Silica sources for arsenic mitigation in rice: machine learning-based predictive modeling and risk assessment

被引：0
|
作者：
Rubina Khanam
Amaresh Kumar Nayak
Pedda Ghouse Peera Sheikh Kulsum
Jajati Mandal
Mohammad Shahid
Rahul Tripathy
Pratap Bhattacharyya
Panneer Selvam
Sushmita Munda
Sivashankari Manickam
Manish Debnath
Raghavendra Goud Bandaru
机构：
[1] National Rice Research Institute,ICAR
[2] C V Raman Global University,Crop Production Division
[3] University of Salford,School of Science, Engineering and Environment
来源：
Environmental Science and Pollution Research | 2023年 / 30卷
关键词：
Arsenic; Rice; Silicon; Machine learning; Random forest model; Human exposure;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：113660 / 113673
页数：13
相关论文
共 50 条
  • [1] Silica sources for arsenic mitigation in rice: machine learning-based predictive modeling and risk assessment
    Khanam, Rubina
    Nayak, Amaresh Kumar
    Kulsum, Pedda Ghouse Peera Sheikh
    Mandal, Jajati
    Shahid, Mohammad
    Tripathy, Rahul
    Bhattacharyya, Pratap
    Selvam, Panneer
    Munda, Sushmita
    Manickam, Sivashankari
    Debnath, Manish
    Bandaru, Raghavendra Goud
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (53) : 113660 - 113673
  • [2] A machine learning-based predictive model for risk assessment in airport areas
    Gugliandolo, Giovanni
    Caccamo, Maria Teresa
    Castorina, Giuseppe
    Chillemi, Domenica Letizia
    Famoso, Fabio
    Munao, Gianmarco
    Raffaele, Marcello
    Schifilliti, Valeria
    Semprebello, Agostino
    Magazu, Salvatore
    2021 IEEE 8TH INTERNATIONAL WORKSHOP ON METROLOGY FOR AEROSPACE (IEEE METROAEROSPACE), 2021, : 53 - 57
  • [3] Machine Learning-Based Predictive Modeling of Postpartum Depression
    Shin, Dayeon
    Lee, Kyung Ju
    Adeluwa, Temidayo
    Hur, Junguk
    JOURNAL OF CLINICAL MEDICINE, 2020, 9 (09) : 1 - 14
  • [4] Machine learning-based assessment of diabetes risk
    Sun, Qi
    Cheng, Xin
    Han, Kuo
    Sun, Yichao
    Ren, He
    Li, Ping
    APPLIED INTELLIGENCE, 2025, 55 (02)
  • [5] Machine learning-based predictive modeling of depression in hypertensive populations
    Lee, Chiyoung
    Kim, Heewon
    PLOS ONE, 2022, 17 (07):
  • [6] Machine Learning-Based Predictive Modeling of Complications of Chronic Diabetes
    Derevitskii, Ilia, V
    Kovalchuk, Sergey, V
    9TH INTERNATIONAL YOUNG SCIENTISTS CONFERENCE IN COMPUTATIONAL SCIENCE, YSC2020, 2020, 178 : 274 - 283
  • [7] Machine learning-based predictive modeling of contact heat transfer
    Anh Tuan Vu
    Gulati, Shrey
    Vogel, Paul-Alexander
    Grunwald, Tim
    Bergs, Thomas
    INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2021, 174
  • [8] Machine Learning-Based Rowhammer Mitigation
    Joardar, Biresh Kumar
    Bletsch, Tyler K.
    Chakrabarty, Krishnendu
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2023, 42 (05) : 1393 - 1405
  • [9] Importance of calibration assessment in machine learning-based predictive analytics
    Staartjes, Victor E.
    Kernbach, Julius M.
    JOURNAL OF NEUROSURGERY-SPINE, 2020, 32 (06) : 985 - 986
  • [10] A Machine Learning-based Method for Cyber Risk Assessment
    Rafaiani, Giulia
    Battaglioni, Massimo
    Compagnoni, Simone
    Senigagliesi, Linda
    Chiaraluce, Franco
    Baldi, Marco
    2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS, 2023, : 263 - 268
12345