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Flood Video Segmentation on Remotely Sensed UAV Using Improved Efficient Neural Network
Naili Suri Inthizami,M. Anwar Ma’sum,Machmud R. Alhamidi,Ahmad Gamal,Ronni Ardhianto,Kurnianingsih,Wisnu Jatmiko 한국통신학회 2022 ICT Express Vol.8 No.3
Semantic segmentation can be used to analyze the video data taken by UAV in the flood monitoring system. An accurate analysis can help rescue teams to assess and mitigate flood disasters. This paper proposed an improved Efficient Neural Network architecture to segment the UAV video of flood disaster. The proposed method consists of atrous separable convolution as the encoder and depth-wise separable convolution as the decoder. The experimental results reveal that the proposed method outperforms Efficient Neural Networks’ other architecture and gives the highest frame per second.
Generative Adversarial Networks for Unbalanced Fetal Heart Rate Signal Classification
Riskyana Dewi Intan Puspitasari,M. Anwar Ma’sum,Machmud R. Alhamidi,Kurnianingsih,Wisnu Jatmiko 한국통신학회 2022 ICT Express Vol.8 No.2
Deep Learning Classification is often used to analyze biomedical data. One of them is to analyze the Fetal Heart Rate (FHR) signal data used to check and monitor maternal and fetal health and prevent mobility and mortality in fetuses at risk of developing hypoxia. The problem that often occurs in the data is data unbalance. Time Series Generative Adversarial Networks (TSGAN) solves data imbalance in the FHR signal and generate more data and better classification performance. Augmentation using the GAN model in this study obtained an increase in the Quality Index of 3%–44% from other models.