Overview and challenges of machine translation for contextually appropriate translations.

Machine translation facilitates cross-linguistic communication by converting text between languages. However, producing contextually accurate translations remains a challenge. This review explores the difficulties in achieving such accuracy, particularly in capturing contextual information, disambiguating polysemous words, and handling idiomatic expressions, cultural nuances, and domain-specific terms.

Machine learning methods to predict particulate matter PM .

Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM , is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API).