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ردیف | عنوان | نوع |
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1 |
Internet of Things-enabled Passive Contact Tracing in Smart Cities
ردیابی تماس غیرفعال با قابلیت اینترنت اشیا در شهرهای هوشمند-2022 Contact tracing has been proven an essential practice during pandemic outbreaks and is a
critical non-pharmaceutical intervention to reduce mortality rates. While traditional con-
tact tracing approaches are gradually being replaced by peer-to-peer smartphone-based
systems, the new applications tend to ignore the Internet-of-Things (IoT) ecosystem that is
steadily growing in smart city environments. This work presents a contact tracing frame-
work that logs smart space users’ co-existence using IoT devices as reference anchors. The
design is non-intrusive as it relies on passive wireless interactions between each user’s
carried equipment (e.g., smartphone, wearable, proximity card) with an IoT device by uti-
lizing received signal strength indicators (RSSI). The proposed framework can log the iden-
tities for the interacting pair, their estimated distance, and the overlapping time duration.
Also, we propose a machine learning-based infection risk classification method to char-
acterize each interaction that relies on RSSI-based attributes and contact details. Finally,
the proposed contact tracing framework’s performance is evaluated through a real-world
case study of actual wireless interactions between users and IoT devices through Bluetooth
Low Energy advertising. The results demonstrate the system’s capability to accurately cap-
ture contact between mobile users and assess their infection risk provided adequate model
training over time.
© 2021 Elsevier B.V. All rights reserved. keywords: بلوتوث کم انرژی | ردیابی تماس | اینترنت اشیا | طبقه بندی خطر عفونت | Bluetooth Low Energy | Contact Tracing | Internet of Things | Infection Risk Classification |
مقاله انگلیسی |
2 |
IoT architecture for continuous long term monitoring: Parkinson’s Disease case study
معماری اینترنت اشیا برای نظارت طولانی مدت مداوم: مطالعه موردی بیماری پارکینسون-2022 In recent years, technological advancements and the strengthening of the Internet of Things
concepts have led to significant improvements in the technology infrastructures for remote
monitoring. This includes telemedicine which is the ensemble of technologies and tools involved
in medical services, from consultations, to diagnosis, prescriptions, treatment and patient
monitoring, all done remotely via an Internet connection.
Developing a telemedicine framework capable of monitoring patients over a continuous long-term monitoring window may encounter various issues related to the battery life of the device or the accuracy of the retrieved data. Moreover, it is crucial to develop an IoT architecture that is adaptable to various scenarios and the ongoing changes of the application scenario under analysis. In this work, we present an IoT architecture for continuous long-term monitoring of patients. Furthermore, as a real scenario case study, we adapt our IoT architecture for Parkinson’s Disease management, building up the PDRMA (Parkinson’s disease remote monitoring architecture). Performance analysis for optimal operation with respect to temperature and daily battery life is conducted. Finally, a multi-parameter app for the continuous monitoring of Parkinson’s patients is presented. keywords: IoT | Telemedicine | Continuous long term monitoring | Parkinson’s disease | e-Health |
مقاله انگلیسی |
3 |
Plant leaf disease detection using computer vision and machine learning algorithms
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین-2022 Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom-
mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to
the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind
the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in
plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing
with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the
samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm-
ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to
256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means
clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted
using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis
and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally,
the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM),
Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is
tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples. keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease |
مقاله انگلیسی |
4 |
تکنیک ها و کاربردهای توالی یابی RNA تک سلولی در تحقیقات تکوین تخمدان و بیماری های مرتبط
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 23 تخمدان یک ارگان بسیار سازمان یافته متشکل از سلول های زایا و انواع مختلف سلول های سوماتیک است که ارتباطات آنها منجر به تکوین تخمدان و تولید تخمک های عملکردی می شود. تفاوت بین سلول های منفرد ممکن است اثرات عمیقی بر عملکرد تخمدان داشته باشد. تکنیکهای توالییابی RNA تک سلولی، رویکردهای امیدوارکنندهای برای کشف ترکیب انواع سلولی ارگانیسم ها، پویایی رونوشتها یا ترنسکریپتوم، شبکه تنظیمکننده بین ژنها و مسیرهای سیگنالدهی بین انواع سلولها در وضوح تک سلولی هستند. در این مطالعه، ما یک مرور کلی از تکنیکهای توالییابی RNA تک سلولی موجود از جمله Smart-seq2 و Drop-seq و همچنین کاربردهای آنها در تحقیقات بیولوژیکی و بالینی ارائه میکنیم تا درک بهتری از مکانیسمهای مولکولی زیربنای تکوین تخمدان و بیماری های مرتبط با آن ارائه کنیم.
کلیدواژگان: تکوین تخمدانن | توالی یابی RNA تک سلولی | شبکه تنظیمی | بیماری ها |
مقاله ترجمه شده |
5 |
پیاده سازی یک راه حل حسابداری هزینه هوش تجاری در یک محیط مراقبت های بهداشتی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 12 محیط سیستم سلامت در پرتغال یک نگرانی دائمی برای جامعه ما است. با توجه به این موضوع، مانند هر بخش دیگری، منطقه بیمارستان دارای ساختار پیچیده ای است که حجم زیادی از اطلاعات را در خود جای داده است که فرآیند تصمیم گیری را دشوار می کند. با این کار، نیاز به بهبود مدیریت خدمات و منابع موسسات بهداشتی وجود دارد. با در نظر گرفتن این موضوع، راه حل شامل تبدیل سیستم فعلی با کمک سیستم های اطلاعاتی برای پیاده سازی می شود. بنابراین، ایده پیادهسازی سیستمهای اطلاعاتی که از هوش تجاری در بیمارستانها استفاده میکنند، مطرح میشود، تمرکز این پروژه کمک به مدیران در تحلیل حسابداری تحلیلی است. با مشارکت Centro Hospitalar Universitário do Porto، تصمیم گرفته شد تا استفاده از هوش تجاری را با هدف پیاده سازی یک راه حل تکمیلی برای طرح حسابداری بهای تمام شده موجود، با هدف بهبود کارایی و ارائه ابزارهای جدید مدیریت به مدیران مورد بررسی قرار دهیم.
کلمات کلیدی: حسابداری بهای تمام شده | هوش تجاری | مراقبت های بهداشتی |
مقاله ترجمه شده |
6 |
A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home
یک معماری مفهومی هشدار اولیه مبتنی بر اینترنت اشیا برای نظارت از راه دور بیماران COVID-19 در بخش ها و در خانه-2022 Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person
contacts, enabling early-detection of severe cases, and remotely assessing patients’ status.
Internet of Things (IoT) technologies have been used for monitoring patients’ health with
wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning
scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results
in detecting the health deterioration of COVID-19 patients. Although the literature presents
several approaches for remote monitoring, none of these studies proposes a customized,
complete, and integrated architecture that uses an effective early-detection mechanism for
COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore,
this article’s objective is to present a comprehensive IoT-based conceptual architecture that
addresses the key requirements of scalability, interoperability, network dynamics, context
discovery, reliability, and privacy in the context of remote health monitoring of COVID-19
patients in hospitals and at home. Since remote monitoring of patients at home (essential
during a pandemic) can engender trust issues regarding secure and ethical data collection,
a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a
configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.
keywords: نظارت از راه دور | کووید-۱۹ | اخبار-2 | معماری | رضایت | اینترنت اشیا | Remote monitoring | COVID-19 | NEWS-2 | Architecture | Consent | IoT |
مقاله انگلیسی |
7 |
AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics
AgroLens: یک معماری مزرعه هوشمند کمهزینه و سبز پسند برای پشتیبانی از تشخیص بیماریهای برگ در زمان واقعی-2022 Agriculture is one of the most significant global economic activities responsible for feeding the
world population of 7.75 billion. However, weather conditions and diseases impact production
efficiency, reducing economic activity and the food sovereignty of economies worldwide. Thus,
computational methods can support disease classification based on an image. This classification
requires training Artificial Intelligence (AI) models on high-performance computing resources,
usually far from the user domain. State of the art has proposed the concept of Edge Computing
(EC), which aims to bring computational resources closer to the domain problem to decrease
application latency and improve computational power closer to the client. In addition, EC has
become an enabling technology for Smart Farms, and the literature has appropriated EC to
support these applications. However, predominantly state-of-the-art architectures are dependent
on Internet connectivity and do not allow diverse real-time classification of diseases based on
crop leaf on mobile devices. This paper sheds light on a new architecture, AgroLens, built with
low-cost and green-friendly devices to support a mobile Smart Farm application, operational
even in areas lacking Internet connectivity. Among our main contributions, we highlight the
functional evaluation of AgroLens for AI-based real-time classification of diseases based on leaf
images, achieving high classification performance using a smartphone. Our results indicate that
AgroLens supports the connectivity of thousands of sensors from a smart farm without imposing
computational overhead on edge-compute. The AgroLens architecture opens up opportunities
and research avenues for deployment and evaluation for large-scale Smart Farm applications
with low-cost devices.
keywords: بیماری گیاهی | مزرعه هوشمند | اینترنت اشیا | یادگیری عمیق | سبز پسند| Plant disease | Smart Farm | Internet of Things | Deep learning | Green-friendly |
مقاله انگلیسی |
8 |
An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches
یک معماری تعاملی مبتنی بر اینترنت اشیا برای نظارت بیسیم بیماران با پچ های حسگر-2022 The alliance between the Internet of Things (IoT) and healthcare has the potential to improve
healthcare assistance at different stages of care through distributed vital sign sensing, paving
the way for domiciliary hospitalization. In this work, we propose an innovative design for
an IoT-based interoperable healthcare system to wirelessly monitor and classify patient status.
To support our research, we identify gaps, and discuss standards, protocols and technologies
based on works that use relevant IoT applications in healthcare. The proposed architecture is
centered on several low-energy unobtrusive sensors attached to the patients’ bodies, as well as
their beds, which encompass data acquisition nodes linked to a smart gateway that aggregates
data. The smart gateway is integrated with an existing hospital information system through the
exchange of Electronic Health Records (EHR), making relevant patient data easily available to
health professionals on systems which are familiar to them. A use case scenario is presented in
order to fulfill functional and non-functional requirements and provide a better understanding
of connection and communication between the distinct entities of the proposed architecture,
which is based on Bluetooth Low Energy (BLE) technology at the data acquisition level, the
Message Queuing Telemetry Transport (MQTT) protocol at the internal level, and on the Fast
Healthcare Interoperability Resources (FHIR) standard at the higher level.
keywords: Internet of Things | Digital healthcare | Wireless patient biomonitoring | System architecture | Interoperability |
مقاله انگلیسی |
9 |
HealthCloud: A system for monitoring health status of heart patients using machine learning and cloud computing
HealthCloud: سیستمی برای نظارت بر وضعیت سلامت بیماران قلبی با استفاده از یادگیری ماشین و محاسبات ابری-2022 In the context of the global health crisis of 2020, the tendency of many people to self-diagnose at
home virtually, prior to any physical interaction with medical professionals, has been increased.
Existing self-diagnosis systems include those accessible via the Internet, which involve entering
one’s symptoms. Several other methods do exist, for example, people read medical blogs or
notes, which are often wrongly interpreted by them and they arrive at a completely different
assumption regarding the cause of their symptoms. In this paper, a system called HealthCloud
is proposed, for monitoring health status of heart patients using machine learning and cloud
computing. This study aims to offer the ‘best of both worlds’, by combining the information
required for the person to understand the disease in sufficient detail, with an accurate prediction
as to whether they may have (in this case) heart disease or not. The presence of heart disease
is predicted using machine learning algorithms such as Support Vector Machine, K-Nearest
Neighbours, Neural Networks, Logistic Regression and Gradient Boosting Trees. This paper
evaluates these machine learning algorithms to obtain the most accurate model, in compliance
with Quality of Service (QoS) parameters. The performance of these machine learning models
is measured and compared using the metrics such as Accuracy, Sensitivity (Recall), Specificity,
AUC scores, Execution Time, Latency, and Memory Usage. For better establishment of the
results, these machine learning algorithms have been cross validated with 5-fold cross validation
technique. With an accuracy rate of 85.96%, it has been found that Logistic Regression is the
most responsive and accurate model amongst those models assessed. The Precision, Recall,
Cross Validation mean and AUC Score for this model were 95.83%, 76.67%, 81.68% and 96%
respectively. The algorithm and the mobile application were tested on Google Cloud Firebase
with existing user inputs from the dataset, as well as with unseen new data. The use of this
system can assist patients, both in reaching self-diagnosis decisions and in monitoring their
health.
keywords: Machine learning | Smart healthcare | Heart disease prediction | Cloud computing |
مقاله انگلیسی |
10 |
Implementation of a teleimmersion homecoming support system for supporting inpatients
پیاده سازی سیستم پشتیبانی از راه دور بازگشت به خانه برای حمایت از بیماران بستری-2022 This research has developed a teleimmersion homecoming support system for inpatients and their
families by combining a head-mounted display, a 360-degree camera, and a remote control robot.
This system realizes real-time remote communication between inpatients and their families and
provides inpatients with a “homecoming” feeling. Furthermore, inpatients can experience a
realistic home space by operating the remote control robot with a 360-degree camera installed at
home and enjoying family conversations with the voice call function. To evaluate this system, we
conducted a questionnaire survey on the operability, necessity, functionality, and presence of this
system. More than 80% of the subjects answered positively in the evaluation of the necessity,
functionality, and presence of this system. However, in the operability evaluation, 30% of the
subjects answered negatively, so it is necessary to improve the operability in the future. keywords: غوطه وری از راه دور | ربات کنترل از راه دور | سیستم پشتیبانی بازگشت به خانه | نمایشگر روی سر | واقعیت مجازی | Teleimmersion | Remote control robot | Homecoming support system | Head mounted display | Virtual reality |
مقاله انگلیسی |