دانلود و نمایش مقالات مرتبط با Prediction::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

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). 

نتیجه جستجو - Prediction

تعداد مقالات یافته شده: 932
ردیف عنوان نوع
1 IoT-based Prediction Models in the Environmental Context: A Systematic Literature Review
مدل‌های پیش‌بینی مبتنی بر اینترنت اشیا در زمینه محیطی: مروری بر ادبیات سیستماتیک-2022
Undoubtedly, during the last years climate change has alerted the research community of the natural environment sector. Furthermore, the advent of Internet of Things (IoT) paradigm has enhanced the research activity in the environmental field offering low-cost sensors. Moreover, artificial intelligence and more specifically, statistical and machine learning methodologies have proved their predictive power in many disciplines and various real-world problems. As a result of the aforementioned, many scientists of the environmental research field have performed prediction models exploiting the strength of IoT data. Hence, insightful information could be extracted from the review of these research works and for this reason, a Systematic Literature Review (SLR) is introduced in the present manuscript in order to summarize the recent studies of the field under specific rules and constraints. From the SLR, 54 primary studies have been extracted during 2017-2021. The analysis showed that many IoT-based prediction models have been applied the previous years in 10 different environmental issues, presenting in the majority of the primary studies promising results.
keywords: Natural Environment | Internet of Things | Prediction Models | Systematic Literature Review
مقاله انگلیسی
2 Understanding the effect of surfactants on two-phase flow using computer vision
درک اثر سورفکتانت ها بر جریان دو فازی با استفاده از بینایی کامپیوتر-2022
The effect of surfactants on vertical gas-liquid flow is experimentally investigated in a 12.7 mm diameter tube at conditions relevant to an ammonia-water bubble absorber. The characteristics of two-phase flow are studied using an air-water mixture, both with and without the addition of 1-octanol as the surfac- tant. High-speed videography is used to study the flow patterns and quantify interfacial areas and bubble velocities. Novel computer vision-based methods are used to analyze and quantify these flow parame- ters. The addition of 1-octanol results in enhancement in interfacial area due to the prevention of bubble coalescence leading to many small diameter bubbles. Measured values of interfacial area are compared with predictions from correlations in the literature, and agreement and differences are interpreted and discussed. The bubble velocity is measured by object tracking using the optical flow method. Surfactants lead to a decrease in bubble velocity and increase in the residence time. These are surmised to be due to the shear stresses caused by the non-uniform concentration distribution of surfactant along the bub- ble surface. Overall, the addition of surfactants can lead to appreciable enhancement in heat and mass transfer rates due to their effect on interfacial areas and residence times.
keywords: سورفکتانت ها | جریان دو فازی | ناحیه رابط | سرعت | تقویت | تجسم جریان | Surfactants | Two-phase flow | Interfacial area | Velocity | Enhancement | Flow visualization
مقاله انگلیسی
3 A novel method of fish tail fin removal for mass estimation using computer vision
یک روش جدید حذف باله دم ماهی برای تخمین جرم با استفاده از بینایی کامپیوتر-2022
Fish mass estimation is extremely important for farmers to get fish biomass information, which could be useful to optimize daily feeding and control stocking densities and ultimately determine optimal harvest time. However, fish tail fin mass does not contribute much to total body mass. Additionally, the tail fin of free-swimming fish is deformed or bent for most of the time, resulting in feature measurement errors and further affecting mass prediction accuracy by computer vision. To solve this problem, a novel non-supervised method for fish tail fin removal was proposed to further develop mass prediction models based on ventral geometrical features without tail fin. Firstly, fish tail fin was fully automatically removed using the Cartesian coordinate system and image processing. Secondly, the different features were respectively extracted from fish image with and without tail fin. Finally, the correlational relationship between fish mass and features was estimated by the Partial Least Square (PLS). In this paper, tail fins were completely automatically removed and mass estimation model based on area and area square has been the best tested on the test dataset with a high coefficient of determination (R2) of 0.991, the root mean square error (RMSE) of 7.10 g, the mean absolute error (MAE) of 5.36 g and the maximum relative error (MaxRE) of 8.46%. These findings indicated that mass prediction model without fish tail fin can more accurately estimate fish mass than the model with tail fin, which might be extended to estimate biomass of free- swimming fish underwater in aquaculture.
keywords: برداشتن باله دم | اتوماسیون | ماهی | تخمین انبوه | بینایی کامپیوتر | Tail fin removal | Automation | Fish | Mass estimation | Computer vision
مقاله انگلیسی
4 ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022
Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive, manual task. This study proposes a novel approach for automated plumage condition assessment using com- puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of input image characteristics, the method was evaluated using images with and without depth information in resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of subjective human annotations, plumage condition predictions were compared to manual assessments of one observer and to matching annotations of two observers. Among all tested settings, performance metrics based on matching manual annotations of two observers were equal or better than the ones based on annotations of a single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of 98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of plumage conditions in commercial laying hen farms.
keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation
مقاله انگلیسی
5 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
6 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
7 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
8 Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network
طبقه بندی مبتنی بر بینایی کامپیوتری شدت پاشش بتن با استفاده از ماشین تقویت کننده گرادیان قویا بهینه شده فراابتکاری و شبکه عصبی پیچیده عمیق-2022
This paper presents alternative solutions for classifying concrete spall severity based on computer vision ap- proaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center sym- metric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern (ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification directly without the need for texture descriptors. Experimental results with a dataset containing real-world concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a classification accuracy rate of roughly 99%.
keywords: شدت ریزش بتن | دستگاه افزایش گرادیان | الگوی باینری محلی | فراماسونری | یادگیری عمیق | Concrete spall severity | Gradient boosting machine | Local binary pattern | Metaheuristic | Deep learning
مقاله انگلیسی
9 Computer vision for solid waste sorting: A critical review of academic research
بینایی کامپیوتری برای تفکیک زباله جامد: مروری انتقادی تحقیقات دانشگاهی-2022
Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV- enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little atten- tion has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are introduced and compared. The distribution of academic research outputs is also examined from the aspects of waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were un- evenly distributed in different sectors such as household, commerce and institution, and construction. Too often, researchers reported some preliminary studies using simplified environments and artificially collected data. Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested researchers to train and evaluate their CV algorithms.
keywords: زباله جامد شهری | تفکیک زباله | بینایی ماشین | تشخیص تصویر | یادگیری ماشین | یادگیری عمیق | Municipal solid waste | Waste sorting | Computer vision | Image recognition | Machine learning | Deep learning
مقاله انگلیسی
10 A computer vision framework using Convolutional Neural Networks for airport-airside surveillance
چارچوب بینایی کامپیوتری با استفاده از شبکه‌های عصبی کانولوشن برای نظارت در فرودگاه-2022
Modern airports often have large and complex airside environments featuring multiple runways, with changing configurations, numerous taxiways for effective circulation of flights and tens, if not hundreds, of gates. With inherent uncertainties in gate push-back and taxiway routing, efficient surveillance and management of airport-airside operations is a highly challenging task for air traffic controllers. An increase in air traffic may lead to gate delays, taxiway congestion, taxiway incursions as well as significant increase in the workload of air traffic controllers. With the advent of Digital Towers, airports are increasingly being equipped with surveillance camera systems. This paper proposes a novel computer vision framework for airport-airside surveillance, using cameras to monitor ground movement objects for safety enhancement and operational efficiency improvement. The framework adopts Convolutional Neural Networks and camera calibration techniques for aircraft detection and tracking, push-back prediction, and maneuvering monitoring. The proposed framework is applied on video camera feeds from Houston Airport, USA (for maneuvering monitoring) and Obihiro Airport, Japan (for push-back prediction). The object detection models of the proposed framework achieve up to 73.36% average precision on Houston airport and 87.3% on Obihiro airport. The framework estimates aircraft speed and distance with low error (up to 6 meters), and aircraft push-back is predicted with an average error of 3 min from the time an aircraft arrives with the error-rate reducing until the aircraft’s actual push-back event.
keywords: Air traffic control | Convolutional Neural Network | Computer vision
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 11163 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 11163 :::::::: افراد آنلاین: 66