scholarly journals Novel Antibiotics from a ‘White Box’ 2D Structural Fingerprint Decision Tree

2021 ◽  
Author(s):  
Gareth Williams
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Black Box ◽  
Chemical Similarity ◽  
Chemical Fingerprint ◽  
The Neural Network ◽  
Fingerprint Pattern ◽  
Tree Approach ◽  
Novel Antibiotics

<p>The paper is concerned with repurposing drugs based on chemical similarity to existing drugs, with an application to antibiotics. A simple ‘white box’ 2D chemical fingerprint-based decision tree approach is shown to largely recapitulate a neural network study in the literature. In particular, the repurposing of halicin is shown to be based on an explicit fingerprint pattern, unlike the neural network ‘black box’ methodology.</p>

scholarly journals Novel Antibiotics from a ‘White Box’ 2D Structural Fingerprint Decision Tree

2021 ◽  
Author(s):  
Gareth Williams
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Black Box ◽  
Chemical Similarity ◽  
Chemical Fingerprint ◽  
The Neural Network ◽  
Fingerprint Pattern ◽  
Tree Approach ◽  
Novel Antibiotics

<p>The paper is concerned with repurposing drugs based on chemical similarity to existing drugs, with an application to antibiotics. A simple ‘white box’ 2D chemical fingerprint-based decision tree approach is shown to largely recapitulate a neural network study in the literature. In particular, the repurposing of halicin is shown to be based on an explicit fingerprint pattern, unlike the neural network ‘black box’ methodology.</p>

scholarly journals The Design of Neural Network Based Accuracy Prediction for Software Testing Black-Box A Method with the Equivalence Partitions Technique

2020 ◽  
Vol 8 (9) ◽  
pp. 01-04
Author(s):  
Zulkifli et al.
Keyword(s):  
Neural Network ◽  
Software Testing ◽  
Black Box ◽  
Real Output ◽  
The Real ◽  
Network Algorithm ◽  
The Neural Network ◽  
Accuracy Level ◽  
Neural Network Algorithm ◽  
Box Method

The absence of a neural network algorithm model to predict the level of accuracy in terms of black-box software testing, equivalence partitions technique is a problem in this research. In this case, the algorithm used for predicting software errors by researchers is the neural network algorithm and testing the software uses the black-box method with the equivalence partitions technique. The neural network algorithm is an artificial neural system, or neural network are the physical cellular system that can acquire, store and use the knowledge gained from experience for activation using bipolar sigmoid output values which range between -1 to 1. Software testing black-box methods is a testing approach where the data comes from defined functional requirements regardless of the final program structure, and the technique used is equivalence partitions. The design prediction accuracy of this research is by determining the college application to be the software to be tested, then tested using the black-box method with the equivalence partitions technique (this test chosen because it will find software errors in several categories, including functions error or missing, interface errors, errors in data structures or external database access, performance errors, initialization errors and terminations), from the black-box test the dataset obtained. This dataset measures the accuracy of the real output and prediction output. The last step is calculating the error, RSME from the real output and prediction output. The results of this research show that the neural network algorithm was being to measure the accuracy level of software testing applied to determine the prediction of the accuracy level of black-box software testing with the equivalence partitions technique, and the average accuracy results are above 80%.

Generalized Constraint Neural Network Model System Parameter Identification

2010 ◽  
Vol 143-144 ◽  
pp. 1207-1212 ◽  
Author(s):  
Shuang Zhang ◽  
Gang Jin ◽  
Jing Xiao ◽  
Shu Li ◽  
Yu Ping Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Black Box ◽  
System Model ◽  
Identification Method ◽  
The Neural Network ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Generalized Constraint

By analyzing and deducing generalized constraint neural network (GCNN) with model some present theories, the identification method of the m-input n-output (MINO) and multiple-input multiple–output (MIMO) systems is acquired. It is possible to improve the transparency of the black box through the practical test. This identification method is useful to enhance identification of GCNN model’s parameters, moreover, the identification ability of the neural network black box system model is further made better.

scholarly journals Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 191-196
Author(s):  
Yun Jie Li ◽  
Hui Song
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Daily Living ◽  
Tree Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Daily Living Activity ◽  
The Neural Network ◽  
Better Than

In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

scholarly journals Comparison of Machine Learning With Logistic Regression for Prediction of Chronic Kidney Disease in the Thai Adult Population

2021 ◽  
Vol 44 (4) ◽  
pp. 1-12
Author(s):  
Ratchainant Thammasudjarit ◽  
Punnathorn Ingsathit ◽  
Sigit Ari Saputro ◽  
Atiporn Ingsathit ◽  
Ammarin Thakkinstian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Prediction Model ◽  
Prediction Models ◽  
The Neural Network

Background: Chronic kidney disease (CKD) takes huge amounts of resources for treatments. Early detection of patients by risk prediction model should be useful in identifying risk patients and providing early treatments. Objective: To compare the performance of traditional logistic regression with machine learning (ML) in predicting the risk of CKD in Thai population. Methods: This study used Thai Screening and Early Evaluation of Kidney Disease (SEEK) data. Seventeen features were firstly considered in constructing prediction models using logistic regression and 4 MLs (Random Forest, Naïve Bayes, Decision Tree, and Neural Network). Data were split into train and test data with a ratio of 70:30. Performances of the model were assessed by estimating recall, C statistics, accuracy, F1, and precision. Results: Seven out of 17 features were included in the prediction models. A logistic regression model could well discriminate CKD from non-CKD patients with the C statistics of 0.79 and 0.78 in the train and test data. The Neural Network performed best among ML followed by a Random Forest, Naïve Bayes, and a Decision Tree with the corresponding C statistics of 0.82, 0.80, 0.78, and 0.77 in training data set. Performance of these corresponding models in testing data decreased about 5%, 3%, 1%, and 2% relative to the logistic model by 2%. Conclusions: Risk prediction model of CKD constructed by the logit equation may yield better discrimination and lower tendency to get overfitting relative to ML models including the Neural Network and Random Forest.  

scholarly journals Development of the classifier based on a multilayer perceptron using genetic algorithm and cart decision tree

2021 ◽  
Vol 5 (9 (113)) ◽  
pp. 82-90
Author(s):  
Lyudmila Dobrovska ◽  
Olena Nosovets
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Decision Tree ◽  
Decision Trees ◽  
Multilayer Perceptron ◽  
Data Sets ◽  
Biomedical Data ◽  
Simulation Accuracy ◽  
The Neural Network

The problem of developing universal classifiers of biomedical data, in particular those that characterize the presence of a large number of parameters, inaccuracies and uncertainty, is urgent. Many studies are aimed at developing methods for analyzing these data, among them there are methods based on a neural network (NN) in the form of a multilayer perceptron (MP) using GA. The question of the application of evolutionary algorithms (EA) for setting up and learning the neural network is considered. Theories of neural networks, genetic algorithms (GA) and decision trees intersect and penetrate each other, new developed neural networks and their applications constantly appear. An example of a problem that is solved using EA algorithms is considered. Its goal is to develop and research a classifier for the diagnosis of breast cancer, obtained by combining the capabilities of the multilayer perceptron using the genetic algorithm (GA) and the CART decision tree. The possibility of improving the classifiers of biomedical data in the form of NN based on GA by applying the process of appropriate preparation of biomedical data using the CART decision tree has been established. The obtained results of the study indicate that these classifiers show the highest efficiency on the set of learning and with the minimum reduction of Decision Trees; increasing the number of contractions usually degrades the simulation result. On two datasets on the test set, the simulation accuracy was »83–87 %. The experiments carried out have confirmed the effectiveness of the proposed method for the synthesis of neural networks and make it possible to recommend it for practical use in processing data sets for further diagnostics, prediction, or pattern recognition

NEURAL NETWORK BASED AUTOMATIC DIAGNOSIS OF CHILDREN WITH BRAIN DYSFUNCTION

2001 ◽  
Vol 11 (04) ◽  
pp. 361-369 ◽  
Author(s):  
SUNGZOON CHO ◽  
MIN SUP SHIN
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Decision Tree ◽  
Experimental Test ◽  
Bayesian Learning ◽  
Brain Dysfunction ◽  
Accuracy Rate ◽  
The Neural Network ◽  
Important Input ◽  
Better Than

This paper proposes the use of multilayer perceptron for brain dysfunction diagnosis. The performance of MLP was better than that of Discriminant Analysis and Decision Tree classifiers, with an 85% accuracy rate in an experimental test involving 332 subjects. In addition, the neural network employing Bayesian learning was able to identify the most important input variable. These two results demonstrate that the neural network can be effectively used in the diagnosis of children with brain dysfunction.

scholarly journals Model-based Damage Detection through Physics Guided Learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Aliirmak
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Simulation Model ◽  
Black Box ◽  
Training Data ◽  
Guided Learning ◽  
Intermediate Layers ◽  
The Neural Network ◽  
Modeling Errors ◽  
Physics Based Simulation

Data-driven learning approaches have gained a lot of interest in evaluating and validating complex dynamic systems. One of the main challenges for developing a reliable learning model is the lack of training data covering a large range of various operational conditions. Extensive training data can be generated using a physics-based simulation model. On the other hand, the learning algorithm should be still tested with experimental data obtained from the actual system. Modeling errors may lead to a statistical divergence between the simulation training data and the experimental testing data, causing poor performance, especially for domain-agnostic black-box learning methods. To close the gap between the simulation and experimental domains, this paper proposes a physics-guided learning approach that combines the power of the neural network with domain-specific physics knowledge. Specifically, the proposed architecture integrates physical parameters extracted from the physics-based simulation model into the intermediate layers of the neural network to constrain the learning process. To demonstrate the effectiveness of the proposed approach, the architecture is adopted to a damage classification problem for a three-story structure. Our results show that the accuracy for localizing the damage correctly based on experimental data improves significantly over black-box models, especially under large modeling errors. In this paper, we also use the physics-based intermediate layers to analyze the interpretability of the classification results.

scholarly journals A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2405
Author(s):  
Ioannis Mallidis ◽  
Volha Yakavenka ◽  
Anastasios Konstantinidis ◽  
Nikolaos Sariannidis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Goal Programming ◽  
Regression Models ◽  
Support Vector ◽  
Threshold Values ◽  
Time Efficiency ◽  
The Neural Network

The paper develops a goal programming-based multi-criteria methodology, for assessing different machine learning (ML) regression models under accuracy and time efficiency criteria. The developed methodology provides users with high flexibility in assessing the models as it allows for a fast and computationally efficient sensitivity analysis of accuracy and time significance weights as well as accuracy and time significance threshold values. Four regression models were assessed, namely the decision tree, random forest, support vector and the neural network. The developed methodology was employed to forecast the time to failures of NASA Turbofans. The results reveal that decision tree regression (DTR) seems to be preferred for low values of accuracy weights (up to 30%) and low accuracy and time efficiency threshold values. As the accuracy weights tend to increase and for higher accuracy and time efficiency threshold values, random forest regression (RFR) seems to be the best choice. The preference for the RFR model however, seems to change towards the adoption of the neural network for accuracy weights equal to and higher than 90%.

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