scholarly journals Revisiting the Contested Role of Natural Resources in Violent Conflict Risk through Machine Learning

2020 ◽  
Vol 12 (16) ◽  
pp. 6574
Author(s):  
Marie K. Schellens ◽  
Salim Belyazid
Keyword(s):  
Machine Learning ◽  
Natural Resources ◽  
Natural Resource ◽  
Network Models ◽  
Violent Conflict ◽  
Machine Learning Techniques ◽  
Economic Conditions ◽  
Neural Network Models ◽  
Logistic Regression Models ◽  
Conflict Risk

The integrated character of the sustainable development goals in Agenda 2030, as well as research in environmental security, flag that sustainable peace requires sustainable and conflict-sensitive natural resource use. The precise relationship between the risk for violent conflict and natural resources remains contested because of the interplay with socio-economic variables. This paper aims to improve the understanding of natural resources’ role in the risk of violent conflicts by accounting for complex interactions with socio-economic conditions. Conflict data was analysed with machine learning techniques, which can account for complex patterns, such as variable interactions. More commonly used logistic regression models are compared with neural network models and random forest models. The results indicate that a country’s natural resource features are important predictors of its risk for violent conflict and that they interact with socio-economic conditions. Based on these empirical results and the existing literature, we interpret that natural resources can be root causes of violent intrastate conflict, and that signals from natural resources leading to conflict risk are reflected in and influenced by interacting socio-economic conditions. More specifically, the results show that variables such as access to water and food security are important predictors of conflict, while resource rents and oil and ore exports are relatively less important than other natural resource variables, contrasting what prior research has suggested. Given the potential of natural resource features to act as an early warning for violent conflict, we argue that natural resources should be included in conflict risk models for conflict prevention.

scholarly journals Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 689 ◽  
Author(s):  
Tyler McCandless ◽  
Susan Dettling ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Solar Power ◽  
Network Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Power Prediction ◽  
Neural Network Models

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.

scholarly journals Machine Learning in KM3NeT

2019 ◽  
Vol 207 ◽  
pp. 05004 ◽  
Author(s):  
Chiara De Sio
Keyword(s):  
Machine Learning ◽  
Network Models ◽  
High Energy ◽  
Particle Identification ◽  
Machine Learning Techniques ◽  
Neutrino Interaction ◽  
Mass Hierarchy ◽  
Reconstruction Algorithms ◽  
Neural Network Models ◽  
Neutrino Mass Hierarchy

The KM3NeT Collaboration is building a network of underwater Cherenkov telescopes at two sites in the Mediterranean Sea, with the main goals of investigating astrophysical sources of high-energy neutrinos (ARCA) and of determining the neutrino mass hierarchy (ORCA). Various Machine Learning techniques, such as Random Forests, BDTs, Shallow and Deep Networks are being used for diverse tasks, such as event-type and particle identification, energy/direction estimation, source identification, signal/background discrimination and data analysis, with sound results as well as promising research paths. The main focus of this work is the application of Convolutional Neural Network models to the tasks of neutrino interaction classification, as well as the estimation of energy and direction of the propagating particles. The performances are also compared to those of the standard reconstruction algorithms used in the Collaboration.

scholarly journals Machine Learning in Agriculture Application: Algorithms and Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 1140-1146
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
Network Models ◽  
Processing Technique ◽  
Machine Learning Techniques ◽  
Image Processing Technique ◽  
Neural Network Models ◽  
Survey Paper ◽  
Learning Techniques

Machine learning techniques with high performance computing technologies can create various new opportunities in the agriculture domain. This paper does comprehensivereview of various papers which are concentrating on machine learning (ML) and deep learning application in agriculture. This paper is categorized into three sections a) Yield prediction using machine learning technique b) Price prediction c) Leaf disease detection using neural networks. In this paper we study the comparison of neural network models with existing models. The findings of this survey paper indicate Deep learning models give high accuracy and outperform traditional image processing technique and ML techniques outperforms various traditional techniques in prediction.

scholarly journals Disease Identification in Chilli Leaves using Machine Learning Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 325-329
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Detection System ◽  
Network Models ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Neural Network Models ◽  
The Past ◽  
Learning Techniques ◽  
Proposed Model

Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

Artificial Metabolic Networks: enabling neural computation with metabolic networks

2022 ◽  
Author(s):  
Leon Faure ◽  
Bastien Mollet ◽  
Wolfram Liebermeister ◽  
Jean-Loup Faulon
Keyword(s):  
Machine Learning ◽  
Recurrent Neural Networks ◽  
Metabolic Networks ◽  
Cross Validation ◽  
Regression Coefficient ◽  
Network Models ◽  
Biotechnological Applications ◽  
Neural Network Models ◽  
Surrogate Constraint ◽  
Constraint Based Modeling

Metabolic networks have largely been exploited as mechanistic tools to predict the behavior of microorganisms with a defined genotype in different environments. However, flux predictions by constraint-based modeling approaches are limited in quality unless labor-intensive experiments including the measurement of media intake fluxes, are performed. Using machine learning instead of an optimization of biomass flux - on which most existing constraint-based methods are based - provides ways to improve flux and growth rate predictions. In this paper, we show how Recurrent Neural Networks can surrogate constraint-based modeling and make metabolic networks suitable for backpropagation and consequently be used as an architecture for machine learning. We refer to our hybrid - mechanistic and neural network - models as Artificial Metabolic Networks (AMN). We showcase AMN and illustrate its performance with an experimental dataset of Escherichia coli growth rates in 73 different media compositions. We reach a regression coefficient of R2=0.78 on cross-validation sets. We expect AMNs to provide easier discovery of metabolic insights and prompt new biotechnological applications.

scholarly journals Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6019
Author(s):  
José Manuel Lozano Domínguez ◽  
Faroq Al-Tam ◽  
Tomás de J. Mateo Sanguino ◽  
Noélia Correia
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Models ◽  
Fuzzy Classifier ◽  
Logistic Regression Models ◽  
The Road ◽  
Learning Agent ◽  
Machine Learning Models

Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.

Sign in / Sign up

Export Citation Format

Share Document