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.
Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus…
Vocal disorders have affected several patients all over the world. Due to the inherent difficulty of diagnosing vocal disorders without sophisticated equipment and trained personnel, a number of patients remain undiagnosed. To alleviate the monetary cost of diagnosis, there has been a recent growth in the use of data a…
Some research institutions demand researchers to distribute the incomes they earn from publishing papers to their researchers and/or co-authors. In this study, we deal with the Impact Factor-based ranking journal as a criteria for the correct distribution of these incomes. We also include the Authorship Credit factor f…
An unsupervised method clusters patient incident reports for content analysis.
problem Lack of methods to extract interpretable content from electronic healthcare records.
method Combines text-embedding with paragraph vectors and graph-theoretical multiscale community detection.
result Extracts high-intrinsic-consistency groups of patient incident reports.
NASirt automates CNN architecture design for spectral data.
problem Designing optimal neural architectures for complex data.
method Neural Architecture Search (NAS) with Item Response Theory (IRT) for instance-level complexity.
result NASirt achieves high accuracy (97.40%) on spectral datasets.
Machine learning predicts sperm motility from videos and participant data.
problem Manual evaluation of semen samples is time-consuming and unreliable.
method Used machine learning techniques including simple linear regression and convolutional neural networks.
result Sperm motility prediction using deep learning is rapid and consistent.
Deep learning detects corrosion in nuclear fuel canisters.
problem Ensuring safety and integrity of used nuclear fuel dry storage canisters.
method Residual neural networks (ResNets) for real-time corrosion detection of canister images.
result Deep learning approach accurately detects corrosion and classifies canisters as corroded or intact.
EL framework certifies and flags bias in ML models without distributional assumptions.
problem Systematic performance disparities across sensitive subpopulations in ML models.
method Empirical likelihood-based approach for non-parametric fairness auditing.
result EL framework outperforms bootstrap methods in certification and subpopulation discovery.
Machine learning models detect COVID-19 from routine blood tests.
problem Separating COVID-19 from other viral pneumonias using blood tests.
method Employed random forests and support vector machines on blood data.
result SVM-based classifier achieves 84% accuracy in detecting COVID-19.
CONTINA provides adaptive confidence intervals for traffic demand prediction.
problem Uncertainty in future traffic demand predictions and the need for valid confidence intervals.
method Adaptive confidence interval method that adjusts based on deployment errors.
result Valid confidence intervals with shorter lengths and theoretical coverage guarantee.
Paper tackles learning win-win solutions in aggregation systems.
problem Learning approximate equilibrium solutions in aggregation systems to keep suppliers.
method Multi-Agent Reinforcement Learning (MARL) with variance reduction mechanism.
result Approach reduces value variance and moves joint solution closer to Nash Equilibrium.
Intel's system identifies and categorizes businesses for sales opportunities.
problem Identifying relevant new markets and customers for large enterprises.
method Mining public business web pages, enriching with external data, and using deep learning.
result Significantly boosts sales personnel's ability to discover new customers and partnerships.
Automatically counts microglial cells in rat spinal cord images, providing precise counts and uncertainty estimates.
problem Counting microglial cells in small, heterogeneous datasets is time-consuming and requires extensive training.
method Pre-processing to filter images, designing a non-parametric, non-linear kernel counter, providing uncertainty estimation.
result The method can provide precise counts and uncertainty estimates in small datasets, even with expert opinions.