scholarly journals Prediction of Perceived Utility of Consumer Online Reviews Based on LSTM Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hu Wang ◽  
Tianbao Liang ◽  
Yanxia Cheng
Keyword(s):  
Neural Network ◽  
Subjective Evaluation ◽  
Perceived Value ◽  
Online Reviews ◽  
Classification Model ◽  
Text Representation ◽  
Labor Costs ◽  
Data Set ◽  
Short Text ◽  
Perceived Utility

Perceived value is the customer’s subjective understanding of the value they obtain and is their subjective evaluation of the product or service they enjoy. This value is deducted from the cost of the product or service. In order to understand and predict the specific cognition of consumers on the value of products or services and distinguish it from the objective value of products or services in the general sense, this paper uses the in-depth learning method based on LSTM to establish a model to predict the perceived benefits of consumers. It is a challenging task to analyze the emotion of consumers or recognize the perceived value of consumers from various texts of online trading platforms. This paper proposes a new short-text representation method based on bidirectional LSTM. This method is very effective for forecasting research. In addition, we also use the attention mechanism to learn the specific emotional vocabulary. Short-text representation can be used for emotion classification and emotion intensity prediction. This paper evaluates the proposed classification model and regression data set. Compared with the baseline of the corresponding data set, the contrast of the results was 93%. The research shows that using deep neural network to predict the perceived utility of consumer comments can reduce the intervention of artificial features and labor costs and help predict the perceived utility of products to consumers.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

scholarly journals Analysis of evaluation problems of the risk situation of patieents suffering from ischemic heart disease

2012 ◽  
Vol 60 (2) ◽  
pp. 125-134 ◽  
Author(s):  
Vladimír Konečný ◽  
Milan Sepši ◽  
Oldřich Trenz
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Complex Analysis ◽  
Partial Solution ◽  
Health Condition ◽  
Ischemic Heart ◽  
Classification Model ◽  
Data Set ◽  
Software Application

The ischemic heart disease represents a very common health issue which, thanks to its seriousness, impacts a big part of the population and is the cause of about one third of all death cases in the Czech Republic. For the analysis itself, data from medicinal practice of one of the authors of the article have been used and this study is a follow up of his PhD thesis. Concretely it was a set of patients which were being rehabilitated after a heart stroke; the results of the medical examination of these patients create 26 parameters. This data has been obtained in the course of the patients’ treatment. In the first phase of generating the classification model, the parameters that didn’t have a detrimental effect on the assessment of health condition of the patients have been removed from the data set and have been kept in the category of additional parameters. For the classification itself, an approach from artificial intelligence – applying a neural network - has been chosen. For the recording and transformation of the entering data a special application has been made. The classification and analysis of the data is performed on an experimental model of the self-learning of a neural network. The conclusions that arise from the initial analysis of this issue and the partial solution can be generalized and when using an appropriate software application they could even be used in medical practice. To do a complex analysis of the influence of all 26 parameters on the overall state of health of the patients is very difficult. A decision-making model appears to be a good solution. Last but not least, the proposed solution has to be verified on a bigger sample of patients afflicted by the ischemic heart disease.

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning

2020 ◽  
Vol 83 (6) ◽  
pp. 602-614
Author(s):  
Hidir Selcuk Nogay ◽  
Hojjat Adeli
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Epileptic Seizure ◽  
Binary Classification ◽  
Classification Model ◽  
Automatic Identification ◽  
Eeg Signal ◽  
Data Set

<b><i>Introduction:</i></b> The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. <b><i>Methods:</i></b> In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. <b><i>Results:</i></b> The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. <b><i>Discussion/Conclusion:</i></b> The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.

Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs

2021 ◽  
Author(s):  
Cansu Görürgöz ◽  
Kaan Orhan ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
True Positive Rate ◽  
Turnaround Time ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Region Detection ◽  
Positive Rate

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. Results: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. Conclusion: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

scholarly journals Long Text QA Matching Model Based on BiGRU–DAttention–DSSM

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1129
Author(s):  
Shihong Chen ◽  
Tianjiao Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Matching Task ◽  
Semantic Model ◽  
Text Representation ◽  
Short Text ◽  
Questions And Answers ◽  
Text Features ◽  
Text Matching

QA matching is a very important task in natural language processing, but current research on text matching focuses more on short text matching rather than long text matching. Compared with short text matching, long text matching is rich in information, but distracting information is frequent. This paper extracted question-and-answer pairs about psychological counseling to research long text QA-matching technology based on deep learning. We adjusted DSSM (Deep Structured Semantic Model) to make it suitable for the QA-matching task. Moreover, for better extraction of long text features, we also improved DSSM by enriching the text representation layer, using a bidirectional neural network and attention mechanism. The experimental results show that BiGRU–Dattention–DSSM performs better at matching questions and answers.

Understanding the role of argument quality in the adoption of online reviews

2015 ◽  
Vol 39 (7) ◽  
pp. 885-902 ◽  
Author(s):  
Xi Xu ◽  
Zhong Yao
Keyword(s):  
Decision Model ◽  
Online Shopping ◽  
Perceived Value ◽  
Online Reviews ◽  
Online Marketing ◽  
Research Model ◽  
Data Set ◽  
Content Type ◽  
Argument Quality ◽  
A Value

Purpose – The purpose of this paper is to develop a research model that examines the effect of argument quality and argument perspective on the adoption of online reviews in a value-based decision model. In addition, the paper investigates the moderating effects of argument quality from needs theory. Design/methodology/approach – To validate the research model, a questionnaire survey was conducted in the Chinese online shopping context. Hypotheses were tested using partial least squares analysis on a data set of 208 online consumers. Findings – The results suggest that argument quality and argument perspective have positive impacts on perceived value, further influencing the adoption of online reviews. Moreover, the results confirm that the information credibility and quantity sufficiency of online reviews are positively associated with argument quality. The findings also indicate that argument quality mediates the relationship between argument perspective and perceived value. Originality/value – This work contributes to the literature by employing a value-based decision model to study the adoption of online reviews in the online shopping context, as well as the moderating effect of argument quality on the argument perspective to perceived value. The main conclusions of this study can be valuable to online marketing managers.

scholarly journals Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Kindie Biredagn Nahato ◽  
Khanna Nehemiah Harichandran ◽  
Kannan Arputharaj
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Heart Disease ◽  
Missing Values ◽  
Classification Model ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Knowledge Mining ◽  
Clinical Dataset ◽  
Two Stages

The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

scholarly journals A BERT-based Hybrid Short Text Classification Model Incorporating CNN and Attention-based BiGRU

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
Feature Fusion ◽  
Feature Representation ◽  
Classification Model ◽  
Short Text ◽  
Learning Techniques ◽  
Proposed Model ◽  
Fusion Framework

Short text classification is a research focus for natural language processing (NLP), which is widely used in news classification, sentiment analysis, mail filtering and other fields. In recent years, deep learning techniques are applied to text classification and has made some progress. Different from ordinary text classification, short text has the problem of less vocabulary and feature sparsity, which raise higher request for text semantic feature representation. To address this issue, this paper propose a feature fusion framework based on the Bidirectional Encoder Representations from Transformers (BERT). In this hybrid method, BERT is used to train word vector representation. Convolutional neural network (CNN) capture static features. As a supplement, a bi-gated recurrent neural network (BiGRU) is adopted to capture contextual features. Furthermore, an attention mechanism is introduced to assign the weight of salient words. The experimental results confirmed that the proposed model significantly outperforms the other state-of-the-art baseline methods.

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