AI Job Loss Tracker
Newly reported layoffs where AI is either explicitly cited or credibly blamed as a material factor. Reporting window starts January 1, 2025.
AI & Environment Resource HubAl-Sadman Chowdhury; Md. Shihab Uddin; Md Rashad Tanjim; Fariha Noor; Rashedur M. Rahman · 2020-08-01
This study applies machine learning, including deep learning with LSTM, to classify and predict Dhaka's Air Quality Index (AQI), incorporating daily temperature as a parameter and demonstrating optimal AQI forecasting performance.
Al-Sadman Chowdhury; Md. Shihab Uddin; Md Rashad Tanjim; Fariha Noor; Rashedur M. Rahman. 2020-08-01. Application of Data Mining Techniques on Air Pollution of Dhaka City. https://www.researchgate.net/publication/344782553_Application_of_Data_Mining_Techniques_on_Air_Pollution_of_Dhaka_City (AI & Environment Resource Hub; record paper-137; collection snapshot 2026-09-15).
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Source sheet: Scientific Paper · Row 141 · Original ID: paper-137.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
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Shared topics: Governance and society; Materials and critical minerals
Newly reported layoffs where AI is either explicitly cited or credibly blamed as a material factor. Reporting window starts January 1, 2025.
Shared topics: Governance and society; Materials and critical minerals
This paper argues that AI's environmental impacts from energy consumption and water use to mineral extraction constitute a global climate justice concern that demands moving beyond efficiency metrics to center the unequal distribution of costs and benefits, particularly in the Global South.
Shared topics: Governance and society; Air pollution
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Shared topics: Materials and critical minerals; Air pollution
This machine learning project explores air quality in Beijing from 2010 to 2014, focusing on predicting fine particulate matter (PM2.5) and how it evolves over time in relation to meteorological and seasonal factors. The dataset is a time series, with hourly readings of PM2.5 and weather conditions such as temperature, wind speed, and pressure.