scholarly journals Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Jacob Tryon ◽  
Ana Luisa Trejos
Keyword(s):  
Machine Learning ◽  
Elbow Flexion ◽  
Relevant Information ◽  
Classification Model ◽  
Learning Technology ◽  
Learning Methods ◽  
Time Frequency ◽  
Machine Learning Methods ◽  
Signal Representations ◽  
Flexion Extension

Wearable robotic exoskeletons have emerged as an exciting new treatment tool for disorders affecting mobility; however, the human–machine interface, used by the patient for device control, requires further improvement before robotic assistance and rehabilitation can be widely adopted. One method, made possible through advancements in machine learning technology, is the use of bioelectrical signals, such as electroencephalography (EEG) and electromyography (EMG), to classify the user's actions and intentions. While classification using these signals has been demonstrated for many relevant control tasks, such as motion intention detection and gesture recognition, challenges in decoding the bioelectrical signals have caused researchers to seek methods for improving the accuracy of these models. One such method is the use of EEG–EMG fusion, creating a classification model that decodes information from both EEG and EMG signals simultaneously to increase the amount of available information. So far, EEG–EMG fusion has been implemented using traditional machine learning methods that rely on manual feature extraction; however, new machine learning methods have emerged that can automatically extract relevant information from a dataset, which may prove beneficial during EEG–EMG fusion. In this study, Convolutional Neural Network (CNN) models were developed using combined EEG–EMG inputs to determine if they have potential as a method of EEG–EMG fusion that automatically extracts relevant information from both signals simultaneously. EEG and EMG signals were recorded during elbow flexion–extension and used to develop CNN models based on time–frequency (spectrogram) and time (filtered signal) domain image inputs. The results show a mean accuracy of 80.51 ± 8.07% for a three-class output (33.33% chance level), with an F-score of 80.74%, using time–frequency domain-based models. This work demonstrates the viability of CNNs as a new method of EEG–EMG fusion and evaluates different signal representations to determine the best implementation of a combined EEG–EMG CNN. It leverages modern machine learning methods to advance EEG–EMG fusion, which will ultimately lead to improvements in the usability of wearable robotic exoskeletons.

scholarly journals Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3597 ◽  
Author(s):  
Qitao Zhang ◽  
Chenji Wei ◽  
Yuhe Wang ◽  
Shuyi Du ◽  
Yuanchun Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reservoir Characterization ◽  
Short Term Memory ◽  
Water Saturation ◽  
Petroleum Industry ◽  
Learning Technology ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Saturation Distribution ◽  
Model Training

Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

Ensemble machine learning methods for spatio-temporal data analysis of plant and ratoon sugarcane

2021 ◽  
Vol 25 (5) ◽  
pp. 1291-1322
Author(s):  
Sandeep Kumar Singla ◽  
Rahul Dev Garg ◽  
Om Prakash Dubey
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Binary Classification ◽  
Temporal Variations ◽  
Classification Model ◽  
Gradient Boosting ◽  
Remotely Sensed Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Classification And Regression

Recent technological enhancements in the field of information technology and statistical techniques allowed the sophisticated and reliable analysis based on machine learning methods. A number of machine learning data analytical tools may be exploited for the classification and regression problems. These tools and techniques can be effectively used for the highly data-intensive operations such as agricultural and meteorological applications, bioinformatics and stock market analysis based on the daily prices of the market. Machine learning ensemble methods such as Decision Tree (C5.0), Classification and Regression (CART), Gradient Boosting Machine (GBM) and Random Forest (RF) has been investigated in the proposed work. The proposed work demonstrates that temporal variations in the spectral data and computational efficiency of machine learning methods may be effectively used for the discrimination of types of sugarcane. The discrimination has been considered as a binary classification problem to segregate ratoon from plantation sugarcane. Variable importance selection based on Mean Decrease in Accuracy (MDA) and Mean Decrease in Gini (MDG) have been used to create the appropriate dataset for the classification. The performance of the binary classification model based on RF is the best in all the possible combination of input images. Feature selection based on MDA and MDG measures of RF is also important for the dimensionality reduction. It has been observed that RF model performed best with 97% accuracy, whereas the performance of GBM method is the lowest. Binary classification based on the remotely sensed data can be effectively handled using random forest method.

Public Sentiments Analysis Based on Fuzzy Logic for Text

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1341-1360 ◽  
Author(s):  
Xinzhi Wang ◽  
Hui Zhang ◽  
Zheng Xu
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Sentiment Analysis ◽  
Web Mining ◽  
Experimental Studies ◽  
Learning Technology ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Public Sentiment ◽  
Similar Accuracy

Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.

scholarly journals Remedies for algorithmic tacit collusion

2020 ◽  
Author(s):  
Francisco Beneke ◽  
Mark-Oliver Mackenrodt
Keyword(s):  
Machine Learning ◽  
Real Life ◽  
Competition Law ◽  
Tacit Collusion ◽  
Learning Technology ◽  
Social Harm ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Social ◽  
Use Efficiency

Abstract There is growing evidence that tacit collusion can be autonomously achieved by machine learning technology, at least in some real-life examples identified in the literature and experimental settings. Although more work needs to be done to assess the competitive risks of widespread adoption of autonomous pricing agents, this is still an appropriate time to examine which possible remedies can be used in case competition law shifts towards the prohibition of tacit collusion. This is because outlawing such conduct is pointless unless there are suitable remedies that can be used to address the social harm. This article explores how fines and structural and behavioural remedies can serve to discourage collusive results while preserving the incentives to use efficiency-enhancing algorithms. We find that this could be achieved if fines and remedies can target structural conditions that facilitate collusion. In addition, the problem of unfeasibility of injunctions to remedy traditional price coordination changes with the use of pricing software, which in theory can be programmed to avoid collusive outcomes. Finally, machine-learning methods can be used by the authorities themselves as a tool to test the effects of any given combination of remedies and to estimate a more accurate competitive benchmark for the calculation of the appropriate fine.

scholarly journals Machine Learning Methods for Quality Prediction in Production

Logistics ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 35
Author(s):  
Sidharth Sankhye ◽  
Guiping Hu
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Classification Methods ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Production Stages ◽  
Smart Factories

The rising popularity of smart factories and Industry 4.0 has made it possible to collect large amounts of data from production stages. Thus, supervised machine learning methods such as classification can viably predict product compliance quality using manufacturing data collected during production. Elimination of uncertainty via accurate prediction provides significant benefits at any stage in a supply chain. Thus, early knowledge of product batch quality can save costs associated with recalls, packaging, and transportation. While there has been thorough research on predicting the quality of specific manufacturing processes, the adoption of classification methods to predict the overall compliance of production batches has not been extensively investigated. This paper aims to design machine learning based classification methods for quality compliance and validate the models via case study of a multi-model appliance production line. The proposed classification model could achieve an accuracy of 0.99 and Cohen’s Kappa of 0.91 for the compliance quality of unit batches. Thus, the proposed method would enable implementation of a predictive model for compliance quality. The case study also highlights the importance of feature construction and dataset knowledge in training classification models.

scholarly journals A Novel and Rapid Serum Detection Technology for Non-Invasive Screening of Gastric Cancer Based on Raman Spectroscopy Combined With Different Machine Learning Methods

2021 ◽  
Vol 11 ◽  
Author(s):  
Mengya Li ◽  
Haiyan He ◽  
Guorong Huang ◽  
Bo Lin ◽  
Huiyan Tian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Classification Model ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Unseen Data ◽  
Raman Spectral Data

Gastric cancer (GC) is the fifth most common cancer in the world and a serious threat to human health. Due to its high morbidity and mortality, a simple, rapid and accurate early screening method for GC is urgently needed. In this study, the potential of Raman spectroscopy combined with different machine learning methods was explored to distinguish serum samples from GC patients and healthy controls. Serum Raman spectra were collected from 109 patients with GC (including 35 in stage I, 14 in stage II, 35 in stage III, and 25 in stage IV) and 104 healthy volunteers matched for age, presenting for a routine physical examination. We analyzed the difference in serum metabolism between GC patients and healthy people through a comparative study of the average Raman spectra of the two groups. Four machine learning methods, one-dimensional convolutional neural network, random forest, support vector machine, and K-nearest neighbor were used to explore identifying two sets of Raman spectral data. The classification model was established by using 70% of the data as a training set and 30% as a test set. Using unseen data to test the model, the RF model yielded an accuracy of 92.8%, and the sensitivity and specificity were 94.7% and 90.8%. The performance of the RF model was further confirmed by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.9199. This exploratory work shows that serum Raman spectroscopy combined with RF has great potential in the machine-assisted classification of GC, and is expected to provide a non-destructive and convenient technology for the screening of GC patients.

scholarly journals Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models

2020 ◽  
Author(s):  
Robson Parmezan Bonidia ◽  
Lucas Dias Hiera Sampaio ◽  
Douglas Silva Domingues ◽  
Alexandre Rossi Paschoal ◽  
Fabrício Martins Lopes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Mathematical Models ◽  
Relevant Information ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Classification Problems ◽  
Learning Methods ◽  
Rna Sequences ◽  
Machine Learning Methods

AbstractThe number of available biological sequences has increased significantly in recent years due to various genomic sequencing projects, creating a huge volume of data. Consequently, new computational methods are needed to analyze and extract information from these sequences. Machine learning methods have shown broad applicability in computational biology and bioinformatics. The utilization of machine learning methods has helped to extract relevant information from various biological datasets. However, there are still several obstacles that motivate new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes to study and analyze a feature extraction pipeline based on mathematical models (Numerical Mapping, Fourier, Entropy, and Complex Networks). As a case study, we analyze Long Non-Coding RNA sequences. Moreover, we divided this work into two studies, e.g., (I) we assessed our proposal with the most addressed problem in our review, e.g., lncRNA vs. mRNA; (II) we tested its generalization on different classification problems, e.g., circRNA vs. lncRNA. The experimental results demonstrated three main contributions: (1) An in-depth study of several mathematical models; (2) a new feature extraction pipeline and (3) its generalization and robustness for distinct biological sequence classification.

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