scholarly journals Autonomous Cognitive Model and Analysis for Survivable System

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yiwei Liao ◽  
Guosheng Zhao ◽  
Jian Wang
Keyword(s):  
Cognitive Performance ◽  
Cognitive Model ◽  
Cognitive State ◽  
Growing Up ◽  
Cognitive Computing ◽  
Recognition Mechanism ◽  
Detection Systems ◽  
System Survivability ◽  
State Transition Graph ◽  
Test Parameters

The research on autonomous recognition mechanism for survivability has vigorously been growing up. A method of autonomous cognitive model and quantitative analysis for survivable system was proposed based on cognitive computing technology. Firstly, a cognitive model for survivable system with cross-layer perception ability was established, a self-feedback evolution mode of cognitive unit based on monitor-decide-execute loop structure was improved, and a self-configuration of cognitive unit is realized. Then, combined with the cognitive state transition graph, the analysis of cognitive performance for survivable systems based on dynamic cognitive behavioral changes was constructed. Finally, the cognitive processes of survivable system were described by using formal modeling. Simulation validated the influence degree of test parameters on system survivability from two perspectives of the probability of intrusion detection systems vulnerability and attacks detected. Results show that enhancing the rate of monitoring actions change and the rate of performing actions change obviously improved the cognitive performance of survivable system.

Electroencephalography based detection of cognitive state during learning tasks: An extensive approach

2021 ◽  
Vol 25 (2) ◽  
pp. 157-178
Author(s):  
Theparambil Asharaf Suhail ◽  
◽  
Kottanayil Pally Indiradevi ◽  
Ekkarakkudy Makkar Suhara ◽  
Poovathinal Azhakan Suresh ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cognitive Performance ◽  
Mental State ◽  
Assessment Model ◽  
Support Vector ◽  
Cognitive State ◽  
Linear Discriminant ◽  
Cognitive States ◽  
Learning Tasks ◽  
The Subject

Detecting cognitive states during learning tasks is an essential component in neurocognitive experiments for assessing and enhancing the cognitive performance of individuals. Studies have demonstrated that mental state recognition systems utilizing brain signals are proficient in the automated monitoring of learners’ cognitive states. The current study focuses on developing an efficient individualized and cross-subject cognitive state assessment model based on Electroencephalography (EEG) patterns during learning tasks. For this study, EEGs of 20 healthy subjects were recorded during a resting state followed by a learning task and examined EEG activations patterns in a wide perspective of feature types and rhythms. The extracted features included time-domain features such as Hjorth parameters, Wavelet-based features, and Spectral entropy. Three classifiers, Support Vector Machine, k-Nearest Neighbor, and Linear Discriminant Analysis were employed to recognize the mental state. A new EEG-based attention index using band ratios is proposed and is demonstrated as an effective predictor for recognizing attentive reading. The proposed model can yield recognition performance with an accuracy of 92.9% in the subject-dependent approach and 77.2% in the subject-independent approach with the Support Vector Machine Classifier. The findings are useful for the design and development of neurofeedback systems that monitor and enhance the cognitive performance in healthy individuals, as well as in individuals with cognitive deficits.

scholarly journals Study partner types and prediction of cognitive performance: implications to preclinical Alzheimer’s trials

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Michelle M. Nuño ◽  
◽  
Daniel L. Gillen ◽  
Joshua D. Grill
Keyword(s):  
Cognitive Performance ◽  
Functional Performance ◽  
Objective Measure ◽  
Cognitive Status ◽  
Cognitive State ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Partner Type ◽  
Study Partner ◽  
The Difference

Abstract Background Alzheimer’s disease (AD) clinical trials require enrollment of a participant and a study partner, whose role includes assessing participant cognitive and functional performance. AD trials now investigate early stages of the disease, when participants are not cognitively impaired. This gives rise to the question of whether study partners or participants provide more information in these trials. Methods We used data from the AD Cooperative Study Prevention Instrument Project (ADCS-PI) to compare participant and study partner predictions of the participant’s current and future cognitive state. We used the Cognitive Function Instrument (CFI) as a measure of evaluation of the participant’s cognitive status and the modified ADCS Preclinical Alzheimer’s Cognitive Composite (mADCS-PACC) as an objective measure of cognition. Stratifying by cognitive status and study partner type and adjusting for other predictors of the participant’s cognitive state, we used random forests along with estimated mean variable importance (eMVI) to assess how well each member of the dyad can predict cognitive state at current and later visits. We also fit linear regression models at each time point and for each scenario. Results Participants were better at predicting future cognitive status compared to their study partners regardless of study partner type, though the difference between participants and partners was greatest for non-spousal dyads in the lowest-performing quartile. Cross-sectional assessments differed substantially by dyad type. Within the lowest cognitive performance quartile, participants having a non-spousal study partner outperformed their partners in assessing cognition at later times. Spousal partners, in contrast, outperformed participants later in the study in predicting current cognitive performance. Conclusions These results indicate that participants tend to be better at predicting future cognition compared to their study partners regardless of the study partner type. When assessing current cognition, however, spousal study partners perform better at later time points and non-spousal study partners do not provide as much information regarding participant cognitive state.

scholarly journals Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

2018 ◽  
Author(s):  
Sundar Mahalingam ◽  
Ritika Kabra ◽  
Shailza Singh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Multilayer Perceptron ◽  
Cognitive Model ◽  
Gene Expression Omnibus ◽  
Good Choice ◽  
Integrated System ◽  
Cognitive Computing ◽  
Feed Forward ◽  
Biochemical Characterisation

AbstractLeishmaniasis is an endemic parasitic disease, predominantly found in the poor locality of Africa, Asia and Latin America. It is associated with malnutrition, weak immune system of people and their housing locality. At present, it is diagnosed by microscopic identification, molecular and biochemical characterisation or serum analysis for parasitic compounds. In this study, we present a new approach for diagnosing Leishmaniasis using cognitive computing. The Genetic datasets of leishmaniasis are collected from Gene Expression Omnibus database and it’s then processed. The algorithm for training and developing a model, based on the data is prepared and coded using python. The algorithm and their corresponding datasets are integrated using TensorFlow dataframe. A feed forward Artificial Neural Network trained model with multi-layer perceptron is developed as a diagnosing model for Leishmaniasis, using genetic dataset. It is developed using recurrent neural network. The cognitive model of the trained network is interpreted using the maps and mathematical formula of the influencing parameters. The credit of the system is measured using the accuracy, loss and error of the system. This integrated system of the leishmaniasis genetic dataset and neural network proved to be the good choice for diagnosis with higher accuracy and lower error. Through this approach, all records of the data are effectively incorporated into the system. The experimental results of feed forward multilayer perceptron model after normalization; mean square error (219.84), loss function (1.94) and accuracy (85.71%) of the model, shows good fit of model with the process and it could possibly serve as a better solution for diagnosing Leishmaniasis in future, using genetic datasets.The code is available in Github repository:https://github.com/shailzasingh/Machine-Learning-code-for-analyzing-genetic-dataset-in-Leishmaniasis

scholarly journals A Cognitive Theory-based Opportunistic Resource-Pooling Scheme for Ad hoc Networks

2017 ◽  
Vol 26 (1) ◽  
pp. 47-68
Author(s):  
Seema B. Hegde ◽  
B. Satish Babu ◽  
Pallapa Venkataram
Keyword(s):  
Ad Hoc Networks ◽  
Traffic Management ◽  
Ad Hoc ◽  
Cognitive Theory ◽  
Cognitive Model ◽  
Network Control ◽  
Cognitive Computing ◽  
Resource Pooling ◽  
Hoc Networks ◽  
Routing Congestion

AbstractResource pooling in ad hoc networks deals with accumulating computing and network resources to implement network control schemes such as routing, congestion, traffic management, and so on. Pooling of resources can be accomplished using the distributed and dynamic nature of ad hoc networks to achieve collaboration between the devices. Ad hoc networks need a resource-pooling technique that offers quick response, adaptability, and reliability. In this context, we are proposing an opportunistic resource-pooling scheme that uses a cognitive computing model to accumulate the resources with faster resource convergence rate, reliability, and lower latency. The proposed scheme is implemented using the behaviors-observations-beliefs cognitive model, in which the resource-pooling decisions are made based on accumulated knowledge over various behaviors exhibited by nodes in ad hoc networks.

scholarly journals A Neuroadaptive Cognitive Model for Dealing With Uncertainty in Tracing Pilots' Cognitive State

2020 ◽  
Vol 12 (3) ◽  
pp. 1012-1029 ◽  
Author(s):  
Oliver W. Klaproth ◽  
Marc Halbrügge ◽  
Laurens R. Krol ◽  
Christoph Vernaleken ◽  
Thorsten O. Zander ◽  
...  
Keyword(s):  
Cognitive Model ◽  
Cognitive State

THE ROLE OF DYSFUNCTIONAL BELIEFS IN INDIVIDUALS WHO EXPERIENCE PSYCHOSIS AND USE SUBSTANCES: IMPLICATIONS FOR COGNITIVE THERAPY AND MEDICATION ADHERENCE

1998 ◽  
Vol 26 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Hermine L. Graham
Keyword(s):  
Substance Use ◽  
Medication Adherence ◽  
Subjective Experience ◽  
Cognitive Model ◽  
Developmental Model ◽  
Cognitive State ◽  
Case Formulation ◽  
Treatment Services ◽  
Adherence To Medication ◽  
Starting Point

It can be argued that an individual’s subjective experience and beliefs about a substance are important. Motives and expectancies regarding the use of alcohol and drugs are often that they are going to modify a cognitive state or help them cope with a particular situation. However, there are growing concerns in the U.S.A. and in the U.K. regarding individuals who experience psychosis and concurrently use substances. Correctly diagnosing individuals with dual presentation is said to be difficult, engagement in treatment is problematic, and medication adherence and prognosis poor. In this paper a cognitive-developmental model is proposed. I suggest that for individuals who experience psychosis and also use drugs or alcohol, the ability to identify the relationship between the substance use and the psychotic illness in terms of a case formulation/conceptualization would provide a good starting point for developing strategies and interventions that are most likely to succeed in treatment. Such an approach would explicitly address key cognitions. Unless the dysfunctional substance-related beliefs are addressed, adherence to medication and engagement with treatment services will be hindered and the possibility of relapsing to problematic substance use and acute psychosis remains. A cognitive treatment component, to target these beliefs, based on the cognitive model of substance misuse and the motivational interviewing approach will also be briefly outlined.

The Use of Executable Cognitive Models in Simulation-Based Intelligent Embedded Training

1997 ◽  
Vol 41 (2) ◽  
pp. 1118-1122 ◽  
Author(s):  
Wayne Zachary ◽  
Joan Ryder ◽  
James Hicinbothom ◽  
Kevin Bracken
Keyword(s):  
Cognitive Model ◽  
Performance Standards ◽  
Cognitive State ◽  
Guided Practice ◽  
Training Strategy ◽  
Training Systems ◽  
Simulation Based ◽  
Knowledge Tracing ◽  
And Performance ◽  
Expert Models

This paper defines a new role for expert models in intelligent embedded training — guiding practice. The integration of problem-based practice with focused, automated instruction has long proven elusive in training systems for complex real-world domains. The training strategy of ‘guided practice’ offers a way to merge the approaches of traditional simulation-based practice and intelligent tutoring's knowledge tracing. The performance of the trainee is dynamically assessed against scenario-specific expectations and performance standards, which are generated during the simulation by embedded models of expert operators. This research developed an executable cognitive model capable of solving realistic simulation scenarios in an expert-level manner, identified and implemented modifications and extensions to this baseline model needed to generate dynamic and adaptive expectations of future trainee actions, and developed means of providing cognitive state information for use in (separate) diagnostic processes, without resorting to full-scale knowledge tracing methods.

scholarly journals The Role of Socioeconomic Inequalities In Transitioning to Neurocognitive Disorders in English Population

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 719-720
Author(s):  
Aswathikutty Gireesh ◽  
Amanda Sacker ◽  
Anne McMunn ◽  
Dorina Cadar
Keyword(s):  
Cognitive Performance ◽  
Transient State ◽  
Later Life ◽  
Neurocognitive Disorders ◽  
Lower Probability ◽  
Rapid Progression ◽  
Cognitive State ◽  
English Population ◽  
Using Data

Abstract The association between socioeconomic position (SEP) and dementia is well studied. However, scant attention has been given to the relationship with mild cognitive impairment (MCI), often considered a transient state between normal cognition and dementia. The purpose of this study was to determine the role of various SEP markers such as education and wealth on transitioning to MCI and dementia over a four-year period using data from the English Longitudinal Study of Ageing, a national representative sample of the English population aged 50+. We ascertained MCI and dementia over four years, using a validated algorithm based on physician diagnosis and lower cognitive performance (1 standard deviation below the mean) on multiple standardised tests adjusted for age and education. A Multistate Markov survival model was utilised to investigate whether different SEP markers increased the risk of specific transitions between normal cognitive performance and MCI or dementia, with the latter being considered an absorbing state. During the study period, a quarter of participants progressed to MCI from the normal state. Being in the lowest quintile of wealth was associated with a lower probability of transitioning back to a normal cognitive state from MCI, compared with those in the highest quintile. Greater wealth was weakly associated with a lower risk of transitioning from normal cognitive state to MCI and from MCI to dementia. The overall results imply that socioeconomic advantage might be protective against rapid progression from mild to more severe neurocognitive disorders such as dementia in later life.

scholarly journals Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis

2017 ◽  
Vol 25 (Suppl. 2) ◽  
pp. 175-189 ◽  
Author(s):  
Enaitz Ezpeleta ◽  
Iñaki Garitano ◽  
Urko Zurutuza ◽  
José María Gómez Hidalgo
Keyword(s):  
Sentiment Analysis ◽  
Online Social Networks ◽  
Security And Privacy ◽  
Short Message ◽  
Growing Up ◽  
Detection Systems ◽  
Automatic Text Classification ◽  
Novel Method ◽  
Personality Recognition ◽  
Short Messages

Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced.

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