DAS benchmarking methodology based on FWZIC II and FDOSM II to support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles

Mahmoud, U. S.; Albahri, A. S.; Alsattar, Hassan A.; Zaidan, A. A.; Talal, Mohammed; Mohammed, R. T.; Albahri, O. S.; Zaidan, B. B.; Alamoodi, A. H.; Qahtan, Sarah

doi:10.1007/s12652-022-04201-4

Cited by 22 publications

(5 citation statements)

References 36 publications

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“…There is no clear and valid method for selecting this type of assessment procedure. Hence, providing a systematic procedure for DAS selection would be a huge leap for research and industrial communities [74].…”

Section: Assessment Methods Of Dasmentioning

confidence: 99%

IoT-Based Water Monitoring Systems: A Systematic Review

Zulkifli

Garfan

Talal

et al. 2022

Water

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Water quality monitoring plays a significant part in the transition towards intelligent and smart agriculture and provides an easy transition to automated monitoring of crucial components of human daily needs as new technologies are continuously developed and adopted in agricultural and human daily life (water). For the monitoring and management of water quality, this effort, however, requires reliable models with accurate and thorough datasets. Analyzing water quality monitoring models by utilizing sensors that gather water properties during live experiments is possible due to the necessity for precision in modeling. To convey numerous conclusions regarding the concerns, issues, difficulties, and research gaps that have existed throughout the past five years (2018–2022), this review article thoroughly examines the water quality literature. To find trustworthy peer-reviewed publications, several digital databases were searched and examined, including IEEE Xplore®, ScienceDirect, Scopus, and Web of Science. Only 50 articles out of the 946 papers obtained, were used in the study of the water quality monitoring research area. There are more rules for article inclusion in the second stage of the filtration process. Utilizing a real-time data acquisition system, the criteria for inclusion for the second phase of filtration looked at the implementation of water quality monitoring and characterization procedures. Reviews and experimental studies comprised most of the articles, which were divided into three categories. To organize the literature into articles with similar types of experimental conditions, a taxonomy of the three literature was created. Topics for recommendations are also provided to facilitate and speed up the pace of advancement in this field of study. By conducting a thorough analysis of the earlier suggested methodologies, research gaps are made clear. The investigation largely pointed out the problems in the accuracy of the models, the development of data-gathering systems, and the types of data used in the proposed frameworks. Finally, by examining critical topics required for the development of this research area, research directions toward smart water quality are presented.

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Section: Assessment Methods Of Dasmentioning

confidence: 99%

IoT-Based Water Monitoring Systems: A Systematic Review

Zulkifli

Garfan

Talal

et al. 2022

Water

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“…FDOSM is a mathematical model that addresses MCDM issues involving individual and group decision-making contexts [ 29 , 118 , 119 ]. The FDOSM process consists of three units: a data input unit, a data transformation unit, and a data processing unit [ 120 ]. It also has two phases for group decision-making, external and internal aggregations [ 121 ], as illustrated in Figure 13 .…”

Section: Evaluation and Benchmarking Of Ssvep-based Bci Applications:...mentioning

confidence: 99%

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

Albahri

Al-qaysi

Alzubaidi

et al. 2023

International Journal of Telemedicine and Applications

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The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% ( n = 21 / 30 ). The second category, recurrent neural network (RNN), accounts for 10% ( n = 3 / 30 ). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% ( n = 30 ). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% ( n = 1 / 30 ). The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

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“…Furthermore, the ranking of E-health systems is challenged by three important issues [19][20][21][22], namely, the presence of multiple evaluation properties [23][24][25], the significance of these properties [26][27][28], and the variety of data [29]. These issues must be addressed to fill the research gap that has been identified [30][31][32]. Previous studies incorporated certain privacy and security properties during the design phase of E-health systems.…”

Section: Related Workmentioning

confidence: 99%

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

Qahtan,

Yatim,

Osman

et al. 2023

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E-health Industry 4.0 systems ranking based on Blockchain is a multi-criteria decision-making (MCDM) problem, considering the multiple evaluation properties, their significance, and data variety. The final closeness between the evaluation sample and ideal solutions also constitutes an optimisation problem. To the authors’ knowledge, no study has provided a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems based on Blockchain. Consequently, this study proposes and discusses a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems, utilising the SFS-FWZIC method to address the significance of properties issue and the Grey-TOPSIS method to tackle data variation, multiple evaluation properties, and the ideal solutions optimisation problem. The methodology of the proposed approach consists of three sections. First, three decision matrices are constructed based on the intersection of E-health systems with the mentioned properties, intersecting Electronic Health Records (EHRs), Electronic Medical Records (EMRs), and Personal Health Records (PHRs) with seven properties. Second, the weights of each privacy and security property are calculated using the SFS-FWZIC method. Finally, the weights determined by the SFS-FWZIC method and the three decision matrices are input into the Grey-TOPSIS method to rank E-health systems across the three categories. The findings reveal the following: (1) The SFS-FWZIC method effectively assigned weights to privacy and security properties, with access control achieving the highest significance weight (0.1934) and Secure-search receiving the lowest weight (0.0603). (2) The Grey-TOPSIS method efficiently ranked E-health systems across the three categories based on various parameters, including α=0.1,α=0.3,α=0.5,α=0.7, and α=0.9. Sensitivity and correlation analysis were conducted to evaluate the results, revealing high correlation results based on each α value across all discussed scenarios of changing property weights. The implications of this study can lead to better decision-making, improved security and privacy, increased competition, and widespread adoption, ultimately contributing to more efficient and effective healthcare delivery.

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DAS benchmarking methodology based on FWZIC II and FDOSM II to support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles

Cited by 22 publications

References 36 publications

IoT-Based Water Monitoring Systems: A Systematic Review

IoT-Based Water Monitoring Systems: A Systematic Review

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

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