A double auction scheme of resource allocation with social ties and sentiment classification for Device-to-Device communications

Zhang, Zufan; Wang, Zhangyi; Gan, Chenquan; Zhang, Porui

doi:10.1016/j.comnet.2019.03.018

Cited by 19 publications

(4 citation statements)

References 32 publications

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“…where P represents the transmission power of the next channel symbol in the fixed modulation method, which is processed by normalization [12,13]. The specific objective function p T formula is…”

Section: Communication Resource Allocationmentioning

confidence: 99%

Optimization Algorithm of Communication Resource Allocation in a Complex Network Based on an Improved Neural Network

Zhang¹,

Liu

2022

Journal of Function Spaces

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The traditional optimization algorithm of communication resource allocation in a complex network has the disadvantage of weak antijamming ability, and the communication quality decreases obviously when the number of users is large. In China’s large urban network applications, mobile phones and other networks can have problems such as reduced network efficiency when there are more access users at some communication base stations, thus affecting user network usage. An optimization algorithm of communication resource allocation in the complex network based on an improved neural network is proposed. Increase inertia improves the traditional BP neural network algorithm, using the average path length, clustering coefficient, and connectivity distribution index analysis of the complex network; the improved Hopfield neural network is utilized to confirm each user volume size; it is concluded that their users are able to get the number of subchannels, through the instantaneous channel coarse pair gain dynamic channel allocation, calculating bit load matrix at the same time, minimize transmission power, and achieve bit loading and power allocation and communication resource allocation optimization. Experimental results show that the proposed method has better application performance by introducing the improved neural network and suppressing the external interference on the basis of enhancing the communication effect.

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Section: Communication Resource Allocationmentioning

confidence: 99%

Optimization Algorithm of Communication Resource Allocation in a Complex Network Based on an Improved Neural Network

Zhang¹,

Liu

2022

Journal of Function Spaces

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“…In addition, it reconstructed the sparse matrix of the target field by extending the codebook and applied it to the real-world data set and proved that the recommendation effect of the "codebook migration method" has obvious advantages. The literature [10] proposed a three-factor factorization model based on joint nonnegative matrix. The model uses an effective alternate minimization algorithm to enhance the crossdomain recommendation effect, and it can not only learn the scoring mode shared between domains but also flexibly control the sharing level.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Psychological Characteristics of Students’ Behavior Based on Improved Machine Learning

2021

Journal of Sensors

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Contemporary classroom teaching requires the combination of students’ classroom behavior and their psychological activities and appropriately changes the teaching mode according to students’ psychological characteristics. This paper analyzes the traditional characteristic recognition algorithm, and after improving its deficiencies, an improved characteristic extraction algorithm is proposed, based on the actual situation of classroom learning. This new algorithm can effectively improve the students’ psychological feature prediction; with the support of this algorithm, a comprehensive analysis model with classroom behavior recognition and psychological feature recognition is constructed; also, the functional structure of the system is built up. Through experimental research, the model proposed in this paper is analyzed, and the experimental data has approved that the systemic model could play an important role in classroom teaching.

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“…In this method, no labeled target domain samples are required. The authors in [ 16 , 17 ] propose a method for financial text sentiment classification based on a generative confrontation network (GAN), which combines the random noise generated in the generative network with text representation vectors. Then a discriminant network module is adopted to distinguish true source domain samples, generated samples, and the sentiment.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Classification for Financial Texts Based on Deep Learning

Dong

Liu

2021

Computational Intelligence and Neuroscience

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Sentiment classification for financial texts is of great importance for predicting stock markets and financial crises. At present, with the popularity of applications in the field of natural language processing (NLP) adopting deep learning, the application of automatic text classification and text-based sentiment classification has become more and more extensive. However, in the field of financial text-based sentiment classification, due to a lack of labeled samples, such applications are limited. A domain-adaptation-based financial text sentiment classification method is proposed in this paper, which can adopt source domain (SD) text data with sentiment labels and a large amount of unlabeled target domain (TD) financial text data as training samples for the proposed neural network. The proposed method is a cross-domain transfer-learning-based method. The domain classification subnetwork is added to the original neural network, and the domain classification loss function is also added to the original training loss function. Therefore, the network can simultaneously adapt to the target domain and then accomplish the classification task. The experiment of the proposed sentiment classification transfer learning method is carried out through an open-source dataset. The proposed method in this paper uses the reviews of Amazon Books, DVDs, electronics, and kitchen appliances as the source domain for cross-domain learning, and the classification accuracy rates can reach 65.0%, 61.2%, 61.6%, and 66.3%, respectively. Compared with nontransfer learning, the classification accuracy rate has improved by 11.0%, 7.6%, 11.4%, and 13.4%, respectively.

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A double auction scheme of resource allocation with social ties and sentiment classification for Device-to-Device communications

Cited by 19 publications

References 32 publications

Optimization Algorithm of Communication Resource Allocation in a Complex Network Based on an Improved Neural Network

Optimization Algorithm of Communication Resource Allocation in a Complex Network Based on an Improved Neural Network

Recognition of Psychological Characteristics of Students’ Behavior Based on Improved Machine Learning

Sentiment Classification for Financial Texts Based on Deep Learning

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