scholarly journals Exploring Deep Physiological Models for Nociceptive Pain Recognition

2019 ◽  
Author(s):  
Patrick Thiam ◽  
Peter Bellmann ◽  
Hans A. Kestler ◽  
Friedhelm Schwenker
Keyword(s):  
Deep Learning ◽  
Pain Intensity ◽  
Expert Knowledge ◽  
Physiological Signals ◽  
Nociceptive Pain ◽  
Feature Engineering ◽  
Learning Approaches ◽  
Inference Model ◽  
Learning Architectures ◽  
Intensity Classification

AbstractStandard feature engineering involves manually designing and assessing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features in order to optimize an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterized by the integration of feature engineering, feature selection and inference model optimization into a single learning process. Such techniques have proven to be very successful in the domain of image processing and have been able to attain state-of-the-art performances while significantly outperforming traditional approaches based on hand-crafted features. In the following work, we explore deep learning approaches for the analysis of physiological signals. More precisely, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. Most of the previous works related to pain intensity classification based on physiological signals rely on a carefully designed set of hand-crafted features in order to achieve a relatively good classification performance. Therefore, the current work aims at building competitive pain intensity classification models without the need of domain specific expert knowledge for the generation of relevant features. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous classification methods reported in the literature, with respective average performances of 85.03% and 83.76% for the binary classification experiment consisting of the discrimination between the baseline level and the pain tolerance level (T0vs.T4) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting.

scholarly journals Exploring Deep Physiological Models for Nociceptive Pain Recognition

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4503 ◽  
Author(s):  
Patrick Thiam ◽  
Peter Bellmann ◽  
Hans A. Kestler ◽  
Friedhelm Schwenker
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Expert Knowledge ◽  
Electrodermal Activity ◽  
Nociceptive Pain ◽  
Feature Engineering ◽  
Inference Model ◽  
Inference Models ◽  
Learning Architectures

Standard feature engineering involves manually designing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features for the subsequent optimisation of an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterised by the integration of feature engineering, feature selection and inference model optimisation into a single learning process. In the following work, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. In contrast to previous works, which rely on carefully designed sets of hand-crafted features, the current work aims at building competitive pain intensity inference models through autonomous feature learning, based on deep neural networks. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous methods reported in the literature, with respective average performances of 84.57 % and 84.40 % for the binary classification experiment consisting of the discrimination between the baseline and the pain tolerance level ( T 0 vs. T 4 ) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting. Moreover, the experimental results clearly show the relevance of the proposed approaches, which also offer more flexibility in the case of transfer learning due to the modular nature of deep neural networks.

scholarly journals Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2075
Author(s):  
Óscar Apolinario-Arzube ◽  
José Antonio García-Díaz ◽  
José Medina-Moreira ◽  
Harry Luna-Aveiga ◽  
Rafael Valencia-García
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Interfaces ◽  
State Of The Art ◽  
Learning Approaches ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Intended Meaning ◽  
Language User ◽  
Learning Architectures

Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.

scholarly journals A comparative analysis on question classification task based on deep learning approaches

2021 ◽  
Vol 7 ◽  
pp. e570
Author(s):  
Muhammad Zulqarnain ◽  
Ahmed Khalaf Zager Alsaedi ◽  
Rozaida Ghazali ◽  
Muhammad Ghulam Ghouse ◽  
Wareesa Sharif ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Web Mining ◽  
Question Answering ◽  
Short Term Memory ◽  
Learning Approaches ◽  
Question Classification ◽  
Considerable Impact ◽  
Turkish Language ◽  
Learning Architectures

Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word embedding with various vector sizes on a large corpus composed of user questions. By comparing analysis, we conducted an experiment on deep learning architectures based on test and 10-cross fold validation accuracy. Experiment results were obtained to illustrate the effectiveness of various Word2vec techniques that have a considerable impact on the accuracy rate using different deep learning approaches. We attained an accuracy of 93.7% by using these techniques on the question dataset.

scholarly journals Malware Detection using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1847-1853
Author(s):  
T. Shiva Rama Krishna
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Image Processing Technique ◽  
Feature Engineering ◽  
Public And Private ◽  
Static And Dynamic Analysis ◽  
Private Datasets ◽  
Learning Architectures

Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently in order to arrive at new enhanced methods for effective zero-day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each other’s and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing-based hybrid approach in a big data environment is a new enhanced method for effective zero-day malware detection.

scholarly journals Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4838
Author(s):  
Philip Gouverneur ◽  
Frédéric Li ◽  
Wacław M. Adamczyk ◽  
Tibor M. Szikszay ◽  
Kerstin Luedtke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Feature Learning ◽  
Recognition System ◽  
Extraction Methods ◽  
Feature Engineering ◽  
Learning Approaches ◽  
Physiological Sensors ◽  
Pain Recognition

While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches have proposed automatic pain evaluation systems using machine learning models trained with data coming from behavioural or physiological sensors. Although yielding promising results, machine learning studies for sensor-based pain recognition remain scattered and not necessarily easy to compare to each other. In particular, the important process of extracting features is usually optimised towards specific datasets. We thus introduce a comparison of feature extraction methods for pain recognition based on physiological sensors in this paper. In addition, the PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for heat-induced pain in 52 subjects, is introduced. In total, five different approaches including techniques based on feature engineering and feature learning with deep learning are evaluated on the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature engineering approaches can still compete with deep learning approaches in terms of performance. (2) More complex deep learning architectures do not yield better performance compared to simpler ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based annotations to build a robust pain recognition system.

scholarly journals Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion

2021 ◽  
Author(s):  
Zhenguang Liu ◽  
Peng Qian ◽  
Xiang Wang ◽  
Lei Zhu ◽  
Qinming He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Expert Knowledge ◽  
Source Code ◽  
Smart Contracts ◽  
Learning Approaches ◽  
Vulnerability Detection ◽  
Security Issues ◽  
The Past ◽  
Smart Contract ◽  
Local Expert

Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.

scholarly journals A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3633
Author(s):  
Reed M. Maxwell ◽  
Laura E. Condon ◽  
Peter Melchior
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Water Depth ◽  
Current Model ◽  
Time Dependent ◽  
Water Model ◽  
Learning Approaches ◽  
Simulation Based ◽  
3D Cnn ◽  
Learning Architectures

While machine learning approaches are rapidly being applied to hydrologic problems, physics-informed approaches are still relatively rare. Many successful deep-learning applications have focused on point estimates of streamflow trained on stream gauge observations over time. While these approaches show promise for some applications, there is a need for distributed approaches that can produce accurate two-dimensional results of model states, such as ponded water depth. Here, we demonstrate a 2D emulator of the Tilted V catchment benchmark problem with solutions provided by the integrated hydrology model ParFlow. This emulator model can use 2D Convolution Neural Network (CNN), 3D CNN, and U-Net machine learning architectures and produces time-dependent spatial maps of ponded water depth from which hydrographs and other hydrologic quantities of interest may be derived. A comparison of different deep learning architectures and hyperparameters is presented with particular focus on approaches such as 3D CNN (that have a time-dependent learning component) and 2D CNN and U-Net approaches (that use only the current model state to predict the next state in time). In addition to testing model performance, we also use a simplified simulation based inference approach to evaluate the ability to calibrate the emulator to randomly selected simulations and the match between ML calibrated input parameters and underlying physics-based simulation.

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