Prediction of Various Sizes of Particles in Deep Opencast Copper Mine Using Recurrent Neural Network: A Machine Learning Approach

Gautam, Sneha; Patra, Aditya Kumar; Brema, J.; Raj, Praveen Vijiya; Raimond, Kumudha; Abraham, Siril Sam; Chudugudu, Krupal Raj

doi:10.1007/s40030-021-00589-y

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…Machine translation systems have also developed from rule systems based on dictionaries and manual rules, case-based translation systems to statistical machine translation systems. Statistical machine translation can quickly learn translation knowledge from a large amount of data [1]. It has obvious advantages in robustness and translation quality compared with rule-based and case-based methods.…”

Section: Introductionmentioning

confidence: 99%

Design of English Translation Model Based on Recurrent Neural Network

Wang

2022

Mathematical Problems in Engineering

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In order to improve the accuracy and stability of English translation, this paper proposes an English translation model based on recurrent neural network. Based on the end-to-end encoder-decoder architecture, a recursive neural network (RNN) English machine translation model is designed to promote machine autonomous learning features, transform the distributed corpus data into word vectors, and directly map the source language and target language through the recurrent neural network. Selecting semantic errors to construct the objective function during training can well balance the influence of each part of the semantics and fully consider the alignment information, providing a strong guidance for the training of deep recurrent neural networks. The experimental results show that the English translation model based on recurrent neural network has high effectiveness and stability. Compared with the baseline system, it has improved about 1.51–1.86 BLEU scores. Conclusion. The model improves the performance and quality of English machine translation model, and the translation effect is better.

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Section: Introductionmentioning

confidence: 99%

Design of English Translation Model Based on Recurrent Neural Network

Wang

2022

Mathematical Problems in Engineering

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“…For rural air quality, meteorological factors play an essential role. Factors such as topography, air movement, and climate determine the amount of pollution in the atmosphere at a given time (Gautam et al 2021a , b , c ). Winds play an important role in the deterioration of air pollutants through the dispersal and dilution process.…”

Section: Resultsmentioning

confidence: 99%

“…Its fundamental research and getting attention (scientific/publically) to understand the behavior of aerosol and its possible impact on water resources and agriculture. The reported literature indicated the total incremental concentration of air pollutants and expected the same over urban and rural Indian regions in the coming future (Ramasubramanian et al 1988 ; Gautam et al 2021a , b , c ). Presented study is the new endeavor to rural sites of the Indian region to understand the behavior of aerosol on rainfall patterns in the last 7 years (2015–2021) using satellite observations.…”

Section: Introductionmentioning

confidence: 97%

“…The amount of water from rainfall is one of the most crucial water cycle and global water components with a significant impact on climate conditions (Copernicus Climate Change Service (C3S) 2017 ; Kalpana et al 2020 ; Gautam et al 2021a , b , c ). Many studies highlighted the possible impact of aerosol on cloud formation and related cycles (Ramanathan et al 2005 ; Ramanathan et al 2001a , b ; Kalpana et al 2020 ; Gautam et al 2021a ).…”

Section: Introductionmentioning

confidence: 99%

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Impact Assessment of Aerosol Optical Depth on Rainfall in Indian Rural Areas

et al. 2022

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Aerosol significantly influences the life cycle of clouds and their formation. Many studies reported worldwide on anthropogenic aerosols and their impact on clouds and their optical properties. Atmospheric remote sensing provides the best way to estimate indirectly air quality surveillance and management in megacities of developing countries like India where many cities have elevated concentration profiles of air pollutants with inadequate coverage of spatial and temporal monitoring. The results of the study highlighted the impact on rainfall patterns due to aerosol optical depth (AOD) and fine particulate matter (PM2.5) for a total of 7 years (2015–2021) over five different Indian rural sites by using MODerate Resolution Imaging Spectroradiometer (MODIS). The AOD (550 nm) and PM2.5 were retrieved from the MODIS sensor Terra satellites and the MEERA 2 model, respectively. Also, we have analyzed in this study the relationship of AOD (550 nm) with PM2.5 and meteorological variables (temperature relative humidity and precipitation) over Indian rural sites during 2015–2021. The maximum concentration of AOD (550 nm) has been measured for Gandhi college (2.94 ± 0.44) and minimum for ARM college (0.01 ± 0.28), while the maximum concentration of PM2.5 has been measured for ARM College 296.37 (µg m −3 ) and minimum for Karunya University 0.02 (µg m −3 ). Also, the relation between AOD (550 nm) with total precipitation is measured positively for all locations except Gandhi college whereby PM2.5 associated with total precipitation is measured negatively for all locations except ARM college. Finally, the relationship between PM2.5 and AOD (550 nm) is measured positively in all selected locations except Singhad Institute. The maximum rainfall has been observed for monsoon months (June–August) and post-monsoon months (October) for all locations during the study period. The maximum total precipitation has been measured for Singhad 11,674.7 (mm) and the minimum for Karunya University 4563.41 (mm). However, the results of the study indicated that there was no direct trend observed in AOD in five different selected rural Indian sites.

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“…In general, AI-based neural network algorithms have been demonstrated to be an excellent predictive tool (Patel et al 2017), as evidenced by particle size prediction (Gautam et al 2021), equipment effectiveness prediction (Sivakumar et al 2022), and paper industry performance prediction (Jauhar et al 2022). Likewise, the robust search algorithm of Back Propagation Neural Network (BPNN) also a good technique for predicting facilities (Zhang and Qu 2021).…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Sivakumar¹,

Singh

Arulkirubakaran

et al. 2022

Qual Quant

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The pandemic recession has caused enormous disturbances in many industrialized countries. The massive disruption of the supply chain of production is affecting manufacturing companies operating in and around India. Particularly the medium-sized bus body building works have been reduced, due to its compound anomalies. The integrated view of the production facility priorities is not an easy task. Since it is difficult for available labour to conduct an entire project, the completion of a production process is delayed. But still, the dilemma remains as to how production managers can correctly interpret the priorities of the facility. Indeed, this is a problem missing from the previous study. Fortunately, in the current competitive environment, it is essentially needed. This study has been used Back Propagation Neural Network (BPNN) approach for predicting production facility priorities. The experimental results confirm the suitability of the model for predicting priorities. A real-world problem is taken into account in making use of the model output. In this sense, this total solution facilitates production managers in assessing and enhancing the production facilities. The findings emphasize the priority of "equipment effectiveness, labour scheduling and communication" in order to strengthen the post-pandemic production facility.

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Prediction of Various Sizes of Particles in Deep Opencast Copper Mine Using Recurrent Neural Network: A Machine Learning Approach

Cited by 11 publications

References 27 publications

Design of English Translation Model Based on Recurrent Neural Network

Design of English Translation Model Based on Recurrent Neural Network

Impact Assessment of Aerosol Optical Depth on Rainfall in Indian Rural Areas

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

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