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"Slope stability prediction using the Artificial Neural Network (ANN)" by Al Muhalab Al-Dughaishi, Shivakumar Karekal et al.

Slope failure is a significant risk in both civil and mining operations. This failure phenomenon is more likely to occur during the high rainfall season, areas with a high probability of seismic activity and in cold countries due to freezing-thawing. Further, a poor understanding of hydrogeology and geotechnical factors can contribute to erroneous engineering designs. Several Limit Equilibrium Methods (LEMs) and numerical modelling tools have been developed over the years. However, the highlighted success of the Artificial Neural Networks (ANNs) in other disciplines/sectors has motivated researchers to implement ANNs to forecast the Factor Of Safety (FOS). This paper aims to develop ANNs to predict the value of the FOS for slopes formed by (i) uniform one soil/rock material and (ii) formed by two soil/rock materials. Each of these slopes contains three sub-models with 6, 7 and 8 input material parameters. Thousands of FOS values were generated for each sub-model using LEMs by rando ....

Artificial Neural Networks Anns , Limit Equilibrium Methods , Artificial Neural Networks , Factor Of Safety , Mean Square Error ,

Stock Market | FinancialContent Business Page

Stock Market | FinancialContent Business Page
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Noord Holland , Tomislaw Dalic , Company Name , Artificial Neural Networks , Vikinglink Artificial Neural Network , Artificial Neural Network , Viking Links , Artificial Neural ,

Turing Institute highlights new AI for studying city behaviour

Turing Institute highlights new AI for studying city behaviour
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City Of , United Kingdom , William Barton , Mark Girolami , Alan Turing Institute , Proceedings Of The National Academy Science , Artificial Neural Networks , Professor Mark Girolami , National Academy ,

"Corner path optimization strategy for wire arc additive manufacturing " by Donghong Ding, Lei Yuan et al.

Wire Arc Additive Manufacturing (WAAM) is a promising method to build large metal structures through multi-pass multi-layer deposition. However, during WAAM processing of complex structures using traditional contour paths, filling gaps are easily left at sharp corners due to excessive self-overlapping and under-filling. This study focuses on the development of a corner path optimization strategy in order to improve the capability of the WAAM process in producing gap-free metal components. Firstly, based on the traditional contour paths, an innovative corner path optimization method was developed to generate multi-pass paths with varying centre distances and extra gap-fill paths at corners which avoid excessive self-overlapping and under-filling. Then an Artificial Neural Networks (ANNs) bead model was established to select the welding parameters which are adaptive to the modified multi-pass paths at corners. The capability of WAAM to deposit the welding bead with varying widths but a c ....

Then An Artificial Neural Networks Anns , Arc Additive Manufacturing , Artificial Neural Networks , Additive Manufacturing , Ap Defects , Path Planning ,

"Standardising Breast Radiotherapy Structure Naming Conventions: A Mach" by Ali Haidar, Matthew Field et al.

In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four repres ....

Macarthur Cancer Therapy Centres , Artificial Neural Networks , Data Standardisation , Multimodal Learning ,