Sentiment analysis with deep neural networks: comparative study and performance assessment

2020 ◽  
Vol 53 (8) ◽  
pp. 6155-6195
Author(s):  
Ramesh Wadawadagi ◽  
Veerappa Pagi
Keyword(s):  
Neural Networks ◽  
Comparative Study ◽  
Performance Assessment ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
And Performance

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

scholarly journals Iterative Algorithm for Parameterization of Two-Region Piecewise Uniform Quantizer for the Laplacian Source

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3091
Author(s):  
Jelena Nikolić ◽  
Danijela Aleksić ◽  
Zoran Perić ◽  
Milan Dinčić
Keyword(s):  
Neural Networks ◽  
Performance Assessment ◽  
Probability Density ◽  
Probability Density Functions ◽  
Density Functions ◽  
Uniform Quantization ◽  
Memory Footprint ◽  
High Bit Rates ◽  
And Performance ◽  
Laplacian Distribution

Motivated by the fact that uniform quantization is not suitable for signals having non-uniform probability density functions (pdfs), as the Laplacian pdf is, in this paper we have divided the support region of the quantizer into two disjunctive regions and utilized the simplest uniform quantization with equal bit-rates within both regions. In particular, we assumed a narrow central granular region (CGR) covering the peak of the Laplacian pdf and a wider peripheral granular region (PGR) where the pdf is predominantly tailed. We performed optimization of the widths of CGR and PGR via distortion optimization per border–clipping threshold scaling ratio which resulted in an iterative formula enabling the parametrization of our piecewise uniform quantizer (PWUQ). For medium and high bit-rates, we demonstrated the convenience of our PWUQ over the uniform quantizer, paying special attention to the case where 99.99% of the signal amplitudes belong to the support region or clipping region. We believe that the resulting formulas for PWUQ design and performance assessment are greatly beneficial in neural networks where weights and activations are typically modelled by the Laplacian distribution, and where uniform quantization is commonly used to decrease memory footprint.

A Unified Framework for Improving Misclassifications in Modern Deep Neural Networks for Sentiment Analysis

2021 ◽  
Author(s):  
Ahoud Alhazmi ◽  
Abdulwahab Aljubairy ◽  
Wei Emma Zhang ◽  
Quan Z Sheng ◽  
Elaf Alhazmi
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Unified Framework

scholarly journals A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

2020 ◽  
Vol 380 ◽  
pp. 1-10 ◽  
Author(s):  
Kia Dashtipour ◽  
Mandar Gogate ◽  
Jingpeng Li ◽  
Fengling Jiang ◽  
Bin Kong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Analysis Framework ◽  
Dependency Grammar
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