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 |
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 |
iRestroom : A smart restroom cyberinfrastructure for elderly people
iRestroom: زیرساخت سایبری سرویس بهداشتی هوشمند برای افراد مسن-2022 According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is
projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors
who live alone must have their health state closely monitored to avoid unexpected events (such as
a fall). This study explains the underlying principles, methodology, and research that went into
developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure
system, that we call as iRestroom to assess the frailty of elderly people for them to live a
comfortable, independent, and secure life at home. The proposed restroom idea is based on the
required situations, which are determined by user study, socio-cultural and technological trends,
and user requirements. The iRestroom is designed as a multi-sensory place with interconnected
devices where carriers of older persons can access interactive material and services throughout
their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave
Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a
constantly updated reference for the data originating from numerous sensors and devices installed
in different locations throughout the restroom. A small sample of pilot data was obtained, as well
as pertinent web data. The Institutional Review Board (IRB) has approved all the methods. keywords: اینترنت اشیا | حسگرها | نگهداری از سالمندان | سیستم های هوشمند | یادگیری ماشین | IoT | Sensors | Elder Care | Smart Systems | Machine Learning |
مقاله انگلیسی |
3 |
Predicting social media engagement with computer vision: An examination of food marketing on Instagram
پیشبینی تعامل رسانههای اجتماعی با بینایی رایانه: بررسی بازاریابی مواد غذایی در اینستاگرام-2022 In a crowded social media marketplace, restaurants often try to stand out by showcasing elaborate “Insta-
grammable” foods. Using an image classification machine learning algorithm (Google Vision AI) on restaurants’
Instagram posts, this study analyzes how the visual characteristics of product offerings (i.e., their food) relate to
social media engagement. Results demonstrate that food images that are more confidently evaluated by Google
Vision AI (a proxy for food typicality) are positively associated with engagement (likes and comments). A follow-
up experiment shows that exposure to typical-appearing foods elevates positive affect, suggesting they are easier
to mentally process, which drives engagement. Therefore, contrary to conventional social media practices and
food industry trends, the more typical a food appears, the more social media engagement it receives. Using
Google Vision AI to identify what product offerings receive engagement presents an accessible method for
marketers to understand their industry and inform their social media marketing strategies. keywords: بازاریابی از طریق رسانه های اجتماعی | تعامل با مصرف کننده | یادگیری ماشین | غذا | روان بودن پردازش | هوش مصنوعی گوگل ویژن | Social media marketing | Consumer engagement | Machine learning | Food | Processing fluency | Google Vision AI |
مقاله انگلیسی |
4 |
Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System
تخمین غیر مخرب و بدون تماس محتویات کلروفیل و آمونیاک در برگ های موشک تازه برش خورده بسته بندی شده توسط یک سیستم کامپیوتر ویژن-2022 Computer Vision Systems (CVS) offer a non-destructive and contactless tool to assign visual quality level to fruit
and vegetables and to estimate some of their internal characteristics. The innovative CVS described in this paper
exploits the combination of image processing techniques and machine learning models (Random Forests) to
assess the visual quality and predict the internal traits on unpackaged and packaged rocket leaves. Its perfor-
mance did not depend on the cultivation system (traditional soil or soilless). The same CVS, exploiting its ma-
chine learning components, was able to build effective models for either the classification problem (visual quality
level assignment) and the regression problems (estimation of senescence indicators such as chlorophyll and
ammonia contents) just by changing the training data. The experiments showed a negligible performance loss on
packaged products (Pearson’s linear correlation coefficient of 0.84 for chlorophyll and 0.91 for ammonia) with
respect to unpackaged ones (0.86 for chlorophyll and 0.92 for ammonia). Thus, the non-destructive and con-
tactless CVS represents a valid alternative to destructive, expensive and time-consuming analyses in the lab and
can be effectively and extensively used along the whole supply chain, even on packaged products that cannot be
analyzed using traditional tools. keywords: Contactless quality level assessment | Diplotaxis tenuifolia L | Image analysis | Packaged vegetables | Senescence indicators prediction |
مقاله انگلیسی |
5 |
Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022 Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors
depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and
treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning
such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for
classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the
synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the
Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human
reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future
many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of
tumors. keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron |
مقاله انگلیسی |
6 |
Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics
هوش مصنوعی در مقابل انتخاب طبیعی: استفاده از تکنیکهای بینایی کامپیوتری برای طبقهبندی زنبورها و تقلیدهای زنبور عسل-2022 Many groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms—specifically, computer vision—to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of and , respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.
keywords: Artificial intelligence | Bioinformatics | Computing methodology | Entomology | Zoology |
مقاله انگلیسی |
7 |
Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification
خوشهبندی کاربردی کوانتومی K-Means: تحلیل عملکرد و کاربردها در طبقهبندی شبکه انرژی-2022 In this work, we aim to solve a practical use-case of unsupervised clustering that has applications in predictive maintenance in the energy operations sector using quantum computers. Using only cloud
access to quantum computers, we complete thorough performance analysis of what some current quantum
computing systems are capable of for practical applications involving nontrivial mid-to-high-dimensional
datasets. We first benchmark how well distance estimation can be performed using two different metrics
based on the swap-test, using angle and amplitude data embedding. Next, for the clustering performance
analysis, we generate sets of synthetic data with varying cluster variance and compare simulation to physical
hardware results using the two metrics. From the results of this performance analysis, we propose a general,
competitive, and parallelized version of quantum k-means clustering to avoid some pitfalls discovered due
to noisy hardware and apply the approach to a real energy grid clustering scenario. Using real-world German
electricity grid data, we show that the new approach improves the balanced accuracy of the standard quantum
k-means clustering by 67.8% with respect to the labeling of the classical algorithm.
INDEX TERMS: Cloud quantum computing | quantum clustering | quantum computing | quantum distance estimation. |
مقاله انگلیسی |
8 |
Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
آموزش طبقهبندیکنندههای ترکیبی کلاسیک-کوانتومی از طریق بهینهسازی تغییرات تصادفی-2022 Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this
work, we study a two-layer hybrid classical-quantum classifier
in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second
classical combining layer. The input to the first, hidden, layer is
obtained via amplitude encoding in order to leverage the exponential size of the fan-in of the quantum neurons in the number of
qubits per neuron. To facilitate implementation of the QGLMs, all
weights and activations are binary. While the state of the art on
training strategies for this class of models is limited to exhaustive
search and single-neuron perceptron-like bit-flip strategies, this
letter introduces a stochastic variational optimization approach
that enables the joint training of quantum and classical layers via
stochastic gradient descent. Experiments show the advantages of
the approach for a variety of activation functions implemented by
QGLM neurons.
Index Terms: Probabilistic machine learning | quantum computing | quantum machine learning. |
مقاله انگلیسی |
9 |
A Quantum Mechanics-Based Framework for EEG Signal Feature Extraction and Classification
یک چارچوب مبتنی بر مکانیک کوانتومی برای استخراج و طبقهبندی ویژگی سیگنال EEG-2022 Quantum machine learning (QML) is an emerging research field, which is devoted to devising
and implementing quantum algorithms that could enable machine learning faster than that of classical computers. In this article, a hierarchic quantum mechanics-based framework is investigated to implement both
the feature extraction and classification in the electroencephalogram (EEG) signal. First, the classical EEG
signal dataset is prepared as a quantum state while the sign of the data point is preserved. The prepared quantum state is then evolved with the quantum wavelet packet transformation (QWPT) and the wavelet packet
energy entropy (WPEE) feature is extracted as the input of the subsequent quantum classifier. We finally propose the improved quantum support vector machine with the arbitrary nonlinear kernel, which is employed to
predict the label of the EEG signal. The complexity analysis indicates that the proposed framework provides
exponential speedup over the same structured classical counterpart. Besides, the quantitative experimental
results verify the feasibility and validity.
INDEX TERMS: Quantum machine learning | feature extraction | classification | EEG signal |
مقاله انگلیسی |
10 |
چه چیزی یک معلم زبان خارجی (EFL) موثر میسازد: ادراکات دانش آموزان دبیرستانی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 16 هدف این مقاله توصیف ویژگیهای معلم زبان خارجی (EFL) موثر است که توسط دانش آموزان دبیرستانی کورد درک شده است. شرکت کنندگان شامل ۱۲۲ دانشآموز سال سوم دبیرستان در شهر دوهوک در منطقه کردستان عراق بودند. دادهها با استفاده از پرسشنامه براساس طبقهبندی پارک و لی (۲۰۰۶) از ویژگیهای معلمان EFL شامل سه بخش دانش موضوعی، دانش آموزشی و مهارتهای اجتماعی - عاطفی جمعآوری شد. دادهها به صورت کمی با استفاده از نرمافزار آماری SPSS (نسخه ۲۵) تجزیه و تحلیل شدند. نتایج نشان داد که دانشجویان اهمیت بیشتری در مهارت زبان انگلیسی قائل هستند. علاوه بر این، هم مردان و هم زنان کمترین میانگین نمرات را در فرهنگ انگلیسی داشتند. با این حال، هیچ تفاوت قابل توجهی نه بین پسران و دختران و نه بین دانش آموزان با دستیابی بالا و دستیابی پایین یافت نشد. یافتهها نشان داد که خوب خواندن زبان انگلیسی، مدیریت کلاس درس به درستی و اعتماد به نفس و داشتن کنترل شخصی بالاترین میانگین نمره را داشتند.
کلمات کلیدی: ویژگیهای معلمان زبان خارجی (EFL) موثر | باورهای دانش آموزان |
مقاله ترجمه شده |