Time Series Prediction of Viable Embryo and Automatic Grading in IVF using Deep Learning

Background: The process of In Vitro Fertilization (IVF) involves collecting multiple samples of mature eggs that are fertilized with sperms in the IVF laboratory. They are eventually graded, and the most viable embryo out of all the samples is selected for transfer in the mother’s womb for a healthy pregnancy. Currently, the process of grading and selecting the healthiest embryo is performed by visual morphology, and manual records are maintained by embryologists. Objectives: Maintaining manual records makes the process very tedious, time-consuming, and error-prone. The absence of a universal grading leads to high subjectivity and low success rate of pregnancy. To improve the chances of pregnancy, multiple embryos are transferred in the womb elevating the risk of multiple pregnancies. In this paper, we propose a deep learning-based method to perform the automatic grading of the embryos using time series prediction with Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). Methods: CNN extracts the features of the images of embryos, and a sequence of such features is fed to LSTM for time series prediction, which gives the final grade. Results: Our model gave an ideal accuracy of 100% on training and validation. A comparison of obtained results is made with those obtained from a GRU model as well as other pre-trained models. Conclusion: The automated process is robust and eliminates subjectivity. The days-long hard work can now be replaced with our model, which gives the grading within 8 seconds with a GPU.

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Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization

Atmosphere ◽

10.3390/atmos11050487 ◽

2020 ◽

Vol 11 (5) ◽

pp. 487 ◽

Cited By ~ 3

Author(s):

Trang Thi Kieu Tran ◽

Taesam Lee ◽

Ju-Young Shin ◽

Jong-Suk Kim ◽

Mohamad Kamruzzaman

Keyword(s):

Neural Network ◽

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Human Life ◽

Time Series Prediction ◽

Vital Role ◽

Maximum Temperature ◽

Hyperparameter Optimization ◽

Meta Learning

Time series forecasting of meteorological variables such as daily temperature has recently drawn considerable attention from researchers to address the limitations of traditional forecasting models. However, a middle-range (e.g., 5–20 days) forecasting is an extremely challenging task to get reliable forecasting results from a dynamical weather model. Nevertheless, it is challenging to develop and select an accurate time-series prediction model because it involves training various distinct models to find the best among them. In addition, selecting an optimum topology for the selected models is important too. The accurate forecasting of maximum temperature plays a vital role in human life as well as many sectors such as agriculture and industry. The increase in temperature will deteriorate the highland urban heat, especially in summer, and have a significant influence on people’s health. We applied meta-learning principles to optimize the deep learning network structure for hyperparameter optimization. In particular, the genetic algorithm (GA) for meta-learning was used to select the optimum architecture for the network used. The dataset was used to train and test three different models, namely the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Our results demonstrate that the hybrid model of an LSTM network and GA outperforms other models for the long lead time forecasting. Specifically, LSTM forecasts have superiority over RNN and ANN for 15-day-ahead in summer with the root mean square error (RMSE) value of 2.719 (°C).

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Deep Learning with Long Short-Term Memory for Time Series Prediction

IEEE Communications Magazine ◽

10.1109/mcom.2019.1800155 ◽

2019 ◽

Vol 57 (6) ◽

pp. 114-119 ◽

Cited By ~ 43

Author(s):

Yuxiu Hua ◽

Zhifeng Zhao ◽

Rongpeng Li ◽

Xianfu Chen ◽

Zhiming Liu ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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

Symmetry ◽

10.3390/sym13071156 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1156

Author(s):

Mohamed Yusuf Hassan

Keyword(s):

Time Series ◽

Deep Learning ◽

Respiratory Infection ◽

Acute Respiratory Infection ◽

Short Term Memory ◽

Mean Deviation ◽

Sarima Model ◽

Under Five ◽

Competing Models ◽

Long Short Term Memory

The most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI) and pneumonia among under-five-year old children in Somaliland. The techniques used in the study include seasonal autoregressive integrated moving averages (SARIMA), mixture transitions distribution (MTD), and long short term memory (LSTM) deep learning. The data used in the study were monthly observations collected from five regions in Somaliland from 2011–2014. Prediction results from the three best competing models are compared by using root mean square error (RMSE) and absolute mean deviation (MAD) accuracy measures. Results have shown that the deep learning LSTM and MTD models slightly outperformed the classical SARIMA model in predicting ARI values.

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2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Energies ◽

10.3390/en14092392 ◽

2021 ◽

Vol 14 (9) ◽

pp. 2392

Author(s):

Antonello Rosato ◽

Rodolfo Araneo ◽

Amedeo Andreotti ◽

Federico Succetti ◽

Massimo Panella

Keyword(s):

Time Series ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Neural Architecture ◽

Network Layers ◽

Learning Scheme ◽

World Energy ◽

Multivariate Prediction ◽

Long Short Term Memory

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

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Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

10.1115/ipack2021-74066 ◽

2021 ◽

Author(s):

Pradeep Lall ◽

Tony Thomas ◽

Ken Blecker

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Remaining Useful Life ◽

Operating Conditions ◽

Short Term ◽

Term Memory ◽

Rectangular Pattern ◽

Useful Life ◽

Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

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