Related papers: Lightweight ML-Based Air Quality Prediction for Io…
The integration of the Internet of Things (IoT) and Artificial Intelligence offers significant opportunities to enhance our ability to monitor and address ecological changes. As environmental challenges become increasingly pressing, the…
The Internet of Things (IoT) relies on resource-constrained devices for data acquisition, but the vast amount of data generated and security concerns present challenges for efficient data handling and confidentiality. Conventional…
Air pollution by Nitrogen Oxides (NOx) is a major concern in large cities as it has severe adverse health effects. However, the statistical properties of air pollutants are not fully understood. Here, we use methods borrowed from…
The real-time prediction of NOx emissions is of great significance for pollutant emission control and unit operation of coal-fired power plants. Aiming at dealing with the large time delay and strong nonlinear characteristics of the…
Air pollution poses a significant threat to public health and well-being, particularly in urban areas. This study introduces a series of machine-learning models that integrate data from the Sentinel-5P satellite, meteorological conditions,…
Accurate air quality index (AQI) forecasting is essential for the protecting public health in rapidly growing urban regions, and the practical model evaluation and selection are often challenged by the lack of rigorous, region-specific…
This study evaluates the performance of various supervised machine learning models in analyzing highly correlated neural signaling data from the Adolescent Brain Cognitive Development (ABCD) Study, with a focus on predicting…
Ambient fine particulate matter less than 2.5 $\mu$m in aerodynamic diameter (PM$_{2.5}$) has been linked to various adverse health outcomes and has, therefore, gained interest in public health. However, the sparsity of air quality monitors…
Predicting Sea Surface Temperature (SST) in the Great Barrier Reef (GBR) region is crucial for the effective management of its fragile ecosystems. This study provides a rigorous comparative analysis of several machine learning techniques to…
With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important. In this work, we consider the paradigm where cloud service…
With the increase of global economic activities and high energy demand, many countries have raised concerns about air pollution. However, air quality prediction is a challenging issue due to the complex interaction of many factors. In this…
A Bayesian multiple change-point model is proposed to analyse violations of air quality standards by pollutants such as nitrogen oxides (NO2 and NO) and carbon monoxide (CO). The model is built on the assumption that the occurrence of…
Accurate modeling of outdoor-to-indoor (O2I) and indoor-to-indoor (I2I) signal loss is important for improving indoor wireless network performance in dense urban areas. Traditional on-site measurements are expensive, time-consuming, and…
The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection…
The rapid proliferation of Internet of Things (IoT) devices has created an urgent demand for adaptive, resource-efficient Intrusion Detection Systems (IDS) capable of handling dynamic and evolving cyber threats. This paper investigates…
A method for online decorrelation of chemical sensor signals from the effects of environmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by…
As air pollution is becoming the largest environmental health risk, the monitoring of air quality has drawn much attention in both theoretical studies and practical implementations. In this article, we present a real-time, fine-grained and…
Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to traditional physics-based dynamical models. However, most of…
Resorbable magnesium (Mg) alloys are promising candidates for temporary medical devices due to their biodegradability and favorable mechanical properties. To accelerate the design of diluted Mg alloys for implants, we developed a…
From the past few years, due to advancements in technologies, the sedentary living style in urban areas is at its peak. This results in individuals getting a victim of obesity at an early age. There are various health impacts of obesity…