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Found 10 papers, 2 papers with code
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning
For farmers and managers, the knowledge of when a picker bag is emptied is important for managing harvesting bins more effectively to minimise the time the picked fruit is left out in the heat (resulting in reduced shelf life).
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data
The increasingly wide usage of location aware sensors has made it possible to collect large volume of trajectory data in diverse application domains.
An operational framework to automatically evaluate the quality of weather observations from third-party stations
With increasing number of crowdsourced private automatic weather stations (called TPAWS) established to fill the gap of official network and obtain local weather information for various purposes, the data quality is a major concern in promoting their usage.
Smart Headset, Computer Vision and Machine Learning for Efficient Prawn Farm Management
Understanding the growth and distribution of the prawns is critical for optimising the feed and harvest strategies.
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries.
Deep Learning for Prawn Farming: Forecasting and Anomaly Detection
We present a decision support system for managing water quality in prawn ponds.
Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction
We show that, though the neural network model achieves an accuracy of 80%, it requires long sequences to achieve this (100 samples or more).
Sequence-to-Sequence Imputation of Missing Sensor Data
Although the sequence-to-sequence (encoder-decoder) model is considered the state-of-the-art in deep learning sequence models, there is little research into using this model for recovering missing sensor data.
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature.
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections
Our contributions are as follows; we first show that constraining the transition matrix to be unitary is a special case of an orthogonal constraint.
