Deep Learning of Human Information Foraging Behavior with a Search Engine

2019 ◽  
Author(s):  
Xi Niu ◽  
Xiangyu Fan
Keyword(s):  
Deep Learning ◽  
Search Engine ◽  
Foraging Behavior ◽  
Information Foraging

scholarly journals Deep Learning Based Question Answering Search Engine

2021 ◽  
pp. 25-32
Author(s):  
Mrunal Malekar
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language ◽  
Search Engine ◽  
Language Processing ◽  
Question Answering ◽  
Research Work ◽  
Construction Company ◽  
Exact Answer ◽  
Search For Information

Domain based Question Answering is concerned with building systems which provide answers to natural language questions that are asked specific to a domain. It comes under Information Retrieval and Natural language processing. Using Information Retrieval, one can search for the relevant documents which may contain the answer but it won’t give the exact answer for the question asked. In the presented work, a question answering search engine has been developed which first finds out the relevant documents from a huge textual document data of a construction company and then goes a step beyond to extract answer from the extracted document. The robust question answering system developed uses Elastic Search for Information Retrieval [paragraphs extraction] and Deep Learning for answering the question from the short extracted paragraph. It leverages BERT Deep Learning Model to understand the layers and representations between the question and answer. The research work also focuses on how to improve the search accuracy of the Information Retrieval based Elastic Search engine which returns the relevant documents which may contain the answer.

Auditor Information Foraging Behavior

2016 ◽  
Vol 92 (4) ◽  
pp. 145-160 ◽  
Author(s):  
Benjamin P. Commerford ◽  
Richard C. Hatfield ◽  
Richard W. Houston ◽  
Curtis Mullis
Keyword(s):  
Foraging Behavior ◽  
Audit Quality ◽  
Information Value ◽  
Information Foraging ◽  
Personal Cost ◽  
Audit Evidence ◽  
Evidence Quality ◽  
Evidence Collection ◽  
Small Change ◽  
The Individual

ABSTRACT In this study, we examine how information foraging by auditors affects audit evidence collection in two distinct contexts, and show how a small change to audit methodology mitigates the potentially harmful effects of foraging. Information Foraging Theory explains how, while navigating an information environment, individuals learn to acquire information through personally experiencing the costs incurred and the values obtained from information. Consistent with the theory, we find that auditors react to the immediately felt costs of information collection (e.g., time and effort) at the expense of a more global consideration of information value (i.e., auditors collect lower-quality audit evidence). However, foraging behavior is moderated by removing the personal cost to the individual auditor (identifying audit evidence for another member of the audit team to collect), further demonstrating that these personally felt costs influence auditor choices in a way that reduces the quality of information collected. We contribute to the literature by demonstrating how information foraging can influence evidence quality and, thus, audit quality, and how a slight alteration of audit methodology can mitigate this behavior.

scholarly journals NSGA-III-Based Deep-Learning Model for Biomedical Search Engines

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Manish Gupta ◽  
Naresh Kumar ◽  
Bhupesh Kumar Singh ◽  
Neha Gupta
Keyword(s):  
Deep Learning ◽  
Search Engine ◽  
Search Engines ◽  
Biomedical Imaging ◽  
Learning Model ◽  
Imaging Data ◽  
Testing Dataset ◽  
Proposed Model ◽  
Deep Learning Model ◽  
Nondominated Sorting

With the advancements in biomedical imaging applications, it becomes more important to provide potential results for searching the biomedical imaging data. During the health emergency, tremors require efficient results at rapid speed to provide results to spatial queries using the Web. An efficient biomedical search engine can obtain the significant search intention and return additional important contents in which users have already indicated some interest. The development of biomedical search engines is still an open area of research. Recently, many researchers have utilized various deep-learning models to improve the performance of biomedical search engines. However, the existing deep-learning-based biomedical search engines suffer from the overfitting and hyperparameter tuning problems. Therefore, in this paper, a nondominated-sorting-genetic-algorithm-III- (NSGA-III-) based deep-learning model is proposed for biomedical search engines. Initially, the hyperparameters of the proposed deep-learning model are obtained using the NSGA-III. Thereafter, the proposed deep-learning model is trained by using the tuned parameters. Finally, the proposed model is validated on the testing dataset. Comparative analysis reveals that the proposed model outperforms the competitive biomedical search engine models.

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