A critical approach to Machine Learning forecast capabilities: creating a predictive biography in the age of the Internet of Behaviour (IoB)

Díaz, Diego; Boj, Clara

doi:10.7238/artnodes.v0i31.405249

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Digital rights, encompassing aspects like access to information and freedom of expression, are also deeply influenced by cultural contexts. The interpretation of these rights can vary widely, influenced by cultural norms, legal frameworks, and societal values (445). Educators and professionals in the field of digital literacy must be cognizant of these variations and work towards digital solutions that respect and uphold these rights in a culturally sensitive manner (444).…”

Section: Ethical Considerations In a Multicultural Digital Worldmentioning

confidence: 99%

Digital Literacy 5.0 to Enhance Multicultural Education

Anurogo,

Hardin La Ramba,

Nabila Diyana Putri

et al. 2023

mier

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In Digital Literacy 5.0, the intersectionality of digital competence across various domains marks a paradigm shift from traditional siloed approaches to a more integrated and holistic framework. This article explores the pervasive influence of digital literacy across diverse fields including health, medicine, nutrition, medical tourism, economy, biomedical sciences, bioinformatics, telemedicine, telehealth, artificial intelligence (AI), and vascular surgery. Emphasizing the necessity of digital literacy not just as a skill for tool utilization but as a cornerstone for understanding and leveraging the potential of digital technologies, this comprehensive exploration underscores the critical role of digital competence in enhancing patient outcomes, driving economic growth, spurring innovation, and revolutionizing healthcare and surgical practices. Through a multidisciplinary lens, the article elucidates the indispensability of digital literacy in a technologically interconnected era, highlighting its implications for policy, educational paradigms, and future technological advancements.

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Section: Ethical Considerations In a Multicultural Digital Worldmentioning

confidence: 99%

Digital Literacy 5.0 to Enhance Multicultural Education

Anurogo,

Hardin La Ramba,

Nabila Diyana Putri

et al. 2023

mier

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“…The IoB aids in specifying the essential effective facets of a patient's behavior. Additionally, the IoB helps buyers to achieve their service demands without spending time directing different purchasing mechanisms for healthcare [23].…”

Section: Introductionmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

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