Machine Learning

One of the leading causes of early health detriment is the increasing levels of air pollution in major cities and eventually in indoor spaces. Monitoring the air quality effectively in closed spaces like educational institutes and hospitals can improve both the health and the life quality of the occupants. In this paper, we propose an efficient Indoor Air Quality (IAQ) monitoring and management system, which uses a combination of cutting-edge technologies to monitor and predict major air pollutants like CO2, PM2.5, TVOCs, and other factors like temperature and humidity.

The Explainable Sentiment Analysis Dataset provides annotated sentiment classification data for Amazon Reviews and IMDB Movie Reviews, facilitating the evaluation of sentiment analysis models with a focus on explainability. It includes ground-truth sentiment labels, model-generated predictions, and fine-grained classification results obtained from various large language models (LLMs), including both proprietary (GPT-4o/GPT-4o-mini) and open-source models (DeepSeek-R1 full and distilled models).

DALHOUSIE NIMS LAB ATTACK IOT DATASET 2025-1 dataset comprises of four prevalent types attacks, namely Portscan, Slowloris, Synflood, and Vulnerability Scan, on nine distinct Internet of Things (IoT) devices. These attacks are very common on the IoT eco-systems because they often serve as precursors to more sophisticated attack vectors. By analyzing attack vector traffic characteristics and IoT device responses, our dataset will aid to shed light on IoT eco-system vulnerabilities.

This dataset includes spectra of 250 corn samples with different vitality levels, with a data size of 250*256, categorized into five vitality grades. The imaging spectrometer employs a series spectrophotometer, model N17E, with a spectral range of 874-1734nm and a spectral resolution of 5nm. The CCD used is model ICL-B1410, featuring 1600×1200 pixels, and is equipped with an OLES22 lens with a focal length of 22mm.