Categorising Vaccine Confidence with Transformer-Based Machine Learning Model: The Nuances of Vaccine Sentiment on Twitter (Preprint)

2021 ◽  
Author(s):  
Per Kummervold ◽  
Sam Martin ◽  
Sara Dada ◽  
Eliz Kilich ◽  
Chermain Denny ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Resource Constraints ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Maternal Vaccination ◽  
Machine Learning Model ◽  
Vaccine Confidence ◽  
The Individual ◽  
Automated Processes

BACKGROUND With growing conversations online and less than desired maternal vaccination uptake rates, these conversations could provide useful insight to inform future interventions. Automated processes for this type of analysis, such as natural language processing (NLP), have faced challenges extracting complex stances, like attitudes toward vaccines, from large text. OBJECTIVE In this study, we aimed to build upon recent advances in Transformer-based machine learning methods, and test if this could be used as a tool to assess the stance of social media posts towards vaccination during pregnancy. METHODS A total of 16,604 Tweets posted between 1 November 2018 and 30 April 2019 were selected by boolean searches related to maternal vaccination. Tweets were coded by three individual researchers into the categories “Promotional”, “Discouraging”, “Ambiguous” and “Neutral” After creating a final dataset of 2,722 unique tweets, multiple machine learning methods were trained on the dataset and then tested and compared to the human annotators. RESULTS We received an accuracy of 81.8% (F-score= 0.78) compared to the agreed score between the three annotators. For comparison, the accuracies of the individual annotators compared to the final score were 83.3%, 77.9% and 77.5%. CONCLUSIONS This study demonstrates the ability to achieve close to the same accuracy in categorising tweets using our machine learning models as could be expected by a single human annotator. The potential to use this reliable and accurate automated process could free up valuable time and resource constraints of conducting this analysis, in addition to inform potentially effective and necessary interventions. CLINICALTRIAL N/A

scholarly journals Modulation Classification of Underwater Communication with Deep Learning Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Wang ◽  
Hao Zhang ◽  
Zhanliang Sang ◽  
Lingwei Xu ◽  
Conghui Cao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Language Processing ◽  
Communication Process ◽  
Underwater Acoustic Communication ◽  
Learning Method ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Machine Learning Methods

Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.

scholarly journals Multi-Level Driver Workload Prediction using Machine Learning and Off-the-Shelf Sensors

2018 ◽  
Vol 2672 (37) ◽  
pp. 141-152 ◽  
Author(s):  
Paul van Gent ◽  
Timo Melman ◽  
Haneen Farah ◽  
Nicole van Nes ◽  
Bart van Arem
Keyword(s):  
Machine Learning ◽  
Future Research ◽  
Learning Methods ◽  
Workload Prediction ◽  
Machine Learning Methods ◽  
Driver Workload ◽  
Multi Level ◽  
Driving Conditions ◽  
The Individual ◽  
Lane Keeping

The present study aims to add to the literature on driver workload prediction using machine learning methods. The main aim is to develop workload prediction on a multi-level basis, rather than a binary high/low distinction as often found in literature. The presented approach relies on measures that can be obtained unobtrusively in the driving environment with off-the-shelf sensors, and on machine learning methods that can be implemented in low-power embedded systems. Two simulator studies were performed, one inducing workload using realistic driving conditions, and one inducing workload with a relatively demanding lane-keeping task. Individual and group-based machine learning models were trained on both datasets and evaluated. For the group-based models the generalizing capability, that is the performance when predicting data from previously unseen individuals, was also assessed. Results show that multi-level workload prediction on the individual and group level works well, achieving high correct rates and accuracy scores. Generalizing between individuals proved difficult using realistic driving conditions but worked well in the highly demanding lane-keeping task. Reasons for this discrepancy are discussed as well as future research directions.

scholarly journals A model for estimating the value of the applied pressure based on the analysis of tactile sensor signals using machine learning methods

2021 ◽  
pp. 119-130
Author(s):  
П.С. Козырь ◽  
Р.Н. Яковлев
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Applied Pressure ◽  
Tactile Sensor ◽  
Least Squares Regression ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Sensor Signals ◽  
Fully Connected

В рамках настоящего исследования был проведен анализ существующих работ, посвященных интерпретации показаний тактильных сенсорных устройств, по результатам которого была предложена модель машинного обучения, позволяющая осуществлять оценку величины приложенного давления к поверхности тактильного сенсора давления емкостного типа. В качестве опорных моделей обработки и интерпретации сигналов данного устройства в работе рассматривались несколько методов машинного обучения: линейная регрессия, полиномиальная регрессия, регрессия дерева решений, частичная регрессия наименьших квадратов и полносвязная нейронная сеть прямого распространения. Обучение опорных моделей и апробация конечного решения проводилась на авторском наборе данных, включающем в себя более 3000 экземпляров данных. Согласно полученным результатам, наилучшее качество определения величины приложенного давления продемонстрирован решением на основе полносвязной нейронной сети прямого распространения. Коэффициент детерминации и средний модуль отклонения для данного решения на тестовой выборке составили 0,93 и 13,14 кПа соответственно. Currently, in the field of developing sensing systems for robotic means, one of the urgent tasks is the problem of interpreting the data of tactile pressure and proximity sensors. As a rule, the solution to this problem is complicated both by the dependence of the indicators of tactile sensors on the type of object’s material and by the design features of each individual device. In this study, an analysis of existing works devoted to the interpretation of the readings of tactile sensor devices was carried out. According to the analysis results a machine learning model was proposed that allows estimating the amount of pressure applied to the surface of a tactile pressure sensor of a capacitive type. The architecture of the proposed model includes two key blocks of data analysis, the first one is aimed at recognizing the type of interaction object’s material and the second is devoted to the direct assessment of the magnitude of the pressure applied to the sensor. Several machine learning methods were considered as supporting models for processing and interpreting the signals of this device: linear regression, polynomial regression, decision tree regression, partial least squares regression and a fully connected feedforward neural network.

Application of machine learning methods to automate the detection of obstacles to the movement of a train through a level crossing

2020 ◽  
pp. 46-51
Author(s):  
Yu.M. Iskanderov ◽  
B.E. Katarushkin ◽  
A.A. Ershov
Keyword(s):  
Machine Learning ◽  
Traffic Control ◽  
Intelligent System ◽  
Learning Methods ◽  
Similar System ◽  
Intelligent Information Systems ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Intelligent Information ◽  
Railway Crossing

Aim. Currently, when creating intelligent information systems in various fields of practical activity, machine learning methods are used. The article shows the possibilities of using these methods in automating the detection of obstacles in the interest of improving safety and reducing the number of emergencies at level crossings. Materials and methods. The article discusses advanced computer vision technologies used as the basis of an intelligent system for detecting obstacles to the movement of a train through a railway crossing. Results. Based on the analysis of the conditions and features of the functioning of the technologies considered, the relevance of introducing a similar system is shown, options for constructing its structure and operating principle are proposed, approaches are formulated when developing a machine learning model for classifying of the used images. Conclusions. The approach underlying the formation of an intelligent system for detecting obstacles to the movement of a train through a railway crossing allows it to be used as an additional independent security tool that implements an alarm for a duty officer on a specific section of the railway and / or to a traffic control dispatch center to prevent emergencies.

scholarly journals Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1113
Author(s):  
Ming Zhong ◽  
Yajin Zhou ◽  
Gang Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Language Processing ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Methods ◽  
Sequential Model ◽  
Machine Learning Methods ◽  
Model Based

IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers.

Effects of individual factors on perceived emotion and felt emotion of music: Based on machine learning methods

2020 ◽  
pp. 030573562092842
Author(s):  
Liang Xu ◽  
Xin Wen ◽  
Jiaming Shi ◽  
Shutong Li ◽  
Yuhan Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Individual Factors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Audio Features ◽  
Computer Scientists ◽  
Individual Features ◽  
The Individual ◽  
Music Information ◽  
Perceived Emotion

Music emotion information is widely used in music information retrieval, music recommendation, music therapy, and so forth. In the field of music emotion recognition (MER), computer scientists extract musical features to identify musical emotions, but this method ignores listeners’ individual differences. Applying machine learning methods, this study formed relations among audio features, individual factors, and music emotions. We used audio features and individual features as inputs to predict the perceived emotion and felt emotion of music, respectively. The results show that real-time individual features (e.g., preference for target music and mechanism indices) can significantly improve the model’s effect, and stable individual features (e.g., sex, music experience, and personality) have no effect. Compared with the recognition models of perceived emotions, the individual features have greater effects on the recognition models of felt emotions.

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