scholarly journals Deciphering anomalous heterogeneous intracellular transport with neural networks

2019 ◽  
Author(s):  
Daniel S Han ◽  
Nickolay Korabel ◽  
Runze Chen ◽  
Mark Johnston ◽  
Viki J. Allan ◽  
...  
Keyword(s):  
Neural Network ◽  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Intracellular Transport ◽  
Time Series Data ◽  
Series Data ◽  
Range Analysis ◽  
The Neural Network ◽  
Rescaled Range Analysis ◽  
Data Points

AbstractBiological intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behaviour. Characterisation of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity adequately. Here, we have developed a deep learning feedforward neural network trained on fractional Brownian motion, which provides a novel, accurate and efficient characterization method for resolving heterogeneous behaviour of intracellular transport both in space and time. Importantly, the neural network requires significantly fewer data points compared to established methods, such as mean square displacements, rescaled range analysis and sequential range analysis. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, we were able to interpret anomalous intracellular dynamics as fractional Brownian motion with a stochastic Hurst exponent.

scholarly journals Deciphering anomalous heterogeneous intracellular transport with neural networks

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Daniel Han ◽  
Nickolay Korabel ◽  
Runze Chen ◽  
Mark Johnston ◽  
Anna Gavrilova ◽  
...  
Keyword(s):  
Neural Network ◽  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Intracellular Transport ◽  
Time Series Data ◽  
Series Data ◽  
The Neural Network ◽  
Heterogeneous Behavior ◽  
Data Points ◽  
Short Time

Intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behavior. Characterization of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity. Here, we developed a deep learning feedforward neural network trained on fractional Brownian motion, providing a novel, accurate and efficient method for resolving heterogeneous behavior of intracellular transport in space and time. The neural network requires significantly fewer data points compared to established methods. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, fractional Brownian motion with a stochastic Hurst exponent was used to interpret, for the first time, anomalous intracellular dynamics, revealing unexpected differences in behavior between closely related endocytic organelles.

International Real Estate Review

2004 ◽  
Vol 7 (1) ◽  
pp. 121-138
Author(s):  
Xin J. Ge ◽  
◽  
G. Runeson ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hong Kong ◽  
Time Series Data ◽  
Series Data ◽  
Residential Property ◽  
Neural Network Approach ◽  
Price Performance ◽  
The Neural Network ◽  
Property Price

This paper develops a forecasting model of residential property prices for Hong Kong using an artificial neural network approach. Quarterly time-series data are applied for testing and the empirical results suggest that property price index, lagged one period, rental index, and the number of agreements for sales and purchases of units are the major determinants of the residential property price performance in Hong Kong. The results also suggest that the neural network methodology has the ability to learn, generalize, and converge time series.

scholarly journals Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 1-26
Author(s):  
Nagaraj V. Dharwadkar ◽  
Anagha R. Pakhare ◽  
Vinothkumar Veeramani ◽  
Wen-Ren Yang ◽  
Rajinder Kumar Mallayya Math
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI

1999 ◽  
Vol 02 (02) ◽  
pp. 221-241 ◽  
Author(s):  
JINGTAO YAO ◽  
CHEW LIM TAN ◽  
HEAN-LEE POH
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Exchange ◽  
Emerging Market ◽  
Neural Nets ◽  
Stock Index ◽  
Range Analysis ◽  
The Neural Network ◽  
Rescaled Range Analysis ◽  
Stock Index Forecasting

This paper presents a study of artificial neural nets for use in stock index forecasting. The data from a major emerging market, Kuala Lumpur Stock Exchange, are applied as a case study. Based on the rescaled range analysis, a backpropagation neural network is used to capture the relationship between the technical indicators and the levels of the index in the market under study over time. Using different trading strategies, a significant paper profit can be achieved by purchasing the indexed stocks in the respective proportions. The results show that the neural network model can get better returns compared with conventional ARIMA models. The experiment also shows that useful predictions can be made without the use of extensive market data or knowledge. The paper, however, also discusses the problems associated with technical forecasting using neural networks, such as the choice of "time frames" and the "recency" problems.

scholarly journals Toward an embedded system for gesture recognition based on artificial neural network using reconfigurable target (case study and review)

2020 ◽  
Vol 10 (4) ◽  
pp. 142-148
Author(s):  
Ahmad Reda ◽  
Tareq Alshoufi ◽  
Ahmed Bouzid ◽  
József Vásárhelyi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Embedded System ◽  
Time Series Data ◽  
Reference Signal ◽  
Series Data ◽  
The Neural Network ◽  
Human Arm ◽  
On Chip

With a view to create an intelligent remote control for robot movements, this article treats the study case of dataset creation using RSG (Reference Signal Generator). Using artificial intelligence, the device recognizes the gestures of an operator. Indeed, a neural network can classify time series data coming from accelerometers, and for a beginning 4 gestures are taken into consideration. The most challenging work is to build a reference dataset that is necessary for the learning process. To train the neural network, a huge amount of reference data should be created (hundreds of thousands of time-series vectors per gesture per sensor), which cannot be done manually by an operator. To overcome the issue, an RSG is created. This article also describes how a 1-DoF arm has been designed to emulate the behavior of the human arm doing gestures as well as the data acquisition system. The system is based on a software/hardware co-design implemented on Programmable System on Chip (PSoC).

scholarly journals SMOOTHING IN NEURAL NETWORK FOR UNIVARIAT TIME SERIES DATA FORECASTING

2020 ◽  
Vol 3 (1) ◽  
pp. 23-30
Author(s):  
Nurfia Oktaviani Syamsiah ◽  
Indah Purwandani
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Exponential Smoothing ◽  
Series Data ◽  
Data Set ◽  
Research Material ◽  
Linear Relationships ◽  
The Neural Network ◽  
Optimal Accuracy

Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.

scholarly journals Study of BP neural network based on improved particle swarm algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032041
Author(s):  
Xiaoqian Ma ◽  
Liyuan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Particle Swarm ◽  
Correction Method ◽  
Series Data ◽  
Particle Swarm Algorithm ◽  
The Neural Network ◽  
Swarm Algorithm

Abstract This paper uses first-order difference to transform non-smooth data into smooth time series data, determines the p and q parameters in the model by judging the trailing and truncated nature of ACF, PACF, and finally establishes the ARIMA model after ACI, BCI detection. According to the parameters of the neural network randomly selected similar to the initial spatial position of the particles in the particle swarm algorithm, the improved particle swarm algorithm is used instead of the gradient correction method to precisely adjust the parameters and establish the BP neural network, which improves the robustness and accuracy of the prediction model.

Neural Approach to Predict Flow Discharge in River Chenab Pakistan

2016 ◽  
Vol 20 (5) ◽  
pp. 730-734 ◽  
Author(s):  
Tanzila Saba ◽  
Keyword(s):  
Neural Network ◽  
Water Flow ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Trial And Error ◽  
The Neural Network ◽  
Daily Discharge ◽  
Intelligent Model ◽  
Standard Learning

River water flow forecast in general and particularly in floods is of worth importance for monitoring operations of floods in canals and rivers. Floods in rivers bring destructions to road, houses, crops and causes human dislocation. The River Chenab is one of the largest rivers in Pakistan and has a historical recording of heavy floods. Prior to heavy floods, in time warning is mandatory to save lives and property. Accordingly, this paper presents an intelligent model to predict an advance alarming water flow from Chenab River. Standard learning algorithm is applied to train the ANN for this task. Inputs to the neural network are taken from the daily discharge values and the output layer composed of four neurons to represent number of predicted days. Moreover, trial and error approach is adopted to select appropriate number of inputs for time-series data. Two different architecture (single and double hidden layers) of neural network are evaluated and compared to find the most suitable one. Additionally, two activation functions are tested. The results thus achieved reveal well in time warning to the surroundings to secure flood victims. However, during low discharge, neural network miscalculated.

scholarly journals Industrial Electrical Energy Consumption Forecasting by using Temporal Convolutional Neural Networks

2021 ◽  
Vol 335 ◽  
pp. 02003
Author(s):  
Kai Lok Lum ◽  
Hou Kit Mun ◽  
Swee King Phang ◽  
Wei Qiang Tan
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Time Series Data ◽  
Electrical Energy ◽  
Series Data ◽  
Percentage Error ◽  
Electrical Energy Consumption ◽  
The Neural Network ◽  
Industrial Energy ◽  
Industrial Energy Consumption

In conjunction with the 4th Industrial Revolution, many industries are implementing systems to collect data on energy consumption to be able to make informed decision on scheduling processes and manufacturing in factories. Companies can now use this historical data to forecast the expected energy consumption for cost management. This research proposes the use of a Temporal Convolutional Neural Network (TCN) with dilated causal convolutional layers to perform forecasting instead of conventional Long-Short Term Memory (LSTM) or Recurrent Neural Networks (RNN) as TCN exhibit lower memory and computational requirements. This approach is also chosen due to traditional regressive methods such as Autoregressive Integrated Moving Average (ARIMA) fails to capture non-linear patterns and features for multi-step time series data. In this research paper, the electrical energy consumption of a factory will be forecasted by implementing a TCN to extract the features and to capture the complex patterns in time series data such daily electrical energy consumption with a limited dataset. The neural network will be built using Keras and TensorFlow libraries in Python. The energy consumption data as training data will be provided by GoAutomate Sdn Bhd. Then, the historical data of economic factors and indexes such as the KLCI will be included alongside the consumption data for neural network training to determine the effects of the economy on industrial energy consumption. The forecasted results with and without the economic data will then be compared and evaluated using Weighted Average Percentage Error (WAPE) and Mean Absolute Percentage Error (MAPE) metrics. The parameters for the neural network will then be evaluated and fined tuned accordingly based on the accuracy and error metrics. This research is able create a CNN to forecast electrical energy consumption with WAPE = 0.083 & MAPE = 0.092, of a factory one (1) week ahead with a small scale dataset with only 427 data points, and has determined that the effects of economic index such as the Bursa Malaysia has no meaningful impact on industrial energy consumption that can be then applied to the forecasting of energy consumption of the factory.

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