Unsupervised novelty detection for time series using a deep learning approach.

In the Smart Homes and IoT devices era, abundant available data offers immense potential for enhancing system intelligence. However, the need for effective anomaly detection models to identify and rectify unusual data and behaviors within Smart Home Systems (SHS) remains a critical challenge. This research delves into the relatively unexplored domain of novelty anomaly detection, particularly in the context of unlabeled datasets.

Improving Water Quality Index Prediction Using Regression Learning Models.

Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties.