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ردیف | عنوان | نوع |
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21 |
Quantum Kernels for Real-World Predictions Based on Electronic Health Records
هستههای کوانتومی برای پیشبینیهای دنیای واقعی بر اساس پروندههای سلامت الکترونیکی-2022 Research on near-term quantum machine learning has explored how classical machine learning
algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely
classical counterparts. Although theoretical work has shown a provable advantage on synthetic data sets,
no work done to date has studied empirically whether the quantum advantage is attainable and with what
data. In this article, we report the first systematic investigation of empirical quantum advantage (EQA) in
healthcare and life sciences and propose an end-to-end framework to study EQA. We selected electronic
health records data subsets and created a configuration space of 5–20 features and 200–300 training samples.
For each configuration coordinate, we trained classical support vector machine models based on radial basis
function kernels and quantum models with custom kernels using an IBM quantum computer, making this
one of the largest quantum machine learning experiments to date. We empirically identified regimes where
quantum kernels could provide an advantage and introduced a terrain ruggedness index, a metric to help
quantitatively estimate how the accuracy of a given model will perform. The generalizable framework introduced here represents a key step toward a priori identification of data sets where quantum advantage could
exist.
INDEX TERMS: Artificial intelligence | digital health | electronic health records (EHR) | empirical quantum advantage (EQA) | machine learning | quantum kernels | real-world data | small data sets | support vector machines (SVM). |
مقاله انگلیسی |
22 |
آموزش آسیب شناسی از راه دور تحت همه گیری COVID-19: برداشت های دانشجویان پزشکی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 12 زمینه: همهگیری COVID-19 آموزش سنتی را مجبور کرده است که دوباره ساختار یافته و به صورت آنلاین ارائه شود. هدف: تجزیه و تحلیل ادراک دانشجویان پزشکی در مورد مزایا و مشکلات آموزش از راه دور پاتولوژی در طول همه گیری COVID-19.
طراحی: یک مطالعه مقطعی با یک نظرسنجی آنلاین برای دانشجویان سال سوم و چهارم فارغالتحصیلی پزشکی، که در آموزش از راه دور پاتولوژی در طول همهگیری COVID-19 شرکت کردند، انجام شد. روشهای تدریس آنلاین شامل فعالیتهای همزمان با سخنرانیهای تعاملی زنده، بحثهای مبتنی بر مورد و فعالیتهای ناهمزمان با سخنرانیهای ضبطشده، آموزشها و متون موجود در پلت فرم آموزش آنلاین است. ادراک دانشجویان در مورد آموزش از راه دور آسیب شناسی از طریق نظرسنجی آنلاین مورد ارزیابی قرار گرفت. یافتهها: 90 دانشجو (47%) از 190 شرکتکننده پرسشنامه را تکمیل کردند که 45 نفر مرد و 52 نفر در سال سوم فارغالتحصیلی پزشکی بودند. شرایط درک شده ای که یادگیری آسیب شناسی را تسهیل می کرد شامل استفاده از پلت فرم آموزش آنلاین و انعطاف پذیری زمانی برای مطالعه بود. دانشجویان سخنرانی های زنده تعاملی را برتر از سخنرانی های سنتی سنتی می دانستند. شرایط درک شده ای که مانع اجرای آموزش آنلاین شد، شامل دشواری جداسازی مطالعه از فعالیت های خانگی، بی انگیزگی و بدتر شدن کیفیت زندگی به دلیل دوری فیزیکی از همکاران و اساتید بود. به طور کلی، آموزش از راه دور آسیب شناسی توسط 80٪ از دانشجویان ارزش مثبت داشت. نتیجهگیری: ابزارهای آنلاین اجازه میدهند تا محتوای پاتولوژی با موفقیت در طول همهگیری COVID-19 به دانشآموزان ارائه شود. این تجربه می تواند الگویی برای فعالیت های آموزشی آتی آسیب شناسی در آموزش علوم بهداشت باشد. کلید واژه ها: پاتولوژی | آموزش از راه دور | کووید -19 | آموزش پزشکی |
مقاله ترجمه شده |
23 |
Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review
کاربردهای یادگیری ماشین کوانتومی در حوزه زیست پزشکی: مرور سیستماتیک-2022 Quantum technologies have become powerful tools for a wide range of application disciplines,
which tend to range from chemistry to agriculture, natural language processing, and healthcare due to
exponentially growing computational power and advancement in machine learning algorithms. Furthermore,
the processing of classical data and machine learning algorithms in the quantum domain has given rise to
an emerging field like quantum machine learning. Recently, quantum machine learning has become quite a
challenging field in the case of healthcare applications. As a result, quantum machine learning has become
a common and effective technique for data processing and classification across a wide range of domains.
Consequently, quantum machine learning is the most commonly used application of quantum computing.
The main objective of this work is to present a brief overview of current state-of-the-art published articles
between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the
biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature
review techniques such as research questions and quality metrics of the articles. Initially, we discovered
3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled
30 articles that comply with the quantum machine learning models and quantum circuits using biomedical
data. Eventually, this article provides a broad overview of quantum machine learning limitations and future
prospects.
INDEX TERMS: Quantum computing | quantum machine learning | biomedical and healthcare. |
مقاله انگلیسی |
24 |
Quantum Pythagorean Fuzzy Evidence Theory: A Negation of Quantum Mass Function View
نظریه شواهد فازی کوانتومی فیثاغورث: نفی عملکرد جرم کوانتومی-2022 Dempster–Shafer (D-S) evidence theory is an effective methodology to handle unknown and imprecise information
because it can assign probability into the power set. However, the
process of obtaining information is a complex task, which can consider the rational, conscious, objective evaluation of utility with behavioral effects. Besides, in most cases, information can be obtained
from different angles at the same time. The quantum model of mass
function (QM) uses amplitude and phase angle to easily express
those properties of information that can extend D-S evidence theory
to the unit circle in a complex plane. Moreover, everything in nature
will have its opposite, which is a kind of universality. The Bayes
theorem is essentially the process of negation. However, in most
cases, decisions can be made by only fully considering the known
information without considering the other side of the information.
Hence, considering the negation of information is a question to
be investigated deeply, which can analyze information from the
other point. This article proposes negation of QM by using the
subtraction of vectors in the unit circle, which can degenerate into
negation proposed by Yager in standard probability theory and
negation proposed by Yin et al. in D-S evidence theory. Negation
can provide us more information to consider the problem from
both positive and negative aspects. In this article, negation can be
understood information, which does not belong to event A, that is
to say, negation can be regarded as nonmembership by using the
fuzzy terms. Based on the above discussion, this article proposes the
quantum pythagorean fuzzy evidence theory (QPFET), which is the
novel work to consider QPFET from the point of negation. Besides,
there are some numerical examplesto explainthe proposed method.
In order to explore the applications of QPFET, this article discusses
the possibility ofthe VIsˇekriterijumskoKompromisno Rangiranje
method underQPFETto handle multicriteria decision-makingthat
enables us to capture 2-D data, considering not only amplitude but
also phase angle.
IndexTerms— Dempster–Shafer(D-S) evidencetheory | negation | pythagorean fuzzy sets (PFSs) | quantum mass function | quantum pythagorean fuzzy evidence theory (QPFET). |
مقاله انگلیسی |
25 |
Simultaneous Estimation of Parameters and the State of an Optical Parametric Oscillator System
تخمین همزمان پارامترها و وضعیت یک سیستم نوسان ساز پارامتری نوری-2022 In this article, we consider the filtering problem of an optical parametric oscillator (OPO).
The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the
system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously.
We formulate this problem using a state-space representation of the OPO dynamics. Under the assumption
of Gaussianity and proper constraints, the dual Kalman filter method and the joint extended Kalman filter
method are employed to simultaneously estimate the system state and the pump power. Numerical examples
demonstrate the effectiveness of the proposed algorithms. keywords: Optical parametric oscillator (OPO) | OPO system | parameter estimation | quantum state estimation | simultaneous estimation. |
مقاله انگلیسی |
26 |
بیوپلیمر: ماده ای پایدار برای کاربردهای غذایی و پزشکی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 22 - تعداد صفحات فایل doc فارسی: 48 پلیمرهای زیستی یک گروه پیشرو از مواد کاربردی مناسب برای کاربردهای با ارزش بالا هستند که مورد توجه محققان و متخصصان در رشتههای مختلف قرار گرفته اند. برای درک جنبه های اساسی و کاربردی بیوپلیمرها برای رسیدگی به چندین مشکل پیچیده مرتبط با سلامت و رفاه مهم به تحقیقات بین رشته ای نیاز است. برای کاهش اثرات زیست محیطی و وابستگی به سوخت های فسیلی، تلاش زیادی برای جایگزینی پلیمرهای مصنوعی با مواد زیست تخریب پذیر، به ویژه آنهایی که از منابع طبیعی به دست می آیند، انجام شده است. در این راستا، بسیاری از انواع پلیمرهای طبیعی یا زیستی برای رفع نیازهای کاربردهای روزافزون توسعه یافته اند. این بیوپلیمرها در حال حاضر در مصارف غذایی مورد استفاده قرار می گیرند و به دلیل خواص منحصر به فردشان در حال گسترش در صنایع دارویی و پزشکی هستند. این بررسی بر روی کاربردهای مختلف پلیمرهای زیستی در صنایع غذایی و پزشکی تمرکز دارد و چشم انداز آینده را برای صنعت بیوپلیمر ارائه می دهد.
واژگان کلیدی: پلیمرهای زیستی | کاربردهای پزشکی و غذایی | مواد زیست تخریب پذیر | پلی ساکاریدهای میکروبی | کیتوزان |
مقاله ترجمه شده |
27 |
Low cost cloud based remote microscopy for biological sciences
میکروسکوپ از راه دور مبتنی بر ابر کم هزینه برای علوم زیستی-2022 A low cost remote imaging platform for biological applications was developed. The ‘‘Picroscope’’
is a device that allows the user to perform longitudinal imaging studies on multi-well cell culture
plates. Here we present the network architecture and software used to facilitate communication
between modules within the device as well as system communication with external cloud
services. A web based console was created to control the device and view experiment results.
Post processing tools were developed to analyze captured data in the cloud. The result is a
platform for monitoring biological experiments from outside the lab.
keywords: میکروسکوپی از راه دور | RaspberryPi | Remotemicroscopy |
مقاله انگلیسی |
28 |
Smart City Data Science: Towards data-driven smart cities with open research issues
علم داده شهر هوشمند: به سوی شهرهای هوشمند مبتنی بر داده با مسائل تحقیقاتی باز-2022 Cities are undergoing huge shifts in technology and operations in recent days, and ‘data science’
is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR).
Extracting useful knowledge or actionable insights from city data and building a corresponding
data-driven model is the key to making a city system automated and intelligent. Data science
is typically the scientific study and analysis of actual happenings with historical data using a
variety of scientific methodologies, machine learning techniques, processes, and systems. In this
paper, we concentrate on and explore ‘‘Smart City Data Science’’, where city data collected from
various sources such as sensors, Internet-connected devices, or other external sources, is being
mined for insights and hidden correlations to enhance decision-making processes and deliver
better and more intelligent services to citizens. To achieve this goal, artificial intelligence,
particularly, machine learning analytical modeling can be employed to provide deeper knowledge
about city data, which makes the computing process more actionable and intelligent in various
real-world city services. Finally, we identify and highlight ten open research issues for future
development and research in the context of data-driven smart cities. Overall, we aim to provide
an insight into smart city data science conceptualization on a broad scale, which can be used
as a reference guide for the researchers, industry professionals, as well as policy-makers of a
country, particularly, from the technological point of view.
keywords: شهرهای هوشمند | علم داده | فراگیری ماشین | اینترنت اشیا | تصمیم گیری داده محور | خدمات هوشمند | امنیت سایبری | Smartcities | Datascience | Machinelearning | InternetofThings | Data-drivendecisionmaking | Intelligentservices | Cybersecurity |
مقاله انگلیسی |
29 |
Ultrasonic physical layers as building blocks of IoT stacks
لایه های فیزیکی اولتراسونیک به عنوان بلوک های سازنده پشته های اینترنت اشیا-2022 Instrumental, Scientific and Medical (ISM) bands are essential to the transmission of IoT traffic
as most of the physical layers of the Wireless Personal Area Network (WPAN) and Low Power
Wide Area Network (LPWAN) families rely on them. ISM bands, however, are associated with
a myriad of problems responsible for signal degradation and pollution that lead to application
Quality of Service (QoS) issues. In this context, the use of alternative transmission mechanisms
serve as backup to traditional schemes. One of such mechanisms relies on sub-ultrasonic and
ultrasonic signals for the propagation of IoT device traffic. In this paper, we recycle a well
known acoustic modulation scheme intended to be used in legacy Public Switching Telephone
Networks (PSTNs) to support sub-ultrasonic and ultrasonic channels. This scheme becomes the
basic building block of a physical layer that is combined with other well known elements of the
IoT layered architecture. This not only includes a customized link layer but also an adaptation
layer that enables full integration with upper layers.
Keywords: 6LoWPAN | Ultrasound | ITU V.23 | CoAP | CSMA/CA | Layers |
مقاله انگلیسی |
30 |
AI for next generation computing: Emerging trends and future directions
هوش مصنوعی برای محاسبات نسل بعدی: روندهای نوظهور و مسیرهای آینده-2022 Autonomic computing investigates how systems can achieve (user) specified ‘‘control’’ outcomes on their own, without the intervention of a human operator. Autonomic computing
fundamentals have been substantially influenced by those of control theory for closed and
open-loop systems. In practice, complex systems may exhibit a number of concurrent and
inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g.,
multiple resources within a data centre), research into integrating Artificial Intelligence (AI)
and Machine Learning (ML) to improve resource autonomy and performance at scale continues
to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and
self-management of systems can be achieved at different levels of granularity, from full to
human-in-the-loop automation. In this article, leading academics, researchers, practitioners,
engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing
join to discuss current research and potential future directions for these fields. Further, we
discuss challenges and opportunities for leveraging AI and ML in next generation computing for
emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing
environments.
Keywords: Next generation computing | Artificial intelligence | Cloud computing | Fog computing | Edge computing | Serverless computing | Quantum computing | Machine learning |
مقاله انگلیسی |