Noninvasive fracture characterization based on the classification of sonic wave travel times

Misra, Siddharth; Li, Hao

doi:10.1016/b978-0-12-817736-5.00009-0

Cited by 104 publications

(61 citation statements)

References 19 publications

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“…Naïve Bayes Classifier is a probabilistic algorithm that utilizes the joint probabilities assuming that the existence of a particular feature in a class is independent of the existence of any other feature. This independence allows the features to be learned separately, simplifying and increasing the computation operations [59].…”

Section: ) Naïve Bayes Classifiermentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

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During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker's existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and correlate emotional aspects of speech signals are quite contrasting quantitatively and qualitatively, which present enormous difficulties in blending knowledge from interdisciplinary fields, particularly speech emotion recognition, applied psychology, and human-computer interface. The paper carefully identifies and synthesizes recent relevant literature related to the SER systems' varied design components/methodologies, thereby providing readers with a state-of-the-art understanding of the hot research topic. Furthermore, while scrutinizing the current state of understanding on SER systems, the research gap's prominence has been sketched out for consideration and analysis by other related researchers, institutions, and regulatory bodies.

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Section: ) Naïve Bayes Classifiermentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

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“…The process of training an SVM decision function involves identifying a reproducible hyperplane that maximizes the distance (i.e., the "margin") between the support vectors of both class labels, and thus, the optimal hyperplane is that which "maximizes the margin" between the classes [71]. RF is a supervised learning algorithm consisting of multiple independent decision trees (DT) that are trained independently on a random subset of data [72,73]. It is an ensemble method that uses bagging (bootstrapping and aggregation) to train several DTs in parallel (i.e., uncorrelated forest of trees) whose prediction by committee is more accurate than that of any individual trees [73,74].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

2021

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Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

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“…Yeni bir örneği sınıflandırmak için, her karar ağacı girdi verileri için bir sınıflandırma sağlar; rastgele orman sınıflandırmaları toplar ve sonuç olarak en çok oylanan tahmini seçer. RF sınıflandırıcı; aşırı öğrenme(overfitting) sorunu olmaksızın, doğruluk açısından diğer sınıflandırma yöntemlerinin çoğundan daha iyi performans gösterme eğilimindedir (Misra & Li, 2019). Random Forest yönteminin çalışma mekanizması, Şekil 2'de verilmiştir.…”

Section: Rastgele Ormanunclassified

Topluluk Öğrenmesi Yöntemleri ile Göğüs Kanseri Teşhisi

SERTBAŞ¹

2021

TurkishStudies

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Noninvasive fracture characterization based on the classification of sonic wave travel times

Cited by 104 publications

References 19 publications

A Comprehensive Review of Speech Emotion Recognition Systems

A Comprehensive Review of Speech Emotion Recognition Systems

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Topluluk Öğrenmesi Yöntemleri ile Göğüs Kanseri Teşhisi

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