scholarly journals Use of Machine Learning Methods for Predicting Amount of Bioethanol Obtained from Lignocellulosic Biomass with the Use of Ionic Liquids for Pretreatment

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 243
Author(s):  
Małgorzata Smuga-Kogut ◽  
Tomasz Kogut ◽  
Roksana Markiewicz ◽  
Adam Słowik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Ionic Liquids ◽  
Random Forest ◽  
Production Process ◽  
Lignocellulosic Biomass ◽  
Bioethanol Production ◽  
Artificial Neural

The study objective was to model and predict the bioethanol production process from lignocellulosic biomass based on an example of empirical study results. Two types of algorithms were used in machine learning: artificial neural network (ANN) and random forest algorithm (RF). Data for the model included results of studying bioethanol production with the use of ionic liquids (ILs) and different enzymatic preparations from the following biomass types: buckwheat straw and biomass from four wastelands, including a mixture of various plants: stems of giant miscanthus, common nettle, goldenrod, common broom, fireweed, and hay (a mix of grasses). The input variables consisted of different ionic liquids (imidazolium and ammonium), enzymatic preparations, enzyme doses, time and temperature of pretreatment, and type of yeast for alcoholic fermentation. The output value was the bioethanol concentration. The multilayer perceptron (MLP) was used in the artificial neural networks. Two model types were created; the training dataset comprised 120 vectors (14 elements for Model 1 and 11 elements for Model 2). Assessment of the optimum random forest was carried out using the same division of experimental points (two random datasets, containing 2/3 for training and 1/3 for testing) and the same criteria used for the artificial neural network models. Data for mugwort and hemp were used for validation. In both models, the coefficient of determination for neural networks was <0.9, while for RF it oscillated around 0.95. Considering the fairly large spread of the determination coefficient, two hybrid models were generated. The use of the hybrid approach in creating models describing the present bioethanol production process resulted in an increase in the fit of the model to R2 = 0.961. The hybrid model can be used for the initial classification of plants without the necessity to perform lengthy and expensive research related to IL-based pretreatment and further hydrolysis; only their lignocellulosic composition results are needed.

Fundamental Categories of Artificial Neural Networks

2021 ◽  
pp. 30-64
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

2022 ◽  
pp. 1-30
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

scholarly journals Prediction of Glass Forming Ability of Bulk Metallic Glasses-Machine-Learning

2021 ◽  
Author(s):  
Jaideep Reddy Gedi ◽  
Tanay Saboo ◽  
KAMESWARI PRASADA Rao AYYAGARI
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Critical Thickness ◽  
Glassy Alloy ◽  
Glass Forming Ability ◽  
Machine Learning Techniques ◽  
Glass Forming ◽  
Artificial Neural

Abstract Bulk-Metallic-Glass has been a fascinating class of metallic systems with remarkable corrosion resistance, elastic modulus and wear resistance, while evaluating the glass forming ability has been a very interesting aspect for decades. Machine learning techniques viz., artificial neural networks and random-forest based models have been developed in this work to predict the glass forming ability, given the composition of the bulk metallic glassy alloy. A new criterion of classification of atoms present in a bulk metallic glassy alloy is proposed. Feature importance analysis confirmed that the accuracy of the prediction depends mainly on change in enthalpy of mixing and change in entropy of mixing. However, among the artificial neural network random forest models developed, the former showed a promising accuracy in prediction of the glass formation ability (critical thickness). It has been successfully demonstrated and validated with experimental critical thickness that the glass forming ability can be predicted using an artificial neural network given the elemental composition alone. A computational algorithm was also developed to classify the atoms as big/ small in a given alloy. The outcome of this algorithm was used by models developed by training with experimental data.

scholarly journals Building and Improving Artificial Neural Network Classifier

2019 ◽  
Vol 8 (9) ◽  
pp. 2742-2747
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Cross Validation ◽  
Parameter Tuning ◽  
Classification Problems ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural

Deep learning is a spectrum of machine learning which uses advanced neural networks to solve the various machine learning problems. Its working is very similar to the working of a human brain where the models take decision based on various input parameters. There are multiple open source libraries which implement neural networks, like Tensorflow, Theano, PyTorch, Keras etc. In this paper we have proposed a generic architecture that can be used for any type of classification problems with binary output or classification output using Deep Learning model: Artificial Neural Network (ANN). In the architectural model after Data preprocessing we first build the ANN classifier using Keras library with Tensorflow backends, second step we have apply Cross-validation method for better accuracy. Then we perform Dropout Regularization method for preventing from overfitting and at last we have applied grid search technique for parameter tuning that basically will test several combinations of Hyperparameter values and will eventually return the best selection choice with K-Fold cross validation. And the experimental results shows in higher accuracy with ours proposed architecture and in our proposed architecture results we remove the randomness from the model. In the proposed architecture we can again rebuild developing our model using Keras Callback function by using this feature in our model it does not create any major difference in terms of accuracy. But as we know the accuracy will vary with parameter tuning. The main advantage of using Keras Callback function method is it’s saves a lot of time for building model and it is easy for debugging the model.

Artificial Neural Network What-If Theory

2015 ◽  
Vol 6 (4) ◽  
pp. 52-81 ◽  
Author(s):  
Paolo Massimo Buscema ◽  
William J Tastle
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Innovative Solutions ◽  
Artificial Neural

Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proximity to the standard. Thus, data that might not lend itself well to traditional methods of analysis could identify new perspectives or views of the data and thus, potentially new perceptions of novel and innovative solutions. This method comes out of the field of artificial intelligence, particularly artificial neural networks, and utilizes both machine learning and pattern recognition to display an innovative analysis.

scholarly journals Characterization based machine learning modeling for the prediction of the rheological properties of water-based drilling mud: an experimental study on grass as an environmental friendly additive

2021 ◽  
Author(s):  
Atif Ismail ◽  
Hafiz Muhammad Awais Rashid ◽  
Raoof Gholami ◽  
Arshad Raza
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Rheological Properties ◽  
Gel Strength ◽  
Random Tree ◽  
Drilling Mud ◽  
Environmental Friendly ◽  
Artificial Neural

AbstractThe successful drilling operation depends upon the achievement of target drilling attributes within the environmental and economic constraints but this is not possible only on the basis of laboratory testing due to the limitation of time and resources. The chemistry of the mud decides its rheological potential and selection of the techniques required for recycling operations. Conductivity, pH, and photometer testing were performed for the physio-chemical characterization of the grass to be used as an environmental friendly drilling mud additive. In this study, different particle sizes (75, 150, and 300 µm) of grass powder were mixed in mud density of 8.5, 8.6, and 8.7 ppg in the measurement of gel strength and viscosity of drilling mud. The grass additive was added in different weight conditions considering no additive, 0.25, 0.5, and 1 g to assess the contribution of grass on the gel strength and viscosity of the drilling mud. The machine learning techniques (Multivariate Linear Regression Analysis, Artificial Neural Network, Support Vector Machine Regression, k-Nearest Neighbor, Decision Stump, Random Forest, and Random Tree approaches) were applied to the generated rheological data. The results of the study show that grass can be used for the improvement of the gel strength and viscosity of the drilling mud. The highest improvement of the viscosity was seen when grass powder of 150 µm was added in the 8.7 ppg drilling mud in 0.25, 0.5, and 1 g weights. The gel strength of the drilling mud was improved when the grass additive was added to the drilling mud 8.7 ppg. Random forest and Artificial Neural Network had the same results of 0.72 regression coefficient (R2) for the estimation of viscosity of the drilling mud. The random tree was found as the most effective technique for the modeling of gel strength at 10 min (GS_10min) of the drilling mud. The predictions of Artificial Neural Network had 0.92 R2 against the measured gel strength at 10 s (GS_10sec) of the drilling mud. On average, Artificial Neural Network predicted the rheological properties of the mud with the highest accuracy as compared to other machine learning approaches. The work may serve as a key source to estimate the net effect of grass additives for the improvement of the gel strength and viscosity of the drilling mud without the performance of any large number of laboratory tests.

scholarly journals Evaluation of machine learning applied to the realignment of hierarchies for image segmentation

2019 ◽  
Author(s):  
Milena Menezes Adão ◽  
Silvio Jamil F. Guimarães ◽  
Zenilton K. G. Patrocı́nio Jr.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Image Segmentation ◽  
Random Forest ◽  
Ground Truth ◽  
Experimental Results ◽  
Hierarchical Segmentation ◽  
Segmentation Methods ◽  
Artificial Neural

A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects can be located at different scales due to their size differences or to their distinct distances from the camera. In literature, many works have been developed to improve hierarchical image segmentation results. One possible solution is to realign the hierarchy such that every region containing an object (or its parts) is at the same level. In this work, we have explored the use of random forest and artificial neural network as regressors models to predict score values for regions belonging to a hierarchy of partitions, which are used to realign it. We have also proposed a new score calculation witch considering all user-defined segmentations that exist in the ground-truth. Experimental results are presented for two different hierarchical segmentation methods. Moreover, an analysis of the adoption of different combination of mid-level features to describe regions and different architectures from random forest and artificial neural network to train regressors models. Experimental results have point out that the use of new proposed score was able to improve final segmentation results.

Artificial Neural Network What-If Theory

2020 ◽  
pp. 1-29
Author(s):  
Paolo Massimo Buscema ◽  
William J. Tastle
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Innovative Solutions ◽  
Artificial Neural

Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proximity to the standard. Thus, data that might not lend itself well to traditional methods of analysis could identify new perspectives or views of the data and thus, potentially new perceptions of novel and innovative solutions. This method comes out of the field of artificial intelligence, particularly artificial neural networks, and utilizes both machine learning and pattern recognition to display an innovative analysis.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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