BOUNDED MEMORY BASED FREQUENT PATTERN GROWTH APPROACH WITH DEEP NEURAL NETWORK AND DECISION TREE FOR ROAD ACCIDENT PREDICTION

Abstract Background: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, the hypothesis that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states was investigated.Methods: A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived time and frequency domain features combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which used the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.Results: The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.Conclusions: The incorporation of four HRV-derived time and frequency domain features and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot of a feasibility study, providing a method to supplement DoA monitoring based on EEG features to improve the accuracy of DoA estimation.

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AN IMPROVED FREQUENT PATTERN-GROWTH APPROACH TO DISCOVER RARE ASSOCIATION RULES

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval ◽

10.5220/0002299600430052 ◽

2009 ◽

Keyword(s):

Association Rules ◽

Frequent Pattern ◽

Rare Association ◽

Pattern Growth ◽

Growth Approach

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Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc42975.2020.9282812 ◽

2020 ◽

Author(s):

Plamen Angelov ◽

Eduardo Soares

Keyword(s):

Neural Network ◽

Decision Tree ◽

Deep Neural Network ◽

Tree Inference

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An Explainable Hybrid Model for Bankruptcy Prediction Based on the Decision Tree and Deep Neural Network

2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII) ◽

10.1109/ickii46306.2019.9042639 ◽

2019 ◽

Author(s):

Tsung-Nan Chou

Keyword(s):

Neural Network ◽

Decision Tree ◽

Hybrid Model ◽

Deep Neural Network ◽

Bankruptcy Prediction

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Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA

Frontiers in Public Health ◽

10.3389/fpubh.2021.821410 ◽

2021 ◽

Vol 9 ◽

Author(s):

Elakkiya R. ◽

Deepak Kumar Jain ◽

Ketan Kotecha ◽

Sharnil Pandya ◽

Sai Siddhartha Reddy ◽

...

Keyword(s):

Neural Network ◽

Decision Tree ◽

Deep Neural Network ◽

Imbalanced Data ◽

Vital Role ◽

Biological Data ◽

Decision Tree Classifier ◽

Genome Data ◽

Tree Classifier

Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such problem. The examinations proved that pre miRNAs (precursor microRNAs) could serve as oncogene or tumor suppressors in various cancer types. This paper introduces a Hybrid Deep Neural Network framework (H-DNN) for the classification of pre miRNA in imbalanced data. The proposed H-DNN framework is an integration of Deep Artificial Neural Networks (Deep ANN) and Deep Decision Tree Classifiers. The Deep ANN in the proposed H-DNN helps to extract the meaningful features and the Deep Decision Tree Classifier helps to classify the pre miRNA accurately. Experimentation of H-DNN was done with genomes of animals, plants, humans, and Arabidopsis with an imbalance ratio up to 1:5000 and virus with a ratio of 1:400. Experimental results showed an accuracy of more than 99% in all the cases and the time complexity of the proposed H-DNN is also very less when compared with the other existing approaches.

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Index split decision tree and compositional deep neural network for text categorization

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.9953 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 449

Author(s):

N Ravikumar ◽

Dr P. Tamil Selvan

Keyword(s):

Neural Network ◽

Machine Learning ◽

Decision Tree ◽

Text Categorization ◽

Deep Neural Network ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Decision Tree Model ◽

Tree Model ◽

The Impact

Text categorization with machine learning algorithms generally reckons to possess horizontal set of classes. Several advanced machine learning algorithms have been designed in the past few decades. With the growing research work for text categorization, it has become important to categorize the research outcome and provide the learners with an effective machine learning method, a framework called, Hierarchical Decision Tree and Deep Neural Network (HDT-DNN).It investigates machine learning algorithms to create horizontal set of classes and it is used for classification of text. With this objective, a novel and efficient text categorization framework based on decision tree model is used in order to categorize text according to superior and subordinate level. The text to be categorized is presented in the form of a tree with parent text category being superior to all. The intermediate level represents the text that is both superior and subordinate. Then Deep Neural Network model is presented initiating compositional model, where the text has to be categorized, as a layered integration of primitives from the constructed decision tree model. The extra layers enable composition of features from lower layers, potentially modeling complex text with fewer units than a similarly carried out shallow network producing hierarchical classification. The significance of the impact of HDT-DNN framework is evaluated through empirical study. Extensive experiments are carried out and the performance of HDT-DNN framework is evaluated and compared with existing state-of-art methods using parameters such as precision, classification accuracy, classification time, with respect to varied number of features and document size.

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