Applications of Machine Learning in Biomedical Text Processing and Food Industry

Paramesha, K.; Gururaj, H L; Jena, Om Prakash

doi:10.1002/9781119792611.ch10

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The data basis was divided into 70% training and 30% testing data. The developed ANN models were trained 100,000 times per model with a random number of neurons in the hidden layer (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). Various activation functions and randomly assigned values for weighting coefficients and biases were employed in the modeling process.…”

Section: Ann Modelingmentioning

confidence: 99%

“…To gauge the efficacy and performance of the Support Vector Machine (SVM) and Artificial Neural Network (ANN) models in predicting output variables from input data, various statistical parameters were computed. These parameters encompassed the reduced chi-square (X 2 ) (4), root mean square error (RMSE) (5), mean systematic error (MBE) (6), mean percentage error (MPE) (7), total squared error (SSE) (8), average absolute relative deviation (AARD) (9), and coefficient of determination (r 2 ) (10). The RMSE values serve as indicators of the model's efficiency by assessing the agreement between calculated values and experimentally measured values.…”

Section: The Accuracy Of the Modelsmentioning

confidence: 99%

“…These advanced computational tools, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), contribute to a more precise understanding of ingredient interactions and their impact on product attributes [ 8 ]. By leveraging machine learning algorithms, researchers can model complex relationships within diverse data sets and optimize food formulations [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

Lončar,

Pezo,

Knežević

et al. 2024

Foods

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This study focuses on predicting and optimizing the quality parameters of cookies enriched with dehydrated peach through the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The purpose of the study is to employ advanced machine learning techniques to understand the intricate relationships between input parameters, such as the presence of dehydrated peach and treatment methods (lyophilization and lyophilization with osmotic pretreatment), and output variables representing various quality aspects of cookies. For each of the 32 outputs, including the parameters of the basic chemical compositions of the cookie samples, selected mineral contents, moisture contents, baking characteristics, color properties, sensorial attributes, and antioxidant properties, separate models were constructed using SVMs and ANNs. Results showcase the efficiency of ANN models in predicting a diverse set of quality parameters with r2 up to 1.000, with SVM models exhibiting slightly higher coefficients of determination for specific variables with r2 reaching 0.981. The sensitivity analysis underscores the pivotal role of dehydrated peach and the positive influence of osmotic pretreatment on specific compositional attributes. Utilizing established Artificial Neural Network models, multi-objective optimization was conducted, revealing optimal formulation and factor values in cookie quality optimization. The optimal quantity of lyophilized peach with osmotic pretreatment for the cookie formulation was identified as 15%.

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