Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning
Pneumonia diagnosis from chest X-rays is subjective and can be confused with other diseases.
Papers
1,138 of 1,138 verified cases.
Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Techniques
Engineering & Materials
Metallurgical Processes and Thermodynamics
ai assisted
importance 4/10
scale stated
Measuring liquid steel temperature in the ladle is challenging, expensive, and unsafe in industrial settings.
Deep Learning-Based PC Member Crack Detection and Quality Inspection Support Technology for the Precise Construction of OSC Projects
Engineering & Materials
Infrastructure Maintenance and Monitoring
ai assisted
importance 4/10
scale stated
Quality inspection of precast concrete (PC) members in off-site construction is inaccurate, slow and costly due to shortage of inspection manpower.
Multimodal deep learning for biomedical data fusion: a review
Biomedical data are increasingly multimodal, and researchers lack organized guidance on which deep learning fusion strategies to use.
neu Print: Analysis Tools for EM Connectomics
New large-scale connectome reconstructions (tens of thousands of neurons, tens of millions of connections) need easy, efficient analysis tools usable by non-specialists.
Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet
Engineering & Materials
Plasma Applications and Diagnostics
partially solved
importance 4/10
scale stated
Feedback control of thermal effects of atmospheric pressure plasma jets on complex substrates, which is difficult due to complex plasma-substrate interactions.
Automatic feature generation for machine learning--based optimising compilation
Software & Coding
Parallel Computing and Optimization Techniques
ai assisted
importance 4/10
scale stated
Machine-learned compiler optimisation heuristics depend on hand-chosen program features, and the space of possible features is effectively infinite.
LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting
Traditional methods struggle to accurately forecast renewable energy demand when processing complex data, limiting sustainable planning.
Trends in forensic microbiology: From classical methods to deep learning
Biology & Life Sciences
Forensic Entomology and Diptera Studies
ai assisted
importance 4/10
scale stated
Traditional microbial culture methods in forensic microbiology are slow, costly, and poorly quantitative for tasks like postmortem interval estimation.
MR Images, Brain Lesions, and Deep Learning
White matter hyperintensities in brain MR images need to be classified and detected to support computer-aided diagnosis of ischemic stroke and demyelinating diseases.
Dexterous robotic manipulation using deep reinforcement learning and knowledge transfer for complex sparse reward‐based tasks
Getting a TriFinger robot to dexterously manipulate a cube along a specified positional trajectory under sparse rewards, where traditional control methods struggled.
Quantifying uncertainty in deep learning approaches to radio galaxy classification
Physics & Astronomy
Galaxies: Formation, Evolution, Phenomena
partially solved
importance 3/10
scale stated
Deep learning classifiers of radio galaxies provide predictions without reliable uncertainty estimates, making their outputs hard to trust.
Explaining deep learning of galaxy morphology with saliency mapping
Measuring galactic bar lengths at scale requires automation beyond human visual classification of galaxy morphology.
Radio Galaxy Zoo: giant radio galaxy classification using multidomain deep learning
Physics & Astronomy
Galaxies: Formation, Evolution, Phenomena
ai assisted
importance 3/10
scale stated
Identifying rare giant radio galaxies in very large radio survey datasets is difficult, and will become harder with next-generation telescopes like the SKA.
A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment
Researchers lacked a large annotated microscopy image dataset for deep-learning-based assessment of plant cell microstructures in potato tubers.
A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images
Farmers and experts have long struggled to estimate the amount and location of weeds in lettuce crops, which is key to crop productivity.
SNNRice6mA: A Deep Learning Method for Predicting DNA N6-Methyladenine Sites in Rice Genome
Identifying DNA N6-methyladenine (6mA) sites in the rice genome experimentally is time-consuming and laborious, and existing machine learning predictors had room for improvement.
Neural network potential energy surface for the low temperature ring polymer molecular dynamics of the H2CO + OH reaction
Accurate low-temperature dynamics for the H2CO + OH reaction requires a potential energy surface, but existing symmetry-adapted neural network schemes suffer spurious long-range interactions.
Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement
Online retailers need to classify user engagement levels from Google Analytics metrics to improve digital marketing decisions.
Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil (Lens culinaris Medik.)
Predicting lentil grain yield, a multifactorial nonlinear phenomenon, from numeric agronomic traits.
Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models
Camel milk is adulterated with cheaper cow milk, threatening food quality and safety, and a rapid detection method was needed.
Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders
Biology & Life Sciences
Lipid Membrane Structure and Behavior
ai assisted
importance 3/10
scale stated
Distinguishing membrane curvature-sensing peptides from general membrane binders is difficult because of their chemical diversity.
Electrocardiogram Quality Assessment Using Unsupervised Deep Learning
Noise and disturbances hinder effective interpretation of recorded ECG signals, and existing quality indicators rely on human-defined properties.
Microbiome Preprocessing Machine Learning Pipeline
Raw 16S microbiome feature counts may be a suboptimal representation for machine learning classification tasks.