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).
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1 |
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 |
2 |
A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach
چارچوب تجزیه و تحلیل تصویر رادیولوژیکی برای غربالگری اولیه عفونت COVID-19: یک رویکرد مبتنی بر بینایی کامپیوتری-2022 Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is
one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm
the presence of this virus, some radiological investigations find some important features from the
CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This
article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful
to automatically process the CT scan images without any manual annotation and helpful in the easy
interpretation. The proposed approach is based on artificial cell swarm optimization and will be
known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented
in the Matlab environment. The proposed approach uses a novel superpixel computation method
which is helpful to effectively represent the pixel intensity information which is beneficial for the
optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm
optimization approach. So, a twofold contribution can be observed in this work which is helpful
to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be
isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical
impact of this work. Both qualitative and quantitative experimental results show the effectiveness of
the proposed approach and also establish it as an effective computer-aided tool to fight against the
COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β
index are used to quantify the segmented results and it is observed that the proposed approach not
only performs well but also outperforms some of the standard approaches. On average, the proposed
approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and
9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art
approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a
contribution to the community.
keywords: کووید-۱۹ | تفسیر تصویر رادیولوژیکی | سوپرپیکسل | سیستم فازی نوع 2 | بهینه سازی ازدحام سلول های مصنوعی | COVID-19 | Radiological image interpretation | Superpixel | Type 2 fuzzy system | Artificial cell swarm optimization |
مقاله انگلیسی |
3 |
Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems
ارزیابی بیومتریک حیوانات با استفاده از بینایی کامپیوتری غیرتهاجمی و یادگیری ماشینی پیشبینیکننده خوبی برای سن و رفاه گاوهای شیری هستند: آینده سیستمهای پشتیبانی خودکار دامپزشکی-2022 Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal
welfare, contributing to the future of automated veterinary support systems. This study proposed using non-
invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the
Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop
two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii)
weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy
cow’s face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96),
slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance
without signs of under or overfitting. Models developed in this study can be used in parallel with other models to
assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms. keywords: هوش مصنوعی | فیزیولوژی گاو | ماستیت | بیومتریک حیوانات | سنجش از راه دور برد کوتاه | Artificial intelligence | Cows physiology | Mastitis | Animal biometrics | Short range remote sensing |
مقاله انگلیسی |
4 |
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 |
5 |
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 |
6 |
Quantum Computing Based Optimization for Intelligent Reflecting Surface (IRS)-Aided Cell-Free Network
بهینهسازی مبتنی بر محاسبات کوانتومی برای شبکههای بدون سلول با کمک سطح بازتابی هوشمند (IRS)-2022 Intelligent reflecting surface (IRS) enables the control of propagation characteristics and is attracting considerable attention
as a technology to improve energy utilization efficiency in 6th generation mobile communication systems. As cell-free networks with
multiple distributed base stations (BSs) can communicate in a coordinated manner, they are being actively researched as a new
network architecture to resolve the problem of inter-cell interference in conventional cellular networks. The introduction of the IRS into
the cell-free network can avoid shadowing at a lower cost with less power consumption. Thus, in this study, we considered the case of
communication with user equipment (UE) in a shadowing environment using IRS in a cell-free network that contained distributed BSs
with a single antenna. Moreover, the selection of multiple access methods was derived according to the numbers of BSs, IRSs, and
UEs. In addition, we proposed a quadratic unconstrained binary optimization formulation to optimize the IRS reflection coefficient using
quantum computing. The simulation results verified that the application of the proposed method resulted in a more efficient
communication. Thus, this study clarifies that the optimum control method in every communication environment and aims to act as a
stepping stone to optimize the entire cell-free system.
Index Terms: Intelligent Reflecting Surface | Cell-Free Network | Quantum Computing | Quantum Annealing | Combinatorial Optimization. |
مقاله انگلیسی |
7 |
The applications of Internet of Things in the automotive industry: A review of the batteries, fuel cells, and engines
کاربردهای اینترنت اشیا در صنعت خودرو: مروری بر باتری ها، سلول های سوختی و موتورها-2022 The current advances in the integration of devices through the internet of things (IoT) have
encouraged researchers to focus on the applications of IoT in the automotive industry. Although
different achievements in the in-vehicle network analysis and traffic management have been
already reviewed, a comprehensive study to bring together the main applications of the IoT in the
automotive industry is required. Internal combustion engines (ICEs) are established as the most
common prime-mover for cars, however, with the depleting fossil-fuel resources, the interest in
the usage of fuel cells and batteries has increased. In this regard, the main goal of the current
study is to evaluate the application of IoT in batteries, fuel cells, and ICEs. This paper is also
centralized on different types of IoT applications and combines them with empirical articles such
as Random Location Detection, Vehicle Theft Prevention, Observation of vehicle performance,
and industrial management of vehicles. As an output of this comprehensive review, different
usages of the IoT in the automotive sector will be clarified. Also, this article can be considered as a
basis for advancing the recent implementation of the IoT in the fuel cell, battery, and ICE
domains.
keywords: اینترنت اشیا (IoT) | باتری | سلول سوختی | موتور احتراق داخلی (ICE) | Internet of Things (IoT) | Battery | Fuel cell | Internal combustion engine (ICE) |
مقاله انگلیسی |
8 |
Optimizing the electrical conductivity of polyacrylonitrile/polyaniline with nickel nanoparticles for the enhanced electrostimulation of Schwann cells proliferation
بهینه سازی رسانایی الکتریکی پلی اکریلونیتریل/پلی آنیلین با نانوذرات نیکل برای تحریک الکتریکی افزایش یافته تکثیر سلول های شوان-2021 Tissue engineering scaffolds made of biocompatible polymers are promising alternatives for nerve reparation. For this application, cell proliferation will be speeded up by electrostimulation, which required
electrically-conductive materials. Here, a biomimicking scaffold with optimized conductivity was developed from electrospun polyacrylonitrile/electrically-conductive polyaniline (PAN/PANI) nanofibers
doped with Ni nanoparticles. PAN/PANI/Ni was biocompatible for Schwann cells and exhibited a suitable
tensile strength and wettability for cell proliferation. When compared with unmodified PAN/PANI, the
electrical conductivity of PAN/PANI/Ni was 6.4 fold higher. Without electrostimulation, PAN/PANI and
PAN/PANI/Ni exhibited similar Schwann cells’ proliferation rates. Upon electrostimulation at
100 mV cm1 for one hour per day over five days, PAN/PANI/Ni accelerated Schwann cells’ proliferation
2.1 times compared to PAN/PANI. These results demonstrate the importance of expanding the electrical
conductivity of the tissue engineering scaffold to ensure optimal electrostimulation of nerve cell growth.
Additionally, this study describes a straightforward approach to modulate the electrical conductivity of
polymeric materials via the addition of Ni nanoparticles that can be applied to different biomimicking
scaffolds for nerve healing.
Keywords: Nerve tissue engineering | Electrospinning | PAN/PANI | Ni nanoparticles | Schwann cells |
مقاله انگلیسی |
9 |
Improved optical and electrical properties for heterojunction solar cell using Al2O3/ITO double-layer anti-reflective coating
بهبود خواص نوری و الکتریکی برای سلول های خورشیدی ناهمگون با استفاده از پوشش ضد انعکاس دو لایه Al2O3/ITO-2021 Silicon heterojunction solar cells have been gaining remarkable attention in the photovoltaic industry in recent
years owing to their low temperature coefficient and high efficiency. This study aimed to maximize the short
circuit current density (Jsc), which is directly correlated with the absorbance of the solar cells. An advanced ray
tracking model and hall effect measurement was used to improve the optical properties of Al2O3/ITO as a double layered anti-reflection coating (DLARC) on the solar cell. RF/DC power sputtering system was used to deposit
ITO layer, while atomic layer deposition was used to deposit Al2O3 on ITO to create a DLARC. An average
decrease in reflection from 9.33% to 4.74% and enhancement in EQE from 76.89% to 84.34% were observed for
the DLARC in the wavelength spectrum at 300–1100 nm. It also exhibited a higher Jsc value of 41.13 mA/cm2
and maximum conversion efficiency of 21.6%. The findings of both simulation and experiments showed that the
Al2O3/ITO DLARC has better anti-reflection properties than a single-layer ITO coating.
Keywords: Silicon heterojunction solar cell | Double layered anti-reflection coating | Optical Properties | Electrical Properties |
مقاله انگلیسی |
10 |
Comparison of electrical energy and power of PV with different cells materials in clear sky day condition
مقایسه انرژی الکتریکی و توان PV با مواد سلول های مختلف در شرایط روز آسمان صاف-2021 In present study, a comparison has been made on the basis of gain of the electrical energy and power
from solar cells or photovoltaic module. To evaluate the electrical energy and power, five different materials or cases have been considered, which are named as: case (i): c-Si, case (ii): p-Si, case (iii): a-Si, case
(iv): CdTe and case (v): CIGS. Each PV module has been considered for the analysis which dimension is
0.6051 m2. For case (i): PV cells are made of silicon crystalline, which is having 0.5 Volts and 4 Amp
and 36 cells are connected in series, which are producing 72 W. Such analysis has been studied for a clear
sky day condition, New Delhi, India. The comparative study is attempted to choose best for generating
electrical energy and power when high electrical enrgy demands in our society. It is also observed that
the maximum electrical energy and power have been found for case (i), whereas minimum for case
(iii), due to high PV cell temperature. The electrical energy and power have been 1.8 times higher in case
(i), than case (iii).
Keywords: Silicon materials | PV | Composite climate | Energy and power |
مقاله انگلیسی |