Neural signatures of teamwork: Evidence from real-world military training

Successful teamwork is fundamental for optimally functioning societies. However, little is known regarding the neural basis of real-world teamwork. Here, we recruited forty individuals paired as twenty dyads and recorded dual-EEG at rest and during realistic training scenarios of increasing complexity using virtual simulation systems. We estimated markers of intrinsic brain activity (i.e., individual alpha frequency and aperiodic activity), as well as task-related theta and alpha oscillations. Using nonlinear modelling and machine learning techniques, we found that resting-state EEG predicts performance and can also reliably differentiate between members within a dyad. Task-related theta and alpha activity during easy training tasks predicted later performance on complex training to a greater extent than prior behaviour. These findings complement laboratory-based research on both oscillatory and aperiodic activity in higher-order cognition and provide evidence that theta and alpha activity play a critical role in complex task performance in team environments.

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CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

10.21203/rs.3.rs-27669/v1 ◽

2020 ◽

Author(s):

Diego Fabian Collazos Huertas ◽

Andres Marino Alvarez Meza ◽

German Castellanos Dominguez

Keyword(s):

Wavelet Transform ◽

Motor Imagery ◽

Continuous Wavelet Transform ◽

Brain Activity ◽

Machine Learning Techniques ◽

Continuous Wavelet ◽

Brain Response ◽

Power Spectral ◽

Learning Techniques ◽

Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

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Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

Electronics ◽

10.3390/electronics10151834 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1834

Author(s):

Abdullah Aljumah

Keyword(s):

Neural Network ◽

Real World ◽

Short Term Memory ◽

Herd Immunity ◽

Machine Learning Techniques ◽

Support Vector ◽

Sensitive Information ◽

K Nearest Neighbor ◽

Learning Techniques ◽

The World

From the end of 2019, the world has been facing the threat of COVID-19. It is predicted that, before herd immunity is achieved globally via vaccination, people around the world will have to tackle the COVID-19 pandemic using precautionary steps. This paper suggests a COVID-19 identification and control system that operates in real-time. The proposed system utilizes the Internet of Things (IoT) platform to capture users’ time-sensitive symptom information to detect potential cases of coronaviruses early on, to track the clinical measures adopted by survivors, and to gather and examine appropriate data to verify the existence of the virus. There are five key components in the framework: symptom data collection and uploading (via communication technology), a quarantine/isolation center, an information processing core (using artificial intelligent techniques), cloud computing, and visualization to healthcare doctors. This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information. A simulation was performed to verify the eight algorithms, after selecting the relevant symptoms, on real-world COVID-19 data values. The results showed that five of these eight algorithms obtained an accuracy of over 90%. Conclusively, it is shown that real-world symptomatic information would enable these three algorithms to identify potential COVID-19 cases effectively with enhanced accuracy. Additionally, the framework presents responses to treatment for COVID-19 patients.

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Breathlessness in COPD: linking symptom clusters with brain activity

European Respiratory Journal ◽

10.1183/13993003.04099-2020 ◽

2021 ◽

pp. 2004099

Author(s):

Sarah L. Finnegan ◽

Olivia K. Harrison ◽

Catherine J. Harmer ◽

Mari Herigstad ◽

Najib M. Rahman ◽

...

Keyword(s):

Machine Learning ◽

Functional Neuroimaging ◽

Brain Activity ◽

Symptom Burden ◽

Functional Brain Imaging ◽

Self Report ◽

Machine Learning Techniques ◽

Chronic Obstructive ◽

Unsupervised Machine Learning ◽

Learning Techniques

RationaleCurrent models of breathlessness often fail to explain disparities between patients' experiences of breathlessness and objective measures of lung function. While a mechanistic understanding of this discordance has thus far remained elusive, factors such as mood, attention and expectation have all been implicated as important modulators of breathlessness. Therefore, we have developed a model to better understand the relationships between these factors using unsupervised machine learning techniques. Subsequently we examined how expectation-related brain activity differed between these symptom-defined clusters of participants.MethodsA cohort of 91 participants with mild-to-moderate chronic obstructive pulmonary disease (COPD) underwent functional brain imaging, self-report questionnaires and clinical measures of respiratory function. Unsupervised machine learning techniques of exploratory factor analysis and hierarchical cluster modelling were used to model brain-behaviour-breathlessness links.ResultsWe successfully stratified participants across four key factors corresponding to mood, symptom burden and two capability measures. Two key groups resulted from this stratification, corresponding to high and low symptom burden. Compared to the high symptom load group, the low symptom burden group demonstrated significantly greater brain activity within the anterior insula, a key region thought to be involved in monitoring internal bodily sensations (interoception).ConclusionsThis is the largest functional neuroimaging study of COPD to date and is the first to provide a clear model linking brain, behaviour and breathlessness expectation. Furthermore, it was possible to stratify participants into groups, which then revealed differences in brain activity patterns. Together, these findings highlight the value of multi-modal models of breathlessness in identifying behavioural phenotypes, and for advancing understanding of differences in breathlessness burden.

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A Threat-Hunting of UNSW-NB15 with Machine Learning Techniques to Achieve Resilience

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1110.1291s419 ◽

2019 ◽

Vol 9 (1S3) ◽

pp. 488-494

Keyword(s):

Machine Learning ◽

Fault Tolerance ◽

Critical Role ◽

Cyber Attacks ◽

Three Dimensions ◽

Machine Learning Techniques ◽

Advanced Persistent Threat ◽

Learning Techniques

In order to focus on the mission and functions of the business of the organizations, cyber resiliency have to play a critical role against the adversaries’ target. The strategy recommended by NIST to reduce the suspect ability of cyber-attacks of the system with the three dimensions such as harden the target, limit the damage to the target and make the target resilient. The threats could be based on cyber and noncyber. The objective is to provide cyber resiliency on the Advanced Persistent Threat (APT), has born with the nature of sophisticated, stealthy, persistent towards target and highly adoptable to the environment. The challenge is to provide cyber resilience to the system from compromising tactics of the adversaries, uncertain in eradication of threat due to its persistent nature, recognizing its adapting ability. The cyber resiliency also links with other disciplines like safety, fault tolerance, privacy, resilience and survivability, reliability and security

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On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

Sustainability ◽

10.3390/su12062544 ◽

2020 ◽

Vol 12 (6) ◽

pp. 2544

Author(s):

Alice Consilvio ◽

José Solís-Hernández ◽

Noemi Jiménez-Redondo ◽

Paolo Sanetti ◽

Federico Papa ◽

...

Keyword(s):

Machine Learning ◽

Case Studies ◽

Real World ◽

Asset Management ◽

Building Blocks ◽

Machine Learning Techniques ◽

First Case ◽

Learning Techniques ◽

Track Circuits

The objective of this study is to show the applicability of machine learning and simulative approaches to the development of decision support systems for railway asset management. These techniques are applied within the generic framework developed and tested within the In2Smart project. The framework is composed by different building blocks, in order to show the complete process from data collection and knowledge extraction to the real-world decisions. The application of the framework to two different real-world case studies is described: the first case study deals with strategic earthworks asset management, while the second case study considers the tactical and operational planning of track circuits’ maintenance. Although different methodologies are applied and different planning levels are considered, both the case studies follow the same general framework, demonstrating the generality of the approach. The potentiality of combining machine learning techniques with simulative approaches to replicate real processes is shown, evaluating the key performance indicators employed within the considered asset management process. Finally, the results of the validation are reported as well as the developed human–machine interfaces for output visualization.

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Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.675978 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yining Xu ◽

Xinran Cui ◽

Yadong Wang

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Gene Expression Data ◽

Real World ◽

Tumor Metastasis ◽

Machine Learning Techniques ◽

Expression Data ◽

Real World Data ◽

Convolutional Network ◽

Learning Techniques

Tumor metastasis is the major cause of mortality from cancer. From this perspective, detecting cancer gene expression and transcriptome changes is important for exploring tumor metastasis molecular mechanisms and cellular events. Precisely estimating a patient’s cancer state and prognosis is the key challenge to develop a patient’s therapeutic schedule. In the recent years, a variety of machine learning techniques widely contributed to analyzing real-world gene expression data and predicting tumor outcomes. In this area, data mining and machine learning techniques have widely contributed to gene expression data analysis by supplying computational models to support decision-making on real-world data. Nevertheless, limitation of real-world data extremely restricted model predictive performance, and the complexity of data makes it difficult to extract vital features. Besides these, the efficacy of standard machine learning pipelines is far from being satisfactory despite the fact that diverse feature selection strategy had been applied. To address these problems, we developed directed relation-graph convolutional network to provide an advanced feature extraction strategy. We first constructed gene regulation network and extracted gene expression features based on relational graph convolutional network method. The high-dimensional features of each sample were regarded as an image pixel, and convolutional neural network was implemented to predict the risk of metastasis for each patient. Ten cross-validations on 1,779 cases from The Cancer Genome Atlas show that our model’s performance (area under the curve, AUC = 0.837; area under precision recall curve, AUPRC = 0.717) outstands that of an existing network-based method (AUC = 0.707, AUPRC = 0.555).

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Deep Learning in Multi-step Forecasting of Chaotic Dynamics

Special Topics in Information Technology - SpringerBriefs in Applied Sciences and Technology ◽

10.1007/978-3-030-85918-3_1 ◽

2022 ◽

pp. 3-14

Author(s):

Matteo Sangiorgio

Keyword(s):

Time Series ◽

Real World ◽

Domain Adaptation ◽

Machine Learning Techniques ◽

Future Evolution ◽

Current State ◽

Learning Techniques ◽

Ideal Situation ◽

Real World Applications ◽

Physically Based

AbstractThe prediction of chaotic dynamical systems’ future evolution is widely debated and represents a hot topic in the context of nonlinear time series analysis. Recent advances in the field proved that machine learning techniques, and in particular artificial neural networks, are well suited to deal with this problem. The current state-of-the-art primarily focuses on noise-free time series, an ideal situation that never occurs in real-world applications. This chapter provides a comprehensive analysis that aims at bridging the gap between the deterministic dynamics generated by archetypal chaotic systems, and the real-world time series. We also deeply explore the importance of different typologies of noise, namely observation and structural noise. Artificial intelligence techniques turned out to provide robust predictions, and potentially represent an effective and flexible alternative to the traditional physically-based approach for real-world applications. Besides the accuracy of the forecasting, the domain-adaptation analysis attested the high generalization capability of the neural predictors across a relatively heterogeneous spatial domain.

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Classification of Fetal State through the application of Machine Learning techniques on Cardiotocography records: Towards Real World Application.

10.1101/2021.06.03.21255808 ◽

2021 ◽

Author(s):

Andrew M V Dadario ◽

Christian Espinoza ◽

Wellington Araujo Nogueira

Keyword(s):

Machine Learning ◽

Real World ◽

Cross Validation ◽

Low Cost ◽

Gaussian Process Regression ◽

Machine Learning Techniques ◽

Real World Application ◽

Learning Techniques ◽

Qualified Personnel

Objective Anticipating fetal risk is a major factor in reducing child and maternal mortality and suffering. In this context cardiotocography (CTG) is a low cost, well established procedure that has been around for decades, despite lacking consensus regarding its impact on outcomes. Machine learning emerged as an option for automatic classification of CTG records, as previous studies showed expert level results, but often came at the price of reduced generalization potential. With that in mind, the present study sought to improve statistical rigor of evaluation towards real world application. Materials and Methods In this study, a dataset of 2126 CTG recordings labeled as normal, suspect or pathological by the consensus of three expert obstetricians was used to create a baseline random forest model. This was followed by creating a lightgbm model tuned using gaussian process regression and post processed using cross validation ensembling. Performance was assessed using the area under the precision-recall curve (AUPRC) metric over 100 experiment executions, each using a testing set comprised of 30% of data stratified by the class label. Results The best model was a cross validation ensemble of lightgbm models that yielded 95.82% AUPRC. Conclusions The model is shown to produce consistent expert level performance at a less than negligible cost. At an estimated 0.78 USD per million predictions the model can generate value in settings with CTG qualified personnel and all the more in their absence.

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Retrieval of Web Pages on Real-World Events related to Physical Objects

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2012010104 ◽

2012 ◽

Vol 2 (1) ◽

pp. 65-80 ◽

Cited By ~ 1

Author(s):

Takeshi Okadome ◽

Hajime Funai ◽

Sho Ito ◽

Junya Nakajima ◽

Koh Kakusho

Keyword(s):

Machine Learning ◽

Search Engine ◽

Real World ◽

Machine Learning Techniques ◽

Web Pages ◽

Learning Techniques ◽

Physical Objects

The method proposed in this paper searches for web pages using an event-related query consisting of a noun, verb, and genre term. It re-ranks web pages retrieved using a standard search engine on the basis of scores calculated from an expression consisting of weighted factors such as the frequency of query words. For the genres that are characterized by their genre terms, the method optimizes the weights of the expression. Furthermore, the method attempts to improve the scores provided of relevant pages by using machine learning techniques. In addition, some evaluations are provided to show the effectiveness of the method.

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Predicting the Signs of the Links in a Network

Journal of Digital Science ◽

10.33847/2686-8296.2.2_2 ◽

2020 ◽

Vol 2 (2) ◽

pp. 14-22

Author(s):

Quang-Vinh Dang

Keyword(s):

Real World ◽

Machine Learning Techniques ◽

Research Approach ◽

New Approach ◽

Online Systems ◽

Novel Approach ◽

Learning Techniques ◽

The Future ◽

Potential Improvement ◽

New Research

It is hard to deny the importance of graph analysis techniques, particularly the problem of link and link-sign prediction, in many real-world applications. Predicting future sign of connections in a network is an important task for online systems such as social networks, e-commerce, scientific research, and others. Several research studies have been presented since the early days of this century to predict either the existence of a link in the future or the property of the link. In this study we present a novel approach that combine both families by using machine learning techniques. Instead of focusing on the established links, we follow a new research approach that focusing on no-link relationship. We aim to understand the move between two states of no-link and link. We evaluate our methods in popular real-world signed networks datasets. We believe that the new approach by understanding the no-link relation has a lot of potential improvement in the future.

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