J. Chem. Inf. Comput. Sci.最新文献_第8页

Constructing Metadata Schema of Scientific and Technical Report Based on FRBR 基于FRBR的科技报告元数据模式构建

J. Chem. Inf. Comput. Sci. Pub Date : 2018-03-26 DOI: 10.5539/CIS.V11N2P34

Xiaozhu Zou, Siyi Xiong, Zhi Li, P. Jiang

引用次数: 2

Finding Nutritional Deficiency and Disease Pattern of Rural People Using Fuzzy Logic and Big Data Techniques on Hadoop 基于Hadoop的模糊逻辑和大数据技术发现农村人口营养缺乏和疾病模式

J. Chem. Inf. Comput. Sci. Pub Date : 2018-03-20 DOI: 10.5539/cis.v11n2p11

Sadia Yeasmin, Muhammad Abrar Hussain, Noor Yazdani Sikder, R. Rahman

引用次数: 2

Vehicle Scheduling Model Based on Data Mining 基于数据挖掘的车辆调度模型

J. Chem. Inf. Comput. Sci. Pub Date : 2018-01-31 DOI: 10.5539/cis.v11n1p104

Guohua Zhang, Ting Xie, Min Liu, Yang Liu

引用次数: 0

A Simulation Model of IEEE 802.15.4 GTS Mechanism and GTS Attacks in OMNeT++ / MiXiM + NETA IEEE 802.15.4 GTS机制及omnet++ / MiXiM + NETA中GTS攻击仿真模型

J. Chem. Inf. Comput. Sci. Pub Date : 2018-01-27 DOI: 10.5539/cis.v11n1p78

Y. M. Amin, A. T. Abdel-Hamid

引用次数: 3

Enhancing Big Data Auditing 加强大数据审计

J. Chem. Inf. Comput. Sci. Pub Date : 2018-01-27 DOI: 10.5539/cis.v11n1p90

Sara Alomari, Mona Alghamdi, F. Alotaibi

引用次数: 2

The Analysis and Implementation of the K - Means Algorithm Based on Hadoop Platform 基于Hadoop平台的K均值算法的分析与实现

J. Chem. Inf. Comput. Sci. Pub Date : 2018-01-27 DOI: 10.5539/cis.v11n1p98

L. Wei

引用次数: 1

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey 上下文感知协同过滤推荐系统的矩阵分解技术综述

J. Chem. Inf. Comput. Sci. Pub Date : 2018-01-24 DOI: 10.5539/cis.v11n2p1

M. H. Abdi, G. Okeyo, R. Mwangi

{"title":"Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey","authors":"M. H. Abdi, G. Okeyo, R. Mwangi","doi":"10.5539/cis.v11n2p1","DOIUrl":"https://doi.org/10.5539/cis.v11n2p1","url":null,"abstract":"Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"126 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2018-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79156754","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: 47

A Class Validation Proposal of a Pedagogic Domain Ontology based on Clustering Analysis 基于聚类分析的教学领域本体类验证方案

J. Chem. Inf. Comput. Sci. Pub Date : 2018-01-16 DOI: 10.5539/cis.v11n1p65

Yuridiana Alemán, M. J. S. García, D. V. Ayala

引用次数: 1

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers 基于混合朴素贝叶斯分类器的Twitter情绪分析股票市场分类模型

J. Chem. Inf. Comput. Sci. Pub Date : 2018-01-11 DOI: 10.5539/cis.v11n1p52

Ghaith Abdulsattar A. Jabbar Alkubaisi, S. S. Kamaruddin, H. Husni

{"title":"Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers","authors":"Ghaith Abdulsattar A. Jabbar Alkubaisi, S. S. Kamaruddin, H. Husni","doi":"10.5539/cis.v11n1p52","DOIUrl":"https://doi.org/10.5539/cis.v11n1p52","url":null,"abstract":"Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naive Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naive Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"19 1","pages":"52-64"},"PeriodicalIF":0.0,"publicationDate":"2018-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88193957","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: 30

Enhanced Firefly Algorithm Using Fuzzy Parameter Tuner 基于模糊参数调谐器的改进萤火虫算法

J. Chem. Inf. Comput. Sci. Pub Date : 2018-01-07 DOI: 10.5539/cis.v11n1p26

Mahdi Bidar, S. Sadaoui, Malek Mouhoub, Mohsen Bidar

引用次数: 7