scholarly journals Review Rating Prediction Based on User Context and Product Context

2018 ◽  
Vol 8 (10) ◽  
pp. 1849 ◽  
Author(s):  
Bingkun Wang ◽  
Shufeng Xiong ◽  
Yongfeng Huang ◽  
Xing Li
Keyword(s):  
Prediction Model ◽  
Language Processing ◽  
Product Information ◽  
Prediction Method ◽  
Training Data ◽  
Context Information ◽  
Research Focus ◽  
User Context ◽  
Online User Reviews ◽  
Rating Prediction

With the explosion of online user reviews, review rating prediction has become a research focus in natural language processing. Existing review rating prediction methods only use a single model to capture the sentiments of review texts, ignoring users who express the sentiment and products that are evaluated, both of which have great influences on review rating prediction. In order to solve the issue, we propose a review rating prediction method based on user context and product context by incorporating user information and product information into review texts. Our method firstly models the user context information of reviews, and then models the product context information of reviews. Finally, a review rating prediction method that is based on user context and product context is proposed. Our method consists of three main parts. The first part is a global review rating prediction model, which is shared by all users and all products, and it can be learned from training datasets of all users and all products. The second part is a user-specific review rating prediction model, which represents the user’s personalized sentiment information, and can be learned from training data of an individual user. The third part is a product-specific review rating prediction model, which uses training datasets of an individual product to learn parameter of the model. Experimental results on four datasets show that our proposed methods can significantly outperform the state-of-the-art baselines in review rating prediction.

scholarly journals Congestion Prediction in FPGA Using Regression Based Learning Methods

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1995
Author(s):  
Pingakshya Goswami ◽  
Dinesh Bhatia
Keyword(s):  
Prediction Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Physical Design ◽  
Design Flow ◽  
Training Data ◽  
Regression Problem ◽  
Data Set ◽  
Congestion Prediction ◽  
Routing Congestion

Design closure in general VLSI physical design flows and FPGA physical design flows is an important and time-consuming problem. Routing itself can consume as much as 70% of the total design time. Accurate congestion estimation during the early stages of the design flow can help alleviate last-minute routing-related surprises. This paper has described a methodology for a post-placement, machine learning-based routing congestion prediction model for FPGAs. Routing congestion is modeled as a regression problem. We have described the methods for generating training data, feature extractions, training, regression models, validation, and deployment approaches. We have tested our prediction model by using ISPD 2016 FPGA benchmarks. Our prediction method reports a very accurate localized congestion value in each channel around a configurable logic block (CLB). The localized congestion is predicted in both vertical and horizontal directions. We demonstrate the effectiveness of our model on completely unseen designs that are not initially part of the training data set. The generated results show significant improvement in terms of accuracy measured as mean absolute error and prediction time when compared against the latest state-of-the-art works.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals Developing an Enhanced Short-Range Railroad Track Condition Prediction Model for Optimal Maintenance Scheduling

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Xu ◽  
Chuanjun Jia ◽  
Ye Li ◽  
Quanxin Sun ◽  
Rengkui Liu
Keyword(s):  
Prediction Model ◽  
Short Range ◽  
Prediction Method ◽  
Maintenance Scheduling ◽  
Railroad Track ◽  
Track Geometry ◽  
Track Maintenance ◽  
Track Condition ◽  
Optimal Maintenance ◽  
Process Work

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well.

Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument

2021 ◽  
pp. 2-11
Author(s):  
David Aufreiter ◽  
Doris Ehrlinger ◽  
Christian Stadlmann ◽  
Margarethe Uberwimmer ◽  
Anna Biedersberger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Research Process ◽  
Training Data ◽  
Future Research ◽  
Manufacturing Companies ◽  
Market Information ◽  
Annotation Scheme ◽  
Export Decisions ◽  
Decision Supporting

On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

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