Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia. A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternative to reduce the limitations of traditional methods. However, the main problem of the semantic segmentation technique is raised since large parasites are dominant, and the tiny parasites are suppressed. In addition, the amount and variance of data are important influences in establishing their models. In this study, we conduct two contributions. First, we collect 559 microscopic images containing 691 malaria parasites of thin blood smears. The dataset is named PlasmoID, and most data comes from rural Indonesia. PlasmoID also provides ground truth for parasite detection and segmentation purposes. Second, this study proposes a malaria parasite segmentation and detection scheme by combining Faster RCNN and a semantic segmentation technique. The proposed scheme has been evaluated on the PlasmoID dataset. It has been compared with recent studies of semantic segmentation techniques, namely UNet, ResFCN-18, DeepLabV3, DeepLabV3plus and ResUNet-18. The result shows that our proposed scheme can improve the segmentation and detection of malaria parasite performance compared to original semantic segmentation techniques.

冠状病毒疾病或Covid-19是由SARS-COV-2病毒引起的一种传染病。该病毒引起的第一个确认病例是在2019年12月底在中国武汉市发现的。然后，此案遍布全球，包括印度尼西亚。因此，联合19案被WHO指定为全球大流行。可以使用多种方法（例如深神经网络（DNN））预测COVID-19病例的增长，尤其是在印度尼西亚。可以使用的DNN模型之一是可以预测时间序列的深变压器。该模型经过多种测试方案的培训，以获取最佳模型。评估是找到最佳的超参数。然后，使用预测天数，优化器，功能数量以及与长期短期记忆（LSTM）（LSTM）和复发性神经网络（RNN）的先前模型进行比较的最佳超参数设置进行了进一步的评估。 。所有评估均使用平均绝对百分比误差（MAPE）的度量。基于评估的结果，深层变压器在使用前层归一化时会产生最佳的结果，并预测有一天的MAPE值为18.83。此外，接受Adamax优化器训练的模型在其他测试优化器中获得了最佳性能。 Deep Transformer的性能还超过了其他测试模型，即LSTM和RNN。