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Found 12 papers, 0 papers with code
Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning
In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classi-fication of bird species and automated removal of false positives in camera trap data.
Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps
Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount - mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian.
Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performance
In this paper, we report the findings of a clinical trial conducted by Mersey Care NHS Foundation Trust that evaluated the performance of a faster region-based convolutional neural network and mobile platform that categorised and documented pressure ulcers.
Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence
Camera traps have transformed how ecologists study wildlife species distributions, activity patterns, and interspecific interactions.
Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection
To address this issue, we propose a Machine Learning (ML) approach to model normal working operation and detect anomalies.
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network
Evaluation of the model is performed using a set of standard metrics which show the proposed model achieves high classification results in both training and validation using our windowing strategy, particularly W=500 (Sensitivity 0. 9705, Specificity 0. 9725, F1 Score 0. 9717, Kappa Score 0. 9430, Log Loss 0. 0836, ROCAUC 0. 9945).
SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity
One of the most important challenges in the analysis of high-throughput genetic data is the development of efficient computational methods to identify statistically significant Single Nucleotide Polymorphisms (SNPs).
Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes
Machine learning models, trained with FIGO and other user derived features extracted from CTG traces, have been shown to increase positive predictive capacity and minimise variability.
Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder
Standard statistical tests for single and multi-locus analysis such as logistic regression, have demonstrated little effect in understanding the genetic architecture of complex human diseases.
Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers
This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pressure ulcers.
Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data
SNPs were filtered based on the effects associations have with BMI.
Deep Learning Classification of Polygenic Obesity using Genome Wide Association Study SNPs
Using a deep learning classifier model and genetic variants with P-value < 1x10-2 (2465 SNPs) it was possible to obtain results (SE=0. 9604, SP=0. 9712, Gini=0. 9817, LogLoss=0. 1150, AUC=0. 9908 and MSE=0. 0300).
