全部文献期刊学位论文会议报纸专利标准年鉴图书|学者科研项目
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作者:Shumin Li , Junjie Chen , Bin Liu
来源:[J].BMC Bioinformatics(IF 3.024), 2017, Vol.18 (1)Springer
摘要:Protein remote homology detection plays a vital role in studies of protein structures and functions. Almost all of the traditional machine leaning methods require fixed length features to represent the protein sequences. However, it is never an easy task to extract the discrimina...
作者:Jingsheng Lei , Wenbin Shi , Zhichao Lei ...
来源:[J].EURASIP Journal on Image and Video Processing(IF 0.57), 2018, Vol.2018 (1), pp.1-14Springer
摘要:... Firstly, we use long short-term memory networks to synthesize the context information in a convolutional neural network. Then, we constructed the Mask LSTM-CNN model by combining the existing Mask R-CNN method and the context information. Further, by extracting the specific f...
作者:Yanbu Guo , Weihua Li , Bingyi Wang ...
来源:[J].BMC Bioinformatics(IF 3.024), 2019, Vol.20 (1), pp.1-12DOAJ
摘要:... Our method efficiently applies asymmetric convolutional neural networks (ACNNs) combined with bidirectional long short-term memory (BLSTM) neural networks to predict PSS, leveraging the feature vector dimension of the protein feature matrix. In DeepACLSTM, the ACNNs extract ...
作者:Kazunori D. Yamada , Kengo Kinoshita
来源:[J].BMC Bioinformatics(IF 3.024), 2018, Vol.19 (1), pp.1-11DOAJ
摘要:Abstract Background Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Amino acid sequence profi...
作者:Yun-Long Kong , Qingqing Huang , Chengyi Wang ...
来源:[J].Remote Sensing(IF 2.101), 2018, Vol.10 (3)DOAJ
摘要:... The framework is based on long short-term memory (LSTM) networks. First, LSTM networks are trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Last, the predicted data are compared with real data, and the noticeable deviatio...
作者:Erick López , Carlos Valle , Héctor Allende ...
来源:[J].Energies(IF 1.844), 2018, Vol.11 (3)DOAJ
摘要:... In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an...
作者:Munir Husein , Il-Yop Chung
来源:[J].Energies(IF 1.844), 2019, Vol.12 (10)DOAJ
摘要:... The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehe...
作者:Benjamin D. Bowes , Jeffrey M. Sadler , Mohamed M. Morsy ...
来源:[J].Water(IF 0.973), 2019, Vol.11 (5)DOAJ
摘要:... This study explores two machine learning models, Long Short-term Memory (LSTM) networks and Recurrent Neural Networks (RNN), to model and forecast groundwater table response to storm events in the flood prone coastal city of Norfolk, Virginia. To determine the effect of ...
作者:Xuan-Hien Le , Hung Viet Ho , Giha Lee ...
来源:[J].Water(IF 0.973), 2019, Vol.11 (7)DOAJ
摘要:... This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a r...
作者:Chen Lyu , Bo Chen , Yafeng Ren ...
来源:[J].BMC Bioinformatics(IF 3.024), 2017, Vol.18 (1)Springer
摘要:Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully ...

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