Computational Intelligence

A new dataset named Sanitation is released to evaluate the HAR algorithm’s performance and benefit the researchers in this field, which collects seven types of daily work activity data from sanitation workers.We provide two .csv files, one is the raw dataset “sanitation.csv”, the other is the pre-processed features dataset which is suitable for machine learning based human activity recognition methods.

The dataset contains measurements taken from four air handling units (AHU) installed in a medium-to-large size academic building. The building is a 7-story, 9000 sqm facility commissioned in 2016 hosting the PRECIS research center. It contains multiple research laboratories, multifunction spaces, meeting rooms, and a large auditorium as well as administrative offices. It is located at 44°2606.0N and 26°0244.0E in a temperate continental climate with hot summers and cold winters. Cooling is handled using on-site electric chillers while heating is provided from a district heating network.

Our goal is to find whether a convolutional neural network (CNN) performs better than the existing blind algorithms for image denoising, and, if yes, whether the noise statistics has an effect on the performance gap. We performed automatic identification of noise distribution, over a set of nine possible distributions, namely, Gaussian, log-normal, uniform, exponential, Poisson, salt and pepper, Rayleigh, speckle and Erlang. Next, for each of these noisy image sets, we compared the performance of FFDNet, a CNN based denoising method, with noise clinic, a blind denoising algorithm.

Date fruit data sets are not publicly available. Previous studies have collected and used their own data set. Almost all these studies have few hundred images per class. As our motive was robust date fruit classification, we did not use the camera to take images of a particular size, angle or images with a particular background, instead to add robustness, we built our date fruit database using Google search engine. Hence the images had the multi-background, noise, different lighting condition, other objects, different packaging and sometimes even partial covering.