A Review of the Use of Machine Learning and Artificial Intelligence in Various Sectors

Naved, Mohd

doi:10.46253/j.mr.v5i4.a3

Cited by 3 publications

References 12 publications

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From Sci-Fi to Reality in the New Space Economy: Unlocking the Potential of Sustainable Interplanetary Supply Chains

Cordova,

Gonzalez-Perez

2024

Space Business

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Interplanetary supply chains (IPSCs) are transitioning from science fiction to reality, presenting unique challenges and opportunities. This chapter aims to discuss IPSCs from a business and management standpoint, focusing on sustainability. Challenges in outer space supply chains, including planetary positioning, outsourcing, and space logistics, are explored. Even though Artificial Intelligence (AI) promises to enhance IPSC resilience and efficiency by predicting risks, optimizing operations, and supporting financial services, trust in AI and Internet of Things remains a challenge too. Adapting Sustainable Development Goals (SDGs) for space activities is crucial, considering environmental, ethical, and resource-specific challenges. Consequently, regarding opportunities, AI can assist in resource management and environmental sustainability in space. Hence, socio-economic impacts and equitable resource distribution in space activities are discussed, emphasizing the need for robust and sustainable AI systems for space’s harsh environment. Also, international collaboration and governance are vital, while future sustainable IPSCs would need to incorporate blockchain integration, efficient modeling, and scalability tools. In conclusion, this chapter contributes to the evolving discussion of IPSCs, the potential for new SDGs, and the multifaceted challenges and opportunities of sustainability in interplanetary business. Also, it underscores the importance of responsible and sustainable space exploration for humanity’s future.

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From Sci-Fi to Reality in the New Space Economy: Unlocking the Potential of Sustainable Interplanetary Supply Chains

Cordova,

Gonzalez-Perez

2024

Space Business

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Technology-Driven Financial Risk Management: Exploring the Benefits of Machine Learning for Non-Profit Organizations

Huang

2024

Systems

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This study explores how machine learning can optimize financial risk management for non-profit organizations by evaluating various algorithms aimed at mitigating loan default risks. The findings indicate that ensemble learning models, such as random forest and LightGBM, significantly improve prediction accuracy, thereby enabling non-profits to better manage financial risk. In the context of the 2008 subprime mortgage crisis, which underscored the volatility of financial markets, this research assesses a range of risks—credit, operational, liquidity, and market risks—while exploring both traditional machine learning and advanced ensemble techniques, with a particular focus on stacking fusion to enhance model performance. Emphasizing the importance of privacy and adaptive methods, this study advocates for interdisciplinary approaches to overcome limitations such as stress testing, data analysis rule formulation, and regulatory collaboration. The research underscores machine learning’s crucial role in financial risk control and calls on regulatory authorities to reassess existing frameworks to accommodate evolving risks. Additionally, it highlights the need for accurate data type identification and the potential for machine learning to strengthen financial risk management amid uncertainty, promoting interdisciplinary efforts that address broader issues like environmental sustainability and economic development.

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Big Data Analytics for Supply Chain Optimization

Vaghani

2024

Advances in Logistics, Operations, and Management Science

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Despite the extensive discussion on the benefits of BDA in supply chains, there remains a lack of systematic understanding of how BDA can be effectively linked to Supply Chain Optimization (SCO). This document explores the cumulative published literature from 2014 and 2024, so as to ascertain the impact of big data analytics on supply chain optimization. 64 articles were selected from diverse academic journals, and from these articles, information was gathered, analyzed, and organized. The results shows that Big data is often used for making predictions in SCO; big data analytics has a lot of difficulties dealing with large amounts of data related to managing the supply chain, which include too much data, problems with data quality, keeping data safe and private, difficulty in analyzing data, ability to grow as needed, and complicated supply chains. The study recommends that big data projects should begin with specific goals instead of just hoping people will use them. Starting with small test projects can help show the benefits of big data analytics.

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A Review of the Use of Machine Learning and Artificial Intelligence in Various Sectors

Cited by 3 publications

References 12 publications

From Sci-Fi to Reality in the New Space Economy: Unlocking the Potential of Sustainable Interplanetary Supply Chains

From Sci-Fi to Reality in the New Space Economy: Unlocking the Potential of Sustainable Interplanetary Supply Chains

Technology-Driven Financial Risk Management: Exploring the Benefits of Machine Learning for Non-Profit Organizations

Big Data Analytics for Supply Chain Optimization

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