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Author

P. Punitha

Bio: P. Punitha is an academic researcher. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.


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Journal ArticleDOI
24 Apr 2023-Energies
TL;DR: In this paper , the authors tried to show the core and frontiers of surface mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction.
Abstract: The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource consumption, and environmental friendliness. In the future, the development of Surface Mining 4.0 will provide a response to the technological shock associated with the acceleration of the digital modernization of the mining sector and the increase in labor productivity, which are reducing the operating costs of raw materials extraction. In this regard, the given review is an attempt to analyze the surface mining digital transformation over the course of the diffusion of Industry 4.0 technologies covered in scientific publications. The authors tried to show the core and frontiers of Surface Mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction. Particular attention was paid to the review of research on the role of Surface Mining 4.0 in achieving sustainable development goals.

1 citations

Proceedings ArticleDOI
17 May 2023
TL;DR: In this article , the authors highlight various ML research projects related to air pollution monitoring and prediction using IoT sensor data in the context of diverse cities, and summarize historical and current data based on AQ prediction models, methods, and methodologies, examines current research methodology, the advantages and disadvantages of AQ prediction, as well as the challenges related to real-time AQ monitoring.
Abstract: A severe and pervasive environmental problem that affects the entire planet is Air Pollution (AP). Numerous researchers have focused on these issues while keeping human health in mind. One of the best methods to educate people about major health issues and safeguard human health from air pollution is through information about air quality predictions. Air pollution is one of the most difficult environmental issues, and it affects many major cities. Real-time monitoring of pollution data can help local officials assess the present state of the city’s traffic and reach well-informed decisions. In order to correctly estimate the pollutant concentrations, it is necessary to have an early system for monitoring and measuring the amount of AP using the Air Quality Index. Incorporating Internet of Things (IoT)-based devices may significantly change the AQ forecast dynamically, hence enhancing the AQ forecast. The accuracy and cost of the Both the AP prediction that is mentioned and the evaluation of it using various known methodologies are quite low. While AP prediction is still permitted in some sectors, machine learning (ML) algorithm development is expanding swiftly and looking into almost all fields and applications. This paper highlights numerous ML research projects related to AP monitoring and prediction using IoT sensor data in the context of diverse cities. This paper also summarizes historical and current data based on AQ prediction models, methods, and methodologies, examines current research methodology, the advantages and disadvantages of AQ prediction, as well as the challenges related to real-time AQ monitoring and prediction.
Proceedings ArticleDOI
17 May 2023
TL;DR: In this paper , the authors highlight various ML research projects related to air pollution monitoring and prediction using IoT sensor data in the context of diverse cities, and summarize historical and current data based on AQ prediction models, methods, and methodologies, examines current research methodology, the advantages and disadvantages of AQ prediction, as well as the challenges related to real-time AQ monitoring.
Abstract: A severe and pervasive environmental problem that affects the entire planet is Air Pollution (AP). Numerous researchers have focused on these issues while keeping human health in mind. One of the best methods to educate people about major health issues and safeguard human health from air pollution is through information about air quality predictions. Air pollution is one of the most difficult environmental issues, and it affects many major cities. Real-time monitoring of pollution data can help local officials assess the present state of the city’s traffic and reach well-informed decisions. In order to correctly estimate the pollutant concentrations, it is necessary to have an early system for monitoring and measuring the amount of AP using the Air Quality Index. Incorporating Internet of Things (IoT)-based devices may significantly change the AQ forecast dynamically, hence enhancing the AQ forecast. The accuracy and cost of the Both the AP prediction that is mentioned and the evaluation of it using various known methodologies are quite low. While AP prediction is still permitted in some sectors, machine learning (ML) algorithm development is expanding swiftly and looking into almost all fields and applications. This paper highlights numerous ML research projects related to AP monitoring and prediction using IoT sensor data in the context of diverse cities. This paper also summarizes historical and current data based on AQ prediction models, methods, and methodologies, examines current research methodology, the advantages and disadvantages of AQ prediction, as well as the challenges related to real-time AQ monitoring and prediction.