Determination of rock depth using artificial intelligence techniques

Viswanathan, R.; Samui, Pijush

doi:10.1016/j.gsf.2015.04.002

Cited by 25 publications

(4 citation statements)

References 30 publications

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“…Many of the above studies have started to be applied to actual case studies in civil and transport engineering topics, hydrology and geological studies, or small-size target detection such as rebars and pipes. For instance, detection of the defects inside tree trunks [41], inspection of railways [42], automated landmine and UXO detection [43], pavement distress detection [44,45], evaluation of pavement thickness [46], and determination of rock depth [47]. Although most of these case studies do not fall directly within the archaeological context, they are all applicable to address archaeological research questions regarding parameter estimation, modelling, and feature extraction problems.…”

Section: Ai Applications On Gprmentioning

confidence: 99%

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

Küçükdemirci

Sarris

2022

Remote Sensing

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Ground penetrating radar (GPR) is a well-established technique used in archaeological prospection and it requires a number of specialized routines for signal and image processing to enhance the data acquired and lead towards a better interpretation of them. Computer-aided techniques have advanced the interpretation of GPR data, dealing with a wide range of operations aiming towards locating, imaging, and diagnosis/interpretation. This article will discuss the novel and recent applications of machine learning (ML) and deep learning (DL) techniques, under the artificial intelligence umbrella, for processing GPR measurements within archaeological contexts, and their potential, limitations, and possible future prospects.

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Section: Ai Applications On Gprmentioning

confidence: 99%

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

Küçükdemirci

Sarris

2022

Remote Sensing

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“…ELM is considered as the single hidden layer feed-forward neural networks(SLFN) [11]. The association between output t and input x is given as [15].…”

Section: The Extreme Learning Machine Technique Of Spam Classificationmentioning

confidence: 99%

Classifying Spam Emails Using Artificial Intelligent Techniques

Roy

Viswanatham

2016

JERA

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Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.

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“…Jahed Armaghani et al [25] focused on predicting the BI using the SVM method. Viswanathan and Samui [26] utilized the GPR method to predict rock depth in their study. Mahmoodzadeh et al [27] predicted rock quality designation (RQD) using several methods and found that the GPR method outperformed other methods.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Rock's Brittleness and Dynamic Properties Utilizing Effective Artificial Intelligence Approaches

Xie,

Wang,

et al. 2024

Period. Polytech. Civil Eng.

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This research aims to determine the brittleness index (BI) and engineering properties of limestone specimens. In addition, this study evaluates the effect of moisture on the developed models to predict the BI and shear wave velocity (Vs), based on the point load index (Is50), dry and saturated tensile strength (Ts-d and Ts-s), and porosity. Gaussian process regression (GPR), multilayer feed-forward neural network (MFFNN), and multiple linear regression (MLR) predictive models were utilized. Microscopic examination of the limestone specimens revealed that calcite is the predominant mineral. It was observed that samples with higher calcite content exhibited greater brittleness and strength properties while having lower porosity. The results obtained from the MLR analysis demonstrated that it is possible to accurately forecast the brittleness index (BI), as well as the dry and saturated shear wave velocities (Vs-d and Vs-s) at the specific sites under investigation. The moisture effect on developed models showed that Vs prediction in dry conditions (Vs-d) was less accurate compared to the saturated conditions (Vs-s). Conversely, the relationships developed for estimating the BI in dry conditions exhibited higher accuracy. The analysis of all model assumptions using MLR indicated that the models could be reliably utilized. However, the MFFNN and GPR methods were found to be more conservative in estimating these properties. Moreover, the study identified the best transfer function and training algorithm for predicting Vs and BI. The evaluation metrics, such as R2 and RMSE revealed that GPR demonstrated higher precision compared to MFFNN and MLR.

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Determination of rock depth using artificial intelligence techniques

Cited by 25 publications

References 30 publications

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

Classifying Spam Emails Using Artificial Intelligent Techniques

Prediction of Rock's Brittleness and Dynamic Properties Utilizing Effective Artificial Intelligence Approaches

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