Enhanced Stacking Ensemble Model in Predictive Analytics of Environmental Sensor Data

Jebamalar, J. Angelin; Kamalakannan, T.

doi:10.1109/icais50930.2021.9395755

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…This prediction model may also take into account the consequences of pitting and crevice corrosion. The method may be used on a variety of constructions exposed to atmospheric corrosion, not just airplanes [1].The framework is based on the hybrid SHM technique and combines a machine learning algorithm in a supervised learning approach with the usage of a calibrated numerical finite element (FE) model to create data from various structural state states under varied environmental circumstances [5]. To gather data from a local and global sensor setup on a genuine bridge under various structural state circumstances, a thorough experimental benchmark research is conducted.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modelling for Environmental Telemetry Data and Damage Prediction

2022

IRJMETS

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Big data is being produced by the IoT more and more. Realizing this hinges on being able to extract relevant information from huge data. Big data may be utilized to optimize processes. This may be done using neural networks and machine learning, which also makes it possible to create intelligent apps that exploit this data. Sensory boxes were used to collect data from rooms, and many LSTM-based neural networks were employed to forecast the sensor values in the future. Calculations were made to determine the absolute mean error and standard deviation of the forecasts. A standard deviation-adjusted absolute mean figure was used to gauge how long it took to create a forecast. The performance and prediction accuracy of the LSTM models were then assessed. Smart healthcare platforms can continually monitor the health and activities of patients and the elderly, as well as the security and safety of the environment, with the use of environmental sensors and wearable technology. When combined with a person's medical history, this constant flow of data can provide tailored diagnosis and treatment and automate crucial duties like medical data archiving and assessing the efficacy of medical therapies. Recent developments in big data processing and artificial intelligence (AI) technology have made it possible to construct extremely dependable, precise, and strong infrastructures for data capture and processing. The main aim of the author is to predict the damage happening in the sensors that are used to detect the environmental data and also the damage prediction of the sensors using random forest as the technique for the prediction.

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Section: Introductionmentioning

confidence: 99%

Predictive Modelling for Environmental Telemetry Data and Damage Prediction

2022

IRJMETS

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Bankruptcy Prediction using Ensemble Support Vector Machine

Mustamin

Jeffry

Wungo

et al. 2022

2022 Seventh International Conference on Informatics and Computing (ICIC)

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A stacking-based ensemble learning model for kneading paste quality intelligent prediction: A real case study

Li,

Xu,

Wang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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Paste kneading is a vital process of prebaked carbon anode production, and the quality of the paste has a great impact on the quality of the final product. However, it is difficult to inspect the paste quality in line. Because the inspection of the paste quality in the laboratory is not real-time, the paste has already entered the next process after the results are obtained. And the manual quality inspection is labor-intensive and unsafe. Therefore, a stacking-based ensemble learning model for kneading paste quality prediction is proposed in this paper. The gradient boosting decision tree, random forest, k-nearest neighbors, and support vector machine are used as base learners, and logistic regression is used as meta-learner. Kneading production data of 572 paste pots are collected for quality prediction, where each pot of paste data contains 44 signals. The correlation coefficient-based feature engineering method was applied, and 10 features with the greatest correlation with paste quality were identified to construct the dataset. The up-sampling and under-sampling methods are used to solve the problem of sample imbalance. Parallel comparison is applied to verify the advantage of the stacking-based ensemble learning model, and the results indicate that the model performs better than every single classifier and has higher accuracy and generalization ability, especially for imbalanced samples.

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Enhanced Stacking Ensemble Model in Predictive Analytics of Environmental Sensor Data

Cited by 3 publications

References 14 publications

Predictive Modelling for Environmental Telemetry Data and Damage Prediction

Predictive Modelling for Environmental Telemetry Data and Damage Prediction

Bankruptcy Prediction using Ensemble Support Vector Machine

A stacking-based ensemble learning model for kneading paste quality intelligent prediction: A real case study

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