scholarly journals A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 359
Author(s):  
Kai Ye ◽  
Yangheran Piao ◽  
Kun Zhao ◽  
Xiaohui Cui
Keyword(s):  
Decision Making ◽  
Essential Role ◽  
Online Discussion ◽  
Professional Community ◽  
Experimental Results ◽  
Agricultural Products ◽  
Price Prediction ◽  
Price Data ◽  
Rich Information ◽  
Lstm Network

Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals Application of Spectral Methods for Synthesis of Symptoms for Qualification of Agricultural Products

2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Radoslava Nikolova Gabrova ◽  
Lena Filipova Kostadinova
Keyword(s):  
Decision Making ◽  
Spectral Methods ◽  
The State ◽  
Agricultural Products ◽  
Initial Information ◽  
Second Stage ◽  
New Space ◽  
Initial Description

The basic stages during automatic qualification of agricultural products are: acquisition of initial information of the quality state, mathematical proceeding of this information and decision making for object qualification to defined sets according to the state of quality. One of the steps in the second stage is transformation the patterns of qualified products to a new space of symptoms. In this paper an approach for transformation the initial description of the objects to a new space of spectral symptoms, independently of the size of the initial description of the object is presented. A relation for synthesis of symptoms for recognition has been generated.

AR-Tri-Training: Tri-Training with Assistant Strategy

2014 ◽  
Vol 513-517 ◽  
pp. 1840-1844 ◽  
Author(s):  
Long Jie Cui ◽  
Hong Li Wang ◽  
Rong Yi Cui
Keyword(s):  
Learning Strategy ◽  
Voice Recognition ◽  
Classification Performance ◽  
Experimental Results ◽  
Training Algorithm ◽  
Information Strategy ◽  
Testing Rate ◽  
Rich Information ◽  
Validation Set

The classification performance of the classifier is weakened because the noise samples are introduced for the use of unlabeled samples in Tri-training. In this paper a new Tri-training style algorithm named AR-Tri-training (Tri-training with assistant and rich strategy) is proposed. Firstly, the assistant learning strategy is posed. Then the supporting learner is designed by combining the assistant learning strategy with rich information strategy. The number of mislabeled samples produced in the iterations of three classifiers mutually labeling are reduced by use of the supporting learner, moreover the unlabeled samples and the misclassified samples of validation set can be fully used. The proposed algorithm is applied to voice recognition. The experimental results show that AR-Tri-training algorithm can compensate for the shortcomings of Tri-training algorithm, further improve the testing rate.

Estimated path information gain-based robot exploration under perceptual uncertainty

Robotica ◽  
2022 ◽  
pp. 1-17
Author(s):  
Jie Liu ◽  
Chaoqun Wang ◽  
Wenzheng Chi ◽  
Guodong Chen ◽  
Lining Sun
Keyword(s):  
Decision Making ◽  
Topological Structure ◽  
Information Gain ◽  
State Of The Art ◽  
Selection Process ◽  
Random Trees ◽  
Experimental Results ◽  
Restricted Area ◽  
Structure Information ◽  
Path Information

Abstract At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

Exploring Managerial Attitudes Toward Various Participation Mechanisms in Response to Citizen Satisfaction Signals on Public Service Quality

2021 ◽  
pp. 009539972110483
Author(s):  
Youngmin Oh ◽  
Heontae Shin ◽  
Jongsun Park
Keyword(s):  
Decision Making ◽  
Service Quality ◽  
Public Service ◽  
Essential Role ◽  
Negativity Bias ◽  
Positive Signal ◽  
Citizen Satisfaction ◽  
Public Managers ◽  
Participation Mechanisms

This study identifies the impacts of different citizen satisfaction signals (positive/negative) on managers’ agreement to use various participation channels. Citizen satisfaction with public service quality plays an essential role in managers’ accountability expectations. Accordingly, it is crucial to examine how public managers use participation mechanisms, reacting to citizen satisfaction signals on public service quality. The results confirm a negativity bias: Managers are more reactive to citizens’ negative signals than a positive signal in their service quality evaluations. However, the negative signal’s effect does not reach the participation tools, where the degree of their decision-making is highly delegated to citizens.

scholarly journals SentiProdBR: Building Domain-Specific Sentiment Lexicons for the Portuguese Language

2021 ◽  
Author(s):  
Tiago de Melo
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Bayes Theorem ◽  
Experimental Results ◽  
Product Categories ◽  
Domain Specific ◽  
Alternative Approaches ◽  
The Web

Online reviews are readily available on the Web and widely used for decision-making. However, only a few studies on Portuguese sentiment analysis are reported due to the lack of resources including domain-specific sentiment lexical collections. In this paper, we present an effective methodology using probabilities of the Bayes’ Theorem for building a set of lexicons, called SentiProdBR, for 10 different product categories for the Portuguese language. Experimental results indicate that our methodology significantly outperforms several alternative approaches of building domain-specific sentiment lexicons.

scholarly journals ACTA A Tool for Argumentative Clinical Trial Analysis

2019 ◽  
Author(s):  
Tobias Mayer ◽  
Elena Cabrio ◽  
Serena Villata
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Online Discussion ◽  
Trial Analysis ◽  
Persuasive Essays

Argumentative analysis of textual documents of various nature (e.g., persuasive essays, online discussion blogs, scientific articles) allows to detect the main argumentative components (i.e., premises and claims) present in the text and to predict whether these components are connected to each other by argumentative relations (e.g., support and attack), leading to the identification of (possibly complex) argumentative structures. Given the importance of argument-based decision making in medicine, in this demo paper we introduce ACTA, a tool for automating the argumentative analysis of clinical trials. The tool is designed to support doctors and clinicians in identifying the document(s) of interest about a certain disease, and in analyzing the main argumentative content and PICO elements.

Evolution of an Executive Information System

2000 ◽  
pp. 148-168
Author(s):  
Hamid Nemati ◽  
Keith Smith
Keyword(s):  
Decision Making ◽  
Information System ◽  
Data Warehouse ◽  
Essential Role ◽  
Successful Outcome ◽  
Development Team ◽  
Number Of Factors ◽  
Basic Concepts ◽  
Synthetic View ◽  
Executive Information System

This case highlights factors that provided the impetus for changing a successful EIS into a data warehouse at the VF Corporation. The data warehouse was developed to aid JeansWear, a division of VF, with its pointof- sale/replenishment activities. The data warehouse provides greater reporting and OLAP capabilities, giving replenishment analysts a detailed and synthetic view of the marketplace. It is estimated that about $100 million in 1998 alone might be attributed to the improved Replenishment decision making due to the data warehouse. The case discusses the basic concepts and architecture of this data warehouse and outlines the development process and the problems that the development team had to overcome. It also examines the essential role that this data warehouse is currently playing in the success of VF Corporation. Finally, the case outlines and discusses a number of factors that should be considered and questions that should be asked prior to initiation of a data warehouse project in order to assure a successful outcome.

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