Conductor minimum safe distance analysis: Application of a 20 kV medium voltage airline (SUTM) system

Machmud, Amir; Suranto, Suranto; Hamimi, Hamimi; Harmono, Setyo

doi:10.24042/ijecs.v1i1.9215

IJECS

2021

DOI: 10.24042/ijecs.v1i1.9215

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Conductor minimum safe distance analysis: Application of a 20 kV medium voltage airline (SUTM) system

Amir Machmud

Suranto Suranto

Hamimi Hamimi

et al.

Abstract: Medium Voltage Air Line Conductor (SUTM) has a voltage of 20 kV. The SUTM network should have the criteria for electricity safety techniques, including minimum safety distances between the trees and the environment and the effectiveness of electricity distribution development. There are ten stages in the installation of the new SUTM 20 kV network. The results of the study concluded that the Conductor used in the planning of the 20 kV SUTM new network construction was AAACS - 150 mm2. The safe distance between … Show more

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2024

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Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models

Moulaei,

Afshari,

Moulaei

et al. 2024

Sci Rep

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Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke. This study analyzed a dataset comprising 663 records from patients hospitalized at Hazrat Rasool Akram Hospital in Tehran, Iran, including 401 healthy individuals and 262 stroke patients. A total of eight established ML (SVM, XGB, KNN, RF) and DL (DNN, FNN, LSTM, CNN) models were utilized to predict stroke. Techniques such as 10-fold cross-validation and hyperparameter tuning were implemented to prevent overfitting. The study also focused on interpretability through Shapley Additive Explanations (SHAP). The evaluation of model’s performance was based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior sensitivity at 96.15%, while FNN exhibited better specificity (96.0%), accuracy (96.0%), F1-score (95.0%), and ROC (98.0%) among DL models. For ML models, RF displayed higher sensitivity (99.9%), accuracy (99.0%), specificity (100%), F1-score (99.0%), and ROC (99.9%). Overall, RF outperformed all models, while DL models surpassed ML models in most metrics except for RF. DL models (CNN, LSTM, DNN, FNN) achieved sensitivities from 93.0 to 96.15%, specificities from 80.0 to 96.0%, accuracies from 92.0 to 96.0%, F1-scores from 87.34 to 95.0%, and ROC scores from 95.0 to 98.0%. In contrast, ML models (KNN, XGB, SVM) showed sensitivities between 29.0% and 94.0%, specificities between 89.47% and 96.0%, accuracies between 71.0% and 95.0%, F1-scores between 44.0% and 95.0%, and ROC scores between 64.0% and 95.0%. This study demonstrates the efficacy of DL and ML models in predicting stroke, with the RF models outperforming all others in key metrics. While DL models generally surpassed ML models, RF’s exceptional performance highlights the potential of combining these technologies for early stroke detection, significantly improving patient outcomes by preventing severe consequences like permanent neurological damage or death. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-82931-5.

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Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models

Moulaei,

Afshari,

Moulaei

et al. 2024

Sci Rep

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show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Conductor minimum safe distance analysis: Application of a 20 kV medium voltage airline (SUTM) system

Cited by 1 publication

References 9 publications

Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models

Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models

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