Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Geng, Xiaoxiao; Wang, Feiyang; Wu, Hong‐Hui; Wang, Shuize; Wu, Guilin; Gao, Junheng; Zhao, Haitao; Zhang, Chaolei; Mao, Xinping

doi:10.1002/mgea.10

Cited by 11 publications

(1 citation statement)

References 97 publications

(154 reference statements)

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“…Furthermore, highlight the importance of human visual explanations in mitigating bias in AI-based assessment of surgeon skills, emphasizing the need for effective mitigation strategies to address bias in AI systems (Kiyasseh et al, 2023). Moreover, 's work presents an in-depth examination of the integration of datadriven and AI technologies in the steel industry, showcasing the potential of AI in accelerating material research and intelligent manufacturing technology, thereby contributing to risk management in the industry (Geng, 2023).…”

Section: Risk Management In Ai-driven Strategiesmentioning

confidence: 99%

Artificial Intelligence in stock broking: A systematic review of strategies and outcomes

Noluthando Zamanjomane Mhlongo,

Titilola Falaiye,

Andrew Ifesinachi Daraojimba

et al. 2024

World J. Adv. Res. Rev.

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Artificial Intelligence (AI) has emerged as a transformative force in the field of stock broking, revolutionizing traditional trading strategies and reshaping financial markets. This systematic review delves into the diverse array of AI-driven strategies employed in stock broking and assesses their outcomes, shedding light on the evolving landscape of algorithmic trading. The study encompasses a comprehensive analysis of various AI models, including machine learning algorithms, deep neural networks, and natural language processing techniques, that have been harnessed to analyze market data, predict stock movements, and optimize trading decisions. By synthesizing existing literature, the review offers insights into the effectiveness and limitations of these strategies, providing a nuanced understanding of their impact on market dynamics. Key findings reveal that AI applications in stock broking exhibit a wide spectrum of approaches, ranging from predictive modeling for price forecasting to sentiment analysis for gauging market sentiment. The review also explores the integration of reinforcement learning in algorithmic trading, highlighting the adaptive nature of AI systems in responding to dynamic market conditions. Furthermore, the outcomes of AI-driven strategies are evaluated in terms of risk management, profitability, and overall market efficiency. The review identifies trends indicating increased efficiency and reduced human biases, but also acknowledges challenges related to model interpretability, ethical considerations, and the potential for algorithmic-driven market volatility. This systematic review contributes to the evolving discourse on the role of AI in stock broking, offering a holistic examination of strategies and outcomes. As financial markets continue to embrace technological advancements, understanding the nuances of AI applications becomes paramount for market participants, regulators, and researchers alike. This study serves as a valuable resource for stakeholders seeking to navigate the complex interplay between artificial intelligence and the stock broking landscape.

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Section: Risk Management In Ai-driven Strategiesmentioning

confidence: 99%

Artificial Intelligence in stock broking: A systematic review of strategies and outcomes

Noluthando Zamanjomane Mhlongo,

Titilola Falaiye,

Andrew Ifesinachi Daraojimba

et al. 2024

World J. Adv. Res. Rev.

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An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information

Gong,

Lei,

Sun

et al. 2024

Materials Genome Engineering Advances

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Accurately and quickly predicting hydrogen embrittlement performance is critical for the service of metal materials. However, due to multi‐source heterogeneity, existing hydrogen embrittlement data are missing, making it impractical to train reliable machine learning models. In this study, we proposed an ensemble learning training strategy for missing data based on the Adaboost algorithm. This method introduced a mask matrix with missing data and enabled each round of training to generate sub‐datasets, considering missing value information. The strategy first trained a subset of features based on the existing dataset and a selected method and continuously focused on the combination of features with the highest error for iterative training, where the mask matrix of the missing data was used as the input to fit the weights of each base learner using a neural network. Compared with directly modeling on highly sparse data, the predictive ability of this strategy was significantly improved by approximately 20%. In addition, in the testing of new samples, the predicted mean absolute error of the new model was successfully reduced from 0.2 to 0.09. This strategy offers good adaptability to the hydrogen embrittlement sensitivity of different sizes and can avoid interference from feature importance caused by filling data.

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Accelerating spin Hall conductivity predictions via machine learning

Zhao,

Lai,

Wang

et al. 2024

Materials Genome Engineering Advances

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Accurately predicting the spin Hall conductivity (SHC) is crucial for designing novel spintronic devices that leverage the spin Hall effect. First‐principles calculations of SHCs are computationally intensive and unsuitable for quick high‐throughput screening. Here, we have developed a residual crystal graph convolutional neural network (Res‐CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. This is enabled by having access to 9249 instances of SHCs data and incorporating extra residual networks into the standard CGCNN framework. We found that Res‐CGCNN surpasses CGCNN, achieving a mean absolute error of 115.4 (ℏ/e) (S/cm) for regression and an area under the receiver operating characteristic curve of 0.86 for classification. Additionally, we utilized Res‐CGCNN to conduct high‐throughput screenings of materials in the Materials Project database that were absent in the training set. This led to the prediction of several previously unreported materials displaying large SHCs exceeding 1000 (ℏ/e) (S/cm), which were validated through first‐principles calculations. This study represents the inaugural endeavor to construct a machine learning model capable of effectively capturing the intricate nonlinear relationship between SHCs and crystal structure and composition, serving as a useful tool for the efficient screening and design of materials exhibiting high SHCs.

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Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Cited by 11 publications

References 97 publications

Artificial Intelligence in stock broking: A systematic review of strategies and outcomes

Artificial Intelligence in stock broking: A systematic review of strategies and outcomes

An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information

Accelerating spin Hall conductivity predictions via machine learning

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