A Hybrid Systematic Review Approach on Complexity Issues in Data-Driven Fuzzy Inference Systems Development

Kalibatiene, Diana; Miliauskaitė, Jolanta

doi:10.15388/21-infor444

Cited by 17 publications

(9 citation statements)

References 129 publications

(179 reference statements)

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“…Multiple databases were used to select data sources: Web of Science, Scopus, ResearchGate, IEEE, IGI Global, Google Scholar, and conference pages with collections of articles with related content. In this review, the author applied the reverse snowball technique to search for keywords in titles, abstracts, and extended keyword sets (Kalibatienė & Miliauskaitė, 2021). The backward snowball technique makes it possible to find appropriate information until relevant papers are found.…”

Section: Methodology For Data Source Selectionmentioning

confidence: 99%

Generation of a Learning Path in E-Learning Environments: Literature Review

Ovtšarenko

2023

New Trends in Computer Sciences

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Education is moving into an e-learning environment, displacing contact and face-to-face learning. However, current e-learning environments cannot still personalise when creating e-learning paths. Identifying existing solutions’ problems and limitations is critical to generate new, more advanced ideas for creating personalised e-learning paths. The literature analysis, for which 28 articles for 2018–2022 were used, describes the existing solutions used to adapt and optimise e-learning. The article provides an overview of existing research in the field of personalisation of e-learning systems and the creation of e-learning trajectories, proposes the development of a taxonomy of studied methods for recommending and forming individual learning trajectories, analysis of the practices described in the articles to identify the most commonly used of them. Limitations, problems and unresolved issues in previous studies are summarised and provide information for further work on improving the results obtained and for choosing the direction of future research, which is given in the final part of the article.

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Section: Methodology For Data Source Selectionmentioning

confidence: 99%

Generation of a Learning Path in E-Learning Environments: Literature Review

Ovtšarenko

2023

New Trends in Computer Sciences

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“…As there are seven input features, we have aggregated six FIS systems to accommodate all inputs; however, two inputs were given at one stage. The reason to use only two input and single output FIS per stage is to limit the number of rules that could further reduce the computational complexity at every stage [ 60 ]. The decision about the organization of input parameters at every stage was made based on the correlation between features and the response variable, as shown in Figure 3 [ 61 , 62 , 63 ].…”

Section: Methodsmentioning

confidence: 99%

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

Saini

Dutta

Marques

2022

Sensors

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Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.

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“…In this study, we have used triangular MF, since it is easily implementable in FIS Kalibatiene, 2020a, 2020b;Kalibatienė and Miliauskaitė, 2021;Choi and Rhee, 2009).…”

Section: The Anfis Structurementioning

confidence: 99%

Development of a Fuzzy Inference Based Solar Energy Controller for Smart Marine Water Monitoring

Kalibatiene¹,

Miliauskaitė²,

Dzemydienė³

et al. 2021

Informatica

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Nowadays, there is a lack of smart marine monitoring systems, which have possibilities to integrate multi-dimensional components for monitoring and predicting marine water quality and making decisions for their optimal operations with minimal human intervention. This research aims to extend the smart coastal marine monitoring by proposing a solar energy planning and control component. The proposed approach involves the adaptive neuro-fuzzy inference system (ANFIS) for the wireless buoys, working online during the whole year in the Baltic Sea near the Lithuanian coast. The usage of our proposed fuzzy solar energy planning and control components allows us to prolong the lifespan of batteries in buoys, so it has a positive impact on sustainable development. The novelty and advantage of the proposed approach lie in establishing the ANFIS-based model to predict and control solar energy in a buoy for different lighting and temperature conditions depending on the four year seasons and to make a decision to transfer the collected data. The energy planning and consumption system for the wireless sensor network of buoys is carefully evaluated, and its prototype is developed. The proposed approach can be practically used for environmental monitoring, providing stakeholders with relevant and timely information for sound decision-making about hydro-meteorological situations in coastal marine water.

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A Hybrid Systematic Review Approach on Complexity Issues in Data-Driven Fuzzy Inference Systems Development

Cited by 17 publications

References 129 publications

Generation of a Learning Path in E-Learning Environments: Literature Review

Generation of a Learning Path in E-Learning Environments: Literature Review

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

Development of a Fuzzy Inference Based Solar Energy Controller for Smart Marine Water Monitoring

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