Application of machine learning algorithms for prediction of sinter machine productivity

Mallick, Arpit; Dhara, Subhra; Rath, Sushant

doi:10.1016/j.mlwa.2021.100186

Cited by 20 publications

(6 citation statements)

References 15 publications

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“…The output per worker at Safelite increased by 44 percent because of productivity effects. This firm had chosen a sub-par pay framework, as benefits likewise expanded with the change (Mallick et al 2021). An information examination and AI approach were utilized to foresee the sintering machine's efficiency.…”

Section: Machine Learning Prediction On Productivitymentioning

confidence: 99%

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

Bijalwan,

Gupta,

Mendiratta

et al. 2024

Economies

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One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important factors that influence the overall organizational performance. This article compares various machine learning algorithms such as XG Boost, Random Forest (RF), Histogram Gradient Boosting Regressor, LGBM Regressor, Ada Boost Regressor, and Gradient Boosting Regressor on the dataset of municipality workers. The study aims to propose a machine learning approach to predict and evaluate the productivity of municipality workers. The evaluation of the overall targeted and actual productivity of each department shows that out of 12 different departments, only 5 departments were able to meet their targeted productivity. A 3D Scatter plot visually displays the incentive given by the department to each worker based on their productivity. The results show that XG Boost performs best in comparison with the other five algorithms, as the value of R Squared is 0.71 and MSE (Mean Squared Error) is 0.01.

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Section: Machine Learning Prediction On Productivitymentioning

confidence: 99%

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

Bijalwan,

Gupta,

Mendiratta

et al. 2024

Economies

View full text Add to dashboard Cite

show abstract

“…In the present CNN training, a fixed value of the learning rate, 0.0005, was selected. The R-value may be further increased if the dynamic learning rate is used [46].…”

Section: Significance Analysismentioning

confidence: 99%

SCORN: Sinter Composition Optimization with Regressive Convolutional Neural Network

Guo

Zhang

2022

Solids

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Sinter composition optimization is an important process of iron and steel companies. To increase companies’ profits, they often rely on innovative technology or the workers’ operating experience to improve final productions. However, the former is costly because of patents, and the latter is error-prone. In addition, traditional linear programming optimization methods of sinter compositions are inefficient in the face of large-scale problems and complex nonlinear problems. In this paper, we are the first to propose a regressive convolutional neural network (RCNN) approach for the sinter composition optimization (SCORN). Our SCORN is a single input and multiple outputs regression model. Sinter plant production is used as the input of the SCORN model, and the outputs are the optimized sintering compositions. The SCORN model can predict the optimal sintering compositions to reduce the input of raw materials consumption to save costs and increase profits. By constructing a new neural network structure, the RCNN model is trained to increase its feature extraction capability for sintering production. The SCORN model has a better performance compared with several regressive approaches. The practical application of this predictive model can not only formulate corresponding production plans without feeding materials but also give better input parameters of sintered raw materials during the sintering process.

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“…A sender's signal must be appropriately detected by a receiver in order to execute dependable wireless communication. Disturbance from transmitted signal and actual wireless link settings are important considerations for the Mallick, Dhara and Rath of [6]. Machine learning can be used to categorize signals in a realistic wireless channel, according to the researchers.…”

Section: Signal Classificationmentioning

confidence: 99%

Review of Algorithms, Frameworks and Implementation of Deep Machine Learning Algorithms

Leonid

2022

JCNS

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Machine Learning (ML) is increasingly being used in intelligent systems that can perform Artificial Intelligence (AI) functions. Analytical model development and solving problems related with it may be automated by machine learning, which explains the ability of computers to learn from problem-specific learning algorithm. Depending on artificial neural networks, "deep learning" is a kind of machine learning. The performance of deep learning techniques is superior to that of superficial machine learning techniques and conventional methods of data analysis in many situations. Deep Machine Learning (DML) algorithms and frameworks that have been implemented to and supported by wireless communication systems have been thoroughly analyzed in this paper. User associations, power latency and allocation; bandwidth assignment and user selections, and; cloud computing technology on the edge have both been suggested as potential DML implementations.

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Application of machine learning algorithms for prediction of sinter machine productivity

Cited by 20 publications

References 15 publications

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

SCORN: Sinter Composition Optimization with Regressive Convolutional Neural Network

Review of Algorithms, Frameworks and Implementation of Deep Machine Learning Algorithms

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