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This paper, using an inverse signal approach, presents a novel deep learning algorithm based on a convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) together with spectrograms for the classification of power quality disturbances (PQDs). The proposed method focuses on the region where the PQD event occurs, using a deep learning‐based spectrogram with the aim of increasing classification success rates. In the proposed approach, the time shift of the signal relative to the pure sine signal was found and relative to this time shift, an inverse sine wave was generated according to the original signal. By collecting this sine wave together with the original signal, spectrograms of both signals were obtained and converted into red/green/blue (RGB) images that were then combined. Finally, classification was carried out via CNN/Bi‐LSTM. In this context, 29 different disturbance events in both single and combined structures were used. The proposed model was applied to the disturbance events and 99.33% classification accuracy was obtained.

The prediction of hospital patients and outpatients with suspected arboviral infection individuals in research-limited settings of the urban areas is defined as a challenging process for clinicians. Dengue, Chikungunya, and Zika arboviruses have gained attention in recent years because of the high prevalence in the society and financial burden of major global health systems. In this study, we proposed a machine learning algorithm based prediction model over retrospective medical records, which are named as SISA (the Severity Index for Suspected Arbovirus) and SISAL (the Severity Index for Suspected Arbovirus with Laboratory) datasets. Therefore, we aim to inform the clinicians about the use of machine learning with transfer learning success for diagnosis and comprehensive comparison of the classification performances over the SISA/SISAL datasets in the resource-limited settings that may cause to the small datasets of arboviral infection. In this study, Convolutional Neural Network and Long Short-Term Memory have achieved 100% accuracy and 1 of area under the curve (AUC) score, Fully Connected Deep Network has provided 92.86% accuracy and 0.969 AUC score in the SISAL dataset with transfer learning. Moreover, 98.73% accuracy and 0.988 AUC score were obtained by Convolutional Neural Network and Long Short-Term Memory for the SISA dataset. Furthermore, Linear Discriminant Analysis (shallow algorithm) has provided reaching up to 96.43% accuracy. Notably, deep learning based models have achieved improved performances compared to the previously reported study.