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The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland

机译：LSTM和MTD模型的深度学习最能预测索马里兰下五岁儿童中的急性呼吸道感染

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Mohamed Yusuf Hassan;
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1. Predicting air quality with deep learning LSTM: Towards comprehensive models [J] . Ecological informatics: an international journal on ecoinformatics and computational ecology . 2020,第期

机译：预测深度学习的空气质量LSTM：朝向综合模型
2. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in young children in 2015: a systematic review and modelling study [J] . Shi Ting, McAllister David A., OBrien Katherine L., The Lancet . 2017,第10098期

机译：2015年幼儿呼吸道合胞病毒引起的全球性，区域和国家疾病负担估计急性呼吸道感染：系统审查和建模研究
3. LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy [J] . Md. Kowsher, Anik Tahabilder, Md. Zahidul Islam Sanjid, Procedia Computer Science . 2021,第a期

机译：LSTM-ANN＆amp; Bilstm-Ann：混合深层学习模型，提高分类准确性
4. The relationship between serum zinc levels and clinical features in under-five Nigerian children with Acute Lower Respiratory Infections [C] . Ibraheem R., Johnson W., Abdulkarim A., World Congress of the World Society for Pediatric Infectious Diseases . 2014

机译：急性下呼吸道呼吸道感染下五盏尼日利亚儿童血清锌水平与临床特征的关系
5. Indoor exposure to particulate matter and acute lower respiratory infections in young children in urban Bangladesh. [D] . Gurley, Emily Suzanne. 2011

机译：孟加拉国城市幼儿室内暴露于颗粒物和急性下呼吸道感染的情况。
6. LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites [O] . Guohua Huang, Qingfeng Shen, Guiyang Zhang, 2021

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7. Using LSTM Neural Networks as Resource Utilization Predictors: The Case of Training Deep Learning Models on the Edge [O] . John Violos, Evangelos Psomakelis, Dimitrios Danopoulos, 2020

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The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland

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