Application of convolutional neural network to traditional data

Zhang, Xiaohang; Wu, F. Q.; Li, Zhengren

doi:10.1016/j.eswa.2020.114185

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…We took 90% of the dataset as training set and the rest of the dataset was treated as the test set. We fitted each of eight ML-based classifiers: random forest (RF) [56], Naïve Bayes (NB) [57], decision tree (DT) [58], XGBoost [59], knearest neighbor (KNN) [60], multilayer perceptron (MLP) [61], support vector machine (SVM) [62], and 1-dimensional convolution network (1D CNN) [63] for the training set. The five ML-based classifiers (RF, DT, KNN, MLP, and SVM) out of eight classifiers had additional parameters, called hyperparameters.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

2022

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Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify children as either having ADHD or healthy. We extracted the data of 45,779 children aged 3–17 years from the 2018–2019 National Survey of Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) of children were ADHD, and the rest of the children were healthy. Since the class label is highly imbalanced, we adopted a combination of oversampling and undersampling approaches to make a balanced class label. We adopted logistic regression (LR) to extract the significant factors for children with ADHD based on p-values (<0.05). Eight ML-based classifiers such as random forest (RF), Naïve Bayes (NB), decision tree (DT), XGBoost, k-nearest neighborhood (KNN), multilayer perceptron (MLP), support vector machine (SVM), and 1-dimensional convolution neural network (1D CNN) were adopted for the prediction of children with ADHD. The average age of the children with ADHD was 12.4 ± 3.4 years. Our findings showed that RF-based classifier provided the highest classification accuracy of 85.5%, sensitivity of 84.4%, specificity of 86.4%, and an AUC of 0.94. This study illustrated that LR with RF-based system could provide excellent accuracy for classifying and predicting children with ADHD. This system will be helpful for early detection and diagnosis of ADHD.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

2022

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“…The performance of the DCNN model was significantly better than that of an MLP model. Zhang et al [23] proposed the application of a CNN model to traditional data. Twelve types of traditional tabular data were used, and ML models such as eXtreme gradient boosting (XGBoost) [24], SVM, RF, MLP, and k-nearest neighbor clustering were compared with the CNN model.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Na²,

Park

et al. 2021

Applied Sciences

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Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

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“…Recently, a deep convolutional neural network (CNN) in the framework of artificial intelligence (AI) is emerging as a potential candidate for performing multivariate predictive analysis in analytical chemistry (Lussier et al, 2020;Ayres et al, 2021;Debus et al, 2021). Deep learning has extensive applications in categorizing various kinds of data, such as sounds, speech, and text (Zhang et al, 2021), but particularly gains increasing attention in classifying image tasks (Obaid Kavi et al, 2019). In the structure of a CNN, multiple layers are responsible for identifying features in the images in a hierarchical manner and making inferences on categorical classification (Murata et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Intelligent framework for cannabis classification using visualization of gas chromatography/mass spectrometry data and transfer learning

Huang

2023

Front. Anal. Sci.

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Introduction: Gas chromatography combined with mass spectrometry (GC/MS) is popular analytical instrumentation for chemical separation and identification. A novel framework for chemical forensics based on the visualization of GC/MS data and transfer learning is proposed.Methods: To evaluate the framework, 228 GC/MS data collected from two standard cannabis varieties, i.e., hemp and marijuana, were utilized. By processing the raw GC/MS data, analytical features, including retention times, mass-to-charge ratios, intensities, and summed ion mass spectra, were successfully transformed into two types of image representations. The GC/MS data transformed images were fed into a pre-trained convolutional neural network (CNN) to develop intelligent classifiers for the sample classification tasks. The effectiveness of several hyper-parameters for improving classification performance was investigated during transfer learning.Results: The proposed analytical workflow could classify hemp and marijuana with 97% accuracy. Furthermore, the transfer-learning-based classifiers were established without requiring big data sets and peak alignment.Discussion: The potential application of the new artificial intelligence (AI)-powered framework for chemical forensics using GC/MS data has been demonstrated. This framework provides unique opportunities for classifying various types of physical evidence using chromatography and mass spectrometry signals.

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Application of convolutional neural network to traditional data

Cited by 14 publications

References 21 publications

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Intelligent framework for cannabis classification using visualization of gas chromatography/mass spectrometry data and transfer learning

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