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

Albahri, A. S.; Joudar, Shahad Sabbar; Hamid, Rula A.; Zahid, Idrees A.; Alqaysi, M. E.; Albahri, O. S.; Alamoodi, A. H.; Kou, Gang; Sharaf, Iman Mohamad

doi:10.1007/s40815-023-01597-9

Cited by 5 publications

(1 citation statement)

References 69 publications

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“…A step towards something far more advanced than machine learning. This advancement utilized the enhanced models in healthcare, building and construction, and specific algorithm developments as well as natural language processing and many other domains [34,35,36]. Improvements on language processing and its application have had a tremendous effect [37].…”

Section: Large Language Model Advancementmentioning

confidence: 99%

Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach

Zahid,

Joudar

2024

Mesopotamian Journal of Big Data

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Considering the prevailing rule of Large Language Models (LLMs) applications and the benefits of XML in a compiler context. This manuscript explores the synergistic integration of Large Language Models with XML-based compiler tools and advanced computing technologies. Marking a significant stride toward redefining compiler construction and data representation paradigms. As computing power and internet proliferation advance, XML emerges as a pivotal technology for representing, exchanging, and transforming documents and data. This study builds on the foundational work of Chomsky's Context-Free Grammar (CFG). Recognized for their critical role in compiler construction, to address and mitigate the speed penalties associated with traditional compiler systems and parser generators through the development of an efficient XML parser generator employing compiler techniques. Our research employs a methodical approach to harness the sophisticated capabilities of LLMs, alongside XML technologies. The key is to automate grammar optimization, facilitate natural language processing capabilities, and pioneer advanced parsing algorithms. To demonstrate their effectiveness, we thoroughly run experiments and compare them to other techniques. This way, we call attention to the efficiency, adaptability, and user-friendliness of the XML-based compiler tools with the help of these integrations. And the target will be the elimination of left-recursive grammars and the development of a global schema for LL(1) grammars, the latter taking advantage of the XML technology, to support the LL(1) grammars construction. The findings in this research not only underscore the signification of these innovations in the field of compilation construction but also indicate a paradigm move towards the use of AI technologies and XML in the context of the resolution of programming traditional issues. The outlined methodology serves as a roadmap for future research and development in compiler technology, which paves the way for open-source software to sweep across all fields. Gradually ushering in a new era of compiler technology featuring better efficiency, adaptability, and all CFGs processed through existing XML utilities on a global basis.

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Section: Large Language Model Advancementmentioning

confidence: 99%

Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach

Zahid,

Joudar

2024

Mesopotamian Journal of Big Data

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A novel dual-level multi-source information fusion approach for multicriteria decision making applications

Sharaf,

Albahri,

Alsalem

et al. 2024

Appl Intell

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

Albahri,

Hamid,

Alzubaidi

et al. 2024

Complex Intell. Syst.

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

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

Cited by 5 publications

References 69 publications

Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach

Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach

A novel dual-level multi-source information fusion approach for multicriteria decision making applications

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

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