Performance of Artificial Neural Network for Predicting Fermentation Characteristics in Biosurfactant Production by Bacillus subtilis ATCC 6633 using Sugar Cane Molasses

2011 ◽  
Vol 7 (6) ◽  
Author(s):  
Yousef Rahimi Kashkouli ◽  
Azadeh Mogharei ◽  
Saman Mousavian ◽  
Farzaneh Vahabzadeh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bacillus Subtilis ◽  
Sugar Cane ◽  
Model Building ◽  
Biosurfactant Production ◽  
Cane Molasses ◽  
Subtilis Atcc ◽  
Bacillus Subtilis Atcc ◽  
Artificial Neural

Artificial neural network (ANN) was successfully applied to model fermentation parameters for biosurfactant production by Bacillus subtilis ATCC 6633 using sugar cane molasses. Cell growth and biosurfactant production were monitored along the surface activity of the cell-free broth. Response surface methodology (RSM) as a formal statistical model building system was used for the ANN development. The network predicted biosurfactant concentration was 0.381 g/l which showed almost no differences with the relevant experimental value which obtained according to the RSM arrangement. Furthermore, the ANN surface tension reduction was 30.48 mN/m, which was within 3.24% of the experimental value. Comparisons between RSM and the ANN showed preference of using ANN as complementary to RSM and not as a replacement to it.

Analysis of Stock Volatility Clustering Using ANN

2015 ◽  
Vol 28 (2) ◽  
pp. 32-45 ◽  
Author(s):  
Manish Kumar ◽  
Santanu Das ◽  
Sneha Govil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Building ◽  
Stochastic Volatility Model ◽  
Propagation Model ◽  
Stock Index ◽  
Support Vector ◽  
Stock Volatility ◽  
Volatility Model ◽  
Artificial Neural

The model building theories broadly categorize the stock index forecasting models into two broad categories: Based on statistical theory consisting models such as Stochastic Volatility model (SV) and General Autoregressive Conditional Heteroskedasticity (GARCH) whereas other one based on artificial intelligence based models, such as artificial neural network (ANN), the support vector machine (SVM) and technique used for optimization such as particle swarm optimization (PSO). In existing literature, many of the statistical models when compared with artificial neural network models were outperformed by these models. This paper analyses stock volatility using ANN models as Multilayer perceptron with back propagation model and Radial Basis function.

Toward Algorithms for Automation of Postgenomic Data Analyses: Bacillus subtilis Promoter Prediction with Artificial Neural Network

2020 ◽  
Vol 24 (5) ◽  
pp. 300-309
Author(s):  
Rafael Vieira Coelho ◽  
Gabriel Dall'Alba ◽  
Scheila de Avila e Silva ◽  
Sergio Echeverrigaray ◽  
Ana Paula Longaray Delamare
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bacillus Subtilis ◽  
Promoter Prediction ◽  
Data Analyses ◽  
Artificial Neural

scholarly journals Crude glycerol from biodiesel industry as substrate for biosurfactant production by Bacillus subtilis ATCC 6633

2014 ◽  
Vol 57 (2) ◽  
pp. 295-301 ◽  
Author(s):  
Marylane de Sousa ◽  
Iuri Torquato Dantas ◽  
Anne Kamilly Nogueira Felix ◽  
Hosiberto Batista de Sant'Ana ◽  
Vânia Maria Maciel Melo ◽  
...  
Keyword(s):  
Bacillus Subtilis ◽  
Crude Glycerol ◽  
Biosurfactant Production ◽  
Subtilis Atcc ◽  
Bacillus Subtilis Atcc ◽  
Biodiesel Industry

Detecting SIM Box Fraud by Using Support Vector Machine and Artificial Neural Network

2015 ◽  
Vol 74 (1) ◽  
Author(s):  
Roselina Sallehuddin ◽  
Subariah Ibrahim ◽  
Azlan Mohd Zain ◽  
Abdikarim Hussein Elmi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Model Building ◽  
Computational Time ◽  
Support Vector ◽  
Ann Model ◽  
Svm Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia.  One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud.   The classification uses nine selected features of data extracted from Customer Database Record.  The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training.

scholarly journals Multi-Scale Biosurfactant Production by Bacillus subtilis Using Tuna Fish Waste as Substrate

Catalysts ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 456
Author(s):  
Jiheng Hu ◽  
Jie Luo ◽  
Zhiwen Zhu ◽  
Bing Chen ◽  
Xudong Ye ◽  
...  
Keyword(s):  
Bacillus Subtilis ◽  
Large Scale ◽  
Biological Activities ◽  
Pilot Scale ◽  
Biosurfactant Production ◽  
Tuna Fish ◽  
Fish Waste ◽  
Subtilis Atcc ◽  
Bacillus Subtilis Atcc ◽  
Surfactin Production

As one of the most effective biosurfactants reported to date, lipopeptides exhibit attractive surface and biological activities and have the great potential to serve as biocatalysts. Low yield, high cost of production, and purification hinder the large-scale applications of lipopeptides. Utilization of waste materials as low-cost substrates for the growth of biosurfactant producers has emerged as a feasible solution for economical biosurfactant production. In this study, fish peptone was generated through enzyme hydrolyzation of smashed tuna (Katsuwonus pelamis). Biosurfactant (mainly surfactin) production by Bacillus subtilis ATCC 21332 was further evaluated and optimized using the generated fish peptone as a comprehensive substrate. The optimized production conduction was continuously assessed in a 7 L batch-scale and 100 L pilot-scale fermenter, exploring the possibility for a large-scale surfactin production. The results showed that Bacillus subtilis ATCC 21332 could effectively use the fish waste peptones for surfactin production. The highest surfactin productivity achieved in the pilot-scale experiments was 274 mg/L. The experimental results shed light on the further production of surfactins at scales using fish wastes as an economical substrate.

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