Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model
Authors
Keywords
-
Journal
ISPRS International Journal of Geo-Information
Volume 10, Issue 8, Pages 547
Publisher
MDPI AG
Online
2021-08-16
DOI
10.3390/ijgi10080547
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Quantitative Analysis of Global Terrorist Attacks Based on the Global Terrorism Database
- (2021) Zhongbei Li et al. Sustainability
- Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method
- (2019) Mengmeng Hao et al. ISPRS International Journal of Geo-Information
- Fourth paradigm GIScience? Prospects for automated discovery and explanation from data
- (2019) Mark Gahegan INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
- Albumin-chaperoned cyanine dye yields superbright NIR-II fluorophore with enhanced pharmacokinetics
- (2019) Rui Tian et al. Science Advances
- A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation
- (2018) Leyuan Fang et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Multi-label classification using a fuzzy rough neighborhood consensus
- (2018) Sarah Vluymans et al. INFORMATION SCIENCES
- Site Selection of Retail Shops Based on Spatial Accessibility and Hybrid BP Neural Network
- (2018) Luyao Wang et al. ISPRS International Journal of Geo-Information
- Grid-Based Crime Prediction Using Geographical Features
- (2018) Ying-Lung Lin et al. ISPRS International Journal of Geo-Information
- On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors
- (2018) Xun Zhang et al. ISPRS International Journal of Geo-Information
- HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source
- (2017) Shixiang Wan et al. PROTEOMICS
- Predicting armed conflict: Time to adjust our expectations?
- (2017) Lars-Erik Cederman et al. SCIENCE
- Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit
- (2017) Gang Mei et al. Royal Society Open Science
- Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach
- (2017) Fangyu Ding et al. PLoS One
- Categorizing feature selection methods for multi-label classification
- (2016) Rafael B. Pereira et al. ARTIFICIAL INTELLIGENCE REVIEW
- How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?
- (2016) Piotr Szymański et al. Entropy
- An approach for multi-label classification by directed acyclic graph with label correlation maximization
- (2016) Jaedong Lee et al. INFORMATION SCIENCES
- Civil conflict sensitivity to growing-season drought
- (2016) Nina von Uexkull et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- GPU-accelerated parallel algorithms for linear rankSVM
- (2015) Jing Jin et al. JOURNAL OF SUPERCOMPUTING
- Hidden Markov models for the activity profile of terrorist groups
- (2013) Vasanthan Raghavan et al. Annals of Applied Statistics
- A Review on Multi-Label Learning Algorithms
- (2013) Min-Ling Zhang et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Quantifying the Influence of Climate on Human Conflict
- (2013) S. M. Hsiang et al. SCIENCE
- On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level
- (2013) Sriram Narayanamoorthy et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities
- (2012) Ting Ma et al. REMOTE SENSING OF ENVIRONMENT
- Climate Change and Violent Conflict
- (2012) J. Scheffran et al. SCIENCE
- Classifier chains for multi-label classification
- (2011) Jesse Read et al. MACHINE LEARNING
- Random k-Labelsets for Multilabel Classification
- (2010) Grigorios Tsoumakas et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Dependence of locally linear embedding on the regularization parameter
- (2010) Rasa Karbauskaitė et al. Top
- An adaptive inverse-distance weighting spatial interpolation technique
- (2008) George Y. Lu et al. COMPUTERS & GEOSCIENCES
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now