scholarly journals AI-based forecasting of ethanol fermentation using yeast morphological data

2021 ◽  
Author(s):  
Kaori Itto-Nakama ◽  
Shun Watanabe ◽  
Naoko Kondo ◽  
Shinsuke Ohnuki ◽  
Ryota Kikuchi ◽  
...  
Keyword(s):  
Supervised Machine Learning ◽  
Morphological Data ◽  
Coefficient Of Determination ◽  
Yeast Fermentation ◽  
Product Yields ◽  
The Neural Network ◽  
Stable Production ◽  
Staining Protocol ◽  
Ethanol Yields ◽  
Neural Network Algorithm

Abstract Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a non-staining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of > 0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.

scholarly journals Neural Model for the Prediction of Volume Losses in the Aging Process of Rums

2020 ◽  
Vol 29 (54) ◽  
pp. e10514
Author(s):  
Beatriz García-Castellanos ◽  
Osney Pérez-Ones ◽  
Lourdes Zumalacárregui-de-Cárdenas ◽  
Idania Blanco-Carvajal ◽  
Luis Eduardo López-de-la-Maza
Keyword(s):  
Aging Time ◽  
Aging Process ◽  
Neural Model ◽  
Correlation Factor ◽  
Coefficient Of Determination ◽  
Training Algorithms ◽  
The Neural Network ◽  
Aging Conditions ◽  
The Mean ◽  
Input Variables

The rum aging process shows volume losses, called wastage. The numerical operation variables: product, boardwalk, horizontal and vertical positions, date, volume, alcoholic degree, temperature, humidity and aging time, recorded in databases, contain valuable information to study the process. MATLAB 2017 software was used to estimate volume losses. In the modeling of the rum aging process, the multilayer perceptron neuronal network with one and two hidden layers was used, varying the number of neurons in these between 4 and 10. The Levenberg-Marquadt (LM) and Bayesian training algorithms were compared (Bay) The increase in 6 consecutive iterations of the validation error and 1,000 as the maximum number of training cycles were the criteria used to stop the training. The input variables to the network were: numerical month, volume, temperature, humidity, initial alcoholic degree and aging time, while the output variable was wastage. 546 pairs of input/output data were processed. The statistical Friedman and Wilcoxon tests were performed to select the best neural architecture according to the mean square error (MSE) criteria. The selected topology has a 6-4-4-1 structure, with an MSE of 2.1∙10-3 and a correlation factor (R) with experimental data of 0.9898. The neural network obtained was used to simulate thirteen initial aging conditions that were not used for training and validation, detecting a coefficient of determination (R2) of 0.9961.

scholarly journals WRF-Chem Simulation of Winter Visibility in Jiangsu, China, and the Application of a Neural Network Algorithm

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 520
Author(s):  
Peishu Zong ◽  
Yali Zhu ◽  
Huijun Wang ◽  
Duanyang Liu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Extinction Coefficient ◽  
Wrf Model ◽  
Jiangsu Province ◽  
Network Algorithm ◽  
The Neural Network ◽  
Noticeable Improvement ◽  
Neural Network Algorithm ◽  
Meteorological Elements

In this paper, the winter visibility in Jiangsu Province is simulated by WRF-Chem (Weather Research and Forecasting (WRF) model coupled with Chemistry) with high spatiotemporal resolutions. Simulation results show that WRF-Chem has good capability to simulate the visibility and related local meteorological elements and air pollutants in Jiangsu in the winters of 2013–2017. For visibility inversion, this study adopts the neural network algorithm. Meteorological elements, including wind speed, humidity and temperature, are introduced to improve the performance of WRF-Chem relative to the visibility inversion scheme, which is based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) extinction coefficient algorithm. The neural network offers a noticeable improvement relative to the inversion scheme of the IMPROVE visibility extinction coefficient, substantially improving the underestimation of winter visibility in Jiangsu Province. For instance, the correlation coefficient increased from 0.17 to 0.42, and root mean square error decreased from 2.62 to 1.76. The visibility inversion results under different humidity and wind speed levels show that the underestimation of the visibility using the IMPROVE scheme is especially remarkable. However, the underestimation issue is essentially solved using the neural network algorithm. This study serves as a basis for further predicting winter haze events in Jiangsu Province using WRF-Chem and deep-learning methods.

Using neural network model to estimate the rental price of residential properties

2019 ◽  
Vol 24 (2) ◽  
pp. 217-230
Author(s):  
Olalekan Shamsideen Oshodi ◽  
Wellington Didibhuku Thwala ◽  
Tawakalitu Bisola Odubiyi ◽  
Rotimi Boluwatife Abidoye ◽  
Clinton Ohis Aigbavboa
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Predictive Accuracy ◽  
Content Type ◽  
The Neural Network ◽  
Residential Properties ◽  
African Context ◽  
Neural Network Algorithm ◽  
The Government

Purpose Estimation of the rental price of a residential property is important to real estate investors, financial institutions, buyers and the government. These estimates provide information for assessing the economic viability and the tax accruable, respectively. The purpose of this study is to develop a neural network model for estimating the rental prices of residential properties in Cape Town, South Africa. Design/methodology/approach Data were collected on 14 property attributes and the rental prices were collected from relevant sources. The neural network algorithm was used for model estimation and validation. The data relating to 286 residential properties were collected in 2018. Findings The results show that the predictive accuracy of the developed neural network model is 78.95 per cent. Based on the sensitivity analysis of the model, it was revealed that balcony and floor area have the most significant impact on the rental price of residential properties. However, parking type and swimming pool had the least impact on rental price. Also, the availability of garden and proximity of police station had a low impact on rental price when compared to balcony. Practical implications In the light of these results, the developed neural network model could be used to estimate rental price for taxation. Also, the significant variables identified need to be included in the designs of new residential homes and this would ensure optimal returns to the investors. Originality/value A number of studies have shown that crime influences the value of residential properties. However, to the best of the authors’ knowledge, there is limited research investigating this relationship within the South African context.

Neural Network Algorithm for Solving Large Scale Travelling Salesman Problems

2012 ◽  
Vol 542-543 ◽  
pp. 1398-1402
Author(s):  
Guo Zhong Cheng ◽  
Wei Feng ◽  
Fang Song Cui ◽  
Shi Lu Zhang
Keyword(s):  
Neural Network ◽  
Time Complexity ◽  
Large Scale ◽  
Travelling Salesman Problem ◽  
Effective Algorithm ◽  
Travelling Salesman ◽  
Satisfactory Solution ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

This study improves the neural network algorithm that was presented by J.J.Hopfield for solving TSP(travelling salesman problem) and gets an effective algorithm whose time complexity is O(n*n), so we can solve quickly TSP more than 500 cities in microcomputer. The paper considers the algorithm based on the replacement function of the V Value. The improved algorithm can greatly reduces the time and space complexities of Hopfield method. The TSP examples show that the proposed algorithm could efficiently find a satisfactory solution and has a fast convergence speed.

scholarly journals Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations

2014 ◽  
Vol 7 (9) ◽  
pp. 9047-9094 ◽  
Author(s):  
A. Di Noia ◽  
O. P. Hasekamp ◽  
G. van Harten ◽  
J. H. H. Rietjens ◽  
J. M. Smit ◽  
...  
Keyword(s):  
Neural Network ◽  
Complex Refractive Index ◽  
Effective Radius ◽  
The Other ◽  
Look Up Table ◽  
The Neural Network ◽  
Retrieval Scheme ◽  
Small Set ◽  
Neural Network Algorithm ◽  
Aerosol Properties

Abstract. In this paper, the use of a neural network algorithm for the retrieval of the aerosol properties from ground-based spectropolarimetric measurements is discussed. The neural network is able to retrieve the aerosol properties with an accuracy that is almost comparable to that of an iterative retrieval. By using the outcome of the neural network as a first guess of the iterative retrieval scheme, the accuracy of the fine and coarse mode optical thickness are further improved while for the other parameters the improvement is small or absent. The resulting scheme (neural network + iterative retrieval) is compared to the original one (look-up table + iterative retrieval) on a set of simulated ground-based measurements, and on a small set of real observations carried out by an accurate ground-based spectropolarimeter. The results show that the use of a neural network based first guess leads to an increase in the number of converging retrievals, and possibly to more accurate estimates of the aerosol effective radius and complex refractive index.

Real-Time Monitor Method of Soil Slope Stability in Seasonal Frozen Area

2013 ◽  
Vol 303-306 ◽  
pp. 777-781
Author(s):  
Ping Jiang ◽  
Meng Su Zhang
Keyword(s):  
Shear Strength ◽  
Slope Stability ◽  
Real Time ◽  
Evaluation System ◽  
Landslide Hazard ◽  
Soil Slope ◽  
Freezing And Thawing ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Freezing And Thawing Cycles

Soil slope in seasonal frozen area subject to rainfall or freezing and thawing cycles, the shear strength of soil damage and lead to landslide hazard. In order to predict the landslide hazard through monitoring the soil slope stability real-time, the soil slope stability evaluation system is established. The Neural Network Algorithm can simulate elastic-plastic finite element method well, and using Genetic Algorithm to search cohesion(c) and internal friction angel(φ) which meet the accuracy requirements. And the intelligent parameter inversion model of soil shear strength (c, φ) based on displacement is established. So, the slope stability can be evaluated real –time based on displacement monitor. Application of this method in actual project shows that this method is accurate and effective.

Image Segmentation of Eggplants in Growing Environment Based on Improved BP Neural Network

2010 ◽  
Vol 40-41 ◽  
pp. 599-603
Author(s):  
Jian Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Bp Neural Network ◽  
Image Features ◽  
The Neural Network ◽  
Training Samples ◽  
Neural Network Algorithm ◽  
Growing Environment ◽  
Improved Bp Algorithm ◽  
Gray Values

Aim at the complex background of eggplant image in the growing environment, a image segmentation method based on BP neural network was put forward. The EXG gray values of 3×3 neighborhood pixels were obtained as image features through by analyzing the eggplant image. 30 eggplant images were taken as training samples and results of manual segmentation images by Photoshop were regarded as teacher signals. The improved BP algorithm was used to train the parameter of the neural network. The effective parameter was achieved after 120 times of training. The result of this experiment showed that the eggplant fruit could be preferably segmented from the background by using BP neural network algorithm and it could totally meet the demands of the picking robots after further processing by way of combining mathematics morphology with median filtering.

The Analysis of Faults Detection Software Based on Improved Neural Network Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2044-2047
Author(s):  
Miao Yan ◽  
Zhi Bao Liu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Improved Method ◽  
Software Faults ◽  
Component Software ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Detection Software

The large-scale software is consisted of the components which are quite different. The detection accuracy of the traditional faults detection methods for the large-scale component software is not satisfactory. This paper proposes a large-scale software faults detection methods based on improved neural network combining the features of the large-scale software by computing the stable probability and building the neural network faults detection models. The proposed method can analyze the serial faults of the large-scale software to determine the positions of the faults. The experiment and simulation results show that the improved method for large-scale software fault detection can greatly improve the accuracy.

scholarly journals An Application of Neural Network Technique to Correct the Dome Temperature Effects on Pyrgeometer Measurements

2006 ◽  
Vol 23 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Amauri P. Oliveira ◽  
Jacyra Soares ◽  
Marija Z. Božnar ◽  
Primož Mlakar ◽  
João F. Escobedo
Keyword(s):  
Neural Network ◽  
Temperature Effects ◽  
Longwave Radiation ◽  
Global Solar Radiation ◽  
Time Of Day ◽  
Network Training ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Using Data ◽  
The City

Abstract This work describes an application of a multilayer perceptron neural network technique to correct dome emission effects on longwave atmospheric radiation measurements carried out using an Eppley Precision Infrared Radiometer (PIR) pyrgeometer. It is shown that approximately 7-month-long measurements of dome and case temperatures and meteorological variables available in regular surface stations (global solar radiation, air temperature, and air relative humidity) are enough to train the neural network algorithm and correct the observed longwave radiation for dome temperature effects in surface stations with climates similar to that of the city of São Paulo, Brazil. The network was trained using data from 15 October 2003 to 7 January 2004 and verified using data, not present during the network-training period, from 8 January to 30 April 2004. The longwave radiation values generated by the neural network technique were very similar to the values obtained by Fairall et al., assumed here as the reference approach to correct dome emission effects in PIR pyrgeometers. Compared to the empirical approach the neural network technique is less limited to sensor type and time of day (allows nighttime corrections).

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