Corrigendum To: Implementing Wireless Sensor Network Through Machine Learning Techniques

The error was noted in the article titled “Implementing Wireless Sensor Network Through Machine Learning Techniques” published in International Journal of Sensors, Wireless Communications and Control, 2025, 15(3), 268-280 [1]. In the abstract of the article, the conclusion was incomplete, which has now been completed. Details of the error and a correction are provided here. Original: Conclusion: The training data accuracies are as follows: Linear Regression (46.64%), Random Forest (99.67%), XG Boost (99.99%), SVM (32.10%), K Nearest Neighbor (98.56%), Naïve Bayes Classifier (96.45%), Principle Component Analysis (97.88%) and NN (34.86%). The testing data accuracies are as follows: Linear Regression (22.00%), Random Forest (97.18%), XG Boost (96.94%), SVM (21.75%), K Nearest Neighbor (97.66%), Naïve Bayes Classifier(64.49%), PCA(99.38%) and NN(28.94%). Linear Regression, Random Forest, SVM, Naive Bayes Classifier, PCA, K Nearest Neighbour, and XG Boost were all examined. Results revealed that Corrected: Conclusion: The training data accuracies are as follows: Linear Regression (46.64%), Random Forest (99.67%), XG Boost (99.99%), SVM (32.10%), K Nearest Neighbor (98.56%), Naïve Bayes Classifier (96.45%), Principle Component Analysis (97.88%) and NN (34.86%). The testing data accuracies are as follows: Linear Regression (22.00%), Random Forest (97.18%), XG Boost (96.94%), SVM (21.75%), K Nearest Neighbor (97.66%), Naïve Bayes Classifier(64.49%), PCA(99.38%) and NN(28.94%). Linear Regression, Random Forest, SVM, Naive Bayes Classifier, PCA, K Nearest Neighbour, and XG Boost were all examined. Results revealed that Random Forest and XGBoost achieved the highest accuracy, making them the most effective models for WSN prediction. We regret the error and apologize to the readers. The original article can be found online at: https://www.benthamscience.com/article/143403