scholarly journals Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2999 ◽  
Author(s):  
Miguel Arevalillo-Herráez ◽  
Maximo Cobos ◽  
Sandra Roger ◽  
Miguel García-Pineda
Keyword(s):  
Detection System ◽  
Data Transformation ◽  
Small Sample ◽  
Detection Methods ◽  
Eeg Signals ◽  
Individual Traits ◽  
Emotional Aspects ◽  
Public Repositories ◽  
Small Sample Problem ◽  
The Individual

Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

scholarly journals Recombination Shapes the Structure of an EnvironmentalVibrio choleraePopulation

2010 ◽  
Vol 77 (2) ◽  
pp. 537-544 ◽  
Author(s):  
Daniel P. Keymer ◽  
Alexandria B. Boehm
Keyword(s):  
Bacterial Species ◽  
Housekeeping Genes ◽  
Small Sample ◽  
Detection Methods ◽  
Significant Linkage Disequilibrium ◽  
Central California ◽  
Intraspecies Diversity ◽  
The Individual ◽  
Significant Linkage ◽  
Sequence Types

ABSTRACTVibrio choleraeconsists of pathogenic strains that cause sporadic gastrointestinal illness or epidemic cholera disease and nonpathogenic strains that grow and persist in coastal aquatic ecosystems. Previous studies of disease-causing strains have shownV. choleraeto be a primarily clonal bacterial species, but isolates analyzed have been strongly biased toward pathogenic genotypes, while representing only a small sample of the vast diversity in environmental strains. In this study, we characterized homologous recombination and structure among 152 environmentalV. choleraeisolates and 13 other putativeVibrioisolates from coastal waters and sediments in central California, as well as four clinicalV. choleraeisolates, using multilocus sequence analysis of seven housekeeping genes. Recombinant regions were identified by at least three detection methods in 72% of ourV. choleraeisolates. Despite frequent recombination, significant linkage disequilibrium was still detected among theV. choleraesequence types. Incongruent but nonrandom associations were observed for maximum likelihood topologies from the individual loci. Overall, our estimated recombination rate inV. choleraeof 6.5 times the mutation rate is similar to those of other sexual bacteria and appears frequently enough to restrict selection from purging much of the neutral intraspecies diversity. These data suggest that frequent recombination amongV. choleraemay hinder the identification of ecotypes in this bacterioplankton population.

Using Photovoice to understand physical and social living environment influence on adherence to diabetes

2021 ◽  
pp. 136345932110200
Author(s):  
Alice Scavarda ◽  
Giuseppe Costa ◽  
Franca Beccaria
Keyword(s):  
Blood Sugar ◽  
Diabetes Management ◽  
Lifestyle Changes ◽  
Living Environment ◽  
Local Health ◽  
Bonding Social Capital ◽  
Physical Context ◽  
Individual Traits ◽  
Italian City ◽  
The Individual

Within the past several years, a considerable body of research on adherence to diabetes regimen has emerged in public health. However, the focus of the vast majority of these studies has been on the individual traits and attitudes affecting adherence. Still little is known on the role of the social and physical context in supporting or hindering diabetes self-management, particularly from a qualitative standpoint. To address these limitations, this paper presents the findings of a Photovoice study on a sample of 10 type 2 diabetic older adults living in a deprived neighbourhood of an Italian city. The findings reveal that the possibility to engage in diet, exercise and blood sugar monitoring seems to be more affected by physical and social elements of the respondents’ environment than by the interviewees’ beliefs and attitudes. Both environmental barriers and social isolation emerge as barriers to lifestyle changes and self-care activities related to blood sugar monitoring. The predominance of bonding social capital, the scant level of trust and the negative perception of local health services result in a low level of social cohesion, a limited circulation of health information on diabetes management and, consequently, in poor health outcomes.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

scholarly journals A Synthetic Biology Approach Using Engineered Bacteria to Detect Perfluoroalkyl Substance (PFAS) Contamination in Water

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 801-807
Author(s):  
Nathaniel A Young ◽  
Ryan L Lambert ◽  
Angela M Buch ◽  
Christen L Dahl ◽  
Jackson D Harris ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Dna Sequences ◽  
Detection System ◽  
The United States ◽  
Detection Methods ◽  
Health Concern ◽  
Contaminated Water ◽  
Engineered Bacteria ◽  
Synthetic Biology Approach ◽  
Rhodococcus Jostii

ABSTRACT Introduction Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic compounds used industrially for a wide variety of applications. These PFAS compounds are very stable and persist in the environment. The PFAS contamination is a growing health issue as these compounds have been reported to impact human health and have been detected in both domestic and global water sources. Contaminated water found on military bases poses a potentially serious health concern for active duty military, their families, and the surrounding communities. Previous detection methods for PFAS in contaminated water samples require expensive and time-consuming testing protocols that limit the ability to detect this important global pollutant. The main objective of this work was to develop a novel detection system that utilizes a biological reporter and engineered bacteria as a way to rapidly and efficiently detect PFAS contamination. Materials and Methods The United States Air Force Academy International Genetically Engineered Machine team is genetically engineering Rhodococcus jostii strain RHA1 to contain novel DNA sequences composed of a propane 2-monooxygenase alpha (prmA) promoter and monomeric red fluorescent protein (mRFP). The prmA promoter is activated in the presence of PFAS and transcribes the mRFP reporter. Results The recombinant R. jostii containing the prmA promoter and mRFP reporter respond to exposure of PFAS by activating gene expression of the mRFP. At 100 µM of perfluorooctanoic acid, the mRFP expression was increased 3-fold (qRT-PCR). Rhodococcus jostii without exposure to PFAS compounds had no mRFP expression. Conclusions This novel detection system represents a synthetic biology approach to more efficiently detect PFAS in contaminated samples. With further refinement and modifications, a similar system could be readily deployed in the field around the world to detect this critical pollutant.

scholarly journals Within-Site Variability of Liana Wood Anatomical Traits: A Case Study in Laussat, French Guiana

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 523 ◽  
Author(s):  
Félicien Meunier ◽  
Sruthi M. Krishna Moorthy ◽  
Hannes P. T. De Deurwaerder ◽  
Robin Kreus ◽  
Jan Van den Bulcke ◽  
...  
Keyword(s):  
Specific Gravity ◽  
French Guiana ◽  
Growth Form ◽  
Vessel Diameter ◽  
Small Sample ◽  
Diameter Distribution ◽  
Hydraulic Architecture ◽  
Woody Vines ◽  
Wood Trait ◽  
The Individual

Research Highlights: We investigated the variability of vessel diameter distributions within the liana growth form among liana individuals originating from a single site in Laussat, French Guiana. Background and Objectives: Lianas (woody vines) are key components of tropical forests. Lianas are believed to be strong competitors for water, thanks to their presumed efficient vascular systems. However, unlike tropical trees, lianas are overlooked in field data collection. As a result, lianas are often referred to as a homogeneous growth form while little is known about the hydraulic architecture variation among liana individuals. Materials and Methods: We measured several wood hydraulic and structural traits (e.g., basic specific gravity, vessel area, and vessel diameter distribution) of 22 liana individuals in a single sandy site in Laussat, French Guiana. We compared the liana variability of these wood traits and the correlations among them with an existing liana pantropical dataset and two published datasets of trees originating from different, but species-rich, tropical sites. Results: Liana vessel diameter distribution and density were heterogeneous among individuals: there were two orders of magnitude difference between the smallest (4 µm) and the largest (494 µm) vessel diameters, a 50-fold difference existed between extreme vessel densities ranging from 1.8 to 89.3 vessels mm−2, the mean vessel diameter varied between 26 µm and 271 µm, and the individual theoretical stem hydraulic conductivity estimates ranged between 28 and 1041 kg m−1 s−1 MPa−1. Basic specific gravity varied between 0.26 and 0.61. Consequently, liana wood trait variability, even within a small sample, was comparable in magnitude with tree surveys from other tropical sites and the pantropical liana dataset. Conclusions: This study illustrates that even controlling for site and soil type, liana traits are heterogeneous and cannot be considered as a homogeneous growth form. Our results show that the liana hydraulic architecture heterogeneity across and within sites warrants further investigation in order to categorize lianas into functional groups in the same way as trees

scholarly journals The comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110283
Author(s):  
Meltem Yurtcu ◽  
Hülya Kelecioglu ◽  
Edward L Boone
Keyword(s):  
Classical Test Theory ◽  
Small Sample ◽  
Test Theory ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Test Equating ◽  
Classical Test ◽  
A Value ◽  
Item Functioning ◽  
The Individual

Bayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.

Using complementary remote detection methods for retrofitted eco-passages: a case study for monitoring individual koalas in south-east Queensland

2016 ◽  
Vol 43 (5) ◽  
pp. 369 ◽  
Author(s):  
C. E. Dexter ◽  
R. G. Appleby ◽  
J. P. Edgar ◽  
J. Scott ◽  
D. N. Jones
Keyword(s):  
Road Construction ◽  
Primary Objective ◽  
Correlated Data ◽  
Camera Traps ◽  
Detection Methods ◽  
Urban Landscapes ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Phascolarctos Cinereus ◽  
The Individual

Context Vehicle-strike has been identified as a key threatening process for koala (Phascolarctos cinereus) survival and persistence in Australia. Roads and traffic act as barriers to koala movement and can impact dispersal and metapopulation dynamics. Given the high cost of wildlife mitigation structures such as purpose-built fauna-specific underpasses or overpasses (eco-passages), road construction and management agencies are constantly seeking cost-effective strategies that facilitate safe passage for fauna across roads. Here we report on an array of detection methods trialled to verify use of retrofitted road infrastructure (existing water culverts or bridge underpasses) by individual koalas in fragmented urban landscapes in south-east Queensland. Aims The study examined whether the retrofitting of existing road structures at six sites facilitated safe passage for koalas across roads. Our primary objective was to record utilisation of retrofitted infrastructure at the level of the individual. Methods We used a combination of existing monitoring methods such as GPS/VHF collars, camera traps, sand plots, and RFID tags, along with a newly developed animal-borne wireless identification (WID) tag and datalogging system, specifically designed for this project, to realise the study aims. Key results We were able to verify 130 crossings by koalas involving a retrofitted structure or a road surface over a 30-month period by using correlated data from complementary methods. We noted that crossings were generally uncommon and mostly undertaken by only a subset of our tagged individuals at each site (21% overall). Conclusions An important element of this study was that crossing events could be accurately determined at the level of the individual. This allowed for detailed assessment of eco-passage usage, rather than the more usual approach of simply recording species’ presence. Implications This study underscores the value of identifying the constraints of each individual monitoring method in relation to site conditions. It also highlights the benefits of contingency planning to limit data loss (i.e. using more than one method to collect data). We suggest an approach that uses complementary monitoring methods has significant advantages for researchers, particularly with reference to improving understanding of whether eco-passages are meeting their prescribed conservation goals.

scholarly journals Hybrid classification model to detect advanced intrusions using data mining techniques

2018 ◽  
Vol 7 (2.4) ◽  
pp. 10
Author(s):  
V Mala ◽  
K Meena
Keyword(s):  
Data Mining ◽  
Learning Algorithm ◽  
Detection System ◽  
Classification Model ◽  
Detection Methods ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Intruder Detection ◽  
Hybrid Classification ◽  
Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

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