chest X-ray images. Various models, including Convolutional Neural Networks (CNNs) and pre-trained models, have achieved accuracies ranging from 85.20% to 99.66%. However, the proposed Fine-Tuned ResNet50 model consiste...
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,
Adetokunbo Macgregor John-Otumu*
