Study uses AI to estimate food demand for businesses.
problem Difficulty in predicting daily food demand for personnel.
method Artificial Neural Networks (ANN) model trained on historical data.
result ANN model predicts daily food demand with low error rate.
Paper identifies food types from Yelp photos using machine learning.
problem Ineffective labeling of food photos on Yelp.
method Image pre-processing, CNN feature extraction, and classification algorithms.
result Identifies up to 10 food types from raw photos with high accuracy.
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.
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…
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.
KitcheNette predicts and recommends food ingredient pairings.
problem Limited study of food ingredient pairings despite many existing pairings.
method Siamese neural networks trained on a dataset of 300K scores.
result KitcheNette outperforms other models and discovers novel pairings.
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.
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…
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 …
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,…
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…
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…
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…
Paper compares machine learning models for food authenticity.
problem Determining correct food labelling from NIR spectroscopic data.
method Applied and compared various classification, dimension reduction, and variable selection approaches to NIR datasets of meat, olive oil, and honey samples.
result Partial least squares outperformed other approaches in classifying food authenticity.
Analyze food popularity trends using social media data.
problem Identify trends and popularity of cuisine types across space and time.
method Use off-the-shelf machine learning techniques, Kernel Density Estimation, and Bayesian Networks.
result Model dependencies among food cuisines popularity.
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…
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.
This paper evaluates investment risks in LATAM AI startups using DCF method.
problem Unique challenges and risks faced by LATAM tech startups.
method Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics; Discounted Cash Flow (DCF) method.
result Developed a ranking of emerging powers in Latin America for tech startup investment.
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.
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.
Recommender system improves recall of omitted foods in online dietary surveys.
problem Improving accuracy of online dietary assessment surveys through recall assistance.
method Developed a recommender algorithm to remind respondents of omitted foods based on past survey data.
result The recommender system captures more omitted foods than hand-coded prompts, but with lower precision.
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.
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.
Technical report predicts eating and food purchasing behaviors of free-living individuals.
problem Predicting eating and food purchasing behaviors of free-living individuals.
method Applied multiple machine learning algorithms (Logistic Regression, RBF-SVM, Random Forest, Gradient Boosting) to minute-level features from sensors and environmental context.
result Gradient Boosting model had the highest mean accuracy score (0.7289) for predicting eating events before 0 to 4 minutes.
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.
We propose two spectral algorithms for partitioning nodes in directed graphs respectively with a cyclic and an acyclic pattern of connection between groups of nodes. Our methods are based on the computation of extremal eigenvalues of the transition matrix associated to the directed graph. The two algorithms outperform …
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.
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…
Generative model creates meal images from ingredient descriptions.
problem Synthesize photo-realistic meal images from ingredient descriptions.
method Attention-based ingredients-image association model, cycle-consistent constraint.
result Model generates meal images corresponding to ingredient descriptions.
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.
Efficiently projects points onto polytopes, especially useful in web-scale applications.
problem Efficiently projecting points onto polytopes in large-scale applications.
method Developed a vertex-oriented incremental algorithm for polytope projection, tailored for simplex and unit-box cut polytopes.
result Majority of projections lie on vertices of polytopes, leading to significant performance improvements.
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.
Recent increases in basic food prices are severely impacting vulnerable populations worldwide. Proposed causes such as shortages of grain due to adverse weather, increasing meat consumption in China and India, conversion of corn to ethanol in the US, and investor speculation on commodity markets lead to widely differin…
The waggle dance that honeybees perform is an astonishing way of communicating the location of food source. After over 60 years of its discovery, researchers still use manual labeling by watching hours of dance videos to detect different transitions between dance components thus extracting information regarding the dis…
Research shows franchised fast food companies' stock prices decline more during recessions.
problem Impact of recession on franchised fast food companies' stock prices.
method Analyzed stock price data with Weibull distribution.
result Recessions have a more severe impact on franchised fast food companies' stock prices.
Study uses non-invasive sensors to assess microbial contamination in green salads.
problem Assessing microbial contamination in ready-to-eat green salads.
method Unified spectra analysis workflow with data normalization, feature selection, and partial least squares regression.
result Unified workflow provides efficient estimation of microbial contamination and shelf life.
Dataset for object detection at Oktoberfest beer tent.
problem Realistic object detection in a busy, diverse setting.
method Hand-annotated 1,110 images, provided trained models.
result Challenging dataset for object detection.
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.
Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this…
OTT services are replacing traditional telecom services, affecting revenue streams.
problem OTT services are reducing traditional telecom revenue streams.
method Quantitative analysis of mobile user trends and revenue changes.
result OTT services are becoming more prevalent and impacting telecom revenue.
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…