LSTM Recurrent Neural Network-Based Frequency Control Enhancement of the Power System with Electric Vehicles and Demand Management

Sundararajan, G.; Sivakumar, P.

doi:10.1155/2022/1281248

Cited by 2 publications

(1 citation statement)

References 45 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the output is 1 then all data will be stored and vice versa if the output is 0 then all data will be discarded. With the following formula [28]:…”

Section: Long Term Short Memory (Lstm)mentioning

confidence: 99%

Utilizing LSTM and K-NN for Anatomical Localization of Tuberculosis: A Solution for Incomplete Data

Rochman,

Miswanto,

Suprajitno

et al. 2023

MMEP

View full text Add to dashboard Cite

Tuberculosis (TB) is a prevalent lung disease that significantly contributes to mortality rates, with an estimated 98,000 fatalities observed in Indonesia alone. TB can be classified into two categories based on its anatomical location: pulmonary, when detected in lung parenchyma tissue, and extrapulmonary, when identified in organs outside the lungs. Current diagnostic procedures necessitate numerous laboratory tests and manual assessments, which are both time-consuming and susceptible to data incompleteness, thereby potentially influencing the diagnostic outcomes. This necessitates the development of a rapid and accurate classification system for the anatomical location of TB, which could aid medical professionals in diagnosis. In this study, we propose a novel classification system that utilizes the K-Nearest Neighbors (K-NN) algorithm to handle missing data, and the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing. For the classification of pulmonary and extrapulmonary TB, the study employs the Long Short-Term Memory (LSTM) method, the performance of which is compared with other models, namely Naï ve Bayes, Support Vector Machine (SVM), and Backpropagation. Although all four models demonstrated high levels of accuracy, the LSTM method outperformed the others, achieving 100% accuracy compared to Naï ve Bayes (99.4%), SVM (99.3%), and Backpropagation (99.7%). These results were obtained after implementing imputation and class balancing stages, and optimizing LSTM features such as the tanh activation function, learning rate of 0.01, 100 LSTM units, and the ADAM optimizer. The proposed system thus presents an effective solution for the rapid and accurate classification of TB based on anatomical location.

show abstract

“…If the output is 1 then all data will be stored and vice versa if the output is 0 then all data will be discarded. With the following formula [28]:…”

Section: Long Term Short Memory (Lstm)mentioning

confidence: 99%