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 |
Prediction of total volatile basic nitrogen (TVB-N) and 2-thiobarbituric acid (TBA) of smoked chicken thighs using computer vision during storage at 4 °C
پیشبینی کل نیتروژن بازی فرار (TVB-N) و اسید ۲-تیوباربیتوریک (TBA) ران مرغ دودی با استفاده از بینایی رایانه در طول نگهداری در دمای ۴ درجه سانتیگراد-2022 As the traditional indicators of freshness measurement of meat products, TVB-N and TBA have the disadvantage
of time-consuming, labor-intensive and destructive to the sample. The objective of this study was to investigate
the possibility of computer vision techniques to visualize the variation of TVB-N and TBA during the storage of
smoked chicken thighs. In this study, freshness indicators (TVB-N and TBA) and images of smoked chicken thighs
were obtained simultaneously every 3 days during storage at 4 ◦C. Then, the RGB color space was converted to
HSI and L*a*b* color spaces by color conversion algorithm, and the color parameters (RGB, HSI and L*a*b*)
were correlated with TVB-N and TBA, respectively, for establishing multiple regression models. Finally, visu-
alization maps of the spoilage were established by applying the multiple regression model to each pixel in the
image. The results showed that the multiple linear regression models of TBA and TVB-N based on the color
parameters L*, a*, I, S and R were well correlated (R 2 = 0.993 for TBA and R 2 = 0.970 for TVB-N). Distribution
maps of TBA and TVB-N changed color gradually from blue to red during storage, respectively. In conclusion, this
study demonstrated that distribution maps can be employed as a rapid, objective, and non-destructive method to
predict the TBA and TVB-N values of smoked chicken thighs during storage. keywords: ران مرغ دودی | بینایی کامپیوتر | خنکی | TVB-N | TBA | Smoked chicken thigh | Computer vision | Freshness |
مقاله انگلیسی |
2 |
Computer vision model for estimating the mass and volume of freshly harvested Thai apple ber ( Ziziphus mauritiana L:) and its variation with storage days
مدل بینایی کامپیوتری برای تخمین جرم و حجم سیب تازه برداشت شده تایلندی (Ziziphus mauritiana L:) و تغییرات آن با روزهای نگهداری-2022 The physical properties of fruits are proportional to their mass and volume; this connection is used to determine
the fruit qualities and in designing the novel postharvest machinery. The present study aimed to forecast the
mass and volume of Thai apple ber (Ziziphus mauritiana L.) as a function of its physical properties measured using
image processing techniques at different stages of ripening (1st day, 4th day, 7th day, and 10th day). The mass
and volume models developed and analyzed the single variable regression, multilinear regressions, and mass
regression based on volume. Among these models, linear support vector machine (SVM) was found appropriate.
The experimental data analysis showed that the R2 of the linear SVM model for mass and volume of the projected
area were 0.955 and 0.965, respectively. In contrast, for the multilinear regression model, R2 values were 0.967
and 0.972, respectively. For the mass prediction model, the R2 was 0.970 based on calculated volume showing a
linear relationship. Thus, it was concluded that real-time measurement of physical properties of Thai apple ber
using an image-processing technique to estimate the mass and volume is a precise and accurate approach. keywords: بینایی کامپیوتر | پردازش تصویر | فراگیری ماشین | پسرفت | ماشین بردار پشتیبانی | Computer vision | Image processing | Machine learning | Regression | Support vector machine |
مقاله انگلیسی |
3 |
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 |
4 |
Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022 There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant
of the successful delivery of site operations. Although manufacturers provide equipment performance hand-
books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis
of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance
monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment
with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated
image libraries are used to train and test several backbone architectures. Experimental results reveal the pre-
cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel
shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the
ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of
potentials to influence current practice of articulated machinery monitoring in projects. keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures |
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 |
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 |
7 |
Quantum Differentially Private Sparse Regression Learning
یادگیری رگرسیون پراکنده خصوصی کوانتومی دیفرانسیل-2022 The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum differentially private (QDP) Lasso estimator to solve sparse regression tasks. Concretely, given N d -dimensional data points with N≪d , we first prove that the optimal classical and quantum non-private Lasso requires Ω(N+d) and Ω(N−−√+d−−√) runtime, respectively. We next prove that the runtime cost of QDP Lasso is dimension independent , i.e., O(N5/2) , which implies that the QDP Lasso can be faster than both the optimal classical and quantum non-private Lasso. Last, we exhibit that the QDP Lasso attains a near-optimal utility bound O~(N−2/3) with privacy guarantees and discuss the chance to realize it on near-term quantum chips with advantages.
keywords: Differential privacy | quantum machine learning | quantum computing. |
مقاله انگلیسی |
8 |
Quantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing
-2022 The increasing availability of quantum computers
motivates researching their potential capabilities in enhancing
the performance of data analysis algorithms. Similarly, as in
other research communities, also in remote sensing (RS), it is
not yet defined how its applications can benefit from the usage
of quantum computing (QC). This letter proposes a formulation
of the support vector regression (SVR) algorithm that can be
executed by D-Wave quantum computers. Specifically, the SVR
is mapped to a quadratic unconstrained binary optimization
(QUBO) problem that is solved with quantum annealing (QA).
The algorithm is tested on two different types of computing
environments offered by D-Wave: the advantage system, which
directly embeds the problem into the quantum processing unit
(QPU), and a hybrid solver that employs both classical and
QC resources. For the evaluation, we considered a biophysical
variable estimation problem with RS data. The experimental
results show that the proposed quantum SVR implementation
can achieve comparable or, in some cases, better results than the
classical implementation. This work is one of the first attempts to
provide insight into how QA could be exploited and integrated in
future RS workflows based on machine learning (ML) algorithms.
Index Terms: Quantum annealing (QA) | quantum computing (QC) | quantum machine learning (QML) | remote sensing (RS) | support vector regression (SVR). |
مقاله انگلیسی |
9 |
An investigation of the transmission success in Lorawan enabled IoT-HAPS communication
An investigation of the transmission success in Lorawan enabled IoT-HAPS communication-2022 As the communication and aviation technology expand, High altitude platform stations (HAPS)
are increasingly gaining a wider usage area in modern Internet of Things (IoT) deployments. One
of the areas in which HAPS can be effectively utilized is the wide area deployment of sensors
that require a costly data acquisition effort in terms of transportation and communication access.
Aerial communication using a low-energy technology such as LoRa can provide significant
advantages in such scenarios. Our work models and simulates LoRAWAN communication in
utilizing HAPS in data acquisition over a large distribution span of IoT devices/sensors. We
conduct experiments on various different scenarios including changing number of devices,
span area, HAPS speed and LoRa duty cycle to draw conclusions about how each of these
parameters affect communication quality. Results of the simulation are used in regression
analysis of equation factors to calculate the expected transmission performance under different
experimental setups. Our results (and simulation code) can be used to reason about certain
properties of IoT deployment (such as sensor count, sensor distribution area, HAPS speed, etc.)
before the real deployment is done in LoRaWAN enabled IoT-HAPS communication.
keywords: High altitude platform station communication | LoRaWAN communication | Wide-area sensor network | IoT deployment simulation | Communication quality estimation |
مقاله انگلیسی |
10 |
Evaluating computing performance of deep neural network models with different backbones on IoT-based edge and cloud platforms
ارزیابی عملکرد محاسباتی مدلهای شبکه عصبی عمیق با ستون فقرات مختلف در بسترهای لبه و ابری مبتنی بر اینترنت اشیا-2022 This paper focuses on evaluating and predicting the computing performance of different archi-
tectures of deep neural network models (DNNs) in cross-platform and cross-inference frame-
works. We test nearly 30 typical DNN models for image recognition on Google Colab cloud
computing platform and Intel neural compute stick 2 embedded edge computing platform and
record the computational performance metrics i.e. the Top-N accuracy, model complexity,
computational complexity, inference time, memory usage, and so on. We compare and analyze
these performance parameters with the previous workstation equipped with NVIDIA Titan X
Pascal and an embedded system based on NVIDIA Jetson TX1 board to evaluate the inference
efficiency of different DNN models using different inference frameworks. The methods of ANOVA
are adopted to quantify the differences between the models. A combination method of cluster
analysis and regression analysis is proposed to find the similar inference time variation processes
across models, which can be used to predict the inference results of unknown models. These
presented results will contribute to better deployment and application of resource-constrained
DNN models on the heterogeneous high-performance computing platform. keywords: شبکه های عصبی عمیق | سکوهای متقابل | چارچوب های استنتاج متقابل | تشخیص تصویر | Deep neural networks | Cross platforms | Cross-inference frameworks | Image recognition |
مقاله انگلیسی |