The Meta-Metaverse: Ideation and Future Directions

Jamshidi, Mohammad (Behdad); Dehghaniyan Serej, Arash; Jamshidi, Alireza; Moztarzadeh, Omid

doi:10.3390/fi15080252

Cited by 20 publications

(4 citation statements)

References 132 publications

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“…In forthcoming investigations, the fusion of digital twin technologies, the metaverse, fuzzy systems, machine learning, Internet of Things (IoT) will present a substantial potential for addressing intricate issues [31][32][33]. This collaborative framework, reminiscent of the envisioned Meta-Metaverse, aims to enrich immersive encounters and construct precise digital duplicates spanning diverse domains [34]. Just as the layered composition introduced in the Meta-Metaverse expedites digital transformation, these amalgamated approaches could hasten our comprehension of the intricacies surrounding the reappearance of CHC.…”

Section: Resultsmentioning

confidence: 99%

Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning

Moztarzadeh,

Liska,

Liskova

et al. 2023

Clinics and Practice

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Chronic hyperplastic candidiasis (CHC) presents a distinctive and relatively rare form of oral candidal infection characterized by the presence of white or white–red patches on the oral mucosa. Often mistaken for leukoplakia or erythroleukoplakia due to their appearance, these lesions display nonhomogeneous textures featuring combinations of white and red hyperplastic or nodular surfaces. Predominant locations for such lesions include the tongue, retro-angular mucosa, and buccal mucosa. This paper aims to investigate the potential influence of specific anatomical locations, retro-angular mucosa, on the development and occurrence of CHC. By examining the relationship between risk factors, we present an approach based on machine learning (ML) to predict the location of CHC occurrence. In this way, we employ Gradient Boosting Regression (GBR) to classify CHC lesion locations based on important risk factors. This estimator can serve both research and diagnostic purposes effectively. The findings underscore that the proposed ML technique can be used to predict the occurrence of CHC in retro-angular mucosa compared to other locations. The results also show a high rate of accuracy in predicting lesion locations. Performance assessment relies on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE), consistently revealing favorable results that underscore the robustness and dependability of our classification method. Our research contributes valuable insights to the field, enhancing diagnostic accuracy and informing treatment strategies.

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Section: Resultsmentioning

confidence: 99%

Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning

Moztarzadeh,

Liska,

Liskova

et al. 2023

Clinics and Practice

Self Cite

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“…These technologies allow users to experience a sense of presence and agency within virtual environments, making learning more engaging and experiential [33,74]. Furthermore, advancements in artificial intelligence (AI) are enhancing the adaptability and interactivity of these environments, enabling personalized and dynamic learning experiences [75].…”

Section: Background and Conceptsmentioning

confidence: 99%

Virtual Worlds for Learning in Metaverse: A Narrative Review

Damaševičius,

Sidekerskienė

2024

Sustainability

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As digital technologies continue to evolve, they offer unprecedented opportunities to transform traditional educational paradigms. Virtual worlds offer a dynamic and immersive platform for fostering sustainability education, bridging the gap between theoretical knowledge and practical application. In these interactive environments, students can engage with complex ecological systems and sustainability challenges in a risk-free setting, allowing for experimentation and exploration that would be impractical or impossible in the real world. This study aims to investigate the application of various types of virtual worlds in educational settings, examine their characteristics and potential, and explore how they foster critical 21st-century skills like critical thinking, creativity, communication, and collaboration. This paper comprehensively explores various types of virtual worlds—Adventure World, Simulation World, Creative World, Role-Playing World, and Collaborative World—assessing their impact on educational processes and outcomes. Adventure Worlds, with narrative-driven quests, engage students in exploratory learning within a story context. Simulation Worlds replicate real-world environments, allowing students to practice and hone practical skills in a risk-free setting. Creative Worlds provide open-ended, sandbox-like environments where innovation and imagination are paramount. Role-Playing Worlds facilitate empathy and perspective-taking through character-driven scenarios, while Collaborative Worlds emphasize teamwork and problem-solving in group projects. The narrative review methodology was adopted for the comprehensive analysis and synthesis of the literature to assess the impact and integration of virtual worlds in education, focusing on identifying trends, challenges, and opportunities within this domain. The evaluation methodology used in this study incorporates a mix of the Theory of Inventive Problem Solving (TRIZ), Concept-Knowledge (C-K) theory, Structure-behavior-function (SBF) modeling, the Framework for 21st Century Learning (P21), and Universal Design for Learning (UDL) to evaluate the characteristics and educational potential of different virtual world types. Findings indicate that virtual worlds effectively support critical thinking, creativity, communication, and collaboration skills, presenting a comprehensive analysis of how these environments can support, supplement, or transform traditional educational models. The main outcome of the study is the comprehensive exploration of various types of virtual worlds—Adventure World, Simulation World, Creative World, Role-Playing World, and Collaborative World—in education, demonstrating their significant potential to enhance learning experiences and outcomes through immersive, interactive environments that foster critical thinking, creativity, communication, and collaboration skills.

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“…Last, Metaverse frameworks, such as Meta-Metaverse [95], could revolutionize how researchers interact with molecular data, simulate drug interactions, and predict outcomes in virtual environments that closely mimic real-world conditions. By leveraging the capabilities of such frameworks, the application of XAI in drug discovery could be significantly enhanced, providing more intuitive, interactive, and detailed explorations of drug efficacy, side effects, and molecular mechanisms.…”

Section: ) Extending Xai In Drug Discovery Through Cloud Computingmentioning

confidence: 99%

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Alizadehsani,

Oyelere,

Hussain

et al. 2024

IEEE Access

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The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

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The Meta-Metaverse: Ideation and Future Directions

Cited by 20 publications

References 132 publications

Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning

Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning

Virtual Worlds for Learning in Metaverse: A Narrative Review

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

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