In-Vehicle Infotainment Systems: Using Bayesian Networks to Model Cognitive Selection of Music Genres

2020 ◽  
pp. 1-10
Author(s):  
George J. Dimitrakopoulos ◽  
Ilias E. Panagiotopoulos
Keyword(s):  
Bayesian Networks ◽  
Music Genres ◽  
Cognitive Selection ◽  
Selection Of

scholarly journals Integration of Healthcare domain Ontologies using Bayesian Networks

2019 ◽  
Vol 9 (2S) ◽  
pp. 46-52
Keyword(s):  
Bayesian Networks ◽  
Knowledge Sharing ◽  
Domain Knowledge ◽  
Bayesian Belief Networks ◽  
Integration Algorithm ◽  
Algorithmic Approach ◽  
Medical Practitioners ◽  
Automated Knowledge ◽  
The Individual ◽  
Selection Of

Semantic Web (SW) was created with the vision of knowledge sharing. Knowledge from the past and present help predict the future with the use of Machine Learning (ML) algorithms. SW powered with ontologies help in realizing machine interactions supporting automated knowledge extraction. Healthcare as a field of medical domain gives lot of importance for timely accurate decisions with the available features. Representing existing information in terms of ontologies, retrieving the decisions upon establishing interaction between the relevant ontologies within the same domain, knowledge sharing & reusing the existing facts are of great benefit to the medical practitioners and researchers which has lot of open challenges to be resolved in order to realize the same. To address the stated issues, an algorithmic approach – Ontologies Integration algorithm using Bayesian Networks (OIBN) based on Bayesian Belief Networks (BBN) working on Naïve beliefs has been proposed which works on symptoms through the attributes of related ontologies within the same domain exploring the symptom dependencies and their probability of occurrences in combination. Selection of features for integration will follow the steps proposed in Sequential Forward Feature Selection algorithm (SFFS). The observation on the correctness of the presented method over diabetic datasets represented in ontological form with integration of relevant features reveals that the knowledge graphs have been efficiently explored discovering the facts based on the probability theory. The experimental results conclude that the proposed technique is showing enhanced prediction accuracy of 80.95% which is better compared to accuracies of the individual ontologies prior to integration and existing state-of-art technique.

Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks

2001 ◽  
Vol 5 (5) ◽  
pp. 300-305
Author(s):  
Wimpie D. Nortje ◽  
◽  
Johann E. W. Holm ◽  
Gerhard P. Hancke ◽  
Imre. J. Rudas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Bayesian Networks ◽  
Training Data ◽  
Computational Time ◽  
Bayesian Techniques ◽  
Multiple Networks ◽  
The Neural Networks ◽  
Computational Errors ◽  
Bias Variance ◽  
Selection Of

Training neural networks involves selection of a set of network parameters, or weights, on account of fitting a non-linear model to data. Due to the bias in the training data and small computational errors, the neural networks’ opinions are biased. Some improvement is possible when multiple networks are used to do the classification. This approach is similar to taking the average of a number of biased opinions in order to remove some of the bias that resulted from training. Bayesian networks are effective in removing some of the bias associated with training, but Bayesian techniques are tedious in terms of computational time. It is for this reason that alternatives to Bayesian networks are investigated.

scholarly journals Security Threat Modelling With Bayesian Networks and Sensitivity Analysis for IAAS Virtualization Stack

2021 ◽  
Vol 33 (4) ◽  
pp. 44-69
Author(s):  
Asvija B. ◽  
Eswari R. ◽  
Bijoy M. B.
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Networks ◽  
Cloud Infrastructure ◽  
Security Threat ◽  
Attack Graph ◽  
Crucial Component ◽  
Threat Modelling ◽  
Definition Of ◽  
Cloud Infrastructures ◽  
Selection Of

Designing security mechanisms for cloud computing infrastructures has assumed importance with the widespread adoption of public clouds. Virtualization security is a crucial component of the overall cloud infrastructure security. In this article, the authors employ the concept of Bayesian networks and attack graphs to carry out sensitivity analysis on the different components involved in virtualization security for infrastructure as a service (IaaS) cloud infrastructures. They evaluate the Bayesian attack graph (BAG) for the IaaS model to reveal the sensitive regions and thus help the administrators to secure the high risk components in the stack. They present a formal definition of the sensitivity analysis and then evaluate using the BAG model for IaaS stack. The model and analysis presented here can also be used by security analysts and designers to make a selection of the security solutions based on the risk profile of vulnerable nodes and the corresponding cost involved in adding a defense against the identified vulnerabilities.

Cluster Analysis for the Cognitive Selection of Nonlinear Programming Algorithms

1990 ◽  
Author(s):  
Jin-xian Ma ◽  
Shi-huai Xie ◽  
Yong Chen
Keyword(s):  
Cluster Analysis ◽  
Nonlinear Programming ◽  
Expert System ◽  
Mechanical Design ◽  
Test Problems ◽  
Comparative Performance ◽  
Nonlinear Programming Algorithms ◽  
Cognitive Selection ◽  
Programming Algorithms ◽  
Selection Of

Abstract In recent years, cluster analysis has played an increasingly important role in statistical pattern recognition. Hoeltzel and Chieng have shown an example on cognitive selection of nonlinear programming algorithms in a mechanical design expert system. In this paper, an improved dynamic clustering of 3000 samples came from a comparative performance evaluation of six typical nonlinear programming softwares with randomly generated test problems has been made. Explanations resulting from the cluster analysis have been used to build rules to form the knowledge base of an optimization expert system.

scholarly journals Normative selection of Bayesian networks

2005 ◽  
Vol 93 (2) ◽  
pp. 340-357 ◽  
Author(s):  
Paola Sebastiani ◽  
Marco Ramoni
Keyword(s):  
Bayesian Networks ◽  
Selection Of

Sensation Seeking and the Use and Selection of Media Materials

2005 ◽  
Vol 97 (1) ◽  
pp. 236-244 ◽  
Author(s):  
Alice Hall
Keyword(s):  
Sensation Seeking ◽  
Regression Analyses ◽  
Television Programming ◽  
Music Genres ◽  
Urban Music ◽  
Urban Universities ◽  
Movie Theatre ◽  
Sensation Seeking Scale ◽  
Film Genres ◽  
Selection Of

175 individuals recruited from urban universities ( n = 82) and the surrounding community (120 women, 55 men; 82 18- to 25-yr.-olds, 26 26- to 34-yr.-olds, 44 35- to 50-yr.-olds, 23 50 yr. old or over) completed a sensation seeking scale and measures of the frequency with which they used specific media and selected specific television programming, film, and music genres. Regression analyses showed Sensation Seeking to be associated positively with Movie Theatre Attendance and with the Selection of Urban Music Genres. Sensation Seeking was also associated negatively with Selection of Light Film Genres.

scholarly journals Cognitive control of orofacial motor and vocal responses in the ventrolateral and dorsomedial human frontal cortex

2020 ◽  
Vol 117 (9) ◽  
pp. 4994-5005 ◽  
Author(s):  
Kep Kee Loh ◽  
Emmanuel Procyk ◽  
Rémi Neveu ◽  
Franck Lamberton ◽  
William D. Hopkins ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Human Brain ◽  
Language Production ◽  
Functional Neuroimaging ◽  
Learning Rule ◽  
Basic Function ◽  
Vocal Responses ◽  
Midcingulate Cortex ◽  
Cognitive Selection ◽  
Selection Of

In the primate brain, a set of areas in the ventrolateral frontal (VLF) cortex and the dorsomedial frontal (DMF) cortex appear to control vocalizations. The basic role of this network in the human brain and how it may have evolved to enable complex speech remain unknown. In the present functional neuroimaging study of the human brain, a multidomain protocol was utilized to investigate the roles of the various areas that comprise the VLF–DMF network in learning rule-based cognitive selections between different types of motor actions: manual, orofacial, nonspeech vocal, and speech vocal actions. Ventrolateral area 44 (a key component of the Broca’s language production region in the human brain) is involved in the cognitive selection of orofacial, as well as, speech and nonspeech vocal responses; and the midcingulate cortex is involved in the analysis of speech and nonspeech vocal feedback driving adaptation of these responses. By contrast, the cognitive selection of speech vocal information requires this former network and the additional recruitment of area 45 and the presupplementary motor area. We propose that the basic function expressed by the VLF–DMF network is to exert cognitive control of orofacial and vocal acts and, in the language dominant hemisphere of the human brain, has been adapted to serve higher speech function. These results pave the way to understand the potential changes that could have occurred in this network across primate evolution to enable speech production.

Cognitive Anchoring of Different Errors in Continuous Serial Learning

1969 ◽  
Vol 25 (1) ◽  
pp. 79-82 ◽  
Author(s):  
E. Rae Harcum
Keyword(s):  
Serial Position ◽  
Serial Position Curve ◽  
Serial Learning ◽  
Cognitive Factors ◽  
Symmetrical Component ◽  
Different Types ◽  
Cognitive Selection ◽  
Selection Of

This paper investigated different types of errors in serial learning, via a further analysis of the data obtained by Harcum, Pschirrer, and Coppage (1968) with continuous presentation of 10-trigram lists. The purpose was to discover whether the particular distributions of extralist intruding errors (ELI), intralist intruding errors (ILI), and failures to respond (FTR), which have been obtained using the conventional temporal gap between successive trials, are found when trials are continuous. Similar distributions would indicate that the relevant determinants are intervening cognitive factors, rather than stimulus variables. The results with continuous trials were similar to those for conventional pacing; FTR errors showed both asymmetrical and symmetrical (bowing) components, the ILI distribution was more nearly symmetrical with the minimum at the cognitively first item, and ELI errors, which were infrequent, varied little among serial positions. These results support the conclusion that an asymmetrical component of the serial-position curve is produced by the cognitive selection of one item to be learned first, to become the anchor for the later learning, and a symmetrical component is the result of associative or positional confusion.

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