scholarly journals Harnessing cytoplasmic particles movement of the human early embryo analysed by advanced imaging and artificial intelligence to predict development to blastocyst stage

2020 ◽  
Author(s):  
Giovanni Coticchio ◽  
Giulia Fiorentino ◽  
Giovanna Nicora ◽  
Raffaella Sciajno ◽  
Federica Cavalera ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Research Question ◽  
Time Lapse ◽  
Blastocyst Stage ◽  
Preimplantation Embryos ◽  
Embryo Viability ◽  
Movement Velocity

AbstractResearch QuestionProgress in artificial intelligence (AI) and advanced image analysis offers unique opportunities to develop novel embryo assessment approaches. In this study, we tested the hypothesis that such technologies can extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst.DesignIn a proof-of principle study, an artificial neural network (ANN) approach was undertaken to assess retrospectively 230 human preimplantation embryos. After ICSI, embryos were subjected to time-lapse monitoring for 44 hours. For comparison as a standard embryo assessment methodology, a single senior embryologist assessed each embryo to predict development to blastocyst stage (BL) based on a single picture frame taken at 42 hours of development. In the experimental approach, in embryos that developed to blastocyst or destined to arrest (NoBL), cytoplasm movement velocity (CMV) was recorded by time-lapse monitoring during the first 44 hours of culture and analysed with a Particle Image Velocimetry (PIV) algorithm to extract quantitative information. Three main AI approaches, the k-Nearest Neighbor (k-NN), the Long-Short Term Memory Neural Network (LSTM-NN) and the hybrid ensemble classifier (HyEC) were employed to classify the two embryo classes.ResultsBlind operator assessment classified each embryo in terms of ability of development to blastocyst, reaching a 75.4% accuracy, 76.5% sensitivity, 74.3% specificity, 74.3% precision and 75.4% F1 score. After integration of results from AI models together with the blind operator classification, the performance metrics improved significantly, with a 82.6% accuracy, 79.4% sensitivity, 85.7% specificity, 84.4% precision and 81.8% F1 score.ConclusionsThe present study suggests the possibility to predict human blastocyst development at early cleavage stages by detection of CMV and AI analysis. This indicates the importance of the dynamics of the cytoplasm as a novel and valuable source of data to assess embryo viability.

scholarly journals Pemanfaatan Asynchronous Advantage Actor-Critic Dalam Pembuatan AI Game Bot Pada Game Arcade

2019 ◽  
Vol 1 (2) ◽  
pp. 74-84
Author(s):  
Evan Kusuma Susanto ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Trial And Error ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Asynchronous Advantage Actor-Critic (A3C) adalah sebuah algoritma deep reinforcement learning yang dikembangkan oleh Google DeepMind. Algoritma ini dapat digunakan untuk menciptakan sebuah arsitektur artificial intelligence yang dapat menguasai berbagai jenis game yang berbeda melalui trial and error dengan mempelajari tempilan layar game dan skor yang diperoleh dari hasil tindakannya tanpa campur tangan manusia. Sebuah network A3C terdiri dari Convolutional Neural Network (CNN) di bagian depan, Long Short-Term Memory Network (LSTM) di tengah, dan sebuah Actor-Critic network di bagian belakang. CNN berguna sebagai perangkum dari citra output layar dengan mengekstrak fitur-fitur yang penting yang terdapat pada layar. LSTM berguna sebagai pengingat keadaan game sebelumnya. Actor-Critic Network berguna untuk menentukan tindakan terbaik untuk dilakukan ketika dihadapkan dengan suatu kondisi tertentu. Dari hasil percobaan yang dilakukan, metode ini cukup efektif dan dapat mengalahkan pemain pemula dalam memainkan 5 game yang digunakan sebagai bahan uji coba.

Prediction and Optimization of WAG Flooding by Using LSTM Neural Network Model in Middle East Carbonate Reservoir

2021 ◽  
Author(s):  
Ruijie Huang ◽  
Chenji Wei ◽  
Baozhu Li ◽  
Jian Yang ◽  
Suwei Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Numerical Simulation ◽  
Short Term Memory ◽  
Oil Production ◽  
Simulation Method ◽  
Analysis Data ◽  
Carbonate Reservoir ◽  
Parameters Optimization ◽  
Model Based

Abstract Production prediction continues to play an increasingly significant role in reservoir development adjustment and optimization, especially in water-alternating-gas (WAG) flooding. As artificial intelligence continues to develop, data-driven machine learning method can establish a robust model based on massive data to clarify development risks and challenges, predict development dynamic characteristics in advance. This study gathers over 15 years actual data from targeted carbonate reservoir and establishes a robust Long Short-Term Memory (LSTM) neural network prediction model based on correlation analysis, data cleaning, feature variables selection, hyper-parameters optimization and model evaluation to forecast oil production, gas-oil ratio (GOR), and water cut (WC) of WAG flooding. In comparison to traditional reservoir numerical simulation (RNS), LSTM neural networks have a huge advantage in terms of computational efficiency and prediction accuracy. The calculation time of LSTM method is 864% less than reservoir numerical simulation method, while prediction error of LSTM method is 261% less than RNS method. We classify producers into three types based on the prediction results and propose optimization measures aimed at the risks and challenges they faced. Field implementation indicates promising outcome with better reservoir support, lower GOR, lower WC, and stabler oil production. This study provides a novel direction for application of artificial intelligence in WAG flooding development and optimization.

scholarly journals Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

2022 ◽  
Vol 30 (7) ◽  
pp. 1-23
Author(s):  
Hongwei Hou ◽  
Kunzhi Tang ◽  
Xiaoqian Liu ◽  
Yue Zhou
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Research Result ◽  
Customer Information ◽  
Rural Governance ◽  
Artificial Intelligence Technology ◽  
Reasonable Prediction

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

scholarly journals Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sujan Ghimire ◽  
Zaher Mundher Yaseen ◽  
Aitazaz A. Farooque ◽  
Ravinesh C. Deo ◽  
Ji Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Flood Control ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Short Term ◽  
Neural Network Models ◽  
Time Intervals ◽  
Term Memory ◽  
Long Short Term Memory

AbstractStreamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s−1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction.

46 Use of time-lapse imaging technology to assess relationships of morphological and phototextural attributes of presumptive ovine zygotes and early embryos with their developmental competence invitro

2021 ◽  
Vol 33 (2) ◽  
pp. 130
Author(s):  
S. Pena ◽  
K. Fryc ◽  
M. Murawski ◽  
A. Nowak ◽  
B. Kij ◽  
...  
Keyword(s):  
Time Lapse ◽  
Blastocyst Stage ◽  
Developmental Competence ◽  
Perivitelline Space ◽  
Embryo Viability ◽  
Pixel Intensity ◽  
Developmental Potential ◽  
Imaging Technology ◽  
Time Lapse Imaging ◽  
Zygote Stage

The assessment of morphology and digital image opacity may provide valuable information on embryo viability because such traits are linked to embryonic gene expression, metabolism and ultrastructure. Time-lapse imaging has been used in research to monitor the dynamic nature of the developing pre-implantation embryo, which includes capturing alterations in various morphological parameters over time. The present study examined the effectiveness of time-lapse technology in assessing several morphometric and phototextural parameters for predicting the developmental potential of ovine embryos. The development of 37 long wool sheep embryos from IVF to the blastocyst stage was monitored and evaluated using Primo Vision time-lapse imaging technology. Image-Pro Plus software was then used to measure zona pellucida thickness, embryo diameter, cellular grey-scale pixel intensity and heterogeneity, and total area of the perivitelline space. A one-way analysis of variance (ANOVA) was done using SigmaPlot® 11.0 for all attributes at various time points during embryo development [i.e. presumptive zygote stage, t(0); first cleavage, t(2) or t(3); second cleavage, t(4) or t(6); and third cleavage, t(7) or t(8)]. Our results indicate that most parameters analysed did not differ among embryos varying in their developmental fate, with the exception of the perivitelline space area, which was greater (P<0.05) for non-dividing embryos than for future blastocysts at the presumptive zygote stage (4040±4137 vs. 857±642µm2, respectively; mean±s.d.). Consequently, the measurement of perivitelline space at t(0) could be used to predict developmental potential of invitro-produced ovine embryos, but further investigation is required.

P–137 Male embryos take longer to develop to the blastocysts stage

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
J Fraire-Zamora ◽  
M Martínez ◽  
D García ◽  
R Vassena ◽  
A Rodríguez
Keyword(s):  
Statistical Difference ◽  
Small Sample Size ◽  
Time Lapse ◽  
Study Groups ◽  
Blastocyst Stage ◽  
Small Sample ◽  
Preimplantation Embryos ◽  
Specific Difference ◽  
Male And Female ◽  
Morphokinetic Parameters

Abstract Study question Are there any differences in developmental timings between male and female preimplantation embryos? Summary answer There is a tendency for statistical difference in the time to reach blastocyst stage for male embryos compared to female embryos What is known already Differences in gene expression and metabolic uptake between male and female preimplantation embryos have been found in animal models and humans. These differences could affect the developmental timings of embryos resulting in differences in either sex. Morphokinetic parameters can precisely assess developmental timings. Only a few studies have analyzed morphokinetic parameters between male and female preimplantation embryos and no consensus has been reached on whether there is any sex-specific difference. The objective of this study is to compare morphokinetic parameters between male and female preimplantation embryos to determine any sex-specific developmental differences. Study design, size, duration This is a retrospective study including 102 preimplantation embryos from February 2018 to February 2020. The morphokinetic parameters obtained from time-lapse records of each embryo were: time to pronuclear fading (tPNf), times to 2–8 cells (t2, t3, t4, t5, t6, t7, t8), time to start of blastulation (tSB) and time to full blastocyst stage (tB). A two-tailed Student’s t-test was used to compare morphokinetic parameters between embryo sexes. A p < 0.05 was considered statistically significant. Participants/materials, setting, methods The study included retrospective time-lapse data from preimplantation embryos giving rise to 51 baby boys and 51 baby girls, as seen at birth. This is a single-center study with standardized culture conditions. Embryos in both study groups issued from cycles with donated oocytes. Only elective blastocyst stage single-embryo transfers (SET) on day 5 were assessed. Main results and the role of chance A tendency to statistical difference (p = [0.1–0.05]) was observed for blastocyst-related morphokinetic parameters: tSB (mean time was 89.6±6.3 hours in male embryos vs. 86.9±8.1 hours in female embryos, p = 0.06) and tB (100.2±5.9 hours versus 97.9±6.5 hours, p = 0.07). Male embryos showed an increased average time of 2.7 hours to tSB and 2.3 hours to tB, while no differences were found in the mean times of all the other morphokinetic paraments measured (p > 0.50): tPNf (∼21.8±3.0 hours) t2 (∼24.4±3.2 hours); t3 (∼35.6±3.9 hours); t4 (∼36.6±4.6 hours); t5 (∼46.9±6.0 hours); t6 (∼53.5±7.0 hours); t7 (∼54.1±7.3 hours) and t8 (∼54.1±7.3 hours). This finding suggests a sex-specific difference in reaching blastocyst stages. Limitations, reasons for caution The main limitation of the study is its retrospective nature and the small sample size. We analyzed the data of embryos leading to a live birth (high-quality embryos), therefore, caution should be made when generalizing results to non-implanting embryos (of potentially lower quality). Wider implications of the findings: Sex-specific differences in developmental timings of preimplantation embryos at blastocyst stage, as evidenced by time-lapse data, should be considered to avoid selection biases during embryo transfers in ART clinic. Trial registration number Not applicable

scholarly journals Artificial neural-network analysis combined with time-lapse imaging predicts embryo ability to develop to the blastocyst stage

2019 ◽  
Vol 112 (3) ◽  
pp. e273-e274
Author(s):  
Giovanni Coticchio ◽  
Raffaella Sciajno ◽  
Giulia Fiorentino ◽  
Federica Cavalera ◽  
Giovanna Nicora ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Time Lapse ◽  
Blastocyst Stage ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural ◽  
Time Lapse Imaging

scholarly journals Comparative Study on Reliability of Transfer Learning to Classify Plant-Based Diseases

2021 ◽  
Vol 10 (6) ◽  
pp. 154-160
Author(s):  
N Rohan Sai ◽  
◽  
T Sudarshan Rao ◽  
G. L. Aruna Kumari ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Network Architecture ◽  
Agricultural Sector ◽  
Performance Metrics ◽  
Primary Objective ◽  
The Other ◽  
Common Disease ◽  
Classification Problems

One of the essential factors contributing to a plant's growth is identifying and preventing diseases in the early stages. Healthy plants are essential for a rich production. Recent advances in Deep learning - a subset of Artificial Intelligence and Machine Learning are playing a pivotal role in solving image classification problems and can be applied to the agricultural sector for crop surveillance and early anomaly identification. For this research, we used an open-source dataset of leaf images divided into three classes, two of which are the most common disease types found on many crops; the graphical characterizations for the three classes are images of leaves with Powdery Residue, images of leaves with Rusty Spots, and images of Healthy leaves. The primary objective of this research is to present a pre-trained ImageNet network architecture that is well suited for dealing with plant-based data, even when sample sizes collected are limited. We used different convolutional neural network-based architectures such as InceptionV3, MobileNetV2, Xception, VGG16, and VGG19 to classify plant leaf images with visually different representations of each disease. Xception, MobileNetV2, and DenseNet had a considerable advantage over all the performance metrics recorded among the other networks trained.

scholarly journals A Survey of Forex and Stock Price Prediction Using Deep Learning

2021 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Zexin Hu ◽  
Yiqi Zhao ◽  
Matloob Khushi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Mean Square Error ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Percentage Error ◽  
Mean Square ◽  
Learning Methods ◽  
Stock Price Prediction

Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.

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