System Safety Analysis for Critical System Applications Using Bayesian Networks

Prabhakaran, Remya; Krishnaprasad, R.; Nanda, Manju; Jayanthi, J.

doi:10.1016/j.procs.2016.07.294

Cited by 4 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This provides the benefit of being able to compute inference “omnidirectionally.” [ 17 ]. In this research, three Bayesian networks were considered: Naïve Bayes [ 15 ], Tree Augmented Naïve Bayes (TAN) [ 12 ], and Sons and Spouses (SS) [ 18 ]. First, Naïve Bayes was not used.…”

Section: Methodsmentioning

confidence: 99%

Identifying High-Risk Factors of Depression in Middle-Aged Persons with a Novel Sons and Spouses Bayesian Network Model

et al. 2020

View full text Add to dashboard Cite

It has been reported repeatedly that depression in middle-aged people may cause serious ramifications in public health. However, previous studies on this important research topic have focused on utilizing either traditional statistical methods (i.e., logistic regressions) or black-or-gray artificial intelligence (AI) methods (i.e., neural network, Support Vector Machine (SVM), ensemble). Previous studies lack suggesting more decision-maker-friendly methods, which need to produce clear interpretable results with information on cause and effect. For the sake of improving the quality of decisions of healthcare decision-makers, public health issues require identification of cause and effect information for any type of strategic healthcare initiative. In this sense, this paper proposes a novel approach to identify the main causes of depression in middle-aged people in Korea. The proposed method is the Sons and Spouses Bayesian network model, which is an extended version of conventional TAN (Tree-Augmented Naive Bayesian Network). The target dataset is a longitudinal dataset employed from the Korea National Health and Nutrition Examination Survey (KNHANES) database with a sample size of 8580. After developing the proposed Sons and Spouses Bayesian network model, we found thirteen main causes leading to depression. Then, genetic optimization was executed to reveal the most probable cause of depression in middle-aged people that would provide practical implications to field practitioners. Therefore, our proposed method can help healthcare decision-makers comprehend changes in depression status by employing what-if queries towards a target individual.

show abstract

Section: Methodsmentioning

confidence: 99%

Identifying High-Risk Factors of Depression in Middle-Aged Persons with a Novel Sons and Spouses Bayesian Network Model

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Khakzad et al (2013) presented a dynamic safety analysis method by mapping the bow-tie to BN. Prabhakaran et al (2016) proposed a safety assessment approach for an unmanned aerial vehicle (UAV) safety-critical system to appropriate monitor the safety-critical outputs. This enabled the use of BNs to secure the system and to ensure its fully successful performance.…”

Section: Safety-critical Systemsmentioning

confidence: 99%

Artificial intelligence in safety-critical systems: a systematic review

Wang

Chung

2021

IMDS

View full text Add to dashboard Cite

PurposeThis study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.Design/methodology/approachA total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.FindingsThe literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.Practical implicationsThis study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.Originality/valueThis is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.

show abstract

“…In this SLR, it is observed that 43 out of 51 BN-based model have used less than 40 nodes. However, the remaining eight have used equal or more than 40, including Xiaoyan et al [65], Song et al [104], Zhiqiang et al [70], Xiqiang et al [85], Barry et al [54], Jiali et al [90], Remya et al [78], and Jinsoo et al [82], 40, 45, 47, 47, 51, 58, 60, and 64, respectively. However, all models that utilized GT and BN simultaneously have used less than 40 nodes in the developed system.…”

Section: Data Sources and Number Of Nodes Used To Construct Bn/gtmentioning

confidence: 99%

“…Mingjing et al [47] Simulation Not Specified Xiaoxue et al [48] Real-time Not Specified Xin et al [49] Real-time Not Specified Meizhi et al [50] Preliminary Not Specified Niamat et al [51] Preliminary Outsider, Insider Chengpeng et al [52] Real-time Not Specified Yi et al [53] Simulation Not Specified Barry et al [54] Preliminary Not Specified Alexandre et al [55] Simulation Outsider Sabarathinam et al [56] Simulation Not Specified Seyedmohsen et al [57] Simulation Not Specified Mario et al [58] Simulation Not Specified Chao et al [59] Preliminary Not Specified Nima et al [60] Real-time Outsider Hui et al [61] Real-time Outsider Xiqiang et al [62] Real-time Not Specified Jamal et al [63] Simulation Not Specified Elvin et al [64] Preliminary Not Specified Xiaoyan et al [65] Real-time Not Specified Ying et al [66] Real-time Outsider Subhojeet et al [67] Simulation Not Specified Huai et al [68] Simulation Not Specified Gabriele et al [69] Preliminary Not Specified Zhiqiang et al [70] Simulation Not Specified Jinsoo et al [71] Real-time Insider Donya et al [72] Preliminary Not Specified Xianyou et al [73] Preliminary Not Specified Galizia et al [74] Simulation Not Specified Francesca et al [75] Simulation Outsider Zhao et al [76] Real-time Not Specified Mark et al [77] Real-time Outsider, Insider Remya et al [78] Simulation Not Specified Xin Chen [79] Simulation Not Specified Mark et al [80] Preliminary Not Specified Martin et al [81] Preliminary Not Specified Jinsoo et al…”

Section: Study Implementation Criteria Threat Actormentioning

confidence: 99%

Towards Integration of Security and Safety Measures for Critical Infrastructures Based on Bayesian Networks and Graph Theory: A Systematic Literature Review

Pirbhulal

Gkioulos

Katsikas

2021

Signals

View full text Add to dashboard Cite

In recent times, security and safety are, at least, conducted in safety-sensitive or critical sectors. Nevertheless, both processes do not commonly analyze the impact of security risks on safety. Several scholars are focused on integrating safety and security risk assessments, using different methodologies and tools in critical infrastructures (CIs). Bayesian networks (BN) and graph theory (GT) have received much attention from academia and industries to incorporate security and safety features for different CI applications. Hence, this study aims to conduct a systematic literature review (SLR) for co-engineering safety and security using BN or GT. In this SLR, the preferred reporting items for systematic reviews and meta-analyses recommendations (PRISMA) are followed. Initially, 2295 records (acquired between 2011 and 2020) were identified for screening purposes. Later on, 240 articles were processed to check eligibility criteria. Overall, this study includes 64 papers, after examining the pre-defined criteria and guidelines. Further, the included studies were compared, regarding the number of required nodes for system development, applied data sources, research outcomes, threat actors, performance verification mechanisms, implementation scenarios, applicability and functionality, application sectors, advantages, and disadvantages for combining safety, and security measures, based on GT and BN. The findings of this SLR suggest that BN and GT are used widely for risk and failure management in several domains. The highly focused sectors include studies of the maritime industry (14%), vehicle transportation (13%), railway (13%), nuclear (6%), chemical industry (6%), gas and pipelines (5%), smart grid (5%), network security (5%), air transportation (3%), public sector (3%), and cyber-physical systems (3%). It is also observed that 80% of the included studies use BN models to incorporate safety and security concerns, whereas 15% and 5% for GT approaches and joint GT and BN methodologies, respectively. Additionally, 31% of identified studies verified that the developed approaches used real-time implementation, whereas simulation or preliminary analysis were presented for the remaining methods. Finally, the main research limitations, concluding remarks and future research directions, are presented

show abstract

System Safety Analysis for Critical System Applications Using Bayesian Networks

Cited by 4 publications

References 8 publications

Identifying High-Risk Factors of Depression in Middle-Aged Persons with a Novel Sons and Spouses Bayesian Network Model

Identifying High-Risk Factors of Depression in Middle-Aged Persons with a Novel Sons and Spouses Bayesian Network Model

Artificial intelligence in safety-critical systems: a systematic review

Towards Integration of Security and Safety Measures for Critical Infrastructures Based on Bayesian Networks and Graph Theory: A Systematic Literature Review

Contact Info

Product

Resources

About