Integration of FDOSM and FWZIC Under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

Alsattar, Hassan A.; Qahtan, Sarah; Mohammed, R. T.; Zaidan, A. A.; Albahri, O. S.; Kou, Gang; Alamoodi, A. H.; Albahri, A. S.; Zaidan, B. B.; Al-Samarraay, Mohammed S.; Malik, Rami Qays; Jasim, Ali Najm

doi:10.1142/s0219622022500511

Cited by 32 publications

(7 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By using fuzzy set theory and a zero-inconsistency approach, FWZIC is able to ensure that each criterion is weighted appropriately, even in the presence of inconsistencies. Another significant advantage of FWZIC is its ability to reduce the time and effort required to weight criteria [29], [30]. Unlike other methods that require direct comparisons between the criteria, FWZIC does not need such comparisons, which can be time-consuming.…”

Section: Why Choose Fwzic To Weight the Criteria?mentioning

confidence: 99%

Evaluation of the Routing Algorithms for NoC-Based MPSoC: A Fuzzy Multi-Criteria Decision-Making Approach

Muhsen,

Husin,

Zolkepli

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Context: Routing algorithms play a crucial role in the performance of Network-on-Chip (NoC)based Multi-Processor Systems-on-Chip (MPSoC). However, the selection of appropriate and effective routing algorithms poses a challenge for designers, given the multitude of assessment criteria, data fluctuations, and varying criteria importance. Objective: In this study, we propose a comprehensive assessment of various routing algorithms, aiming to identify the most suitable and effective routing algorithm that satisfies designers' systemlevel requirements and assessment criteria. Method: This research integrates the Fuzzy-Weighted Zero-Inconsistency (FWZIC) method and the Fuzzy Decision by Opinion Score Method (FDOSM). The utilisation of the Z-Cloud Rough Numbers (ZCRNs) environment addresses the challenge of two types of uncertainty, providing a framework for managing ambiguity in the data and achieving a higher level of data freedom. Our methodology consists of two main phases. Firstly, the decision matrix is constructed based on the performance assessment criteria and routing algorithms. Secondly, we employ the ZCR-FWZIC method to derive the weights for each criterion and subsequently employ the ZCR-FDOSM-BM approach to rank the routing algorithms. Result: The analysis reveals that Adaptive Dimensional Bubble Routing (ADBR), Message-based Congestion-Aware Routing (MCAR), and Dynamic and Adaptive Routing Algorithm (DyAd) are ranked as the top three routing algorithms, respectively. Conclusion: This research presents essential implications for designers and system engineers involved in NoCbased MPSoC, offering insights to enhance decision-making processes and facilitate the selection of an appropriate routing algorithm.

show abstract

Section: Why Choose Fwzic To Weight the Criteria?mentioning

confidence: 99%

Evaluation of the Routing Algorithms for NoC-Based MPSoC: A Fuzzy Multi-Criteria Decision-Making Approach

Muhsen,

Husin,

Zolkepli

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The FDOSM is an MCDM method for ranking alternatives, such as in a benchmarking process for SSVEP-based BCI applications. 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 ].…”

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

View full text Add to dashboard Cite

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.

show abstract

“…So, the second question should be discussed: "How can ML and AI techniques benefit the ASD diagnosis process?" ML and AI techniques play an important role in the diagnosis process contributing to early ASD diagnosis and providing excellent support for controlling and treatment [11][12][13]. In addition, after considerable advancements in computer science and information technology, ML is being used to detect and assess a variety of illnesses, such as lung cancer, hepatitis, heart disease, COVID-19, and diabetes [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to zero pairwise comparisons, FWZIC eliminates the potential for mistakes. Recently, the FWZIC method acquired attention and has been used in several studies [13,[53][54][55][56]. FWZIC method can process zero inconsistency.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

Alqaysi

Albahri

Hamid

2022

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

Background and Contexts. Autism spectrum disorder (ASD) is difficult to diagnose, prompting researchers to increase their efforts to find the best diagnosis by introducing machine learning (ML). Recently, several available challenges and issues have been highlighted for the diagnosis of ASD. High consideration must be taken into the feature selection (FS) approaches and classification process simultaneously by using medical tests and sociodemographic characteristic features in autism diagnostic. The constructed ML models neglected the importance of medical tests and sociodemographic features in a training and evaluation dataset, especially since some features have different contributions to the processing data and possess more relevancies to the classification information than others. However, the role of the physician’s experience towards feature contributions remains limited. In addition, the presence of many evaluation criteria, criteria trade-offs, and criteria importance categorize the evaluation and benchmarking of diagnosis ML models concerning the intersection between FS approaches and ML classification methods given under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an evaluation framework for benchmarking the best hybrid diagnosis models to classify autism patients’ emergency levels considering multicriteria evaluation solutions. Method. The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and preprocessed. Secondly, developing the hybrid diagnosis models using the intersection process between three FS techniques and five ML algorithms introduces 15 models. The selected medical tests and sociodemographic features from each FS technique are weighted before feeding the five ML algorithms using the fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate a dynamic decision matrix for all developed models based on seven evaluation metrics, including classification accuracy, precision, F1 score, recall, test time, train time, and AUC. (ii) The fuzzy decision by opinion score method (FDOSM) is used to evaluate and benchmark the 15 models concerning the seven evaluation metrics. Results. Results reveal that (i) the three FS techniques have obtained a size different from the others in the number of the selected features; the sets were 39, 38, and 41 out of 48 features. Each set has its weights constructed by FWIZC. Considered sociodemographic features have been mostly selected more than medical tests within FS techniques. (ii) The first three best hybrid models were “ReF-decision tree,” “IG-decision tree,” and “Chi2-decision tree,” with score values 0.15714, 0.17539, and 0.29444. The best diagnosis model (ReF-decision tree) has obtained 0.4190, 0.0030, 0.9946, 0.9902, 0.9902, 0.9902, 0.9902, and 0.9951 for the C1=train time, C2=test time, C3=AUC, C4=CA, C5=F1 score, C6=precision, and C7=recall, respectively. The developed framework would be beneficial in advancing, accelerating, and selecting diagnosis tools in therapy with ASD. The selected model can identify severity as light, medium, or intense based on medical tests and sociodemographic weighted features.

show abstract

Integration of FDOSM and FWZIC Under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

Cited by 32 publications

References 73 publications

Evaluation of the Routing Algorithms for NoC-Based MPSoC: A Fuzzy Multi-Criteria Decision-Making Approach

Evaluation of the Routing Algorithms for NoC-Based MPSoC: A Fuzzy Multi-Criteria Decision-Making Approach

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

Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

Contact Info

Product

Resources

About