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نتیجه جستجو - پیش‌بینی

تعداد مقالات یافته شده: 65
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 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 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
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 A combined real-time intelligent fire detection and forecasting approach through cameras based on computer vision method
یک رویکرد تشخیص و پیش‌بینی حریق هوشمند ترکیبی در زمان واقعی از طریق دوربین‌ها بر اساس روش بینایی کامپیوتری-2022
Fire is one of the most common hazards in the process industry. Until today, most fire alarms have had very limited functionality. Normally, only a simple alarm is triggered without any specific information about the fire circumstances provided, not to mention fire forecasting. In this paper, a combined real-time intelligent fire detection and forecasting approach through cameras is discussed with extracting and predicting fire development characteristics. Three parameters (fire spread position, fire spread speed and flame width) are used to charac- terize the fire development. Two neural networks are established, i.e., the Region-Convolutional Neural Network (RCNN) for fire characteristic extraction through fire detection and the Residual Network (ResNet) for fire forecasting. By designing 12 sets of cable fire experiments with different fire developing conditions, the accu- racies of fire parameters extraction and forecasting are evaluated. Results show that the mean relative error (MRE) of extraction by RCNN for the three parameters are around 4–13%, 6–20% and 11–37%, respectively. Meanwhile, the MRE of forecasting by ResNet for the three parameters are around 4–13%, 11–33% and 12–48%, respectively. It confirms that the proposed approach can provide a feasible solution for quantifying fire devel- opment and improve industrial fire safety, e.g., forecasting the fire development trends, assessing the severity of accidents, estimating the accident losses in real time and guiding the fire fighting and rescue tactics.
keywords: ایمنی آتش سوزی صنعتی | تشخیص حریق | پیش بینی آتش سوزی | تجزیه و تحلیل آتش سوزی | هوش مصنوعی | Industrial fire safety | Fire detection | Fire forecasting | Fire analysis | Artificial intelligence
مقاله انگلیسی
9 Computer vision technique for freshness estimation from segmented eye of fish image
تکنیک بینایی کامپیوتری برای تخمین تازگی از چشم تقسیم شده تصویر ماهی-2022
Preserving the quality of fish is a challenging task. Several different cooling methods and materials are used during their storage, transportation purpose. These are responsible factors that decide the freshness of a post harvested fish. In this proposed algorithm, a computer vision-based technique is developed to predict the freshness level of fish from its image. Eyes of the fish are considered as the region of interest, as a good corre- lation has been observed between the colour of the eye and different duration of storage day. It is segmented from the image of a fish sample and then a strategic framework is used for extraction of the discriminatory features. These extracted features show a degradation pattern which acts as an indicative parameter to determine the level of freshness of sample of fish. The proposed method provides a recognition accuracy of 96.67%. The experimental results indicate that this is an efficient and non-destructive methodology for detecting the fish freshness. The high accuracy of freshness detection and low computation time makes this non-destructive methodology efficient for real-world usage in the fish industry and market.
keywords: استخراج ویژگی | چشم ماهی | تکنیک های پردازش تصویر | سطح تازگی | تقسیم بندی | Feature extraction | Fish eye | Image processing techniques | Level of freshness | Segmentation
مقاله انگلیسی
10 Model-Predictive Quantum Control via Hamiltonian Learning
مدل-کنترل کوانتومی پیش‌بینی‌کننده از طریق یادگیری همیلتونی-2022
This work proposes an end-to-end framework for the learning-enabled control of closed quantum systems. The proposed learning technique is the first of its kind to utilize a hierarchical design which layers probing control, quantum state tomography, quantum process tomography, and Hamiltonian learning to identify both the internal and control Hamiltonians. Within this context, a novel quantum process tomography algorithm is presented which involves optimization on the unitary group, i.e., the space of unitary operators, to ensure physically meaningful predictions. Our scalable Hamiltonian learning algorithms have low memory requirements and tunable computational complexity. Once the Hamiltonians are learned, we formalize data-driven model-predictive quantum control (MPQC). This technique utilizes the learned model to compute quantum control parameters in a closed-loop simulation. Then, the optimized control input is given to a physical quantum system in an open-loop fashion. Simulations show modelpredictive quantum control to be more efficient than the current state-of-the-art, quantum optimal control, when sequential quadratic programming (SQP) is used to solve each control problem.
INDEX TERMS: Quantum Hamiltonian learning | quantum process tomography | quantum control | quantum consensus | quantum networks | quantum computing
مقاله انگلیسی
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