Integration of Sensors and Predictive Analysis with Machine Learning as a Modern Tool for Economic Activities and a Major Step to Fight Against Climate Change
Journal Title: Journal of Green Economy and Low-Carbon Development - Year 2022, Vol 1, Issue 1
Abstract
Environmental issues have remained one of the most challenging social-economic impacts on the world and most countries. Tackling these challenges has remained an underlying issue as a concise approach, method, and policy are yet to be globally made available. Machine learning (ML) with support from IoTs, big data, NLP, and cloud computing is radicalizing the development of a modern-day economy via human support systems. With technical devices, systems, and processes intricately oriented to human understanding. Little environmental needs have been developed to give humans a comfortable place. Even though sensors capture and satisfy human needs, global ecosystem barriers have weighed beyond. Following changes in the world today, automated restrictions and barriers have been seen limiting humans from enjoying opportunities offered by IoTs, big data, NLP, and cloud computing due to environmental impact. Machine learning with capabilities to help humans become more informed is insignificantly exploited on the environmental needs. To suggest an integrated system, method, and areas that IoTs, big data, NLP, and cloud computing should focus on to fight negative environmental impact as a major step to fight climate change. In the study, two research questions and a hypothesis were used. Daily data on emission accusations was collected and used to respond to research questions and hypotheses. In 30 minutes per day and within a month, 412 diesel cars emitted 54,384 g CO2/km, 636 petrol cars emitted 76,320 g CO2/km, and 157 LPG cars emitted 9,577 g CO2/km. Predictions and forecasts were determined based on the data collected. Data accusations reveal they worsen the future impact as both hypotheses and research questions positively support findings that integration of sensors with machine learning can predict future climate situations. Improved gardens are needed, limit artificial items and diesel cars, and improved afforestation is needed in this city.
Authors and Affiliations
Pascal Muam Mah, Iwona Skalna, Tomasz Pełech-Pilichowski, John Muzam, Eric Munyeshuri, Promise Offiong Uwakmfon, Polycap Mudoh
Keywords
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- EP ID EP731835
- DOI https://doi.org/10.56578/jgelcd010103
- Views 60
- Downloads 0
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