Ismail Essamlali

Urban air pollution is a pressing global issue driven by factors such as swift urbanization, population expansion, and heightened industrial activities. To address this challenge, the integration of Machine Learning (ML) into smart cities presents a promising avenue. Our article offers comprehensive insights into recent advancements in air quality research, employing the PRISMA method as a cornerstone for the reviewing process, while simultaneously exploring the application of frequently employed ML methodologies. Focusing on supervised learning algorithms, the study meticulously analyzes air quality data, elucidating their unique benefits and challenges. These frequently employed ML techniques, including LSTM (Long Short-Term Memory), RF (Random Forest), ANN (Artificial Neural Networks), and SVR (Support Vector Regression), are instrumental in our quest for cleaner, healthier urban environments. By accurately predicting key pollutants such as particulate matter (PM), nitrogen oxides (NO_x), carbon monoxide (CO), and ozone (O₃), these methods offer tangible solutions for society. They enable informed decision-making for urban planners and policymakers, leading to proactive, sustainable strategies to combat urban air pollution. As a result, the well-being and health of urban populations are significantly improved. In this revised abstract, the importance of frequently employed ML methods in the context of air quality is explicitly emphasized, underlining their role in improving urban environments and enhancing the well-being of urban populations.

The SARS-CoV-2 virus, responsible for the COVID-19 pandemic, has left an indelible mark on a global scale. This illness, exhibiting a spectrum of mild to severe symptoms, has triggered a widespread health crisis. Within this context, Machine Learning has emerged as a versatile tool, playing a pivotal role in pandemic management. It has found applications in predicting virus transmission patterns, analyzing medical imaging data, and exploring potential therapeutic avenues. This comprehensive paper delves into the multifaceted involvement of Machine Learning in COVID-19 research, spanning from data aggregation to vaccine advancement. Furthermore, we delve into the ethical and societal dimensions inherent in leveraging Machine Learning for pandemic-related inquiries. In conclusion, we spotlight promising avenues for future exploration and advancement in this burgeoning field.