Optimization of process parameters for improved chitinase activity from<i>Thermomyces</i>sp. by using artificial neural network and genetic algorithm

Suryawanshi, Nisha; Sahu, Jyoti; Moda, Yash; Eswari, J. Satya

doi:10.1080/10826068.2020.1780612

Cited by 23 publications

(11 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The regression 46 chart from the MATLAB software for the training phase of estimation is shown in Figure 9 below. The regression graph produces a correlation coefficient based on the relationship between estimates and realized values.…”

Section: Findings and Discussionmentioning

confidence: 99%

Estimation of target station data using satellite data and deep learning algorithms

Yayla

Harmanci

2020

Int J Energy Res

View full text Add to dashboard Cite

In this study, an innovative model has been developed for wind speed estimation through the Deep Learning method using hourly wind speed data from the measurement stations of the General Directorate of Meteorology in Van and Hakkari provinces in Turkey in conjunction with simultaneous satellite images from Eumetsat. Obtained satellite images were used during the introduction of the model, while wind speed data were used at the output stage. As a result of the findings, it was found that 85% accuracy performance could be achieved to provide sufficient insight for systems that are widely established worldwide. The model, developed as a result of the study, eliminates the need to install wind measuring stations for any region on earth within the satellite field in terms of determining wind potential. Since the field of view of the Meteosat 7 satellite covers the whole of Eastern Europe, it was determined that it could predict a high rate of up to 6 hours later by the method used in image analysis. The systems to be controlled with this method will be able to examine the weather events instantly at each point in the satellite field of view and make more accurate decisions. Also, companies will be able to perform a more detailed and rapid field scan compared to existing limited methods, and reduce initial investment costs and operating costs in terms of renewable energy resources investments.

show abstract

Section: Findings and Discussionmentioning

confidence: 99%

Estimation of target station data using satellite data and deep learning algorithms

Yayla

Harmanci

2020

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…[41]. e Levenberg-Marquardt method implements the least damped square method with respect to weights [42][43][44]. e Levenberg-Marquardt backpropagation algorithm uses the conjugate gradient technique to reduce the sum of squares at each iteration [45].…”

Section: Levenberg-marquardtmentioning

confidence: 99%

“…is method avoids a line-search per learning literature by using a step size scaling mechanism, and it makes this algorithm faster as compared with other secondorder algorithms. Scaled Conjugate Gradient offers a controlled learning algorithm with a super linear convergence rate [43].…”

Section: Scaled Conjugate Gradientmentioning

confidence: 99%

“…This method avoids a line-search per learning literature by using a step size scaling mechanism, and it makes this algorithm faster as compared with other second-order algorithms. Scaled Conjugate Gradient offers a controlled learning algorithm with a super linear convergence rate [ 43 ]. The Scale Conjugate method is comparatively faster than the standard backpropagation as it does not include any critical user-dependent parameter.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Risk Prediction by Using Artificial Neural Network in Global Software Development

Iftikhar

Alam

Ahmed

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.

show abstract

“…For instance, genome annotation, host strain selection, pathway discovery, metabolic pathway reconstruction, metabolic flux optimization, multi-omic data integration, and protein modeling can be obtained through machine learning methods [3,19]. Besides, due to the availability of the large amounts of fermentation parameter values from empirical studies, machine learning algorithms can be implemented directly to this multivariate system to fine-tune the fermentation conditions [20,21].…”

Section: Introductionmentioning

confidence: 99%

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

Isp

Lewis

et al. 2021

Preprint

View full text Add to dashboard Cite

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable models are produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

show abstract

Optimization of process parameters for improved chitinase activity fromThermomycessp. by using artificial neural network and genetic algorithm

Cited by 23 publications

References 28 publications

Estimation of target station data using satellite data and deep learning algorithms

Estimation of target station data using satellite data and deep learning algorithms

Risk Prediction by Using Artificial Neural Network in Global Software Development

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

Contact Info

Product

Resources

About