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 Deep learning for constructing microblog behavior representation to identify social media user's personality

2016 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

scholarly journals Deep learning for constructing microblog behavior representation to identify social media user's personality

2016 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

scholarly journals Artificial intelligence in surgical pathology

2019 ◽  
Vol 9 (1) ◽  
pp. I
Author(s):  
Gopi Aryal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Feedback Loop ◽  
Feature Learning ◽  
Machine Intelligence ◽  
Feature Engineering ◽  
Learning Machines ◽  
Complex Features

Artificial intelligence (AI) is machine intelligence that mimics human cognitive function. It denotes the intelligence presented by some artificial entities including computers and robots.  In supervised learning, a machine is trained with data that contain pairs of inputs and outputs. In unsupervised learning, machines are given data inputs that are not explicitly programmed.1 Machine learning refines a model that predicts outputs using sample inputs (features) and a feedback loop. It relies heavily on extracting or selecting salient features, which is a combination of art and science (“feature engineering”).  A subset of feature learning is deep learning, which harnesses neural networks modeled after the biological nervous system of animals. Deep learning discovers the features from the raw data provided during training. Hidden layers in the artificial neural network represent increasingly more complex features in the data. Convolutional neural network is a type of deep learning commonly used for image analysis.

scholarly journals Using Deep Learning for Human Computer Interface via Electroencephalography

2015 ◽  
Vol 4 (4) ◽  
pp. 292 ◽  
Author(s):  
Sangram Redkar
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Motor Imagery ◽  
Autonomous Vehicles ◽  
Ad Hoc ◽  
Feature Learning ◽  
Computer Interface ◽  
Learning Techniques ◽  
Robust Signal Processing

<table class="Heading1Char" width="593" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>In this paper, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised and unsupervised machine learning models, for the EEG motor imagery classification are identified. Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, it is important to have robust signal processing and machine learning modules which operate on the EEG signals and estimate the current thought or intent of the user. Motor Imagery (imaginary hand and leg movements) signals are acquired using the Emotiv EEG headset. The signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques are applied and validated. The performances of various ML techniques are compared and some important observations are reported. Further, Deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is presented and analyzed by performing classification by using the ML techniques. It is shown that hand engineered ‘ad-hoc’ feature extraction techniques are less reliable than the automated (‘Deep Learning’) feature learning techniques. All the findings in this research, can be used by the BCI research community for building motor imagery based BCI applications such as Gaming, Robot control and autonomous vehicles.</p></td></tr></tbody></table>

scholarly journals A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2514
Author(s):  
Tharindu Kaluarachchi ◽  
Andrew Reis ◽  
Suranga Nanayakkara
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Review Paper ◽  
Learning Systems ◽  
Learning Approaches ◽  
Application Development ◽  
Research Gaps ◽  
Domain Experts ◽  
Working Definition ◽  
Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

scholarly journals Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1694
Author(s):  
Mathew Ashik ◽  
A. Jyothish ◽  
S. Anandaram ◽  
P. Vinod ◽  
Francesco Mercaldo ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Support Vector ◽  
Malware Analysis ◽  
Learning Approaches ◽  
Dynamic Features ◽  
System Calls ◽  
Prevention Methods ◽  
Structural Aspects

Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

scholarly journals A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2764
Author(s):  
Xin Yu Liew ◽  
Nazia Hameed ◽  
Jeremie Clos
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Main Process ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Conventional Methods ◽  
The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

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