2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP)最新文献_第4页

Improving the Accuracy in Classification Using the Bayesian Relevance Feedback (BRF) Model 利用贝叶斯相关反馈(BRF)模型提高分类精度

2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) Pub Date : 2018-03-01 DOI: 10.1109/INFRKM.2018.8464783

Fatihah Mohd, M. Jalil, N. M. Mohamad Noor, Z. Bakar

{"title":"Improving the Accuracy in Classification Using the Bayesian Relevance Feedback (BRF) Model","authors":"Fatihah Mohd, M. Jalil, N. M. Mohamad Noor, Z. Bakar","doi":"10.1109/INFRKM.2018.8464783","DOIUrl":"https://doi.org/10.1109/INFRKM.2018.8464783","url":null,"abstract":"In this study, the components of a decision support system (DSS) are discussed. One of the main components is the inference engine (IE). In order to improve the IE in supporting the system's output, Bayesian Relevance Feedback (BRF) model is proposed. This BRF model as suggested has the potential to generate the most relevant target or classes (stage) based on relevance feedback knowledge. Subsequently, eight classifiers: Bayesian Model (BM), K-Nearest Neighbors (KNN), Meta MultiClass Classifier (MCC), Rule OneR (OneR), Random Tree (RT), Multilayer Perceptron (MLP), Naive Bayes (NB), and SMO-Poly Kernel (E-1.O) (SVM) are used in order to evaluate the efficiency of the proposed model. The empirical comparison shows that the BRF significantly improved the accuracy of the entire classification algorithm used for the oral cancer data set with a mean accuracy of 95.83%. It is also noted that the proposed model, BRF contributes to solving the posterior probabilities that do not exist (probability with zero values) in order to improve the decision-making in the oral cancer diagnosis.","PeriodicalId":196731,"journal":{"name":"2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123730310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Neural Machine Translation for English to Hindi 神经机器翻译英语到印地语

2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) Pub Date : 2018-03-01 DOI: 10.1109/INFRKM.2018.8464781

Sandeep Saini, V. Sahula

引用次数: 41

Effective Predicate Identification Algorithm for XML Retrieval XML检索中的有效谓词识别算法

2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) Pub Date : 2018-03-01 DOI: 10.1109/INFRKM.2018.8464696

Roko Abubakar, S. Doraisamy, B. Nakone

引用次数: 0

Constructing a Knowledge Base for Al-Qur'an Utilizing Principles of Human Communication 利用人际交往原理构建《古兰经》知识库

2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) Pub Date : 2018-03-01 DOI: 10.1109/INFRKM.2018.8464823

Sharyar Wani, Tengku Mohd Tengku Sembok, M. Wahiddin

引用次数: 1

Performance Evaluation of Distributed Indexing Using Solr and Terrier Information Retrievals 基于Solr和Terrier信息检索的分布式索引性能评价

2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) Pub Date : 2018-03-01 DOI: 10.1109/INFRKM.2018.8464814

Ali Y. Aldailamy, Nor Asila Wati Abdul Hamid, Mohammed Abdulkarem

{"title":"Performance Evaluation of Distributed Indexing Using Solr and Terrier Information Retrievals","authors":"Ali Y. Aldailamy, Nor Asila Wati Abdul Hamid, Mohammed Abdulkarem","doi":"10.1109/INFRKM.2018.8464814","DOIUrl":"https://doi.org/10.1109/INFRKM.2018.8464814","url":null,"abstract":"The continuous growing datasets and the emergence terabyte-scale data pose great challenges to Information Retrieval (IR) systems. Tremendously, a large amount of data from various aspects is collected every day making the amount of raw data extremely large. As a result, indexing a large volume of data is a time-consuming problem. Therefore, efficient indexing of large collections is getting more challenging. MapReduce is a programming model for the computing of large document collections by distributing data and processing tasks over multiple computing machines. In this study, Solr and Terrier distributed indexing will be evaluated as they are the most popular information retrieval frameworks among researchers and enterprises. To be more specific, this paper will compare and analyze the distributed indexing performance over MapReduce for the indexing strategies of Solr and Terrier using 1GB, 3GB, 6GB, and 9GB datasets. In the experiments, the indexing average time, speedup, and throughput are observed as the number of machines involved in the experiments increases for both indexing frameworks. The experimental results show that Terrier is more efficient with large datasets in the presence of processing resource scalability. On the other hand, Solr performed better with small datasets using limited computing resources.","PeriodicalId":196731,"journal":{"name":"2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123379003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Effective Method for Sentiment Lexical Dictionary Enrichment Based on Word2Vec for Sentiment Analysis 基于Word2Vec的情感词典充实方法

2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) Pub Date : 2018-03-01 DOI: 10.1109/INFRKM.2018.8464775

Eissa Alshari, A. Azman, S. Doraisamy, N. Mustapha, Mostafa Alkeshr

引用次数: 38

Communication Management in Global Software Development Projects 全球软件开发项目中的沟通管理

2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) Pub Date : 2018-03-01 DOI: 10.1109/INFRKM.2018.8464824

Y. Y. Jusoh, Rozi Nor Haizan Nor, B. Mahmood, M. Wafeeq, Mohamed Abdullahi Ali, Muhammad Nur Baihaqi Jusoh

{"title":"Communication Management in Global Software Development Projects","authors":"Y. Y. Jusoh, Rozi Nor Haizan Nor, B. Mahmood, M. Wafeeq, Mohamed Abdullahi Ali, Muhammad Nur Baihaqi Jusoh","doi":"10.1109/INFRKM.2018.8464824","DOIUrl":"https://doi.org/10.1109/INFRKM.2018.8464824","url":null,"abstract":"Many organizations that have the global software development (GSD) projects use communication technologies to connect their virtual teams. However, the virtual team faces various challenges and issues in the process of the GSD. One of the significant challenges is obtaining an efficient communication among team members. This study focuses on the communication factors among the virtual teams highlighted in the literature. The communication factors are related to the temporal distances, geographical distances, socio-cultural, access to training, technological problems that hindering communications, the communication within the status of development process, personal communication skills and language differences. The objective of this study is to examine the communication factors and identify the related issues which are commonly occurring between the virtual teams in the global software development. A survey was conducted in different sectors. The findings indicate that some of important points related to the communication factors contribute to the success of the GSD.","PeriodicalId":196731,"journal":{"name":"2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122663643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Association Between Logical Reasoning Ability and Quality of Relevance Judgments in Crowdsourcing 众包中逻辑推理能力与相关性判断质量的关系

2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) Pub Date : 2018-03-01 DOI: 10.1109/INFRKM.2018.8464689

Parnia Samimi, Prabha Rajagopal, Sri Devi Ravana

{"title":"Association Between Logical Reasoning Ability and Quality of Relevance Judgments in Crowdsourcing","authors":"Parnia Samimi, Prabha Rajagopal, Sri Devi Ravana","doi":"10.1109/INFRKM.2018.8464689","DOIUrl":"https://doi.org/10.1109/INFRKM.2018.8464689","url":null,"abstract":"Human assessors are in charge of creating relevance judgments set in a typical test collection. Nevertheless, this approach is not that efficient as it is expensive and time consuming while scales deficiently. Crowdsourcing as a recent technique for data acquisition is a low-cost and fast method for building relevance judgments. One of the most important issues for using crowdsourcing instead of human expert assessors is the quality of crowdsourcing in building relevance judgments. In order to assess this issue, factors that may have significant effects on the quality of crowdsourcing relevance judgments should be identified. The main objective of this study is to find out whether cognitive characteristics of crowdsourced workers significantly associated with quality of crowdsourced judgments, and to evaluate the effect(s) that each of those characteristics may have on judgment quality, as compared with the gold standard dataset (i.e. human assessment). Thus, the judgments of the crowdsourced workers is compared to that of a human judgment, as the overlap between relevance assessments, and by comparing the system effectiveness evaluation provided by human judgment and from worker assessors. In this study, we assess the effects of the cognitive ability namely logical reasoning ability on quality of relevance judgment. The experiment shows that logical reasoning ability of individuals is remarkably correlated with quality of relevance judgments.","PeriodicalId":196731,"journal":{"name":"2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130354254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2