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

International Journal of Telemedicine and Applications, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 24

Published: April 30, 2023

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

Language: Английский

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Dynamic decision-making framework for benchmarking brain–computer interface applications: a fuzzy-weighted zero-inconsistency method for consistent weights and VIKOR for stable rank

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(17), P. 10355 - 10378

Published: March 16, 2024

Language: Английский

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Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issues

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(S1), P. 53 - 117

Published: June 21, 2023

Language: Английский

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Fuzzy Decision-Making Framework for Sensitively Prioritizing Autism Patients with Moderate Emergency Level

Applied Data Science and Analysis, Journal Year: 2023, Volume and Issue: unknown, P. 16 - 41

Published: March 15, 2023

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that requires careful assessment and management. The prioritization of ASD patients involves navigating through complexities such as conflicts, trade-offs, the importance different criteria. Therefore, this study focuses on prioritizing with in healthcare setting an evaluation benchmarking framework. aim to develop framework utilizes Multi-Criteria Decision Making (MCDM) methods assist professionals patients, particularly those moderate injury levels. methodology outlines several phases, including dataset identification, development decision matrix, weighting 19 criteria using FWZIC method, ranking 432 VIKOR evaluating proposed four sensitivity analysis scenarios. Among criteria, criterion 'verbal communication' obtained highest weight. Additionally, 'laughing for no reason', 'nodding', 'patient movement at home', 'pointing index finger' similar higher weights, indicating their potential impact patients. experimental results highlight significance adjusting weights influencing final rankings method. This emphasizes need consideration when assigning ensure accurate prioritization. Moreover, provides valuable insights into improving care support provided individuals autism Iraq. findings contribute existing body knowledge field pave way future research interventions aimed enhancing quality

Language: Английский

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Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(5), P. 6159 - 6188

Published: June 4, 2024

Abstract This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic solution is challenging resolving conflicts or trade-offs among ASD criteria. research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In first phase, identifies dataset, considering 19 critical medical and sociodemographic criteria The second phase introduces new Decision Matrix (DM) designed to manage effectively. third focuses extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) construct weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). fourth formulates Multi-Attributive Border Approximation Area Comparison (MABAC) method rank within each level. Results from SVN2TL-FWZIC offer significant insights, including higher values "C12 = Laughing no reason" "C16 Notice sound bell" with 0.097358 0.083832, indicating their significance in identifying potential symptoms. base prioritizing triage levels MABAC, encompassing behavioral dimensions. methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming consistency results points. compares benchmark studies, distinct points, achieves remarkable 100% congruence these prior investigations. implications this are far-reaching, offering valuable guide clinical psychologists cases patients.

Language: Английский

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Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: June 17, 2024

Abstract In the context of autism spectrum disorder (ASD) triage, robustness machine learning (ML) models is a paramount concern. Ensuring ML faces issues such as model selection, criterion importance, trade-offs, and conflicts in evaluation benchmarking models. Furthermore, development must contend with two real-time scenarios: normal tests adversarial attack cases. This study addresses this challenge by integrating three key phases that bridge domains fuzzy multicriteria decision-making (MCDM). First, utilized dataset comprises authentic information, encompassing 19 medical sociodemographic features from 1296 autistic patients who received diagnoses via intelligent triage method. These were categorized into one labels: urgent, moderate, or minor. We employ principal component analysis (PCA) algorithms to fuse large number features. Second, fused forms basis for rigorously testing eight models, considering scenarios, evaluating classifier performance using nine metrics. The third phase developed robust framework encompasses creation decision matrix (DM) 2-tuple linguistic Fermatean opinion score method (2TLFFDOSM) multiple-ML perspectives, accomplished through individual external group aggregation ranks. Our findings highlight effectiveness PCA algorithms, yielding 12 components acceptable variance. ranking, logistic regression (LR) emerged top-performing terms 2TLFFDOSM (1.3370). A comparative five benchmark studies demonstrated superior our across all six checklist comparison points.

Language: Английский

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Towards Trustworthy Myopia Detection: Integration Methodology of Deep Learning Approach, XAI Visualization, and User Interface System

Applied Data Science and Analysis, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 15

Published: Feb. 23, 2023

Myopia, a prevalent vision disorder with potential complications if untreated, requires early and accurate detection for effective treatment. However, traditional diagnostic methods often lack trustworthiness explainability, leading to biases mistrust. This study presents four-phase methodology develop robust myopia system. In the initial phase, dataset containing training testing images is located, preprocessed, balanced. Subsequently, two models are deployed: pre-trained VGG16 model renowned image classification tasks, sequential CNN convolution layers. Performance evaluation metrics such as accuracy, recall, F1-Score, sensitivity, logloss utilized assess models' effectiveness. The third phase integrates trustworthiness, transparency through application of Explainable Artificial Intelligence (XAI) techniques. Specifically, Local Interpretable Model-Agnostic Explanations (LIME) employed provide insights into decision-making process deep learning model, offering explanations myopic or normal. final user interface implemented XAI bringing together aforementioned phases. outcomes this contribute advancement objective explainable in field detection. Notably, achieves an impressive accuracy 96%, highlighting its efficacy diagnosing myopia. LIME results valuable interpretations cases. proposed enhances transparency, interpretability, trust process.

Language: Английский

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Multi-Objective Optimization of High-Power Microwave Sources Based on Multi-Criteria Decision-Making and Multi-Objective Micro-Genetic Algorithm

IEEE Transactions on Electron Devices, Journal Year: 2023, Volume and Issue: 70(7), P. 3892 - 3898

Published: June 8, 2023

This article presents a fuzzy simple additive weighting multi-objective micro-genetic algorithm (FSAW-MO-MGA) for high-power microwave (HPM) sources optimization. The FSAW-MO-MGA and single-objective genetic are independently used to optimize the same Ka -band relativistic backward-wave oscillator (RBWO) device. use of allowed us obtain an optimized device structure RBWO with comprehensively improved performance. Particle-in-cell (PIC) simulation results show that can generate pulses output power 616.0 MW operating frequency 30.33 GHz under diode voltage 625.6 kV, current is 6.59 kA at guiding magnetic field 0.8 T. Compared original RBWO, has been increased by 197.6%, beam-to-microwave conversion efficiency from 5.0% 14.9%, beam-current transmission 70.13% 87.72%. reasons these improvements in performance analyzed detail.

Language: Английский

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Explainable Artificial Intelligence Multimodal of Autism Triage Levels Using Fuzzy Approach-Based Multi-criteria Decision-Making and LIME

International Journal of Fuzzy Systems, Journal Year: 2023, Volume and Issue: 26(1), P. 274 - 303

Published: Nov. 17, 2023

Language: Английский

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Exploring decision-making techniques for evaluation and benchmarking of energy system integration frameworks for achieving a sustainable energy future

Energy Strategy Reviews, Journal Year: 2023, Volume and Issue: 51, P. 101251 - 101251

Published: Nov. 27, 2023

Energy Systems Integration (ESI) involves coordinating and planning energy systems to provide reliable affordable services while minimizing environmental harm. It optimizes interactions among different sources achieve sustainability goals promotes efficient resource usage. However, evaluating benchmarking ESI frameworks select the most suitable transparent ones is a complex Multi-Criteria Decision-Making (MCDM) problem. This complexity arises from trade-offs, conflicts, importance considerations of six evaluation characteristics: Multidimensional, Multivectoral, Systemic, Futuristic, Systematic, Applied. Hence, this study aims address by integrating Fuzzy-Weighted Zero-Inconsistency (FWZIC) Multi-Attributive Border Approximation Area Comparison (MABAC). The proposed methodology consists two phases. Firstly, development Dynamic Decision Matrix (DDM) handle 26 as alternatives characteristics criteria. Secondly, integration mathematical processes formulated based on FWZIC-MABAC methods. Using FWZIC technique, criteria were weighted preferences twelve experts. ESI-C2 (Multivectoral) ESI-C1 (Multidimensional) received highest weights 0.195 0.190, respectively, ESI-C5 (Systematic) criterion lowest weight 0.110. remaining criteria, namely ESI-C3 (Systemic), ESI-C6 (Applied), ESI-C4 (Futuristic) obtained 0.189, 0.168, 0.147, respectively. MABAC results showed that A11 (Energy Security) A15 Security under decarbonization) ranked first with score value 0.28081 for both. Conversely, A19 (EJM) had −0.17022. systematic rank sensitivity analysis assessments conducted verify efficiency methodology. We benchmarked against three other benchmark studies achieved 100 % across key perspectives. offers valuable support in making informed sustainable decisions sector.

Language: Английский

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