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Article
Publication date: 15 January 2024

Faris Elghaish, Sandra Matarneh, Essam Abdellatef, Farzad Rahimian, M. Reza Hosseini and Ahmed Farouk Kineber

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly…

Abstract

Purpose

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

Design/methodology/approach

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

Findings

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

Practical implications

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

Originality/value

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 1 December 2005

Göran Svensson

The objective is to describe and conceptualize leadership performance in total quality management (TQM).

6808

Abstract

Purpose

The objective is to describe and conceptualize leadership performance in total quality management (TQM).

Design/methodology/approach

A contingency approach to leadership performance in TQM is undertaken.

Findings

Contingency models of leadership performance in TQM are introduced. Principal parameters in these models are timely contextual accuracies – as well as they contain foresight versus improvidence accuracies – of TQM. A contingency process of leadership performance accuracy in TQM is also introduced. The accuracy parameters are linked by a process accuracy zone. It serves as a descriptive tool of leadership performance. Finally, a typology of leadership performances in TQM is conceptualised.

Research limitations/implications

Generally, this paper is restricted to the core values of TQM, in which a set of core values unites the descriptions of TQM. In particular, this paper is limited to the core value of leadership/management commitment. An important area of further research is to examine the actual accuracy of leadership performance across contexts and over time, as well as foresight versus improvidence accuracy in TQM.

Practical implications

The models, process and typology introduced may be applicable to examine and describe corporate leadership performance in TQM. They may also be used for teaching and training purposes, and in particular as an eye‐opener to the leadership itself, as well as to the employees, the shareholders and other stakeholders (e.g. analysts) in the marketplace. Furthermore, they may be used to position an organisation's specific leadership performance and to compare it with the leadership performance of others (e.g. competitors, suppliers and customers).

Originality/value

The contributions of this paper are: two linked contingency models of leadership performance in TQM, a contingency process, and a typology, both of which are of interest to both practitioners and scholars.

Details

The TQM Magazine, vol. 17 no. 6
Type: Research Article
ISSN: 0954-478X

Keywords

Book part
Publication date: 25 August 2022

Dipankar Ghosh and Lori Olsen

Financial analysts' forecasts serve as a proxy for market earnings expectations, and research provides mixed evidence of the relation between financial analysts' expertise and…

Abstract

Financial analysts' forecasts serve as a proxy for market earnings expectations, and research provides mixed evidence of the relation between financial analysts' expertise and forecast accuracy. The judgment and decision-making (J/DM) literature suggests that those with more expertise will not perform better when tasks exhibit either extremely high or extremely low complexity. Expertise is expected to contribute to superior performance for tasks between these two extremes. Using archival data, this research examines the effect of analysts' expertise on forecasting performance by taking into consideration the forecasting task's complexity. Results indicate that expertise is not an explanatory factor for forecast accuracy when the forecasting task's complexity is extremely high or low. However, when task complexity falls between these two extremes, expertise is a significant explanatory variable of forecast accuracy. Both results are consistent with our expectations.

Details

Advances in Accounting Behavioral Research
Type: Book
ISBN: 978-1-80382-802-2

Keywords

Article
Publication date: 30 November 2023

Xing’an Xu, Najuan Wen and Juan Liu

Artificial intelligence (AI) agents have been increasingly applied in the tourism and hospitality industry. However, AI service failure is inevitable. Thus, AI service recovery…

Abstract

Purpose

Artificial intelligence (AI) agents have been increasingly applied in the tourism and hospitality industry. However, AI service failure is inevitable. Thus, AI service recovery merits empirical investigation. This study aims to explore how AI empathic accuracy affects customers’ satisfaction in the context of AI service recovery.

Design/methodology/approach

A moderated mediation model was presented to describe the effect of empathic accuracy on customer satisfaction via four scenario-based experiments.

Findings

The results reveal the positive impact of AI empathic accuracy on customer satisfaction and the mediating effects of perceived agency and perceived experience. Moreover, anthropomorphism moderates the empathic accuracy effect.

Originality/value

This paper expanded AI service studies by exploring the significance of empathic accuracy in customer recovery satisfaction. The results provide a novel theoretical viewpoint on retaining customers following AI service failure.

目的

人工智能(AI)设备已越来越多地应用于旅游业和酒店业。然而, AI服务失败是不可避免的。因此, AI服务补救值得进一步实证研究。本研究探讨了AI共情准确性如何影响顾客对AI服务补救的满意度。

设计/方法/途径

通过四个基于场景的实验, 提出了一个有调节的中介模型来描述共情准确性对顾客满意度的影响。

研究结果

结果揭示了AI共情准确性对顾客满意度有积极影响, 感知能动性和感知感受性具有中介效应。此外, 拟人化调节了共情准确性的效应。

独创性

本文通过探讨共情准确性在顾客服务补救满意度中的作用, 拓展了AI服务研究。研究结果为AI服务失败后如何留住顾客提供了新的理论视角。

Propósito

Las agentes de inteligencia artificial (IA) se aplican cada vez más en el sector del turismo y la hostelería. Sin embargo, los fallos de los servicios de IA son inevitables. Por lo tanto, la recuperación de servicios de IA merece una investigación empírica. Esta investigación explora cómo la precisión empática de la IA afecta a la satisfacción de los clientes con la recuperación del servicio de IA.

Diseño/Metodología/Enfoque

Se presentó un modelo de mediación moderado para describir el efecto de la precisión empática en la satisfacción del cliente mediante cuatro experimentos basados en escenarios.

Hallazgos

Los resultados revelan el impacto positivo de la precisión empática de la IA en la satisfacción del cliente y los efectos mediadores de la agencia percibida y la experiencia percibida. Además, el antropomorfismo modera el efecto de la precisión empática.

Originalidad

Este artículo amplía los estudios sobre los servicios de IA al investigar el papel de la precisión empática en la satisfacción del cliente. Los resultados aportan un punto de vista teórico novedoso sobre la retención de clientes tras el fallo de un servicio de IA.

Book part
Publication date: 30 September 2020

B. G. Deepa and S. Senthil

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the…

Abstract

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

Article
Publication date: 30 August 2023

Donghui Yang, Yan Wang, Zhaoyang Shi and Huimin Wang

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and…

Abstract

Purpose

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

Design/methodology/approach

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

Findings

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

Originality/value

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 20 October 2023

Dan-Yi Wang and Xueqing Wang

In construction projects, engineering variations are very common and create breeding grounds for opportunistic claims. This study investigates the complementary effect between an…

Abstract

Purpose

In construction projects, engineering variations are very common and create breeding grounds for opportunistic claims. This study investigates the complementary effect between an inspection mechanism and a reputation system in deterring opportunistic claims, considering an employer with limited inspection accuracy and a contractor, which can be either reputation-concerned or opportunistic.

Design/methodology/approach

This paper applies a signaling game to investigate the complementary effect between the employer's inspection and a reputation system in deterring the contractor's possible opportunistic claim, considering the information-flow influence of claiming prices.

Findings

This study finds that in the exogenous-inspection-accuracy case, the employer does not always inspect the claim. A more stringent reputation system complements a less accurate inspection only when the inspection cost is lower than a threshold, but may decline the employer's surplus or social welfare. In the optimal-inspection-accuracy case, the employer always inspects the claim. However, only a sufficiently stringent reputation system can guarantee the effectiveness of an optimal inspection in curbing opportunistic claims. A more stringent reputation system has a value-stepping effect on the employer's surplus but may unexpectedly impair social welfare, whereas a higher inspection cost efficiency always reduces social welfare.

Originality/value

This article contributes to the project management literature by combing the signaling game theory with the reputation theory and thus embeds the problem of inspection mechanism design into a broader socio-economic framework.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

Open Access
Article
Publication date: 18 July 2023

Nishant Agarwal and Amna Chalwati

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

Abstract

Purpose

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

Design/methodology/approach

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

Findings

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

Research limitations/implications

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

Practical implications

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

Originality/value

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

Details

China Accounting and Finance Review, vol. 25 no. 4
Type: Research Article
ISSN: 1029-807X

Keywords

Article
Publication date: 11 July 2023

Abhinandan Chatterjee, Pradip Bala, Shruti Gedam, Sanchita Paul and Nishant Goyal

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for…

Abstract

Purpose

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

Design/methodology/approach

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

Findings

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

Originality/value

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 6 February 2023

G. Edward Gibson, Mounir El Asmar, Abdulrahman Yussef and David Ramsey

Assessing front end engineering design (FEED) accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule…

163

Abstract

Purpose

Assessing front end engineering design (FEED) accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions. A framework to measure FEED accuracy does not exist in the literature or in practice, not does systematic data directly linking FEED accuracy to project performance. This paper aims to focus first on gauging and quantifying FEED accuracy, and second on measuring its impact on project performance in terms of cost change, schedule change, change performance, financial performance and customer satisfaction.

Design/methodology/approach

A novel measurement scheme was developed for FEED accuracy as a comprehensive assessment of factors related to the project leadership and execution teams, management processes and resources; to assess the environment surrounding FEED. The development of this framework built on a literature review and focus groups, and used the research charrettes methodology, guided by a research team of 20 industry professionals and input from 48 practitioners representing 31 organizations. Data were collected from 33 large industrial projects representing over $8.8 billion of installed cost, allowing for a statistical analysis of the framework's impact on performance.

Findings

This paper describes: (1) twenty-seven critical FEED accuracy factors; (2) an objective and scalable method to measure FEED accuracy; and (3) data showing that projects with high FEED accuracy outperformed projects with low FEED accuracy by 20 percent in terms of cost growth in relation to their approved budgets.

Practical implications

FEED accuracy is defined as the degree of confidence in the measured level of maturity of the FEED deliverables to serve as a basis of decision at the end of detailed scope, prior to detailed design. Assessing FEED accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions.

Originality/value

FEED accuracy has not been assessed before, and it turned out to have considerable project performance implications. The new framework presented in this paper is the first of its kind, it has been tested rigorously, and it contributes to both the literature body of knowledge as well as to practice. As one industry leader recently stated, “it not only helped to assess the quality and adequacy of the technical documentation required, but also provided an opportunity to check the organization's readiness before making a capital investment decision.”

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

1 – 10 of over 75000