Data Mining for Adivasi Vikas Vibhag Mobile App Usage Predictions and Recommendations

被引:0
|
作者
Khode-Chaware, Madhavi [1 ]
Karandikar, Aarti [2 ]
机构
[1] Tribal Dev, Nagpur, Maharashtra, India
[2] Shri Ramdeobaba Coll Engn & Management, Nagpur, Maharashtra, India
来源
关键词
ADIVASI VIKAS; TRIBAL DEVELOPMENT; GOVERNMENT SCHEMES FOR ADIVASIS; DATA SCIENCE;
D O I
10.21786/bbrc/13.14/25
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The main purpose of digital technology is to connect every strata of the society. The utilization of technology by means of 'mobile government' or 'm-government' can be used to bridge the gap between public needs and services that the government provides. One form of the 'm-government' is the usage of mobile application (mobile app) as a medium of communication. Tribals or Adivasis as they are referred in India are one of the less connected and cut off communities from mainstream. Due to many geographical and technical reasons, both sides - the adivasis and the administration - find it difficult to mingle with each other. At the same time taking every policy to their door step is financially and practically a daunting task as they live in far remote places and hence the communication gap. This paper is a study of the "Adivasi Vikas Vibhag" Mobile App which is one such step towards bridging the communication gap between tribal and administration on digital platform. The data collected through this app has tremendous potential for application of data science tools. Mobile data science is a fast emerging field that involves collecting the mobile phone data from various sources and building data-driven models using machine learning techniques, which in turn will help in making dynamic decisions intelligently in various day-to-day situations of the users. In this paper we discuss how mobile data science can be applied to data collected through Adivasi Vikas Vibhag Mobile App to make intelligent decisions and to enhance user experience.
引用
收藏
页码:102 / 105
页数:4
相关论文
共 29 条
  • [1] Mining smartphone data for app usage prediction and recommendations: A survey
    Cao, Hong
    Lin, Miao
    PERVASIVE AND MOBILE COMPUTING, 2017, 37 : 1 - 22
  • [2] Mining Exploratory Behavior to Improve Mobile App Recommendations
    He, Jiangning
    Liu, Hongyan
    ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2017, 35 (04)
  • [3] Mobile App Usage Patterns Aware Smart Data Pricing
    Yin, Jieli
    Fan, Yali
    Xia, Tong
    Li, Yong
    Chen, Xiang
    Zhou, Zhi
    Chen, Xu
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2020, 38 (04) : 645 - 654
  • [4] COMPARING THE USAGE DATA OF AN APP AND A MOBILE WEBSITE FOR AN ACADEMIC LIBRARY
    Arroyo-Vazquez, Natalia
    Merlo-Vega, Jose-Antonio
    PROFESIONAL DE LA INFORMACION, 2017, 26 (06): : 1119 - 1126
  • [5] A Study on the Application of Mobile Medical APP in Medical Data Mining
    Zheng, Xuan
    Chen, Xuejiao
    PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON MECHATRONICS, MATERIALS, CHEMISTRY AND COMPUTER ENGINEERING (ICMMCCE 2017), 2017, 141 : 144 - 148
  • [6] Describing Patterns and Disruptions in Large Scale Mobile App Usage Data
    Van Canneyt, Steven
    Bron, Marc
    Haines, Andy
    Lalmas, Mounia
    WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, 2017, : 1579 - 1584
  • [7] Anomaly Detection Techniques in Mobile App Usage Data among Older Adults
    Kyritsis, Athanasios I.
    Deriaz, Michel
    Konstantas, Dimitri
    2018 IEEE 20TH INTERNATIONAL CONFERENCE ON E-HEALTH NETWORKING, APPLICATIONS AND SERVICES (HEALTHCOM), 2018,
  • [8] Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations
    Liu, Bulou
    Bai, Bing
    Xie, Weibang
    Guo, Yiwen
    Chen, Hao
    PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 3347 - 3356
  • [9] A Mobile English Learning Platform Based on Data Mining and Personalized Recommendations
    Zhao, Qiong
    Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [10] Mining user requirements to facilitate mobile app quality upgrades with big data
    Chen, Runyu
    Wang, Qili
    Xu, Wei
    ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2019, 38