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Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. [ABSTRACT FROM AUTHOR]

This study presents a review of literature on the usage of artificial neural networks (ANNs) architecture contribution method and structural equation modeling (SEM), and proposes a new selection process in the context of algorithm -based SEM-ANNs schemes. This study enriches academic literature by providing a review of all the main aspects of customization in ANNs and contribution methods in combination with SEM. Academic databases are examined for exhibition findings, yielding 253 papers published between 2016 and 2022. The retrieved papers are categorized according to inclusion criteria, and the final set of 73 articles are discussed based on two directions, namely, 'Sector-based' and 'Algorithm-based' as a new representation of taxonomy research. A state-of-the-art bibliographic analysis is presented. This review also identifies modern challenges and open issues in terms of multiple evaluation criteria, importance criteria, and data variations related to the selection of customizations in ANNs and contribution methods combined with SEM in different industrial cases. Several issues fall under multicriteria decision making for handling complexity problems in different ANNs and contribution methods. Thus, this study also presents a research proposal and recommends a solution based on a three-phase methodology for handling the selection and overcoming the identified issues, subsequently completing a strategic guideline solution. [ABSTRACT FROM AUTHOR]

The aim of this article is to review and analyse previous academic articles associated with car behaviour analysis for the period of 2010 to June 10, 2021 and understand the benefits of using data collection devices. Articles related to car driver behaviour and sensor utilisation are systematically searched. Three major databases – ScienceDirect, IEEE Xplore and Web of Science – were searched. A set of inclusion and exclusion criteria were developed for the search protocol. All articles were coherently classified via taxonomy. Also. The motives that have led researchers to continue their investigations are explored. The challenges and issues of driver behaviour analysis are illustrated with respect to power consumption, data analysis, detection, cost, security and privacy, sensor usage and individual challenges. The research direction of this review points towards different aspects based on the critical analysis of the different scenarios of driver behaviour studies in real time situations. Here, the critical behaviour analysis of intelligent transportation system development is addressed. The gaps in the reviewed articles include the following: sensors used during experiments, the effect of thresholds on labelling processes or data balancing and classification accuracy, the thresholds in identifying driving styles in the car-following model, insufficient experiment size (large scale or small scale) and limitations in data pre-processing. An implementation map depicting the steps of the case study is provided to give insights into the procedures and the problems they address. This review is expected to offer valid and clear points, contributing to the enhancement of driver behaviour research. [ABSTRACT FROM AUTHOR]