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Open Access
Article
Publication date: 10 October 2024

Yahui Zhang

The wavelet neural network (WNN) has the drawbacks of slow convergence speed and easy falling into local optima in data prediction. Although the artificial bee colony (ABC…

Abstract

Purpose

The wavelet neural network (WNN) has the drawbacks of slow convergence speed and easy falling into local optima in data prediction. Although the artificial bee colony (ABC) algorithm has strong global optimization ability and fast convergence speed, it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.

Design/methodology/approach

This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model. Based on the example of the Jinan Yuhan underground tunnel project, the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed, and the analysis results are compared with the actual detection amount.

Findings

The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data, with a maximum relative error of only 4.73%. On this basis, the results show that the statistical features of ABC-WNN are the lowest, with the errors at 0.566 and 0.573, compared with the single back propagation (BP) neural network model and WNN model. Therefore, it can be derived that the ABC-WNN model has higher prediction accuracy, better computational stability and faster convergence speed for deformation.

Originality/value

This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels. This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multi-arch tunnels and small clearance tunnels. It can provide a new and effective way for deformation prediction in similar projects.

Details

Railway Sciences, vol. 3 no. 6
Type: Research Article
ISSN: 2755-0907

Keywords

Open Access
Article
Publication date: 21 October 2022

Amber L. Cushing and Giulia Osti

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It…

8311

Abstract

Purpose

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

Design/methodology/approach

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Findings

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

Research limitations/implications

The volunteer basis of this study meant that the sample was not representative or generalisable.

Originality/value

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

Details

Journal of Documentation, vol. 79 no. 7
Type: Research Article
ISSN: 0022-0418

Keywords

Open Access
Article
Publication date: 12 June 2023

Wasanthi Madurapperuma

GDP growth, money growth and inflation are essential to an economy's macroeconomic stability and have a direct impact on the policymaking process. Sri Lanka is currently concerned…

11461

Abstract

Purpose

GDP growth, money growth and inflation are essential to an economy's macroeconomic stability and have a direct impact on the policymaking process. Sri Lanka is currently concerned about high inflation. Inflation is a monetary phenomenon. Inflation has been caused by monetary policy in several nations. According to the economic theories of Karl Marx, Irving Fisher and Milton Friedman, a continuous increase in the money supply causes inflation. This paper aims to investigate the relationship between Sri Lanka's GDP growth, money growth and inflation.

Design/methodology/approach

An econometric model and the economic theories of Fisher and Friedman are used to figure out how money supply, inflation and economic growth are linked. Between 1990 and 2021, data were gathered from secondary sources.

Findings

The increase in the money supply is found to cause inflation. Inflation has negative effects on both short- and long-term economic growth. Long-term, the increase in money supply has a negative effect on economic growth.

Research limitations/implications

According to research, the money supply and inflation are inextricably linked, and the money supply has a direct impact on economic growth. As a result, the government should have an appropriate monetary policy and proposals to control inflation levels and stimulate economic growth.

Originality/value

The paper adds to the existing literature in two ways. First, it fills in the lack of studies in Sri Lanka, where there are no papers on this important relationship, especially with a modern econometric study. Second, it tries to shed light on the asymmetric shocks (both positive and negative shocks and changes) between the three variables, which was not done in previous studies.

Details

Journal of Money and Business, vol. 3 no. 2
Type: Research Article
ISSN: 2634-2596

Keywords

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