scholarly journals Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3187
Author(s):  
Jaechoon Jo ◽  
Gyeongmin Kim ◽  
Kinam Park
Keyword(s):  
Target Word ◽  
High Performance ◽  
Product Information ◽  
Syntactic Structure ◽  
Recall Rate ◽  
Product Reviews ◽  
Korean Language ◽  
System Method ◽  
Proposed Model ◽  
Extraction Model

Product information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting information in documents is referred to as sentiment analysis. Finding sentiment-target word pairs is an important sentiment mining research issue. With the Korean language, as the predicate appears at the very end, it is not easy to find the exact word pairs without first identifying the syntactic structure of the sentence. In this study, we propose a model that parses sentence structures and extracts sentiment-target word pairs from the parse tree. The proposed model extracts the sentiment-target word pairs that appear in the sentence by using parsing and statistical methods. For extracting sentiment-target word pairs, this model uses a sentiment word extractor and a target word extractor. After testing data from 4000 movie reviews, the applicable model showed high performance in both accuracy 93.25 (+14.45) and F1-score 82.29 (+3.31) compared with others. However, improvements in the recall rate (−0.35) are needed and computational costs must be reduced.

EQUILIBRIUM AND ATTRACTIVITY ANALYSIS FOR A CLASS OF HETERO-ASSOCIATIVE NEURAL MEMORIES

1996 ◽  
Vol 07 (03) ◽  
pp. 287-304
Author(s):  
DONQ-LIANG LEE ◽  
WEN-JUNE WANG
Keyword(s):  
High Performance ◽  
Sufficient Conditions ◽  
High Capacity ◽  
Neural Model ◽  
Recall Rate ◽  
Basins Of Attraction ◽  
Holographic Memory ◽  
Proposed Model ◽  
Training Pair ◽  
Associative Recall

Based on the natural structure of Kosko’s Bidirectional Associative Memories (BAM), a high-performance, high-capacity associative neural model is proposed which is capable of simultaneous hetero-associative recall. The proposed model, Modified Bidirectional Decoding Strategy (MBDS), improves the recall rate by adding some association fascicles to Kosko’s BAM. The association fascicles are sparse coding neuron structures that provide activating strengths between two neuron fields (say, field X and field Y). The sufficient conditions for a state to become an equilibrium state of the MBDS network is derived. Based on these results, we discuss the basins of attraction of the training pairs in one iteration. The upper bound of the number of error bits which can be tolerated by MBDS is also derived. Because the attractivity of a stored training pair can be increased markedly with the aid of its corresponding association fascicles, we recommend a high capacity realization of MBDS, Bidirectional Holographic Memory (BHM), so that each training pair is stored uniquely and directly in the connection weights rather than encoded in a correlation matrix. Finally, computer simulations demonstrate the attractiveness of three different realizations of MBDS to verify our results.

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

An analytical model for predicting the shear strength of unsaturated soils

2022 ◽  
pp. 1-57
Author(s):  
Tuan A. Pham ◽  
Melis Sutman
Keyword(s):  
Shear Strength ◽  
Analytical Model ◽  
Unsaturated Soils ◽  
High Performance ◽  
Micromechanical Model ◽  
Experimental Studies ◽  
Stress Equilibrium ◽  
Proposed Model ◽  
New Equation ◽  
Multi Phase

The prediction of shear strength for unsaturated soils remains to be a significant challenge due to their complex multi-phase nature. In this paper, a review of prior experimental studies is firstly carried out to present important pieces of evidence, limitations, and some design considerations. Next, an overview of the existing shear strength equations is summarized with a brief discussion. Then, a micromechanical model with stress equilibrium conditions and multi-phase interaction considerations is presented to provide a new equation for predicting the shear strength of unsaturated soils. The validity of the proposed model is examined for several published shear strength data of different soil types. It is observed that the shear strength predicted by the analytical model is in good agreement with the experimental data, and get high performance compared to the existing models. The evaluation of the outcomes with two criteria, using average relative error and the normalized sum of squared error, proved the effectiveness and validity of the proposed equation. Using the proposed equation, the nonlinear relationship between shear strength, saturation degree, volumetric water content, and matric suction are observed.

scholarly journals Spatial regression methods to evaluate beekeeping production in the state of Rio de Janeiro

2013 ◽  
Vol 65 (2) ◽  
pp. 553-558
Author(s):  
W.S. Tassinari ◽  
M.C. Lorenzon ◽  
E.L. Peixoto
Keyword(s):  
Rio De Janeiro ◽  
High Performance ◽  
Spatial Regression ◽  
The State ◽  
Spatial Effects ◽  
Regression Methods ◽  
Honey Production ◽  
Production Factors ◽  
Proposed Model ◽  
Wild Vegetation

Brazilian beekeeping has been developed from the africanization of the honeybees and its high performance launches Brazil as one of the world´s largest honey producer. The Southeastern region has an expressive position in this market (45%), but the state of Rio de Janeiro is the smallest producer, despite presenting large areas of wild vegetation for honey production. In order to analyze the honey productivity in the state of Rio de Janeiro, this research used classic and spatial regression approaches. The data used in this study comprised the responses regarding beekeeping from 1418 beekeepers distributed throughout 72 counties of this state. The best statistical fit was a semiparametric spatial model. The proposed model could be used to estimate the annual honey yield per hive in regions and to detect production factors more related to beekeeping. Honey productivity was associated with the number of hives, wild swarm collection and losses in the apiaries. This paper highlights that the beekeeping sector needs support and help to elucidate the problems plaguing beekeepers, and the inclusion of spatial effects in the regression models is a useful tool in geographical data.

scholarly journals Efficient Low-Complexity Digital Predistortion for Power Amplifier Linearization

2016 ◽  
Vol 6 (3) ◽  
pp. 1096
Author(s):  
Siba Monther Yousif ◽  
Roslina M. Sidek ◽  
Anwer Sabah Mekki ◽  
Nasri Sulaiman ◽  
Pooria Varahram
Keyword(s):  
Power Amplifier ◽  
High Performance ◽  
Low Complexity ◽  
Memory Effects ◽  
Model Complexity ◽  
Model Parameters ◽  
Digital Predistortion ◽  
Nonlinear Memory ◽  
Proposed Model ◽  
Code Division Multiple

<span lang="EN-US">In this paper, a low-complexity model is proposed for linearizing power amplifiers with memory effects using the digital predistortion (DPD) technique. In the proposed model, the linear, low-order nonlinear and high-order nonlinear memory effects are computed separately to provide flexibility in controlling the model parameters so that both high performance and low model complexity can be achieved. The performance of the proposed model is assessed based on experimental measurements of a commercial class AB power amplifier by applying a single-carrier wideband code division multiple access (WCDMA) signal. The linearity performance and the model complexity of the proposed model are compared with the memory polynomial (MP) model and the DPD with single-feedback model. The experimental results show that the proposed model outperforms the latter model by 5 dB in terms of adjacent channel leakage power ratio (ACLR) with comparable complexity. Compared to MP model, the proposed model shows improved ACLR performance by 10.8 dB with a reduction in the complexity by 17% in terms of number of floating-point operations (FLOPs) and 18% in terms of number of model coefficients.</span>

scholarly journals Dual Protectıon for Data usıng Steganographıc Technıques with Embedded Framework

2019 ◽  
Vol 8 (5S3) ◽  
pp. 155-159
Keyword(s):  
High Performance ◽  
Data Communication ◽  
Original Data ◽  
Cover Image ◽  
Original Text ◽  
Dual Protection ◽  
Proposed Model ◽  
Encryption And Decryption ◽  
Secured Data

As the world is getting digitalized, the rush for need of secured data communication is overtop. Provoked by the vulnerability of human visual system to understand the progressive changes in the scenes, a new steganography method is proposed. The paper represents a double protection methodology for secured transmission of data. The original data is hidden inside a cover image using LSB substitution algorithm. The image obtained is inserted inside a frame of the video producing a stego-video. Stego-video attained is less vulnerable to attacks. After decryption phase, the original text is obtained which is error-free and the output image obtained is similar as the cover image. The quality of stego-video is high and there is no need for additional bandwidth for transmission. The hardware implement is required in order to calculate the corresponding analytical results. The proposed algorithm is examined and realized for various encryption standards using Raspberry Pi3 embedded hardware. The results obtained focuses on the attributes of the proposed model. On comparing with other conventional algorithms, the proposed scheme exhibits high performance in both encryption and decryption process with increase in efficiency of secured data communication.

Experience Sharing Service Value Co-Creation Model (ESSVC) and Its Application to Korean Language Service

2013 ◽  
Vol 4 (2) ◽  
pp. 46-57 ◽  
Author(s):  
Wooseok Dong ◽  
Kunio Shirahada ◽  
Michitaka Kosaka
Keyword(s):  
Information Technology ◽  
Social Network ◽  
Value Creation ◽  
Language Education ◽  
Korean Language ◽  
Direct Service ◽  
Service Experience ◽  
Service Value ◽  
Proposed Model ◽  
Service Field

In this paper, the authors propose a service value creation model based on sharing service experience. Experiences for services still remain in customers’ brain or heart after the services are finished. Their main aim is to share customers’ experiences and find suitable service by analyzing information shared after service providing. In the proposed model, the direct service field and the indirect service field are prepared. In the indirect service field, customers share their service experience by using Information Technology (Web2.0, Social Network). Suitable services can be found by analyzing information in the indirect service field. The effectiveness of this new model is demonstrated through its application to Korean language education during a 14week period.

A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

2018 ◽  
Vol 9 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Harendra Kumar ◽  
Nutan Kumari Chauhan ◽  
Pradeep Kumar Yadav
Keyword(s):  
Mathematical Model ◽  
Distributed Computing ◽  
High Performance ◽  
Computing System ◽  
Performance Model ◽  
Communication Costs ◽  
Clustering Technique ◽  
Proposed Model ◽  
Two Stages ◽  
The Mathematical Model

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of examples have compared with some existing models.

scholarly journals The Compressive Strength of High-Performance Concrete and Ultrahigh-Performance

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
E. H. Kadri ◽  
S. Aggoun ◽  
S. Kenai ◽  
A. Kaci
Keyword(s):  
Experimental Data ◽  
Compressive Strength ◽  
Silica Fume ◽  
High Performance ◽  
High Performance Concrete ◽  
Cementitious Materials ◽  
Strength Gain ◽  
Proposed Model ◽  
Partial Cement Replacement ◽  
Better Than

The compressive strength of silica fume concretes was investigated at low water-cementitious materials ratios with a naphthalene sulphonate superplasticizer. The results show that partial cement replacement up to 20% produce, higher compressive strengths than control concretes, nevertheless the strength gain is less than 15%. In this paper we propose a model to evaluate the compressive strength of silica fume concrete at any time. The model is related to the water-cementitious materials and silica-cement ratios. Taking into account the author's and other researchers’ experimental data, the accuracy of the proposed model is better than 5%.

Research on Technology Oriented Framework of Aspects Extraction from Customer Reviews

2014 ◽  
Vol 488-489 ◽  
pp. 1358-1362
Author(s):  
Shi Li ◽  
Ming Yu Ji
Keyword(s):  
Sentiment Analysis ◽  
Product Information ◽  
Theoretical Framework ◽  
The Internet ◽  
Product Reviews ◽  
Valuable Data ◽  
Research Framework ◽  
Customer Reviews ◽  
Feature Mining ◽  
Oriented Research

As e-business develops rapidly, more and more product information and product reviews are posted on the Internet. These contents will have a great significance for companies and consumers. This paper focus on customer reviews of product, and construct a technology oriented research framework for the sentiment analysis. Further more an improved theoretical framework of aspects extraction is proposed, which based on products feature mining issues from customer reviews. This two theoretical framework can help researchers acquire supported valuable data for additional researches including the study of behavioral.

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