Accident Identification Using Fuzzy Cognitive Maps with Adaptive Non-Linear Hebbian Learning Algorithm

Sivabalaselvamani, D.; Harishankher, A.S; Rahunathan, L.; Tamilarasi, A.

doi:10.2139/ssrn.3125251

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Through this work, to know the purposes behind the accident, the Hebbian calculation on Non-straight units is utilized for preparing FCM. [9] An Intelligent Transportation System (ITS) is an impelled program that endeavors, without embodying data, to get ready new offices, to relate to the various vehicle and traffic the executives' strategies , and to permit recognized clients to be the best point by point and to make transport frameworks more steady, more arranged and that's just the beginning 'quicker witted' use. The machine comprises generally an On-Board Unit (OBU), a SmartLicense-(SL), a PC, and Road Side Units (RSUs).…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental Analysis using Deep Learning Techniques for Safety and Riskless Transport - A Sustainable Mobility Environment for Post Covid-19

Selvakarthi¹,

Sivabalaselvamani²,

Ashwath³

et al. 2021

2021 6th International Conference on Inventive Computation Technologies (ICICT)

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Safety living in the society is the greatest challenge due to COVID-19. Transportation has a crucial role in ensuring the safety of society. To bring back the normality of the society after COVID-19, proper wearing of a mask and strict distancing can play a vital role in the solutions of environment and health. Hence the development of smart and riskless transport with active surveillance of passengers to enforcing the wear of mask and maintaining the social distance can be attained through deep learning algorithms (thermal imaging and sensor technologies). In this proposed work, the various image patterns have analyzed using deep learning algorithms. Passengers' health and count inside the bus are measured using sensor technologies. Hence the development of this model has capable of ensuring social distancing among the people and avoiding the crowd for riskless transport.

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Section: Literature Reviewmentioning

confidence: 99%

Experimental Analysis using Deep Learning Techniques for Safety and Riskless Transport - A Sustainable Mobility Environment for Post Covid-19

Selvakarthi¹,

Sivabalaselvamani²,

Ashwath³

et al. 2021

2021 6th International Conference on Inventive Computation Technologies (ICICT)

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“…Because of the ability of this approach to improve expert-defined weight matrices, several works in the current literature have used it for problem-solving in a variety of domains. For instance, Sivabalaselvamani et al (2018) used an adaptive nonlinear Hebbian learning algorithm in training FCM for accident identification. Anninou and Groumpos (2014) modeled Parkinson’s disease using FCMs with nonlinear Hebbian learning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hebbian-based approaches are unsupervised procedures that use available data and a learning formula (based on several modifications of the Hebbian law) to adjust the weight matrix iteratively (Felix et al, 2019;Papageorgiou, 2011). The goal of this approach is to yield weight matrices based on expert knowledge and improve the accuracy of the expert-defined weights (Felix et al, 2019;Papageorgiou, 2011;Sivabalaselvamani et al, 2018). Because of the ability of this approach to improve expertdefined weight matrices, several works in the current literature have used it for problem-Analysis of green computing adoption solving in a variety of domains.…”

Section: Fuzzy Cognitive Mappingmentioning

confidence: 99%

A systemic analysis of green computing adoption using genetically evolved fuzzy cognitive map: a Philippine scenario

Abellana

2020

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Purpose This paper aims to propose a new genetically evolved fuzzy cognitive mapping approach as a decision-making framework for analyzing the relationships between the drivers and strategies for green computing adoption. Design/methodology/approach A focus group discussion among stakeholders in the Philippines is used to establish the relationships between the drivers and strategies of green computing adoption. Findings The proposed approach significantly reduces the time complexity for developing the fuzzy cognitive maps and provides a basis for comprehensively clustering drivers and strategies that share similar characteristics. Research limitations/implications This paper’s results provide insights into how the drivers and strategies of green computing adoption facilitate the intention of adopting stakeholders. Moreover, it provides a framework for analyzing structural relationships that exist between factors in a compliant manner. Originality/value To the best of the author’s knowledge, the paper is the first to analyze the drivers and strategies of green computing under a complex systems’ perspective. Moreover, this is the first study to offer lenses in a Philippine scenario.

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Artificial Intelligence in Data-Driven Analytics for the Personalized Healthcare

Sivabalaselvamani¹,

Selvakarthi²,

Yogapriya³

et al. 2021

2021 International Conference on Computer Communication and Informatics (ICCCI)

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Accident Identification Using Fuzzy Cognitive Maps with Adaptive Non-Linear Hebbian Learning Algorithm

Cited by 4 publications

References 9 publications

Experimental Analysis using Deep Learning Techniques for Safety and Riskless Transport - A Sustainable Mobility Environment for Post Covid-19

Experimental Analysis using Deep Learning Techniques for Safety and Riskless Transport - A Sustainable Mobility Environment for Post Covid-19

A systemic analysis of green computing adoption using genetically evolved fuzzy cognitive map: a Philippine scenario

Artificial Intelligence in Data-Driven Analytics for the Personalized Healthcare

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