scholarly journals Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2

2020 ◽  
Vol 17 (169) ◽  
pp. 20200494 ◽  
Author(s):  
A. S. Fokas ◽  
N. Dikaios ◽  
G. A. Kastis
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Networks ◽  
Short Term ◽  
Analytical Formulae ◽  
Memory Network ◽  
Long Short Term Memory ◽  
The Impact ◽  
Number Of Individuals ◽  
The Given

We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. The significance of these results for evaluating the impact of easing the lockdown measures is discussed.

Deep learning-based container throughput forecasting: a triple bottom line approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonali Shankar ◽  
Sushil Punia ◽  
P. Vigneswara Ilavarasan
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Bottom Line ◽  
Content Type ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
Memory Network ◽  
Long Short Term Memory

PurposeContainer throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.Design/methodology/approachA novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.FindingsThe result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”Originality/valueA novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

scholarly journals Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3433 ◽  
Author(s):  
Seon Kim ◽  
Gyul Lee ◽  
Gu-Young Kwon ◽  
Do-In Kim ◽  
Yong-June Shin
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Small Scale ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Term Memory ◽  
Load Profile ◽  
Memory Network ◽  
Long Short Term Memory

Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition.

scholarly journals Recurrent Neural Network based Models for Word Prediction

2019 ◽  
Vol 8 (4) ◽  
pp. 7433-7437
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
English Language ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
The Given ◽  
Current Word

Globally, people are spending a cumulative amount of time on their mobile device, laptop, tab, desktop, etc,. for messaging, sending emails, banking, interaction through social media, and all other activities. It is necessary to cut down the time spend on typing through these devices. It can be achieved when the device can provide the user more options for what the next word might be for the current typed word. It also increases the speed of typing. In this paper, we suggest and presented a comparative study on various models like Recurrent Neural Network, Stacked Recurrent Neural Network, Long Short Term Memory network (LSTM) and Bi-directional LSTM that gives solution for the above said problem. Our primary goal is to suggest the best model among the four models to predict the next word for the given current word in English Language. Our survey says that for predicting next word RNN provide accuracy 60% and loss 40%, Stacked RNN provide accuracy 62% and loss 38%, LSTM provide accuracy 64% and loss 36% and Bidirectional LSTM provide accuracy 72% and loss 28%.

Exploiting Network Fusion for Organizational Turnover Prediction

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Mingfei Teng ◽  
Hengshu Zhu ◽  
Chuanren Liu ◽  
Hui Xiong
Keyword(s):  
Social Networks ◽  
Intellectual Capital ◽  
Short Term Memory ◽  
Short Term ◽  
Convolutional Network ◽  
Multiple Networks ◽  
Memory Network ◽  
Long Short Term Memory ◽  
The Impact ◽  
Made In

As an emerging measure of proactive talent management, talent turnover prediction is critically important for companies to attract, engage, and retain talents in order to prevent the loss of intellectual capital. While tremendous efforts have been made in this direction, it is not clear how to model the influence of employees’ turnover within multiple organizational social networks. In this article, we study how to exploit turnover contagion by developing a Turnover Influence-based Neural Network (TINN) for enhancing organizational turnover prediction. Specifically, TINN can construct the turnover similarity network which is then fused with multiple organizational social networks. The fusion is achieved either through learning a hidden turnover influence network or through integrating the turnover influence on multiple networks. Taking advantage of the Graph Convolutional Network and the Long Short-Term Memory network, TINN can dynamically model the impact of social influence on talent turnover. Meanwhile, the utilization of the attention mechanism improves the interpretability, providing insights into the impact of different networks along time on the future turnovers. Finally, we conduct extensive experiments in real-world settings to evaluate TINN. The results validate the effectiveness of our approach to enhancing organizational turnover prediction. Also, our case studies reveal some interpretable findings, such as the importance of each network or hidden state which potentially impacts future organizational turnovers.

scholarly journals A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 755
Author(s):  
Yuchen Wang ◽  
Yinke Dou ◽  
Wangxiao Yang ◽  
Jingxue Guo ◽  
Xiaomin Chang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Coastal Region ◽  
Sensor Data ◽  
Learning Networks ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
The Antarctic

Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station’s sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.

scholarly journals Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction

2018 ◽  
Author(s):  
Sen Su ◽  
Ningning Jia ◽  
Xiang Cheng ◽  
Shuguang Zhu ◽  
Ruiping Li
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
Relation Extraction ◽  
Short Term ◽  
Sequential Prediction ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Model Training ◽  
The Impact ◽  
Model Relation

In this paper, we present an encoder-decoder model for distant supervised relation extraction. Given an entity pair and its sentence bag as input, in the encoder component, we employ the convolutional neural network to extract the features of the sentences in the sentence bag and merge them into a bag representation. In the decoder component, we utilize the long short-term memory network to model relation dependencies and predict the target relations in a sequential manner. In particular, to enable the sequential prediction of relations, we introduce a measure to quantify the amounts of information the relations take in their sentence bag, and use such information to determine the order of the relations of a sentence bag during model training. Moreover, we incorporate the attention mechanism into our model to dynamically adjust the bag representation to reduce the impact of sentences whose corresponding relations have been predicted. Extensive experiments on a popular dataset show that our model achieves significant improvement over state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document