Visibility estimation via deep label distribution learning in cloud environment

Song, Mofei; Xu, Han; Liu, Xiao Fan; Li, Qian

doi:10.1186/s13677-021-00261-7

Cited by 11 publications

(6 citation statements)

References 57 publications

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“…The authors Mofei Song and others(2021) examine a novel deep-label distribution learning technique for visibility estimations in cloud systems in a study [30]. To enhance user experience and optimize resource allocation in cloud computing, the authors provide a system that uses advanced machine learning algorithms to estimate visibility levels.…”

Section: Literature Reviewmentioning

confidence: 99%

Recurrent Neural Network (RNN) Inference Cloud Computing

2024

IJFMR

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The purpose of this technical article is to investigate the convergence of Recurrent Neural Networks (RNNs) with Cloud Computing considering the potential benefits and challenges that may be encountered in bringing these two systems together. Recurrent Neural Networks (RNNs) constitute a particular type of artificial neural network that is capable of processing data sequentially in such a way that it becomes well-suited for applications that involve handling time series or language processing. On the other hand, cloud computing provides computer infrastructures on a scalable basis as well as flexible computation services that are accessible whenever they are needed. This paper targets making RNN applications more efficient and faster with the use of RNN libraries on cloud computing platforms through which the cloud services can be used for things like data processing power and storage. By combining RNNs with Cloud Computing, this paper aims to enhance the efficiency and performance of RNN-based applications by leveraging the cloud's computational power and storage capabilities. The study investigates the impact of deploying RNN inference tasks in the cloud environment, analyzing factors such as latency, cost-effectiveness, and scalability.

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Section: Literature Reviewmentioning

confidence: 99%

Recurrent Neural Network (RNN) Inference Cloud Computing

2024

IJFMR

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“…Nowadays, deep learning has become a powerful technique for big data analysis [27], which can learn the feature representation and the classifier in an end-to-end way. The technique is widely used in many applications, such as flooding process prediction [28], visibility estimation [10], shape recognition [29], object detection [30], visual question answering [31], insect pest recognition [32], advertising click-through rate prediction [33], event extraction [34], sneaker recognition [35], modulation recognition [36], sentiment analysis [37], intrusion detection [38][39][40], climate prediction [41], internet of vehicles [42], healthcare [43], and face clustering [44]. Due to the advantage of deep learning, various researchers apply deep learning to predict credit scores.…”

Section: Credit Scoringmentioning

confidence: 99%

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

Shen¹,

Zhang²,

Song³

2023

Computers, Materials &Amp; Continua

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With the rapid development of the internet of things (IoT), electricity consumption data can be captured and recorded in the IoT cloud center. This provides a credible data source for enterprise credit scoring, which is one of the most vital elements during the financial decision-making process. Accordingly, this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data. Instead of predicting the credit rating, our method can generate an absolute credit score by a novel deep ranking model-ranking extreme gradient boosting net (rankXGB). To boost the performance, the rankXGB model combines several weak ranking models into a strong model. Due to the high computational cost and the vast amounts of data, we design an edge computing framework to reduce the latency of enterprise credit evaluation. Specially, we design a two-stage deep learning task architecture, including a cloud-based weak credit ranking and an edge-based credit score calculation. In the first stage, we send the electricity consumption data of the evaluated enterprise to the computing cloud server, where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results. In the second stage, the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result, which is used to calculate an absolute credit score by score normalization. The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

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“…They implemented a deep neural network (DNN) and polynomial regression to predict the visibility of an account on a social media network. In Song et al (2021), a visibility estimation method based on deep label distribution learning (LDL) in a cloud environment was proposed. The model combined cloud computing and image processing to estimate visibility efficiently.…”

Section: Sentiment Analysismentioning

confidence: 99%

CoMoSAOVA: Computational model for sentiment analysis and online visibility assessment

Ejodamen,

Ekong,

Asuquo

2023

wojast

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The online visibility of a brand, product or organization could influence customers’ decision to patronize it. Online reviews are opinions or emotions of customers that reveal their perception about the product over a period of time. The manual identification of features and sentiments toward an entity is a difficult task. In this study, a computational model was proposed to measure online visibility by mining Twitter data on discourse about a corporate entity – University of The Gambia. The numbers of Twitter posts, followers, followings, likes, retweets, quotes, replies and mentions serves as metrics for online visibility assessment. A linear regression evaluation between the age of the entity’s account and the number of its followers showed 96.81% correlation. This shows that the older an active Twitter account, the higher the chance of increasing its followers and its visibility. Another section of the model predicts the tweet sentiment of the entity’s followers with an accuracy of 93.68% using support vector machine and multilayer perceptron neural network. The computed average sentiment score of the case study was 0.3531996 based on Valence Aware Dictionary of sEntiment Reasoning (VADER) model. This means that positive sentiments were expressed in discussions on various issues where the entity was mentioned. The model will enable decision makers understand the sentiments expressed towards an entity. It also estimates online visibility of the entity based on its number of followers and the accounts’ lifespan. The perceived sentiments will aid better decisions that could advance loyalty to the entity. Future studies would examine other computational models to predict various Twitter features that increases an entity's online visibility.

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Visibility estimation via deep label distribution learning in cloud environment

Cited by 11 publications

References 57 publications

Recurrent Neural Network (RNN) Inference Cloud Computing

Recurrent Neural Network (RNN) Inference Cloud Computing

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

CoMoSAOVA: Computational model for sentiment analysis and online visibility assessment

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