Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach
暂无分享,去创建一个
Imre Felde | Gergo Pinter | Amir Mosavi | A. Mosavi | G. Pintér | I. Felde
[1] Riccardo Poli,et al. Particle swarm optimization , 1995, Swarm Intelligence.
[2] Sadko Mandžuka,et al. Model for Estimating Urban Mobility Based on the Records of User Activities in Public Mobile Networks , 2020 .
[3] Balázs Csanád Csáji,et al. Exploring the Mobility of Mobile Phone Users , 2012, ArXiv.
[4] Stefan Poslad,et al. A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records , 2020, Remote. Sens..
[5] M. W Gardner,et al. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .
[6] Nilanjan Dey,et al. Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings , 2016, Neural Computing and Applications.
[7] G. Nabel,et al. Dimerization of NF-KB2 with RelA(p65) regulates DNA binding, transcriptional activation, and inhibition by an I kappa B-alpha (MAD-3) , 1993, Molecular and cellular biology.
[8] Maarten Vanhoof,et al. Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities , 2018, ISPRS Int. J. Geo Inf..
[9] Naiqi Wu,et al. A Comparative Study on Contract Recommendation Model: Using Macao Mobile Phone Datasets , 2020, IEEE Access.
[10] Gabriel Cadamuro,et al. Predicting poverty and wealth from mobile phone metadata , 2015, Science.
[11] Ross Maciejewski,et al. VAUD: A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data , 2018, IEEE Transactions on Visualization and Computer Graphics.
[12] Yunfeng Hu,et al. Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone , 2019, Sustainability.
[13] Fan Zhang,et al. Modeling real-time human mobility based on mobile phone and transportation data fusion , 2018, Transportation Research Part C: Emerging Technologies.
[14] Sanyam Shukla,et al. Dynamic selection of normalization techniques using data complexity measures , 2018, Expert Syst. Appl..
[15] V. Vanitha,et al. Crowd estimation at a social event using call data records , 2018, Int. J. Bus. Inf. Syst..
[16] Chaoming Song,et al. Modelling the scaling properties of human mobility , 2010, 1010.0436.
[17] Carlo Ratti,et al. Understanding house price appreciation using multi-source big geo-data and machine learning , 2020, Land Use Policy.
[18] Alexei Pozdnoukhov,et al. Connected Population Synthesis for Transportation Simulation , 2019, Transportation Research Part C: Emerging Technologies.
[19] Roy E. Welsch,et al. Comprehensive Predictions of Tourists' Next Visit Location Based on Call Detail Records Using Machine Learning and Deep Learning Methods , 2017, 2017 IEEE International Congress on Big Data (BigData Congress).
[20] Angus M. Marshall,et al. CaseNote: Mobile phone call data obfuscation & techniques for call correlation , 2019, Digit. Investig..
[21] Moncef Gabbouj,et al. Evolutionary artificial neural networks by multi-dimensional particle swarm optimization , 2009, Neural Networks.
[22] Ajay Vikram Singh,et al. Trust based Intelligent Routing Algorithm for Delay Tolerant Network using Artificial Neural Network , 2016, Wireless Networks.
[23] Tongyu Zhu,et al. Identifying Significant Places Using Multi-day Call Detail Records , 2014, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence.
[24] Mohamed Ettaouil,et al. Multilayer Perceptron: Architecture Optimization and Training , 2016, Int. J. Interact. Multim. Artif. Intell..
[25] Zbigniew Smoreda,et al. An analytical framework to nowcast well-being using mobile phone data , 2016, International Journal of Data Science and Analytics.
[26] Aleksandar Jevremović,et al. What Image Features Boost Housing Market Predictions? , 2020, IEEE Transactions on Multimedia.
[27] Albert-László Barabási,et al. Understanding individual human mobility patterns , 2008, Nature.
[28] Cherie Armour,et al. Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data , 2018, JMIR mental health.
[29] Dino Pedreschi,et al. Returners and explorers dichotomy in human mobility , 2015, Nature Communications.
[30] Anupam Nanda. Residential Real Estate , 2019 .
[31] J. P. Verma,et al. Data Analysis in Management with SPSS Software , 2012 .
[32] Carlo Ratti,et al. Human mobility and socioeconomic status: Analysis of Singapore and Boston , 2018, Comput. Environ. Urban Syst..
[33] M. Sudheep Elayidom,et al. Call detail record-based traffic density analysis using global K-means clustering , 2019 .
[34] Georgios K. Ouzounis,et al. Smart cities of the future , 2012, The European Physical Journal Special Topics.
[35] S R Parija,et al. Mobility pattern of individual user in dynamic mobile phone network using call data record , 2019, Int. J. Wirel. Mob. Comput..
[36] Alexander Erath,et al. Transport modelling in the age of big data , 2017 .
[37] Albert Solé-Ribalta,et al. Measuring and mitigating behavioural segregation using Call Detail Records , 2020, EPJ Data Science.
[38] Qingquan Li,et al. Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach , 2015, Transportation.
[39] Wright-Patterson Afb,et al. Feature Selection Using a Multilayer Perceptron , 1990 .
[40] Carlo Ratti,et al. Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome , 2011, IEEE Transactions on Intelligent Transportation Systems.
[41] Zbigniew Smoreda,et al. Comparing Regional Patterns of Individual Movement Using Corrected Mobility Entropy , 2018 .
[42] Tohru Ogawa,et al. A new algorithm for three-dimensional voronoi tessellation , 1983 .
[43] Andrew Blake,et al. Real-time traffic monitoring , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.