Performance Improvement of Non Invasive Blood Glucose Measuring System With Near Infra Red Using Artificial Neural Networks

Pamungkas, Rizaldi Ramdlani; Putrada, Aji Gautama; Abdurohman, Maman

doi:10.22219/kinetik.v4i4.844

Cited by 4 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We benchmark our proposed method with three state-of-the-art methods, namely MLP [7], LSTM [8], and ARIMA [9]. MLP, or any feedforward artificial neural network (ANN), is a graph that resembles a sentient being's neuron network [34]. Each input and output from a node in one of the layers in the graph forwards information to the node in the next layer with a non-linear function [35].…”

Section: Benchmark Methods and Performance Metricsmentioning

confidence: 99%

Forecasting Model for Lighting Electricity Load with a Limited Dataset using XGBoost

Abdurohman

Putrada

2023

KINETIK

View full text Add to dashboard Cite

Energy forecasting is an important application of machine learning in renewable energy because it is used for operational, management, and planning purposes. However, using the electricity load dataset during COVID-19 is a research challenge in the forecasting model due to the limited dataset and non-stationarity. This paper proposes an extreme gradient boosting (XGBoost) forecasting model for a limited dataset. Forecasting models require large amounts of data to create high-accuracy models. We conduct research using the PT Biofarma office electricity usage dataset for eight months during the COVID-19 period. Because office activities were limited during the pandemic, the datasets obtained were few. Several methods are used for modeling limited datasets, namely XGBoost, multi-layer perceptron (MLP), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM). We have conducted several experiments using a limited dataset with these four methods. The test results with the t-test show that the electricity load data for work-from-office (WFO) and work-from-home (WFH) periods have a significant average difference. Then the test results with the augmented Dickey–Fuller (ADF) test show that our data is non-stationary. Compared to the benchmark method, the XGBoost method shows the best forecasting performance with mean absolute percentage error (MAPE), root mean squared error (RMSE), mean absolute error (MAE), and R2 of 0.48, 5.00, 3.09, and 0.61 respectively.

show abstract

Section: Benchmark Methods and Performance Metricsmentioning

confidence: 99%

Forecasting Model for Lighting Electricity Load with a Limited Dataset using XGBoost

Abdurohman

Putrada

2023

KINETIK

View full text Add to dashboard Cite

show abstract

“…Furthermore, MLP is a type of ANN that has at least three layers of neurons: input, hidden, and output [27]. MLP goes through a learning process where, in each iteration of the learning process, the weights and biases of each neuron are adjusted to minimize loss to actual labels [28].…”

Section: Airplane Failure Detection By Bird Strikementioning

confidence: 99%

ImbGAFS: GA Feature Selection for AUC in Bird Strike Prediction

Putrada,

Prabowo

2023

Machine Learning Techniques and NLP

View full text Add to dashboard Cite

Several studies discuss airplane failure prediction due to bird strikes. However, these studies need to analyze further the imbalance in their dataset. Our research aim is to create an airplane failure prediction by bird strike using a machine learning method optimized using GA feature selection. GA feature selection uses AUC maximization as the objective function to tackle imbalance problems in the bird strike dataset. First, we obtained the airplane bird strike dataset from Kaggle. We carry out preprocessing on the dataset.We then compared and chose one of four stateof-the-art machine learning methods: SVM, MLP, logistic regression, and random forest. The selection process involves oversampling methods, synthetic minority oversampling technique (SMOTE), and optimum threshold selection, which involves geometric mean (g- mean) and area under curve (AUC) values. Finally, we optimize airplane failure prediction by performing AUC maximization using GA feature selection. Our test results show that random forest is the best machinelearning method in airplane failure prediction compared to SVM, logistic regression, and MLP. SMOTE can increase random forest AUC from 0.845 to 0.878. Finally, the random forest model from ImbGAFS is better than the conventional method without feature selection. The increase in the AUC value is from 0.878 to 0.889. Then, after carrying out optimal threshold selection, ImbGAFS+random forest also has better sensitivity, specificity, and g-mean than conventional methods. The increase is from 0.7737, 0.8350, and 0.8037 to 0.8033, 0.8301, and 0.8166, respectively.

show abstract

“…In the training process, several metrics need to be considered, namely epoch, learning rate, and momentum [179]. In addition, the number of neurons in the input layer, the number of hidden layers, and the number of neurons in each hidden layer can also vary to obtain the optimum model [180].…”

Section: ) Supervised Learningmentioning

confidence: 99%

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

View full text Add to dashboard Cite

Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird's eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof's comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

show abstract

Performance Improvement of Non Invasive Blood Glucose Measuring System With Near Infra Red Using Artificial Neural Networks

Cited by 4 publications

References 11 publications

Forecasting Model for Lighting Electricity Load with a Limited Dataset using XGBoost

Forecasting Model for Lighting Electricity Load with a Limited Dataset using XGBoost

ImbGAFS: GA Feature Selection for AUC in Bird Strike Prediction

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

Contact Info

Product

Resources

About