Research on a working face gas concentration prediction model based on LASSO-RNN time series data

Song, Shuang; Chen, Juntao; Ma, Li; Zhang, Lei; He, Suinan; Du, Guanyi; Wang, Junyan

doi:10.1016/j.heliyon.2023.e14864

Cited by 11 publications

(3 citation statements)

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“…RNNs are an Artificial Neural Network (ANN) that allows past knowledge to be used through a recurrent architecture [15]. Representative RNNs, such as Long Short Term Memory (LSTM), have made breakthrough progress in speech and video processing, social applications, text sentiment analysis, and more [16]. The outputs for each RNN layer employ Dense Layers, and spatial contributions are captured by combining information using additional fully connected layers [17].…”

Section: Introductionmentioning

confidence: 99%

LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media

Yunida,

Faisal,

Muliadi

et al. 2023

j.electron.electromedical.eng.med.inform

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Natural disaster events are occurrences that cause significant losses, primarily resulting in environmental and property damage and in the worst cases, even loss of life. In some cases of natural disasters, social media has been utilized as the fastest information bridge to inform many people, especially through platforms like Twitter. To provide accurate categorization of information, the field of text mining can be leveraged. This study implements a combination of the word2vec and LSTM methods and the combination of word2vec and Bi-LSTM to determine which method is the most accurate for use in the case study of news related to disaster events. The utility of word2vec lies in its feature extraction method, transforming textual data into vector form for processing in the classification stage. On the other hand, the LSTM and Bi-LSTM methods are used as classification techniques to categorize the vectorized data resulting from the extraction process. The experimental results show an accuracy of 70.67% for the combination of word2vec and LSTM and an accuracy of 72.17% for the combination of word2vec and Bi-LSTM. This indicates an improvement of 1.5% achieved by combining the word2vec and Bi-LSTM methods. This research is significant in identifying the comparative performance of each combination method, word2vec + LSTM and word2vec + Bi-LSTM, to determine the best-performing combination in the process of classifying data related to earthquake natural disasters. The study also offers insights into various parameters present in the word2vec, LSTM, and Bi-LSTM methods that researchers can determine.

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Section: Introductionmentioning

confidence: 99%

LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media

Yunida,

Faisal,

Muliadi

et al. 2023

j.electron.electromedical.eng.med.inform

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“…In addition to object detection using a Convolutional Neural Network (CNN), there are several other deep learning methods that can be used for image processing and computer vision tasks. Some of them are: Recurrent Neural Networks (RNNs), (Song et al, 2023), (Ajitha et al, 2022) are a type of neural network architecture that allow information to flow forward and backward through the network, so that they can be used to process sequential data such as text or time. RNNs can be used to recognize handwriting, predict the next word in a sentence, and so on.…”

Section: Introductionmentioning

confidence: 99%

Drowsy Detection in the Eye Area using the Convolutional Neural Network

Wedha

Rahman

Hindarto

et al. 2023

SinkrOn

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Detection of a drowsy driver is an important aspect of driving safety. For this reason, it is necessary to have technology to carry out early detection before fatigue occurs. Mainly focused on driver fatigue that occurs at night. Analysis can be done quickly and accurately. These conditions can be sent via data so that they can be monitored and analyzed in real time. The results of the analysis can be sent by communication via the internet network. In addition, it functions as an early warning and can be used as logging or records that can be stored. This research does not discuss data communication but makes a prototype for detecting sleepy drivers. Prototype created using the Convolutional Neural Network Algorithm. The detection area is in the eye and testing is carried out with the brightness level of the light. In this study, building a prototype to detect signs of driver fatigue using the Convolutional Neural Network algorithm. The detection area used is in the eye, by testing at different light brightness levels. The dataset used in this study consists of a series of eye images, which are divided into two classes, namely open eyes, and closed eyes. After conducting the training process on Convolutional Neural Network, we get results of detection accuracy reaching 90%.

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“…In 2022, Chen Qian [10] used the LASSO regression algorithm for the first time to predict the gas emission in the mining face, which showed that the LASSO regression algorithm was significantly better than the principal component analysis regression model. In 2023, Song [11] proposed a mine working face gas concentration prediction model based on LASSO-RNN by combining the Least Absolute Shrinkage and Selection Operator (LASSO) with the Recurrent Neural Network (RNN), showing that the LASSO effectively alleviates the problems of RNN overfitting and computational overhead.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Gas Emission in the Working Face Based on LASSO-WOA-XGBoost

Song,

Han,

2023

Atmosphere

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In order to improve the prediction accuracy of gas emission in the mining face, a method combining least absolute value convergence and selection operator (LASSO), whale optimization algorithm (WOA), and extreme gradient boosting (XGBoost) was proposed, along with the LASSO-WOA-XGBoost gas emission prediction model. Aiming at the monitoring data of gas emission in Qianjiaying mine, LASSO is used to perform feature selection on 13 factors that affect gas emission, and 9 factors that have a high impact on gas emission are screened out. The three main parameters of n_estimators, learning_rate, and max_depth in XGBoost are optimized through WOA, which solves the problem of difficult parameter adjustment due to the large number of parameters in the XGBoost algorithm and improves the prediction effect of the XGBoost algorithm. "When comparing PCA-BP, PCA-SVM, LASSO-XGBoost, and PCA-WOA-XGBoost prediction models, the results indicate that utilizing LASSO for feature selection is more effective in enhancing model prediction accuracy than employing principal component analysis (PCA) for dimensionality reduction." The average absolute error of the LASSO-WOA-XGBoost model is 0.1775, and the root mean square error is 0.2697, which is the same as other models. Compared with the four prediction models, the LASSO-WOA-XGBoost prediction model reduced the mean absolute error by 7.43%, 8.81%, 4.16%, and 9.92%, respectively, and the root mean square error was reduced by 0.24%, 1.13%, 5.81%, and 8.78%. It provides a new method for predicting the gas emission from the mining face in actual mine production.

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Research on a working face gas concentration prediction model based on LASSO-RNN time series data

Cited by 11 publications

References 1 publication

LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media

LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media

Drowsy Detection in the Eye Area using the Convolutional Neural Network

Prediction of Gas Emission in the Working Face Based on LASSO-WOA-XGBoost

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