Predictive Maintenance of Relative Humidity Using Random Forest Method

Prihatno, Aji Teguh; Nurcahyanto, Himawan; Jang, Yeong Min

doi:10.1109/icaiic51459.2021.9415213

Cited by 19 publications

(5 citation statements)

References 2 publications

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“…Many RFC based applications have been proposed. RFC was initially employed to predict relative humidity in the smart factory environment and showed 82.49% accuracy, which is considered excellent [ 21 ]. The random forest classification, decision tree classification, gradient boosting classification, and Naive Bayes classification image processing technologies were used to classify the types of rice leaf disease in Thailand.…”

Section: System Description and Methodsmentioning

confidence: 99%

“…Additionally, there are three popular classification algorithms for sleeping posture monitoring: the multilayer perceptron (MLP) algorithm [ 7 , 12 , 15 ], the support vector machine (SVM) algorithm [ 16 , 17 , 18 , 19 ], and random forest classification (RFC) [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. An MLP, also known as a feedforward neural network, is an artificial neural network (ANN) with a forward structure.…”

Section: Introductionmentioning

confidence: 99%

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Raspberry Pi-Based Sleep Posture Recognition System Using AIoT Technique

Chen

Wang

2022

Healthcare

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The relationship between sleep posture and sleep quality has been studied comprehensively. Over 70% of chronic diseases are highly correlated with sleep problems. However, sleep posture monitoring requires professional devices and trained nursing staff in a medical center. This paper proposes a contactless sleep-monitoring Internet of Things (IoT) system that is equipped with a Raspberry Pi 4 Model B; radio-frequency identification (RFID) tags are embedded in bed sheets as part of a low-cost and low-power microsystem. Random forest classification (RFC) is used to recognize sleep postures, which are then uploaded to the server database via Wi-Fi and displayed on a terminal. The experimental results obtained using RFC were compared to those obtained via the support vector machine (SVM) method and the multilayer perceptron (MLP) algorithm to validate the performance of the proposed system. The developed system can be also applied for sleep self-management at home and wireless sleep monitoring in medical wards.

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Section: System Description and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Raspberry Pi-Based Sleep Posture Recognition System Using AIoT Technique

Chen

Wang

2022

Healthcare

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“…The tree depth in this application is set to 5 to avoid the interference caused by overtraining. A Random Forest, 30 as shown in Figure 9, is simply an ensemble model composed of a collection of decision trees. The prediction outcomes of each decision tree are averaged to obtain one final outcome.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Branched Neural Network based model for cutter wear prediction in machine tools

Kuo

Cai

Luan

et al. 2022

Structural Health Monitoring

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Cutter wear has a great impact on machining quality, which is particularly true when demand for machining accuracy is high. Therefore, cutter wear analysis is critical in assuring high machining quality and long tool life. However, it is highly dangerous and difficult to monitor and determine tool wear conditions during machining. This paper proposes a method of real-time machining status monitoring using the data collected by external sensors without interfering with the machining process. A tool wear forecast model is introduced in this article. Multiple process parameters and sensor data are collected. Due to missing data, however, data preprocessing is done applying the interpolation or extrapolation approach and data are standardized in order to create an artificial intelligence-based model. The said model will then be used to forecast tool wear during different processing stages and be compared with other different models, such as: AdaBoost, Support Vector Machine, Decision Tree, and Random Forest. The model developed in this study is based on a Branched Neural Network, which generates the best prediction results among all publicly available algorithms. This approach helps reduce the mean absolute error and root-mean-square error values and can improve by 0.11 in R2.

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“…If there are too many trees, the error tends to a limit. We can use some algorithms to find the optimal number of trees for an estimated error and can be used to predict or classify data [35].…”

Section: Random Forestmentioning

confidence: 99%

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks

Muñoz

Martínez

Hernández

2022

Sensors

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Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

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Predictive Maintenance of Relative Humidity Using Random Forest Method

Cited by 19 publications

References 2 publications

Raspberry Pi-Based Sleep Posture Recognition System Using AIoT Technique

Raspberry Pi-Based Sleep Posture Recognition System Using AIoT Technique

Branched Neural Network based model for cutter wear prediction in machine tools

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks

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