scholarly journals Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7375
Author(s):  
Carlos R. Morales ◽  
Fernando Rangel de Sousa ◽  
Valner Brusamarello ◽  
Nestor C. Fernandes
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Base Station ◽  
Good Representation ◽  
Learning Methods ◽  
Data Prediction ◽  
Prediction Scheme ◽  
Final Data ◽  
Transmission Data ◽  
Using Data

One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.

scholarly journals Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis (Preprint)

2020 ◽  
Author(s):  
Xiaofeng Wang ◽  
Shuai Chen ◽  
Tao Li ◽  
Wanting Li ◽  
Yejie Zhou ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Deep Learning ◽  
Risk Prediction ◽  
Health Conditions ◽  
Learning Methods ◽  
Mental Health Conditions ◽  
Depression Risk ◽  
Language Representation ◽  
Using Data

BACKGROUND Depression is a serious personal and public mental health problem. Self-reporting is the main method used to diagnose depression and to determine the severity of depression. However, it is not easy to discover patients with depression owing to feelings of shame in disclosing or discussing their mental health conditions with others. Moreover, self-reporting is time-consuming, and usually leads to missing a certain number of cases. Therefore, automatic discovery of patients with depression from other sources such as social media has been attracting increasing attention. Social media, as one of the most important daily communication systems, connects large quantities of people, including individuals with depression, and provides a channel to discover patients with depression. In this study, we investigated deep-learning methods for depression risk prediction using data from Chinese microblogs, which have potential to discover more patients with depression and to trace their mental health conditions. OBJECTIVE The aim of this study was to explore the potential of state-of-the-art deep-learning methods on depression risk prediction from Chinese microblogs. METHODS Deep-learning methods with pretrained language representation models, including bidirectional encoder representations from transformers (BERT), robustly optimized BERT pretraining approach (RoBERTa), and generalized autoregressive pretraining for language understanding (XLNET), were investigated for depression risk prediction, and were compared with previous methods on a manually annotated benchmark dataset. Depression risk was assessed at four levels from 0 to 3, where 0, 1, 2, and 3 denote no inclination, and mild, moderate, and severe depression risk, respectively. The dataset was collected from the Chinese microblog Weibo. We also compared different deep-learning methods with pretrained language representation models in two settings: (1) publicly released pretrained language representation models, and (2) language representation models further pretrained on a large-scale unlabeled dataset collected from Weibo. Precision, recall, and F1 scores were used as performance evaluation measures. RESULTS Among the three deep-learning methods, BERT achieved the best performance with a microaveraged F1 score of 0.856. RoBERTa achieved the best performance with a macroaveraged F1 score of 0.424 on depression risk at levels 1, 2, and 3, which represents a new benchmark result on the dataset. The further pretrained language representation models demonstrated improvement over publicly released prediction models. CONCLUSIONS We applied deep-learning methods with pretrained language representation models to automatically predict depression risk using data from Chinese microblogs. The experimental results showed that the deep-learning methods performed better than previous methods, and have greater potential to discover patients with depression and to trace their mental health conditions.

scholarly journals Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis

10.2196/17958 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e17958
Author(s):  
Xiaofeng Wang ◽  
Shuai Chen ◽  
Tao Li ◽  
Wanting Li ◽  
Yejie Zhou ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Deep Learning ◽  
Risk Prediction ◽  
Health Conditions ◽  
Learning Methods ◽  
Mental Health Conditions ◽  
Depression Risk ◽  
Language Representation ◽  
Using Data

Background Depression is a serious personal and public mental health problem. Self-reporting is the main method used to diagnose depression and to determine the severity of depression. However, it is not easy to discover patients with depression owing to feelings of shame in disclosing or discussing their mental health conditions with others. Moreover, self-reporting is time-consuming, and usually leads to missing a certain number of cases. Therefore, automatic discovery of patients with depression from other sources such as social media has been attracting increasing attention. Social media, as one of the most important daily communication systems, connects large quantities of people, including individuals with depression, and provides a channel to discover patients with depression. In this study, we investigated deep-learning methods for depression risk prediction using data from Chinese microblogs, which have potential to discover more patients with depression and to trace their mental health conditions. Objective The aim of this study was to explore the potential of state-of-the-art deep-learning methods on depression risk prediction from Chinese microblogs. Methods Deep-learning methods with pretrained language representation models, including bidirectional encoder representations from transformers (BERT), robustly optimized BERT pretraining approach (RoBERTa), and generalized autoregressive pretraining for language understanding (XLNET), were investigated for depression risk prediction, and were compared with previous methods on a manually annotated benchmark dataset. Depression risk was assessed at four levels from 0 to 3, where 0, 1, 2, and 3 denote no inclination, and mild, moderate, and severe depression risk, respectively. The dataset was collected from the Chinese microblog Weibo. We also compared different deep-learning methods with pretrained language representation models in two settings: (1) publicly released pretrained language representation models, and (2) language representation models further pretrained on a large-scale unlabeled dataset collected from Weibo. Precision, recall, and F1 scores were used as performance evaluation measures. Results Among the three deep-learning methods, BERT achieved the best performance with a microaveraged F1 score of 0.856. RoBERTa achieved the best performance with a macroaveraged F1 score of 0.424 on depression risk at levels 1, 2, and 3, which represents a new benchmark result on the dataset. The further pretrained language representation models demonstrated improvement over publicly released prediction models. Conclusions We applied deep-learning methods with pretrained language representation models to automatically predict depression risk using data from Chinese microblogs. The experimental results showed that the deep-learning methods performed better than previous methods, and have greater potential to discover patients with depression and to trace their mental health conditions.

scholarly journals Modeling Traders’ Behavior with Deep Learning and Machine Learning Methods: Evidence from BIST 100 Index

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Afan Hasan ◽  
Oya Kalıpsız ◽  
Selim Akyokuş
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Financial Market ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Support Vector ◽  
Human Beings ◽  
Learning Methods ◽  
Technical Indicators ◽  
Machine Learning Methods

Although the vast majority of fundamental analysts believe that technical analysts’ estimates and technical indicators used in these analyses are unresponsive, recent research has revealed that both professionals and individual traders are using technical indicators. A correct estimate of the direction of the financial market is a very challenging activity, primarily due to the nonlinear nature of the financial time series. Deep learning and machine learning methods on the other hand have achieved very successful results in many different areas where human beings are challenged. In this study, technical indicators were integrated into the methods of deep learning and machine learning, and the behavior of the traders was modeled in order to increase the accuracy of forecasting of the financial market direction. A set of technical indicators has been examined based on their application in technical analysis as input features to predict the oncoming (one-period-ahead) direction of Istanbul Stock Exchange (BIST100) national index. To predict the direction of the index, Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classification techniques are used. The performance of these models is evaluated on the basis of various performance metrics such as confusion matrix, compound return, and max drawdown.

scholarly journals A Survey of Forex and Stock Price Prediction Using Deep Learning

2021 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Zexin Hu ◽  
Yiqi Zhao ◽  
Matloob Khushi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Mean Square Error ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Percentage Error ◽  
Mean Square ◽  
Learning Methods ◽  
Stock Price Prediction

Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.

scholarly journals Unsupervised feature learning-based encoder and adversarial networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Endang Suryawati ◽  
Hilman F. Pardede ◽  
Vicky Zilvan ◽  
Ade Ramdan ◽  
Dikdik Krisnandi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Learning ◽  
Object Classification ◽  
Learning By Doing ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Good Representation ◽  
Learning Methods ◽  
Adversarial Networks ◽  
Unsupervised Deep Learning

AbstractIn this paper, we propose a novel deep learning-based feature learning architecture for object classification. Conventionally, deep learning methods are trained with supervised learning for object classification. But, this would require large amount of training data. Currently there are increasing trends to employ unsupervised learning for deep learning. By doing so, dependency on the availability of large training data could be reduced. One implementation of unsupervised deep learning is for feature learning where the network is designed to “learn” features automatically from data to obtain good representation that then could be used for classification. Autoencoder and generative adversarial networks (GAN) are examples of unsupervised deep learning methods. For GAN however, the trajectories of feature learning may go to unpredicted directions due to random initialization, making it unsuitable for feature learning. To overcome this, a hybrid of encoder and deep convolutional generative adversarial network (DCGAN) architectures, a variant of GAN, are proposed. Encoder is put on top of the Generator networks of GAN to avoid random initialisation. We called our method as EGAN. The output of EGAN is used as features for two deep convolutional neural networks (DCNNs): AlexNet and DenseNet. We evaluate the proposed methods on three types of dataset and the results indicate that better performances are achieved by our proposed method compared to using autoencoder and GAN.

scholarly journals Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification

2021 ◽  
Vol 11 (22) ◽  
pp. 10542
Author(s):  
Tanu Sharma ◽  
Kamaldeep Kaur
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Statistical Tests ◽  
Sentiment Classification ◽  
Ranking Methods ◽  
Learning Methods ◽  
Statistical Comparison ◽  
Benchmark Datasets ◽  
Overall Performance ◽  
Selection Of

With the advancements in processing units and easy availability of cloud-based GPU servers, many deep learning-based methods have been proposed for Aspect Level Sentiment Classification (ALSC) literature. With this increase in the number of deep learning methods proposed in ALSC literature, it has become difficult to ascertain the performance difference of one method over the other. To this end, our study provides a statistical comparison of the performance of 35 recent deep learning methods with respect to three performance metrics-Accuracy, Macro F1 score, and Time. The methods are evaluated for eight benchmark datasets. In this study, the statistical comparison is based on Friedman, Nemenyi, and Wilcoxon tests. As per the results of statistical tests, the top-ranking methods could not significantly outperform several other methods in terms of Accuracy and Macro F1 score and performed poorly on-time metric. However, the time taken by any method is crucial to analyze the overall performance. Thus, this study aids the selection of the Deep Learning method, which maximizes the accuracy and Macro F1 score and takes minimal time. Our study also establishes a framework for validating the performance of new and alternate methods in ALSC that can be helpful for researchers and practitioners working in this area.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

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