با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy
هوش مصنوعی و یادگیری ماشین | برنامه های کاربردی در فیزیوتراپی عضلانی اسکلتی-2019 Introduction: Artificial intelligence (AI) is a field of mathematical engineering which has potential to enhance
healthcare through new care delivery strategies, informed decision making and facilitation of patient engagement.
Machine learning (ML) is a form of narrow artificial intelligence which can be used to automate decision
making and make predictions based upon patient data.
Purpose: This review outlines key applications of supervised and unsupervised machine learning in musculoskeletal
medicine; such as diagnostic imaging, patient measurement data, and clinical decision support. The
current literature base is examined to identify areas where ML performs equal to or more accurately than human
levels.
Implications: Potential is apparent for intelligent machines to enhance various areas of physiotherapy practice
through automization of tasks which involve data analysis, classification and prediction. Changes to service
provision through applications of ML, should encourage physiotherapists to increase their awareness of and
experiences with emerging technologies. Data literacy should be a component of professional development plans
to assist physiotherapists in the application of ML and the preparation of information technology systems to use
these techniques. Keywords: Artificial intelligence | Machine learning | Low back pain | Physiotherapy |
مقاله انگلیسی |
2 |
Data infrastructure literacy
سواد اطلاعاتی زیرساخت داده-2018 A recent report from the UN makes the case for ‘‘global data literacy’’ in order to realise the opportunities afforded by
the ‘‘data revolution’’. Here and in many other contexts, data literacy is characterised in terms of a combination of
numerical, statistical and technical capacities. In this article, we argue for an expansion of the concept to include not just
competencies in reading and working with datasets but also the ability to account for, intervene around and participate in
the wider socio-technical infrastructures through which data is created, stored and analysed – which we call ‘‘data
infrastructure literacy’’. We illustrate this notion with examples of ‘‘inventive data practice’’ from previous and ongoing
research on open data, online platforms, data journalism and data activism. Drawing on these perspectives, we argue that
data literacy initiatives might cultivate sensibilities not only for data science but also for data sociology, data politics as
well as wider public engagement with digital data infrastructures. The proposed notion of data infrastructure literacy is
intended to make space for collective inquiry, experimentation, imagination and intervention around data in educational
programmes and beyond, including how data infrastructures can be challenged, contested, reshaped and repurposed to
align with interests and publics other than those originally intended.
Keywords: Data infrastructures | information infrastructure studies | science and technology studies | digital methods | data activism | data literacy | data publics | data journalism | critical data studies | data critique | data worlds |
مقاله انگلیسی |