Aquaculture effluent is often associated with increased organic carbon, suspended solids,
phosphates, nitrogenous species (nitrates, nitrites, and ammonia), chemical oxygen demand …

Agricultural greenhouse production has to require a stable and acceptable environment, it is
therefore essential for future greenhouse production to obtain full and precisely internal …

Recirculating aquaculture has received more and more attention because of its high
efficiency of treatment and recycling of aquaculture wastewater. The content of dissolved …

Recent research demonstrated that the superficially well-trained machine learning (ML)
models are highly vulnerable to adversarial examples. As ML techniques are becoming a …

Identifying and understanding dynamic concepts in co-evolving sequences is crucial for
analyzing complex systems such as IoT applications, financial markets, and online activity …

Recurrent and convolutional neural networks are the most common architectures used for
time-series forecasting in deep learning literature. Owing to parameter sharing and …

Predicting trends in water quality plays an essential role in the field of environmental
modeling. Though artificial neural networks (ANN) have been involved in predicting water …

In this paper we have presented a novel approach to predict dissolved oxygen in prawn
ponds. It is necessary to maintain dissolved oxygen above a certain level in the ponds for …

Highlights•Machine learning to relate aquatic production to water quality.•Identifying the
most influential variables for harvest outcome in freshwater ponds.•Sensor data analytics to …

Multivariate time series data suffer from the problem of missing values, which hinders the
application of many analytical methods. To achieve the accurate imputation of these missing …