Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies最新文献_第3页

Enhancing the degree of personalization through Vector Space Model and Profile Ontology 通过向量空间模型和轮廓本体增强个性化程度

Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies Pub Date : 1900-01-01 DOI: 10.1109/RIVF.2013.6719902

Safiya Al Sharji, M. Beer, Elizabeth Uruchurtu

{"title":"Enhancing the degree of personalization through Vector Space Model and Profile Ontology","authors":"Safiya Al Sharji, M. Beer, Elizabeth Uruchurtu","doi":"10.1109/RIVF.2013.6719902","DOIUrl":"https://doi.org/10.1109/RIVF.2013.6719902","url":null,"abstract":"Web browsers need to match the users' queries to the information available data bases. However, matching the users' needs with their interests and preferences to provide personalized search results in a ranked order of relevance entails a complex interaction of information attributes and as such, it remains one of the main challenges researchers face. Information Retrieval (IR) techniques focusing specifically on using Vector Space Model (VSM) with Profile Ontology (PO) hybridizationproved an improvement on personalized search results. We improve the degree of personalization by incorporating a new metric, the Dwell Time of each search session to optimize a learned re-ranked model. For a longitudinal naturalistic study of Web interactions, search logs were gathered as stimuli for the ranking algorithms of our personalized search engine. The performance of our re-ranking mechanism using Discounted Cumulative Gain (DCG) and F-measurewas tested. The scheme devised in this study was compared with the Google search engine. It was shown that, at the 10 top ranks of our personalized search engine, 14% improvement in the relevance is achieved.","PeriodicalId":171525,"journal":{"name":"Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128389342","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

Imbalanced educational data classification: An effective approach with resampling and random forest 不平衡教育数据分类:一种有效的重采样和随机森林方法

Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies Pub Date : 1900-01-01 DOI: 10.1109/RIVF.2013.6719882

Thi Ngoc Chau Vo, Hua Phung Nguyen

引用次数: 2

The Lattice Structure of Rotor-Router Model 转子-路由器模型的晶格结构

Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies Pub Date : 1900-01-01 DOI: 10.1109/RIVF.2010.5633377

Le Manh Ha

引用次数: 1

Using Bert Embedding to improve memory-based collaborative filtering recommender systems 利用Bert嵌入改进基于记忆的协同过滤推荐系统

Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies Pub Date : 1900-01-01 DOI: 10.1109/RIVF51545.2021.9642103

Bui Nguyen Minh Hoang, H. T. N. Vy, Hong Tiet Gia, Vu Thi Minh Hang, H. Nhung, Le Nguyen Hoai Nam

引用次数: 1

Determining Restricted Damerau-Levenshtein Edit-Distance of Two Languages by Extended Automata 用扩展自动机确定两种语言的受限Damerau-Levenshtein编辑距离

Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies Pub Date : 1900-01-01 DOI: 10.1109/RIVF.2010.5632914

D. Q. Thang, P. Huy

引用次数: 6

An Empirical Study on Bankruptcy Prediction using Ensemble Learning 基于集成学习的破产预测实证研究

Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies Pub Date : 1900-01-01 DOI: 10.1109/RIVF55975.2022.10013848

Hoang Luu Quang Tien, L. Tran, Trong-Hop Do

{"title":"An Empirical Study on Bankruptcy Prediction using Ensemble Learning","authors":"Hoang Luu Quang Tien, L. Tran, Trong-Hop Do","doi":"10.1109/RIVF55975.2022.10013848","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013848","url":null,"abstract":"Bankruptcy prediction helps to assess the financial condition of a company and its future perspectives within the context of long-term operation on the market. Using machine learning to solve this problem can be a time-efficient and cost-effective approach. This paper introduces an approach using Ensemble learning methods to tackle the bankruptcy classification problem, which achieved the fourth position on the leader board of The 3rd Annual International Data Science & AI Competition 2022 - Structured Data Track. The data set given by the committee of the competition has a lot of challenges so we perform some preprocessing and feature engineering techniques to make the data set become cleaner for modelling. We use three ensemble algorithms, namely Random Forest, Catboost, and LightGBM to compare the performance of three algorithms on the bankruptcy classification problem and find the best result to submit to the competition. After experimenting, we achieve the best result at 98.21% Accuracy on the private leader board of the competition. The result comes from the LightGBM model trained on the data set which is enhanced through feature engineering techniques.","PeriodicalId":171525,"journal":{"name":"Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461640","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

RFL-IoT: An IoT Reconfiguration Framework Applied Fuzzy Logic for Context Management RFL-IoT:应用模糊逻辑的物联网重构框架

Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies Pub Date : 1900-01-01 DOI: 10.1109/RIVF.2019.8713619

Tuan Nguyen-Anh, Q. Trung

{"title":"RFL-IoT: An IoT Reconfiguration Framework Applied Fuzzy Logic for Context Management","authors":"Tuan Nguyen-Anh, Q. Trung","doi":"10.1109/RIVF.2019.8713619","DOIUrl":"https://doi.org/10.1109/RIVF.2019.8713619","url":null,"abstract":"Internet of Things (IoTs) applications normally demand n-tier distributed architecture with numerous diverse components, e.g., sensors attached over IoTs end devices in the front-end, and the web application servers, database servers in the back-end. To deal with the dynamic changes and unpredicted events in the environments, the reconfiguration and re-programming of IoTs applications on IoTs end devices are of vital importance. This issue is also called over-the-air programming (OTAP) of IoTs applications remotely through wireless IoTs network protocols. In this paper, we propose an IoTs reconfiguration framework, namely RFL-IoT, for the reconfiguration of IoTs applications upon the changes of the context and derived by the fuzzy logic for the smart context management. To validate our proposed framework, experiments have been carried out in the smart Air Conditioner application by updating new firmware to IoTs end devices to change the behaviors of the IoTs application upon the reported temperature. Results show that our proposed framework is suitable for the dynamic reconfiguration of IoTs applications, at a reasonable cost in terms of energy consumption and time for complete reconfiguration process.","PeriodicalId":171525,"journal":{"name":"Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130705719","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: 10