A machine learning model for multi-class classification of quenched and partitioned steel microstructure type by the k-nearest neighbor algorithm

Gupta, Ashutosh Kumar; Chakroborty, Subhendu; Ghosh, S. K.; Ganguly, Subhas

doi:10.1016/j.commatsci.2023.112321

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…So far, commonly used ML models and algorithms in HEA design include neural networks (NNs) [31][32][33][34][35][36][37][38][39][40][41][42][43], support vector machine (SVM) [44][45][46][47][48][49][50][51][52][53][54], Gaussian process (GP) [36,[55][56][57][58][59][60][61], k-nearest neighbors (KNN) [62][63][64][65][66], and random forests (RFs) models and algorithms [67,68] etc.…”

Section: Common ML Models and Algorithms In Hea Designmentioning

confidence: 99%

Machine Learning Design for High-Entropy Alloys: Models and Algorithms

Liu,

Yang

2024

Metals

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High-entropy alloys (HEAs) have attracted worldwide interest due to their excellent properties and vast compositional space for design. However, obtaining HEAs with low density and high properties through experimental trial-and-error methods results in low efficiency and high costs. Although high-throughput calculation (HTC) improves the design efficiency of HEAs, the accuracy of prediction is limited owing to the indirect correlation between the theoretical calculation values and performances. Recently, machine learning (ML) from real data has attracted increasing attention to assist in material design, which is closely related to performance. This review introduces common and advanced ML models and algorithms which are used in current HEA design. The advantages and limitations of these ML models and algorithms are analyzed and their potential weaknesses and corresponding optimization strategies are discussed as well. This review suggests that the acquisition, utilization, and generation of effective data are the key issues for the development of ML models and algorithms for future HEA design.

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Section: Common ML Models and Algorithms In Hea Designmentioning

confidence: 99%

Machine Learning Design for High-Entropy Alloys: Models and Algorithms

Liu,

Yang

2024

Metals

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“…A Decision Tree (DT) [ 50 ], for example, is a popular algorithm that constructs a hierarchical structure comprising decision nodes and leaf nodes, enabling efficient decision-making processes. SVC (C-Support Vector Classification) [ 51 ] takes a different approach by utilizing hyperplanes to classify data into distinct categories. Logistic Regression offers a probabilistic framework for predicting binary outcomes, while Random Forest [ 52 ] leverages an ensemble to improve predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Logistic Regression offers a probabilistic framework for predicting binary outcomes, while Random Forest [ 52 ] leverages an ensemble to improve predictive accuracy. KNN [ 51 ], on the other hand, makes use of proximity measurements to classify new instances, and Naïve Bayes applies Bayes’ theorem to calculate probabilities for classification purposes. Furthermore, the literature review highlights the rising prominence of XGBoost (eXtreme Gradient Boosting) [ 51 , 53 , 54 ] as a state-of-the-art static learning model.…”

Section: Introductionmentioning

confidence: 99%

“…KNN [ 51 ], on the other hand, makes use of proximity measurements to classify new instances, and Naïve Bayes applies Bayes’ theorem to calculate probabilities for classification purposes. Furthermore, the literature review highlights the rising prominence of XGBoost (eXtreme Gradient Boosting) [ 51 , 53 , 54 ] as a state-of-the-art static learning model. XGBoost is an optimized implementation of gradient boosting, combining weak models to create a powerful ensemble model.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Potential of Machine Learning Algorithms Associated with the Use of Inertial Sensors for Goat Kidding Detection

Gonçalves,

Marques,

Belo

et al. 2024

Animals

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The autonomous identification of animal births has a significant added value, since it enables for a prompt timely human intervention in the process, protecting the young and the mothers’ health, without requiring continuous human surveillance. Wearable inertial sensors have been employed for a variety of animal monitoring applications, thanks to their low cost and the fact that they allow less invasive monitoring process. Alarms triggered by the occurrence of events must be generated close to the events to avoid delays caused by communication latency, which is why this type of mechanism is typically implemented at the network’s edge and integrated with existing auxiliary mechanisms on the Internet. Although the detection of births in cattle has been carried out commercially for some years, there is no solution for small ruminants, especially goats, where the literature does not even report any attempts. The current work consisted of a first attempt at developing an automatic birth monitor using inertial sensing, as well as detection techniques based on Machine Learning, implemented in a network edge device to assure real-time alarm triggering. Thus, two concept drift detection techniques and seven kidding detection mechanisms were developed using data classification models. The work also includes the testing and comparison of learning results, both in terms of accuracy and of computational costs of the detection module, for algorithms implemented. The results revealed that, despite their simplicity, concept drift algorithms do not allow kidding detection, whereas classification-algorithm-based static learning models do, despite the unbalanced character of the dataset and its reduced size. The learning findings are quite promising in terms of computational cost and its suitability for deployment on edge devices. The algorithm demonstrates behavior changes four hours before kidding and allows for the identification of the kidding hour with an accuracy of 61%, as well as the capacity to improve the overall learning process with a larger dataset.

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