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Integration & Implication of Machine Learning: Barriers to Aid Environmental Monitoring & Management

机译:集成&放大器; 机器学习的含义:援助环境监测的障碍& amp; 管理

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With the development of artificial intelligence and other associated models like machine learning, data science, industrial internet of things etc. it has become a significant challenge for the majority of the practitioners and researchers in field of environmental monitoring and management to keep pace with. Though many international universities in developed countries are making significant contributions to this field, the obstacle remained constant in Bangladesh. Focusing the background, this study is conducted to understand the challenges to integrate and implication of machine learning regarding environmental monitoring and management in Bangladesh. In this study, 20 surveys and 5 In-depth Interviews were conducted with practitioners from eight top institutes those are working on environmental monitoring and management related issues in government, non-government and academia sectors of Bangladesh. Findings revealed that in case of absence of reliable resources on an average intensity of participants is 9.15, where the Intensity of participants in favor of absence of less exposure of research upshots (average is 8.50). Also, lack of sharing information and absence of available funding are identified as major obstacles. This study may help stakeholders to take proper initiatives to encourage researchers and practitioners regarding utilization of machine learning in Bangladesh.
机译:随着人工智能和其他相关模型的发展,如机器学习,数据科学,工业互联网等。它对环境监测和管理领域的大多数从业者和研究人员来说是一项重大挑战,以保持步伐。虽然发达国家的许多国际大学对这一领域作出重大贡献,但孟加拉国仍然存在持续。本研究致力于背景,了解孟加拉国环境监测和管理对机器学习的挑战和对环境监测和管理的挑战。在这项研究中,从8个顶级机构的从业者进行了20个调查和5个深入访谈,这些研究所正在致力于孟加拉国政府,非政府和学术界的环境监测和管理相关问题。调查结果显示,如果在参与者的平均强度没有可靠的资源的情况下,参与者的强度有利于缺乏缺乏研究的研究结果(平均为8.50)。此外,缺乏分享信息和缺乏可用资金被确定为主要障碍。本研究可能有助于利益相关者采取适当的举措,鼓励研究人员和从业者在孟加拉国利用机器学习。

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