Sustainable development of global mineral resources market in Industry 4.0 context

Hushko, Serhii; Botelho, José; Maksymova, Irina; Slusarenko, Kateryna; Kulishov, Volodymyr

doi:10.1088/1755-1315/628/1/012025

Cited by 9 publications

(5 citation statements)

References 2 publications

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“…Further, the surface maps can be combined with AI-based spatial estimation models [72] applied to subsurface geochemical and geophysical data to develop 3D geological models of potential mineralized zones. The approaches will enable the mineral exploration and mining industry to achieve Industry 4.0 [73] through IoT [74][75][76] and blockchain [77] solutions for secure data sharing within the mining industry.…”

Section: Discussionmentioning

confidence: 99%

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

et al. 2022

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Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

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Section: Discussionmentioning

confidence: 99%

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

et al. 2022

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“…Video records of talks are available at the New Trends in Economy YouTube channel (https://www.youtube.com/@user-vu7ic9jy7z). • Bård Borch Michalsen, UiT The Arctic University of Norway, Norway [72] • Ramesh Chandra Rath, Einstein Academy of Technology and Management, India [73] • Nadiia Shmygol, Poznań University of Life Science, Poland [74] • Sofiia Kafka, National Technical University of Oil and Gas, Ukraine [75] • Ketevan Goletiani, Batumi Navigation Teachenng University, Georgia [76] • Giuseppe T. Cirella, University of Gdansk, Poland [77] • Abdukhakim Mamanazarov, Founder of the Center of Economic Culture Development, Uzbekistan [78] • Pankaj Srivastava, FATER Academy of India, India [79] • Ewa Matuska, Pomeranian University in Slupsk, Poland [80] • Olena Panukhnyk, Ternopil Ivan Puluj National Technical University, Ukraine [81] • Ma lgorzata Sidor-Rzadkowska, Warsaw University of Technology, Poland [82] • Vladimir N. Soloviev, Kryvyi Rih State Pedagogical University, Ukraine [83] • W lodzimierz Strelcow, Pomeranian University, Poland [84] • Victoria Solovieva, State University Economics of Technology, Ukraine [85] • Serhii Hushko, State University Economics of Technology, Ukraine [86] • José Manuel Macedo Botelho, Evora University PhD, Portugal [87] • Petia Tanova, Frederick University of Cyprus, Cyprus [88] • Victoria Prokhorova, Ukrainian Engineering Pedagogics Academy, Ukraine [89] • Daina Znotiņa, Rezekne Academy of Tehnologies, Latvia [90] • Oleg Pursky, Kyiv National University of Trade and Economics, Ukraine [91] • Arnold Kiv, Ben-Gurion University of the Negev, Israel [92] • Hanna Kucherova, Classic Private University, Zaporizhzhia, Ukraine [93] • Iluta Arbidane, Rezekne Academy of Tehnologies, Latvia [94] • George Abuselidze, Batumi Shota Rustaveli State University, Batumi, Georgia [95] • Iryna Fadyeyeva, Ivano-Frankivsk National Technical University of Oil and Gas, Ukraine [96] • Serhiy Semerikov, Kryvyi Rih State Pedagogical University, Ukraine [97] • Jozef Za ťko, European institute of further education, Slovakia [98] • Abdel-Badeeh M. Salem, Ain Shams University, Eg...…”

Section: Conference Overviewmentioning

confidence: 99%

9th International Scientific Conference on Sustainability in Energy and Environmental Science

Семеріков

Khvostina²,

Horal³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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This paper represents a preface to the Proceedings of the 9th International Scientific Conference on Sustainability in Energy and Environmental Science (ISCSEES 2022) held worldwide on June 22–24 2022. Background information and the organizational structure of the meeting, program committee, and acknowledgments of the contributions of the many people who made the conference a success are presented.

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“…However, the global discourse lacks an integrated framework for such integration to take place at the level of national economy. The importance of reassessing the international system of «eco-digital» linkages for achieving the decarbonisation targets is considered in the works by Geels et al (2017), Victor et al (2019), , Hushko et al (2021). The authors emphasize the need to fundamentally reconstruct global socio-technological clusters in the sectors of electrical power generation, manufacturing, agriculture, transport, and heating, as well as to develop international sectoral policies and introduce national incentives in order to boost technological progress in these industries.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Digitalization-Based Integration of Climate Policies of Ukraine and the Eu

Maksymova

2023

JEE

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The article investigates the problem of climate policy integration between the European Union and Ukraine, taking into consideration the current targets of climate-resilient development and the possibilities for digitalization of the components of this process. The author performs a systematization of the modern programme documents and mechanisms that are fundamental to the implementation of climate policies in the EU and Ukraine. The author also highlights the innovations of the «Fit for 55» programme and identifies the EU’s climate policy targets for the coming decade. In particular, the author performs an analysis of the potential impact of the Carbon Border Adjustment Mechanism (CBAM) on Ukrainian producers. The findings of the study show that the existing pace of emissions reductions in line with Nationally Determined Contributions of Ukraine and the EU is insufficient to achieve the net-zero level of emissions and to build the climateneutral economy before the internationally adopted milestone years of 2030 and 2050. The author substantiates the need for climate policy intensification through imposition of stricter restrictions on GHG emissions, as well as determines the expediency of increasing Ukraine’s NDC2 from 65% to at least 72%, which would ensure the sufficient pace of climate strategy implementation that would align with the Paris Agreement. Digitalization in this article is viewed as a tool, a process and an enabling environment for acceleration of climate-friendly changes, as well as a factor of overcoming the barriers to integration between the climate policies of the EU and Ukraine.

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Sustainable development of global mineral resources market in Industry 4.0 context

Cited by 9 publications

References 2 publications

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

9th International Scientific Conference on Sustainability in Energy and Environmental Science

Digitalization-Based Integration of Climate Policies of Ukraine and the Eu

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