Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals

This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer. The classification was performed on the sound and force data generated during machining aluminum sheets for different levels of spindle speed, feed rate, depth of cut, and end-mill diameter, and it was trained on 30 s machining data (10–40 s) of the machining experiments. Since a raw audio waveform is seldom used in DL models, Mel-Spectrogram and Mel Frequency Cepstral Coefficients (MFCCs) audio feature extraction techniques were used in the DL models. The results of DL models were compared for the training–validation accuracy, training epochs, and training parameters of each model. Although the roughness classification by all the DL models was satisfactory (except for CNN with Mel-Spectrogram), the transformer-based modes had the highest training (>96%) and validation accuracies (≈90%). The CNN model with Mel-Spectrogram exhibited the worst training and inference accuracy, which is influenced by limited training data. Confusion matrices were plotted to observe the classification accuracy visually. The confusion matrices showed that the transformer model trained on Mel-Spectrogram and the transformer model trained on MFCCs correctly predicted 366 (or 91.5%) and 371 (or 92.7%) out of 400 test samples. This study also highlights the suitability and superiority of the transformer model for time series sound and force data and over other DL models.

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AOHDL: Archimedes Optimization Based Hybrid Deep Learning Model for Soybean Plant Disease Classification

10.21203/rs.3.rs-302084/v1 ◽

2021 ◽

Author(s):

J. Annrose ◽

N. Herald Anantha Rufus ◽

C. R. Edwin Selva Rex ◽

D. Godwin Immanuel

Keyword(s):

Deep Learning ◽

Optimization Algorithm ◽

Short Term Memory ◽

Learning Model ◽

Disease Classification ◽

Training Data ◽

Machine Learning Techniques ◽

Soybean Plant ◽

Lower Accuracy ◽

Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

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Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

10.21203/rs.3.rs-281525/v1 ◽

2021 ◽

Author(s):

J. Annrose ◽

N. Herald Anantha Rufus ◽

C. R. Edwin Selva Rex ◽

D. Godwin Immanuel

Keyword(s):

Deep Learning ◽

Optimization Algorithm ◽

Short Term Memory ◽

Learning Model ◽

Disease Classification ◽

Training Data ◽

Machine Learning Techniques ◽

Soybean Plant ◽

Lower Accuracy ◽

Deep Learning Model

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Predictive Analysis of Cryptocurrency Price Using Deep Learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.27.17889 ◽

2018 ◽

Vol 7 (3.27) ◽

pp. 258 ◽

Cited By ~ 4

Author(s):

Yecheng Yao ◽

Jungho Yi ◽

Shengjun Zhai ◽

Yuwen Lin ◽

Taekseung Kim ◽

...

Keyword(s):

Deep Learning ◽

International Relations ◽

Short Term Memory ◽

Training Data ◽

Short Term ◽

Effective Learning ◽

Learning Techniques ◽

Benchmark Datasets ◽

Novel Method ◽

Long Short Term Memory

The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The proposed approach is implemented in Python and validated for benchmark datasets. The results verify the applicability of the proposed approach for the accurate prediction of cryptocurrency price.

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Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

10.36227/techrxiv.15103602 ◽

2021 ◽

Author(s):

Jaydip Sen ◽

Sidra Mehtab ◽

Abhishek Dutta

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Stock Prices ◽

Stock Price ◽

Regression Models ◽

Short Term Memory ◽

Efficient Market Hypothesis ◽

Training Data ◽

Stock Price Prediction ◽

Price Prediction

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. Researchers have also worked on technical analysis of stocks with a goal of identifying patterns in the stock price movements using advanced data mining techniques. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records from December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. Using the grid-searching technique, the hyperparameters of the LSTM models are optimized so that it is ensured that validation losses stabilize with the increasing number of epochs, and the convergence of the validation accuracy is achieved. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week's open value of the NIFTY 50 time series is the most accurate model.

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Jamming Prediction for Radar Signals Using Machine Learning Methods

Security and Communication Networks ◽

10.1155/2020/2151570 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Gyeong-Hoon Lee ◽

Jeil Jo ◽

Cheong Hee Park

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Model ◽

Training Data ◽

Electronic Warfare ◽

New Type ◽

Long Short Term Memory ◽

Radar Signals ◽

Feature Values ◽

Deep Learning Model

Jamming is a form of electronic warfare where jammers radiate interfering signals toward an enemy radar, disrupting the receiver. The conventional method for determining an effective jamming technique corresponding to a threat signal is based on the library which stores the appropriate jamming method for signal types. However, there is a limit to the use of a library when a threat signal of a new type or a threat signal that has been altered differently from existing types is received. In this paper, we study two methods of predicting the appropriate jamming technique for a received threat signal using deep learning: using a deep neural network on feature values extracted manually from the PDW list and using long short-term memory (LSTM) which takes the PDW list as input. Using training data consisting of pairs of threat signals and corresponding jamming techniques, a deep learning model is trained which outputs jamming techniques for threat signal inputs. Training data are constructed based on the information in the library, but the trained deep learning model is used to predict jamming techniques for received threat signals without using the library. The prediction performance and time complexity of two proposed methods are compared. In particular, the ability to predict jamming techniques for unknown types of radar signals which are not used in the stage of training the model is analyzed.

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Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Sensors ◽

10.3390/s20082359 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2359 ◽

Cited By ~ 3

Author(s):

Meriem Zerkouk ◽

Belkacem Chikhaoui

Keyword(s):

Deep Learning ◽

Behavioral Problems ◽

Short Term Memory ◽

Training Data ◽

Abnormal Behavior ◽

Elderly Persons ◽

Learning Models ◽

Accuracy Measure ◽

Public Data ◽

Abnormal Behaviors

The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.

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Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions

Measurement and Control ◽

10.1177/00202940211001904 ◽

2021 ◽

Vol 54 (3-4) ◽

pp. 439-445

Author(s):

Chih-Ta Yen ◽

Sheng-Nan Chang ◽

Cheng-Hong Liao

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Algorithm ◽

Training Data ◽

Short Term ◽

Deep Learning Algorithm ◽

Testing Dataset ◽

Data Points ◽

Optimal Classification ◽

Long Short Term Memory

This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset consisted of 2100 data points. During the experiment, the proportion of training and testing datasets was 8:2. The model demonstrated an optimal classification accuracy of 76% when the testing dataset was used.

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Hybrid Deep Learning Models for Sentiment Analysis

Complexity ◽

10.1155/2021/9986920 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Cach N. Dang ◽

María N. Moreno-García ◽

Fernando De la Prieta

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Computation Time ◽

Hybrid Models ◽

Training Data ◽

Support Vector ◽

Learning Models ◽

Hybrid Techniques ◽

Wide Range

Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher.

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Classification of User Comment Using Word2vec and Deep Learning

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae0521_01 ◽

2021 ◽

Vol 11 (5) ◽

pp. 1-8

Author(s):

Rafly Indra Kurnia ◽

◽

Abba Suganda Girsang

Keyword(s):

Social Media ◽

Deep Learning ◽

User Satisfaction ◽

Rating Scale ◽

Short Term Memory ◽

Learning Algorithm ◽

Training Data ◽

Deep Learning Algorithm ◽

User Comments

This study will classify the text based on the rating of the provider application on the Google Play Store. This research is classification of user comments using Word2vec and the deep learning algorithm in this case is Long Short Term Memory (LSTM) based on the rating given with a rating scale of 1-5 with a detailed rating 1 is the lowest and rating 5 is the highest data and a rating scale of 1-3 with a detailed rating, 1 as a negative is a combination of ratings 1 and 2, rating 2 as a neutral is rating 3, and rating 3 as a positive is a combination of ratings 4 and 5 to get sentiment from users using SMOTE oversampling to handle the imbalance data. The data used are 16369 data. The training data and the testing data will be taken from user comments MyTelkomsel’s application from the play.google.com site where each comment has a rating in Indonesian Language. This review data will be very useful for companies to make business decisions. This data can be obtained from social media, but social media does not provide a rating feature for every user comment. This research goal is that data from social media such as Twitter or Facebook can also quickly find out the total of the user satisfaction based from the rating from the comment given. The best f1 scores and precisions obtained using 5 classes with LSTM and SMOTE were 0.62 and 0.70 and the best f1 scores and precisions obtained using 3 classes with LSTM and SMOTE were 0.86 and 0.87

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Data Augmentation and Transfer Learning Strategies for Reaction Prediction in Low Chemical Data Regimes

10.26434/chemrxiv.13383275 ◽

2020 ◽

Author(s):

Yun Zhang ◽

Ling Wang ◽

Xinqiao Wang ◽

Chengyun Zhang ◽

Jiamin Ge ◽

...

Keyword(s):

Deep Learning ◽

Learning Strategies ◽

Transfer Learning ◽

Data Augmentation ◽

Learning Strategy ◽

Predictive Performance ◽

Learning Model ◽

Training Data ◽

Reaction Prediction ◽

Transformer Model

Abstract: Effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery. Despite the outstanding capability of deep learning in retrosynthesis and forward synthesis, predictions based on small chemical datasets generally result in low accuracy due to an insufficiency of reaction examples. Here, we introduce a new state art of method, which integrates transfer learning with transformer model to predict the outcomes of the Baeyer-Villiger reaction which is a representative small dataset reaction. The results demonstrate that introducing transfer learning strategy markedly improves the top-1 accuracy of the transformer-transfer learning model (81.8%) over that of the transformer-baseline model (58.4%). Moreover, we further introduce data augmentation to the input reaction SMILES, which allows for better performance and improves the accuracy of the transformer-transfer learning model (86.7%). In summary, both transfer learning and data augmentation methods significantly improve the predictive performance of transformer model, which are powerful methods used in chemistry field to eliminate the restriction of limited training data.

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