Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature

Warin, Thierry; Stojkov, Aleksandar

doi:10.3390/jrfm14070302

Cited by 18 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thanks to its potential, AI can be used for innumerable purposes and fields such as healthcare [7], international security [8], banking and finance [9], or network security [10].…”

Section: Related Work 21 Hints On Ai Basicsmentioning

confidence: 99%

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.

show abstract

“…Thanks to its potential, AI can be used for innumerable purposes and fields such as healthcare [7], international security [8], banking and finance [9], or network security [10].…”

Section: Related Work 21 Hints On Ai Basicsmentioning

confidence: 99%

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…As an interdisciplinary subject covering computer science, mathematics, statistics and engineering, machine learning is dedicated to optimizing the performance of computer programs by using data or previous experience, which is also one of the important directions of artificial intelligence development 1,2 . In recent years, machine learning has been widely used in many fields such as finance, medical care, industry, and biology [3][4][5][6][7][8][9][10] . In 2011, the concept of material genome initiative (MGI) was proposed to shorten the material development cycle through computational tools, experimental facilities and digital data.…”

Section: Introductionmentioning

confidence: 99%

Small data machine learning in materials science

et al. 2023

npj Comput Mater

125

View full text Add to dashboard Cite

This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed.

show abstract

“…Using SHAP in conjunction with machine learning models, it is possible to assess the importance and relative contribution of various factors on the prediction. Tey have been used in a variety of felds, such as the safety assessment of infrastructure projects [18,19]; clinical, medicine, and healthcare modeling [20][21][22][23][24][25][26][27]; transportation and trafc safety [28][29][30][31][32][33][34][35][36][37][38]; fnance and economics risk analysis [39][40][41][42]; and ofshore safety analysis [43].…”

Section: Introductionmentioning

confidence: 99%

Missed Approach, a Safety-Critical Go-Around Procedure in Aviation: Prediction Based on Machine Learning-Ensemble Imbalance Learning

Khattak

Chan

Chen

et al. 2023

Advances in Meteorology

View full text Add to dashboard Cite

The final approach phase of an aircraft accounts for nearly half of all aviation incidents worldwide due to low-level wind shear, heavy downpours, runway excursions, and unsteady approaches. Adopting the missed approach (MAP) procedures may prevent a risky landing, which is usually executed in those situations, but it is safety-critical and a rare occurrence. This study employed machine learning-ensemble imbalance learning to predict MAPs under low-level wind shear conditions based on environmental and situational parameters. The models were developed using the 2017–2021 Hong Kong International Airport (HKIA) Pilot Reports (PIREPs). Initially, imbalance data were applied to machine learning models such as the random forest (RF), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), but these were unable to accurately predict the occurrence of MAPs. Then, these models were used as base estimators for ensemble imbalance learning methods, including the self-paced ensemble (SPE) framework, the balance cascade model, and the easy ensemble model. The SPE framework utilizing XGboost as the base estimator performed better than other frameworks in terms of recall, F1-score, balanced accuracy, and geometric mean. Afterwards, SHAP was utilized to interpret the SPE framework with XGboost as the base estimator. Results showed that low-level wind shear magnitude, runway orientation, and vertical location of low-level wind shear contributed most to MAPs. Runways 07C and 07R had the most MAPs. Most MAPs were initiated when low-level wind shear was within 500 feet of the ground. Strong tailwind triggered MAPs more than headwind. For aviation safety researchers and airport authorities, the framework proposed in this study is a valuable tool.

show abstract

Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature

Cited by 18 publications

References 50 publications

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

Small data machine learning in materials science

Missed Approach, a Safety-Critical Go-Around Procedure in Aviation: Prediction Based on Machine Learning-Ensemble Imbalance Learning

Contact Info

Product

Resources

About