Using Machine Learning and Data Mining to Evaluate Modern Financial Management Techniques

Ahmed, Alim Al Ayub; Rajesh, Sudha; Lohana, Sarika; Ray, Samrat; Maroor, Jnaneshwar Pai; Naved, Mohd

doi:10.1007/978-981-19-0108-9_26

Cited by 29 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Especially when considering rapidly changing data, finding methods of using data mining to reduce time expenditure is the goal of many research endeavors. Many dynamic algorithms have been developed not only to maintain the accuracy of the calculation, but also to enhance the computation speed [36].…”

Section: Renewable Energymentioning

confidence: 99%

Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion

Huang,

Liang,

Gung

et al. 2023

Sustainability

View full text Add to dashboard Cite

As developing economies become more industrialized, the energy problem has become a major challenge in the twenty-first century. Countries around the world have been developing renewable energy to meet the Sustainable Development Goals (SDGs) of the United Nations (UN) and the 26th UN Climate Change Conference of the Parties (COP26). Leaders of enterprises have been made aware of the need to protect the environment and have been practicing environmental marketing strategies and green information systems (GISs) as part of ESG practices. With the rapid growth of the available data from renewable electricity suppliers, the analyses of multi-attribute characteristics across different fields of studies use data mining to obtain viable rule induction and achieve adaptive management. Rough set theory is an appropriate method for multi-attribute classification and rule induction. Nevertheless, past studies for Big Data analytics have tended to focus on incremental algorithms for dynamic databases. This study entails rough set theory from the perspective of the decrement decay alternative rule-extraction algorithm (DAREA) to explore rule induction and present case evidence with managerial implications for the emerging renewable energy industry. This study innovates rough set research to handle data deletion in a Big Data system and promotes renewable energy with valued managerial implications.

show abstract

Section: Renewable Energymentioning

confidence: 99%

Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion

Huang,

Liang,

Gung

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Yet, like any tool or methodology, it comes with inherent limitations and risks. Recognizing and addressing these constraints is crucial for businesses aiming for comprehensive, sustainable solutions [7].…”

Section: Limitations and Potential Risksmentioning

confidence: 99%

Machine Learning Technologies for Automated Audience Targeting in Telemarketing

Irina Igorevna

2023

IJLEMR

View full text Add to dashboard Cite

The advent of machine learning technologies has ushered in a transformative phase for telemarketing, particularly in the domain of audience targeting. This comprehensive research undertakes a meticulous examination of various machine learning algorithms, delineating their roles and efficacy within the telemarketing paradigm. Delving deep into their applications for audience targeting, the study juxtaposes machine learning approaches with traditional marketing strategies, elucidating nuances that inform their respective merits and challenges. As the assessment unfolds, it sheds light on the inherent benefits and potential pitfalls of each algorithmic approach, providing a robust analytical framework for practitioners and scholars alike. Furthermore, in acknowledging the contemporary challenges, the study extensively addresses potential risks, underpinning ethical dilemmas, and methodological limitations that are intrinsic to the integration of advanced computational techniques in marketing. Drawing its conclusions, the research not only encapsulates the current state of affairs but also charts out avenues for prospective investigations. By weaving a narrative that seamlessly combines theoretical postulates with practical implications, this document stands as a beacon for those navigating the intricate corridors of machine learning's role in modern-day telemarketing strategies.

show abstract

“…In [1], Al Ayub Ahmed et al, study the relationship between machine business organizations. They argue that these technologies can be used to analyze data on various aspects of financial management, such as financial planning, budgeting, and risk management, and use this information to optimize the operation of these systems.…”

Section: The Use Of Machine Learning Managementmentioning

confidence: 99%

“…To illustrate the use of machine learning and AI in [1] present an algorithm for predicting stock prices. The algorithm is based on the following equation:…”

Section: The Use Of Machine Learning Managementmentioning

confidence: 99%

“…They argue that these technologies can be used to analyze data on various aspects of financial management, such as financial planning, budgeting, and risk management, and use the operation of these systems. By analyzing data on financial planning, for example, machine learning algorithms can identify patterns and trends that may be missed by traditional methods of analysis, and use this information to optimize financial decisionet al, in [1] discuss the potential for machine learning and AI to enhance the efficiency of financial management by automating tasks and processes. For example, machine learning algorithms can be used to analyze data on risk management, and use this information to optimize the allocation of financial resources.…”

Section: Machine Learning and Artificial Intelligence In Financialmentioning

confidence: 99%

See 1 more Smart Citation

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

Naved¹

2022

View full text Add to dashboard Cite

In this paper, we review the use of machine learning and artificial intelligence in various sectors, including the social and business ecosystem, supply chain management, financial management, marketing management, and performance management. We show that these technologies have the potential to significantly enhance the efficiency and effectiveness of these systems by enabling the analysis and interpretation of large datasets in real-time, and by automating tasks and processes. We also discuss opportunities for further research in these areas and suggest that the use of machine learning and artificial intelligence will continue to grow in importance in the coming years. The use of machine learning and artificial intelligence has already shown tremendous potential in various sectors, including finance, healthcare, transportation, and many others.

show abstract

Using Machine Learning and Data Mining to Evaluate Modern Financial Management Techniques

Cited by 29 publications

References 11 publications

Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion

Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion

Machine Learning Technologies for Automated Audience Targeting in Telemarketing

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

Contact Info

Product

Resources

About