数据分析和信息处理(英文)最新文献_第4页

Research on Personal Credit Evaluation Based on Mobile Telecommunications Data 基于移动通信数据的个人信用评价研究

数据分析和信息处理(英文) Pub Date : 2021-07-08 DOI: 10.4236/jdaip.2021.93010

Shaoyong Hong, Yan Zhang, Chun Yang

{"title":"Research on Personal Credit Evaluation Based on Mobile Telecommunications Data","authors":"Shaoyong Hong, Yan Zhang, Chun Yang","doi":"10.4236/jdaip.2021.93010","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93010","url":null,"abstract":"With the rapid development of big data technology, the personal credit evaluation industry has entered a new stage. Among them, the evaluation of personal credit based on mobile telecommunications data is one of the hotspots of current research. However, due to the complexity and diversity of personal credit evaluation variables, in order to reduce the complexity of the model and improve the prediction accuracy of the model, we need to reduce the dimension of the input variables. According to the data provided by a mobile telecommunications operator, this paper divides the data into a training sets and verification sets. We perform correlation analysis on each indicator of the data in the training set, and calculate the corresponding IV value based on the WOE value of the selected index, then binning data with SPSS Modeler. The selected variables were modeled using a logistic regression algorithm. In order to make the regression results more practical, we extract the scoring rules according to the results of logistic regression, convert them into the form of score cards, and finally verify the validity of the model.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46426760","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

Review of Dimension Reduction Methods 尺寸缩减方法综述

数据分析和信息处理(英文) Pub Date : 2021-07-08 DOI: 10.4236/jdaip.2021.93013

S. Nanga, A. T. Bawah, Ben Acquaye, Mac-Issaka Billa, Francisco Baeta, N. Odai, Samuel Kwaku Obeng, Ampem Darko Nsiah

{"title":"Review of Dimension Reduction Methods","authors":"S. Nanga, A. T. Bawah, Ben Acquaye, Mac-Issaka Billa, Francisco Baeta, N. Odai, Samuel Kwaku Obeng, Ampem Darko Nsiah","doi":"10.4236/jdaip.2021.93013","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93013","url":null,"abstract":"Purpose: This study sought to review the characteristics, strengths, weaknesses \u0000variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The \u0000most commonly used databases employed to search for the papers were ScienceDirect, \u0000Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was \u0000used for the study where 341 papers were reviewed. Results: The linear \u0000techniques considered were Principal Component Analysis (PCA), Linear Discriminant \u0000Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis \u0000(LSA), Locality Preserving Projections (LPP), Independent Component Analysis \u0000(ICA) and Project Pursuit (PP). The non-linear techniques which were developed \u0000to work with applications that have complex non-linear structures considered were Kernel Principal Component \u0000Analysis (KPCA), Multi-dimensional \u0000Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map \u0000(SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and \u0000Projection (UMAP). DR techniques can further be categorized into supervised, \u0000unsupervised and more recently semi-supervised learning methods. The supervised \u0000versions are the LDA and LVQ. All the other techniques are unsupervised. \u0000Supervised variants of PCA, LPP, KPCA and MDS have been developed. \u0000Supervised and semi-supervised variants of PP and t-SNE have also been \u0000developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR \u0000techniques were explored. The different data types that have been applied on \u0000the various DR techniques were also explored.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48542897","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: 19

Application of Machine Learning Techniques for Okra Shelf Life Prediction 机器学习技术在秋葵保质期预测中的应用

数据分析和信息处理(英文) Pub Date : 2021-07-08 DOI: 10.4236/jdaip.2021.93009

I. B. Iorliam, B. A. Ikyo, A. Iorliam, E. O. Okube, K. D. Kwaghtyo, Y. Shehu

引用次数: 4

Internet of Things (IoT) 物联网(IoT)

数据分析和信息处理(英文) Pub Date : 2021-03-18 DOI: 10.4236/JDAIP.2021.92006

Radouan Ait Mouha

{"title":"Internet of Things (IoT)","authors":"Radouan Ait Mouha","doi":"10.4236/JDAIP.2021.92006","DOIUrl":"https://doi.org/10.4236/JDAIP.2021.92006","url":null,"abstract":"the world is \u0000experiencing a strong rush towards modern technology, while specialized \u0000companies are living a terrible rush in the information technology towards the \u0000so-called Internet of things IoT or Internet of objects, which is the integration of things with the \u0000world of Internet, by adding hardware or/and software to be smart and so be \u0000able to communicate with each other and participate effectively in all aspects \u0000of daily life, so enabling new forms of \u0000communication between people and things, and between things themselves, that’s \u0000will change the traditional life into a high style of living. But it won’t be \u0000easy, because there are still many challenges and issues that need to be addressed and have to be \u0000viewed from various aspects to realize their full potential. The main objective of this \u0000review paper will provide the reader with a detailed discussion from a \u0000technological and social perspective. The various IoT challenges and issues, \u0000definition and architecture were discussed. Furthermore, a description of \u0000several sensors and actuators and their smart communication. Also, the most important application areas of IoT were \u0000presented. This work will help readers and researchers understand the IoT and \u0000its potential application in the real world.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44082674","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

Analysis of Evaluation Data Collected on Likert Type Items: Humanities-Courses Likert型项目人文课程评价数据分析

数据分析和信息处理(英文) Pub Date : 2021-03-18 DOI: 10.4236/JDAIP.2021.92007

Raghavendra Dwivedi, N. N. Pandey

{"title":"Analysis of Evaluation Data Collected on Likert Type Items: Humanities-Courses","authors":"Raghavendra Dwivedi, N. N. Pandey","doi":"10.4236/JDAIP.2021.92007","DOIUrl":"https://doi.org/10.4236/JDAIP.2021.92007","url":null,"abstract":"To improve high quality and/or retain achieved high quality of an academic program, time to time evaluation for quality of each covered course is often an integrated aspect considered in reputed institutions, however, there has been little effort regarding humanities courses. This research article deals with analysis of evaluation data collected regarding humanities course from a College of Commerce & Economics, Mumbai, Maharashtra, India, on Likert type items. Appropriateness of one parametric measure and three non-parametric measures are discussed and used in this regard which could provide useful clues for educational policy planners. Keeping in view of the analytical results using these four measures, regardless of the threshold regarding satisfaction among students, overall performance of almost every subject has been un-satisfactory. There is a need to make a focused approach to take every course at the level of high performance. The inconsistency noticed under every threshold further revealed that under such poorly performing subjects globally, one needs to analyze merely at the global level item. Once the global level analysis reveals high performance of a course, then only item specific analysis may need to be focused to find out the items requiring further improvements.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44719030","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: 3

Deep Learning for Robotics 机器人的深度学习

数据分析和信息处理(英文) Pub Date : 2021-03-18 DOI: 10.4236/JDAIP.2021.92005

Radouan Ait Mouha

{"title":"Deep Learning for Robotics","authors":"Radouan Ait Mouha","doi":"10.4236/JDAIP.2021.92005","DOIUrl":"https://doi.org/10.4236/JDAIP.2021.92005","url":null,"abstract":"The application of deep learning to robotics over \u0000the past decade has led to a wave of research into deep artificial neural \u0000networks and to a very specific problems and questions that are not usually \u0000addressed by the computer vision and machine learning communities. Robots have \u0000always faced many unique challenges as the robotic platforms move from the lab \u0000to the real world. Minutely, the sheer amount of diversity we encounter in \u0000real-world environments is a huge challenge to deal with today’s robotic \u0000control algorithms and this necessitates the use of machine learning algorithms \u0000that are able to learn the controls of a given data. However, deep learning \u0000algorithms are general non-linear models capable of learning features directly \u0000from data making them an excellent choice for such robotic applications. \u0000Indeed, robotics and artificial intelligence (AI) are increasing and amplifying \u0000human potential, enhancing productivity and moving from simple thinking towards \u0000human-like cognitive abilities. In this paper, lots of learning, thinking and incarnation challenges \u0000of deep learning robots were discussed. The problem addressed was robotic \u0000grasping and tracking motion planning for robots which was the most fundamental \u0000and formidable challenge of designing autonomous robots. This paper hope to provide the reader an overview of DL and robotic grasping, also the problem of \u0000tracking and motion planning. The system is tested on simulated data and real \u0000experiments with success.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43558673","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: 4

On Lemon Defect Recognition with Visual Feature Extraction and Transfers Learning 基于视觉特征提取和迁移学习的柠檬缺陷识别研究

数据分析和信息处理(英文) Pub Date : 2021-01-01 DOI: 10.4236/jdaip.2021.94014

Yizhi He, Tianchen Zhu, Mingxu Wang, Hanqing Lu

引用次数: 4

Case Study on Data Analytics and Machine Learning Accuracy 数据分析和机器学习准确性案例研究

数据分析和信息处理(英文) Pub Date : 2021-01-01 DOI: 10.4236/jdaip.2021.94015

Abdullah Z. Alruhaymi, Charles J. Kim

引用次数: 1

Realization of UAV Routing Protocol Evaluation System Based on Game Theory Comprehensive Weighting 基于博弈论综合加权的无人机路由协议评价系统实现

数据分析和信息处理(英文) Pub Date : 2021-01-01 DOI: 10.4236/jdaip.2021.94016

Jinze Huang, Fengbiao Zan, Xin Liu, Da Chen