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 |
Knowledge of healthcare providers in the management of anaphylaxis
آگاهی از ارائه دهندگان خدمات بهداشتی در مدیریت آنافیلاکسی-2021 Introduction: Anaphylaxis is defined as a severe, life-threatening systemic hypersensitivity reaction. Early diagnosis and treatment of a severe allergic reaction requires recognition of the signs
and symptoms, as well as classification of severity. It is a clinical emergency, and healthcare
providers should have the knowledge for recognition and management. The aim of the study is to
evaluate the level of knowledge in the management of anaphylaxis in healthcare providers.
Methods: It is an observational, descriptive, cross-sectional study conducted among healthcare providers over 18 years old via a Google Forms link and shared through different social media platforms. A 12-item questionnaire was applied which included the evaluation of the management of anaphylaxis, from June 2020 to May 2021. Results: A total of 1023 surveys were evaluated; 1013 met inclusion criteria and were included in the statistical analysis. A passing grade was considered with 8 or more correct answers out of 12; the overall approval percentage was 28.7%. The group with the highest percentage of approval in the questionnaire was health-care providers with more than 30 years of work experience. There was a significant difference between the proportions of approval between all specialty groups, and in a post-hoc analysis, allergy and immunology specialists showed greater proportions of approval compared to general medicine practitioners (62.9% vs 25%; p¼<0.001). Conclusions: It is important that healthcare providers know how to recognize, diagnose, and treat anaphylaxis, and later refer them to specialists in Allergy and Clinical Immunology in order to make a personalized diagnosis and treatment. Keywords: Anaphylaxis | Epinephrine | Healthcare providers | Knowledge |
مقاله انگلیسی |
2 |
Drawing the premises for personalized learning: Illustrations of management and accounting
طراحی مکان برای یادگیری شخصی: تصاویر مدیریت و حسابداری-2021 This paper reports on a teaching innovation using participant-generated drawings.
Experienced managers were asked to produce a drawing to illustrate their work from an
accounting perspective. The drawings were then used to make the managerial context of
the participants the explicit starting point for personalized executive learning. This study
is the first in the sphere of accounting education and research to take drawing seriously
as a learning method. The results of the experiment show how drawing can be further used
as a tool in management education by facilitating the visualization of the managerial contexts participants work within.
keywords: حسابداری | تحصیلات | نقاشی کردن | آموزش حسابداری | کار مدیریتی | حسابداری مدیریت | روش های بصری | Accounting | Education | Drawing | Accounting education | Managerial work | Management accounting | Visual methods |
مقاله انگلیسی |
3 |
AI-based Reference Ankle Joint Torque Trajectory Generation for Robotic Gait Assistance: First Steps
تولید مسیر حرکت گشتاور مفصل مچ پا مبتنی بر هوش مصنوعی برای کمک به راه رفتن رباتیک: اولین قدم ها-2020 Robotic-based gait rehabilitation and assistance
have been growing to augment and to recover motor function in
subjects with lower limb impairments. There is interest in
developing user-oriented control strategies to provide
personalized assistance. However, it is still needed to set the
healthy user-oriented reference joint trajectories, namely,
reference ankle joint torque, that would be desired under healthy
conditions. Considering the potential of Artificial Intelligence (AI)
algorithms to model nonlinear relationships of the walking
motion, this study implements and compares two offline AI-based
regression models (Multilayer Perceptron and Long-Short Term
Memory-LSTM) to generate healthy reference ankle joint torques
oriented to subjects with a body height ranging from 1.51 to 1.83
m, body mass from 52.0 to 83.7 kg and walking in a flat surface
with a walking speed from 1.0 to 4.0 km/h. The best results were
achieved for the LSTM, reaching a Goodness of Fit and a
Normalized Root Mean Square Error of 79.6 % and 4.31 %,
respectively. The findings showed that the implemented LSTM
has the potential to be integrated into control architectures of
robotic assistive devices to accurately estimate healthy useroriented
reference ankle joint torque trajectories, which are
needed in personalized and Assist-As-Needed conditions. Future
challenges involve the exploration of other regression models and
the reference torque prediction for remaining lower limb joints,
considering a wider range of body masses, heights, walking speeds,
and locomotion modes. Keywords: Ankle Joint Torque Prediction | Artificial Intelligence | Control Strategies | Regression Models | Robotic Gait Rehabilitation |
مقاله انگلیسی |
4 |
Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine
یکپارچه سازی هوش مصنوعی و داروی آزمایشگاهی در پزشکی دقیق قلب و عروق-2020 Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models
and in turn allows the development of complex algorithms which are capable to simulate human intelligence
such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse
areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the
heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart
disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in
augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to
support decision‑making and improve diagnostic and prognostic performance. Attempt has also been made to
explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care,
to enable cost-effectiveness with reduce readmission and mortality rates. Our review addresses the integration of
AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need
of value creation services in cardiovascular medicine. Keywords: Artificial intelligence | Cardiology | Laboratory | Biomarkers | Data | Machine learning | Personalized |
مقاله انگلیسی |
5 |
An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2020 Ever since the beginning of civilization, travel for various causes exists as an essential part of
human life so as travel recommendations, though the early form of recommendations were the
accrued experiences shared by the community. Modern recommender systems evolved along
with the growth of Information Technology and are contributing to all industry and service
segments inclusive of travel and tourism. The journey started with generic recommender engines
which gave way to personalized recommender systems and further advanced to contextualized
personalization with advent of artificial intelligence. Current era is also witnessing a boom in
social media usage and the social media big data is acting as a critical input for various analytics
with no exception for recommender systems. This paper details about the study conducted on the
evolution of travel recommender systems, their features and current set of limitations. We also
discuss on the key algorithms being used for classification and recommendation processes and
metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders. Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism |
مقاله انگلیسی |
6 |
Towards Security and Privacy for Edge AI in IoT/IoE based Digital Marketing Environments
به سمت امنیت و حفظ حریم خصوصی برای هوش مصنوعی لبه در محیط های بازاریابی دیجیتال مبتنی بر IoT / IoE-2020 Abstract—Edge Artificial Intelligence (Edge AI) is a crucial
aspect of the current and futuristic digital marketing Internet of
Things (IoT) / Internet of Everything (IoE) environment.
Consumers often provide data to marketers which is used to enhance
services and provide a personalized customer experience (CX).
However, use, storage and processing of data has been a key concern.
Edge computing can enhance security and privacy which has been
said to raise the current state of the art in these areas. For example,
when certain processing of data can be done local to where
requested, security and privacy can be enhanced. However, Edge AI
in such an environment can be prone to its own security and privacy
considerations, especially in the digital marketing context where
personal data is involved. An ongoing challenge is maintaining
security in such context and meeting various legal privacy
requirements as they themselves continue to evolve, and many of
which are not entirely clear from the technical perspective. This
paper navigates some key security and privacy issues for Edge AI in
IoT/IoE digital marketing environments along with some possible
mitigations. Keywords: edge security | edge privacy | edge AI | edge intelligence | artificial intelligence | AI | machine learning | ML | IoT | IoE | edge | cybersecurity | legal | law | digital marketing | smart | GDPR | CCPA | security | privacy |
مقاله انگلیسی |
7 |
Reinforcement learning application in diabetes blood glucose control : A systematic review
کاربرد یادگیری تقویتی در کنترل قند خون دیابت : یک بررسی سیستماتیک-2020 Background: Reinforcement learning (RL) is a computational approach to understanding and automating goaldirected
learning and decision-making. It is designed for problems which include a learning agent interacting
with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where
the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms
could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen
based exclusively on the patient’s own data.
Objective: In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in
DM patients, reporting successfully implemented RL algorithms in closed-loop, insulin infusion, decision support
and personalized feedback in the context of DM.
Methods: An exhaustive literature search was performed using different online databases, analyzing the literature
from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most
relevant papers according to the title, keywords and abstract. Research questions were established and answered
in a second stage, using the information extracted from the articles selected during the preliminary selection.
Results: The initial search using title, keywords, and abstracts resulted in a total of 404 articles. After removal of
duplicates from the record, 347 articles remained. An independent analysis and screening of the records against
our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51
relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29
relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test,
and disagreements were resolved through discussion.
Conclusions: The advances in health technologies and mobile devices have facilitated the implementation of RL
algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused
on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are
designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area,
therefore we foresee RL algorithms will be used more frequently for BG control in the coming years.
Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and
physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform
clinical validation of the algorithms. Keywords: Reinforcement learning | Blood glucose control | Artificial pancreas | Closed-loop | Insulin infusion |
مقاله انگلیسی |
8 |
Supply and Demand of Ai-Aided Language Education
تأمین و تقاضای آموزش زبان به کمک هوش مصنوعی-2020 Intelligent technology empowers foreign language
education, boosting the development of various kinds of learning
APPS and websites. Artificial intelligence -assisted foreign
language education can balance education supply and demand,
meanwhile it can reduce education costs, bringing more benefits
to education participants. Furthermore, it will improve learning
efficiency, and promote education equity. Big data, learning and
analysis technology can offer learners individualized content,
personalized guidance and diagnostic evaluation, increasing
education supply of learning materials, tutors and learning
venues. Keywords: Artificial Intelligence | language education | supply and demand |
مقاله انگلیسی |
9 |
Therapy-driven Deep Glucose Forecasting
پیش بینی گلوکز عمیق درمان محور-2020 The automatic regulation of blood glucose for Type 1 diabetes patients is the main goal of the artificial
pancreas, a closed-loop system that exploits continue glucose monitoring data to define an optimal insulin
therapy. One of the most successful approaches for developing the artificial pancreas is the model predictive
control, which exhibits promising results on both virtual and real patients. The performance of such controller
is highly dependent on the reliability of the glucose–insulin model used for prediction purpose, which is usually
implemented with classic mathematical models. The main limitation of these models consists in the difficulties
of modeling the physiological nonlinear dynamics typical of this system. The availability of big amount of in
silico and in vivo data moved the attention to new data-driven methods which are able to easily overcome
this problem. In this paper we propose Deep Glucose Forecasting, a deep learning approach for forecasting
glucose levels, based on a novel, two-headed Long-Short Term Memory implementation. It takes in input the
previous values obtained through continue glucose monitoring, the carbohydrate intake, the suggested insulin
therapy and forecasts the interstitial glucose level of the patient. The proposed architecture has been trained
on 100 virtual adult patients of the UVA/Padova simulator, and tested on both virtual and real patients.
The proposed solution is able to generalize to new unseen data, outperforms classical population models and
reaches performance comparable to classical personalized models when fine-tuning is exploited on real patients. Keywords: Diabetes | Forecasting | Prediction | Deep learning | LSTM |
مقاله انگلیسی |
10 |
A reinforcement learning model for personalized driving policies identification
یک مدل یادگیری تقویتی برای شناسایی شخصیت های سیاسی محور -2020 Optimizing driving performance by addressing personalized aspects of driving behavior
and without posing unrealistic restrictions on personal mobility may have far reaching
implications to traffic safety, flow operations and the environment, as well as significant
benefits for users. The present work addresses the problem of delivering personalized driving
policies based on Reinforcement Learning for enhancing existing Intelligent
Transportation Systems (ITS) to the benefit of traffic management and road safety. The proposed
framework is implemented on appropriate driving behavior metrics derived from
smartphone sensors’ data streams. Aggressiveness, speeding and mobile usage are considered
to describe the driving profile per trip and are presented as inputs to the Q-learning
algorithm. The implementation of the proposed methodological approach produces personalized
quantified driving policies to be exploited for self-improvement. Finally, this
paper establishes validation measures of the quality and effectiveness of the produced policies
and methodological tools for comparing and classifying the examined drivers. Keywords: Reinforcement learning | Q-learning | Machine learning | Intelligent transportation systems | Traffic data |
مقاله انگلیسی |