Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of food trade networks are still poorly documented, especially from a multi-network perspective. In particular,…
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48 results for “food trading networks”
The interdependent nature of the global economy has become stronger with increases in international trade and investment. We propose a new model to reconstruct the international trade network and associated cost network by maximizing entropy based on local information about inward and outward trade. We show that the tr…
Impacts of Regional Trade Agreements(RTAs) on Food Security: A Case of ASEAN Free Trade Agreementq-fin.GN
Discriminatory trade liberalization policies are becoming more popular among world economies. Countries are motivated to enter for regional trade agreements to capture faster economic growth for alleviating poverty. In developing economies like most of the member countries of the Association of South East Asian Nations…
Study proposes hybrid machine learning models for crop yield prediction.
problem Crop yield prediction for food security.
method Hybrid machine learning models (ANN-ICA and ANN-GWO).
result ANN-GWO model outperformed ANN-ICA in crop yield prediction.
Analyzing available FAO data from 176 countries over 21 years, we observe an increase of complexity in the international trade of maize, rice, soy, and wheat. A larger number of countries play a role as producers or intermediaries, either for trade or food processing. In consequence, we find that the trade networks bec…
A new method clusters data from multiple sources using a mixture of multilayer SBMs.
problem Aggregating multiple clustering results from different data sources.
method Uses a mixture of multilayer Stochastic Block Models (SBM) to group co-membership matrices.
result Identifies and clusters observations based on their specificities within components.
Paper uses clustering to identify food assistance deserts in Ohio.
problem Food insecurity and accessibility issues in Ohio.
method Gaussian Mixture Model (GMM) clustering technique.
result Identifies food assistance deserts within Ohio.
A data driven network approach to rank countries production diversity and food specializationphysics.soc-ph
The easy access to large data sets has allowed for leveraging methodology in network physics and complexity science to disentangle patterns and processes directly from the data, leading to key insights in the behavior of systems. Here we use to country specific food production data to study binary and weighted topologi…
KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networkscs.LG
As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been adopted by chefs and studied by food researchers. In this work, we propose KitcheNette which is a model that predicts food ingredient pairing scores and recommends opt…
Study forecasts food security trends using real-time data.
problem Food insecurity prediction for sub-national regions.
method Quantitative methodology combining various machine learning models.
result Reservoir Computing model performs best in food security prediction.
Impact of shocks to economies on the efficiency and robustness of the international pesticide trade networksphysics.soc-ph
This study assesses how economic shocks affect the efficiency and robustness of international pesticide trade networks.
problem Economic shocks impact the efficiency and robustness of international pesticide trade networks.
method Simulations were used to quantify efficiency and robustness under different economic shocks. Three strategies were tested: descending, random, and ascending node removal.
result The international pesticide trade networks became more efficient and robust except for clustering coefficient. Import-oriented economies were more vulnerable to shocks.
Optimal transport and neural networks improve trade modeling accuracy.
problem Capturing subtler drivers of trade beyond supply and demand.
method Employing optimal transport and deep neural networks to learn a time-dependent cost function from data.
result Consistently outperforms traditional gravity models in accuracy.
Range-Based Volatility Estimators for Monitoring Market Stress: Evidence from Local Food Price Dataq-fin.ST
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
problem Limited use of range-based volatility estimators in local commodity markets.
method Adapted OHLC volatility estimators to monitor market distress across various contexts.
result OHLC-based volatility indicators detect market disruptions missed by standard momentum indicators.
Improved sales forecasting for new products using transfer learning.
problem Insufficient training data for new products leads to inaccurate sales forecasts.
method Network-based Transfer Learning approach for deep neural networks.
result Deep neural networks' prediction accuracy for food sales forecasting can be effectively increased.
Yelp has been one of the most popular local service search engine in US since 2004. It is powered by crowd-sourced text reviews and photo reviews. Restaurant customers and business owners upload photo images to Yelp, including reviewing or advertising either food, drinks, or inside and outside decorations. It is obviou…
A network analysis of the global energy market: an insight on the entanglement between crude oil and the world economyphysics.soc-ph
One major hurdle in the road toward a low carbon economy is the present entanglement of developed economies with oil. This tight relationship is mirrored in the correlation between most of economic indicators with oil price. This paper addresses the role of oil compared to the other three main energy commodities -coal,…
The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-…
Over the past decade, the stellar growth of Indian economy has been challenged by persistently high levels of inflation, particularly in food prices. The primary reason behind this stubborn food inflation is mismatch in supply-demand, as domestic agricultural production has failed to keep up with rising demand owing to…
High and volatile global food prices have led to food riots and played a critical role in triggering the Arab Spring revolutions in recent years. The severe drought in the US in the summer of 2012 led to a new increase in food prices. Through the fall, they remained at a threshold above which the riots and revolutions …
Deep learning model reduces food waste by stabilizing online food delivery supply chains.
problem Wastage and bullwhip effect in online food delivery services.
method Two-phase LSTM network for demand forecasting, newsvendor model for inventory management.
result Significant reduction in bullwhip effect and food waste, improved forecasting accuracy.
Recently, along with the emergence of food scandals, food supply chains have to face with ever-increasing pressure from compliance with food quality and safety regulations and standards. This paper aims to explore critical factors of compliance risk in food supply chain with an illustrated case in Vietnamese seafood in…
The study finds that the export shares of machinery and food/crude materials are significantly correlated with GDP.
problem Understanding the relationship between export shares and GDP across different commodity sectors.
method Analysis of GDP and international trade data using the SITC classification from 1962 to 2000.
result The export shares of machinery and food/crude materials are significantly correlated with GDP, following a power-law relationship.
UPDATE July 2012 | The Food Crises: The US Droughtphysics.soc-ph
Recent droughts in the midwestern United States threaten to cause global catastrophe driven by a speculator amplified food price bubble. Here we show the effect of speculators on food prices using a validated quantitative model that accurately describes historical food prices. During the last six years, high and fluctu…
FOOD detects out-of-distribution samples quickly without needing OOD data.
problem Detecting out-of-distribution samples efficiently in neural networks.
method Extended DNN classifier with Gaussian layer and log likelihood ratio test.
result FOOD achieves state-of-the-art performance and is fast and applicable.
An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networkscs.LG
Today, many public or private institutions provide professional food service for personnels working in their own organizations. Regarding the planning of the said service, there are some obstacles due to the fact that the number of the personnel working in the institutions is generally high and the personnel are out of…
This work develops a model to distinguish network and covariate information.
problem Identifying unique network and covariate information.
method Low-rank model with two-step estimation: spectral method followed by refinement.
result The method accurately recovers joint and individual components.
Deep learning system improves accuracy of food packaging date verification.
problem Improper labeling of food packaging poses health risks.
method Multi-source deep domain adaptation for domain-invariant representations and class boundary alignment.
result Significant improvement in classification accuracy of use-by date verification.
Predicts food ingredient amounts from images.
problem Predicting relative amounts of ingredients from food images.
method Proposes two deep learning models for sparse and dense predictions, with semi-automatic data pre-processing.
result Encouraging experimental results on a recipe dataset.
Increases in global food prices have led to widespread hunger and social unrest---and an imperative to understand their causes. In a previous paper published in September 2011, we constructed for the first time a dynamic model that quantitatively agreed with food prices. Specifically, the model fit the FAO Food Price I…
In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual descriptions of its ingredients. Previous works on synthesis of images from text typically rely on pre-trained text models to extract text features, followed by a genera…
Wavelet analysis reveals financialization effects on oil-food price correlation.
problem Investigating the correlation between oil and food prices and their determinants.
method Wavelet analysis and energy-based measures to differentiate high and low frequency movements.
result Significant local correlation between food and oil is due to financialization and emerging economies' demand.
Model predicts human food choices based on demographics.
problem Predicting human food choices from demographic data.
method Non-deterministic model based on NHANES dataset and behavioral studies.
result Generates synthetic data similar to original dataset.
Multiplex Dirichlet stochastic block model for clustering multidimensional compositional networksstat.ME
Proposes a new model for clustering multiplex networks with compositional data.
problem Clustering multiplex networks with multiple types of relations and compositional data.
method Multiplex Dirichlet stochastic block model for compositional networks.
result Validated through simulation and applied to international export data.
Study reveals clusters of resilient and vulnerable Spanish agri-food firms post-Ukraine-Russia war.
problem Financial resilience of agri-food companies in Spain during the Ukraine-Russia conflict.
method Cluster analysis using centred log-ratios for compositional data of financial ratios.
result Increase in resilient firms by 2023, highlighting sectoral adaptation to economic challenges.
The paper compares clustering techniques for personalized food kits.
problem Designing food kits that match individual consumer preferences.
method Comparative analysis of clustering techniques using conjoint analysis data.
result SVD clustering outperforms k-means in personalized food kit design.
Algorithm improves resource allocation for food outreach to homeless.
problem Resource-constrained outreach for homeless individuals and food rescue.
method Thompson sampling with Markov chain recovery (via Stein variational gradient descent) for partially-observed episodic restless bandits.
result Significantly outperforms baselines in both organizations' problems.
The World Trade Web (WTW) is a weighted network whose nodes correspond to countries with edge weights reflecting the value of imports and/or exports between countries. In this paper we introduce to this macroeconomic system the notion of extinction analysis, a technique often used in the analysis of ecosystems, for the…
Geometric modeling for human food and chemical sensitivities.
problem Modeling biochemical processes in humans with sensitivities.
method Geometric approach to biochemical modeling.
result Geometric models improve understanding of sensitivities.
Study predicts purchasing decisions of online food delivery customers.
problem Understanding and predicting consumer purchasing decisions in online food delivery.
method Used machine learning techniques including CART, C4.5, random forest, and rule-based classifiers to predict purchasing decisions.
result C4.5 decision tree model outperformed others with 91.67% accuracy.
The underlying objective of food authentication studies is to determine whether unknown food samples have been correctly labelled. In this paper we study three near infrared (NIR) spectroscopic datasets from food samples of different types: meat samples (labelled by species), olive oil samples (labelled by their geogra…
Network structure of inter-industry flowsphysics.soc-ph
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The comm…
Machine learning models are vulnerable to adversarial examples. An adversary modifies the input data such that humans still assign the same label, however, machine learning models misclassify it. Previous approaches in the literature demonstrated that adversarial examples can even be generated for the remotely hosted m…
Study finds WACC negatively impacts firm profitability in Bangladesh's food industry.
problem Determining the impact of Weighted Average Cost of Capital (WACC) on firm profitability.
method Fixed Effects Panel Regression Model using 12 food and allied industry companies from 2005-2019.
result WACC negatively correlates with firm profitability (ROA), significant relationship.
Conversion of corn to ethanol in the US since 2005 has been a major cause of global food price increases during that time and has been shown to be ineffective in achieving US energy independence and reducing environmental impact. We make three key statements to enhance understanding and communication about ethanol prod…
Validation of a recommender system for prompting omitted foods in online dietary assessment surveyscs.CY
Recall assistance methods are among the key aspects that improve the accuracy of online dietary assessment surveys. These methods still mainly rely on experience of trained interviewers with nutritional background, but data driven approaches could improve cost-efficiency and scalability of automated dietary assessment.…
In a recent work (Chattopadhyay, A. K. et al, Europhys. Lett. {\bf 91}, 58003, 2010) based on food consumption statistics, we showed how a stochastic agent based model could represent the time variation of the income distribution statistics in a developing economy, thereby defining an alternative \enquote{poverty index…
Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started …
Researchers develop a machine learning model to improve smell perception for food quality monitoring.
problem Limited data in target domain makes it hard to accurately classify food quality.
method Weakly supervised domain adaptation framework using multiple models in supervised and unsupervised settings.
result The approach generalizes well from one domain to another, improving food quality classification.