Mahdi Aghaei, Ali Nasr Isfahani, Amineh Ghorbani and Omid Roozmand
This study aims to adopt a follower-centric approach in leadership and ethics research by investigating the impact of implicit followership theories (IFTs) on followers’…
Abstract
Purpose
This study aims to adopt a follower-centric approach in leadership and ethics research by investigating the impact of implicit followership theories (IFTs) on followers’ constructive resistance to leaders’ unethical requests. Specifically, it analyzes the mediating role of organizational citizenship behavior in the relationship between IFTs and constructive resistance. Indeed, this study aims to examine whether followers with more positive beliefs about the characteristics that a follower should have IFTs are more likely to resist unethical leadership and whether this relationship is mediated by organizational citizenship behavior as volunteering acts that exceed the formal job requirements.
Design/methodology/approach
The proposed hypotheses were tested using survey data from 273 employees working in a steel manufacturer company in Iran. The variance-based structural equation modeling technique was used to analyze data.
Findings
The results show that followership antiprototype negatively affects both follower’s constructive resistance and organizational citizenship behavior. Furthermore, organizational citizenship behavior mediates the relationship between IFTs and follower’s constructive resistance. Also, both followership prototype and organizational citizenship behavior have a positive effect on follower’s constructive resistance.
Originality/value
Contrary to the dominant leader-centric approach in leadership and organizational ethics research, few studies have examined the role of followers and their characteristics. The results of this study provide important insights into the role of followers in resistance against the leader’s unethical request.
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Wioleta Kucharska and Denise Bedford
This chapter addresses the potential for knowledge, learning, and collaboration (KLC) cultures in public sector organizations. Public sector organizations are among the most…
Abstract
Chapter Summary
This chapter addresses the potential for knowledge, learning, and collaboration (KLC) cultures in public sector organizations. Public sector organizations are among the most complex for introducing or nourishing a KLC approach because there are multiple levels of cultures with varying levels of influence. We describe these complex cultures as tiers. First, we define public sector organizations’ business goals, purpose, and strategies. Then, the authors translate and interpret all five levels of culture for public sector organizations. The chapter also details the nature of cultural complexity, namely the four tiers of public sector cultures: (1) the company culture (Tier 1); (2) the public service culture (Tier 2); (3) the culture of the external environment (Tier 3); and (4) the internal KLC cultures (Tier 4). This chapter establishes a framework for describing an organization’s complex culture and determining the best KLC approach for the context.
Cemalettin Akdoğan, Tolga Özer and Yüksel Oğuz
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of…
Abstract
Purpose
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).
Design/methodology/approach
Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.
Findings
In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.
Originality/value
An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.
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This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship…
Abstract
Purpose
This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models.
Design/methodology/approach
The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection.
Findings
The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance.
Originality/value
The authors draw our conclusions based on their own research and experimental works.
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Tianfeng Wang, Yingying Xu and Zhenzhou Tang
Timely intrusion detection in extensive traffic remains a pressing and complex challenge, including for Web services. Current research emphasizes improving detection accuracy…
Abstract
Purpose
Timely intrusion detection in extensive traffic remains a pressing and complex challenge, including for Web services. Current research emphasizes improving detection accuracy through machine learning, with scant attention paid to the dataset’s impact on the capability for fast detection. Many datasets rely on flow-level features, requiring entire flow completion before determining if it constitutes an attack, reducing efficiency. This paper aims to introduce a new feature extraction method and construct a new security dataset that enhances detection efficiency.
Design/methodology/approach
This paper proposes a novel partial-flow feature extraction method that extracts packet-level features efficiently to reduce the high latency of flow-level extraction. The method also integrates statistical and temporal features derived from partial flows to improve accuracy. The method was applied to the original packet capture (PCAP) files utilized in creating the CSE-CIC-IDS 2018 dataset, resulting in the development of the WKLIN-WEB-2023 dataset specifically designed for web intrusion detection. The effectiveness of this method was evaluated by training nine classification models on both the WKLIN-WEB-2023 and CSE-CIC-IDS 2018 datasets.
Findings
The experimental results show that models trained on the WKLIN-WEB-2023 dataset consistently outperform those on the CSE-CIC-IDS 2018 dataset across precision, recall, f1-score, and detection latency. This demonstrates the superior effectiveness of the new dataset in enhancing both the efficiency and accuracy of intrusion detection.
Originality/value
This study proposes the partial-flow feature extraction method, creating the WKLIN-WEB-2023 dataset. This novel approach significantly enhances detection efficiency while maintaining classification performance, providing a valuable foundation for further research on intrusion detection efficiency.
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Mohammed Hamza Momade, Serdar Durdyev, Dave Estrella and Syuhaida Ismail
This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.
Abstract
Purpose
This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.
Design/methodology/approach
A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.
Findings
The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.
Practical implications
The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.
Originality/value
There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.
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Ehsan Sadrossadat, Behnam Ghorbani, Rahimzadeh Oskooei and Mahdi Kaboutari
This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression…
Abstract
Purpose
This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (qult) of rock foundations, which is a considerable civil and geotechnical engineering problem.
Design/methodology/approach
The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of qult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.
Findings
A new and explicit formula generated by GEP is proposed for the estimation of the qult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.
Originality/value
The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.
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Saeed Ghorbani, Amin Emamian, Amin Amiri Delouei, R. Ellahi, Sadiq M. Sait and Mohamed Bechir Ben Hamida
The purpose of this study is to investigate heat transfer and electrokinetic non-Newtonian flow in a rectangular microchannel in the developed and transient states.
Abstract
Purpose
The purpose of this study is to investigate heat transfer and electrokinetic non-Newtonian flow in a rectangular microchannel in the developed and transient states.
Design/methodology/approach
The Carreau–Yasuda model was considered to capture the non-Newtonian behavior of the fluid. The dimensionless forms of governing equations, including the continuity equation for the Carreau–Yasuda fluid, are numerically solved by considering the volumetric force term of electric current (DC).
Findings
The impact of pertinent parameters such as electrokinetic diameter (R), Brinkman number and Peclet number is examined graphically. It is observed that for increasing R, the bulk velocity decreases. The velocity of the bulk fluid reaches from the minimum to the maximum state across the microchannel over time. At the electrokinetic diameter of 400, the maximum velocity was obtained. Temperature graphs are plotted with changes in the various Brinkman number (0.1 <
Originality/value
This study contributes to discovering the effects of transient flow of electroosmotic flow for non-Newtonian Carreau–Yasuda fluid and transient heat transfer through rectangular microchannel. To the authors’ knowledge, the said investigation is yet not available in existing literature.
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Yasir Mehmood and Vimala Balakrishnan
Research on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms…
Abstract
Purpose
Research on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach.
Design/methodology/approach
An enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from Twitter for a period of three months, resulting in 694,141 tweets. Of these, 2,500 tweets were segregated into two datasets for evaluation purposes after filtering and pre-processing.
Findings
The enhanced approach outperformed ten online sentiment analysis tools with an overall accuracy of 81.4 and 82.3% for dataset 1 and 2, respectively as opposed to ten other sentiment analysis tools.
Originality/value
The study is novel in the sense that data pertaining to a social issue were used instead of products and services, which require different mechanism due to the nature of the issue itself.
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Dimitrios Papandreou, Pavlos Malindretos and Israel Rousso
The aim of this study is to record the prevalence of overweight and obesity and to explore any relationship with nutritional status in Greek children aged 6‐15 years.
Abstract
Purpose
The aim of this study is to record the prevalence of overweight and obesity and to explore any relationship with nutritional status in Greek children aged 6‐15 years.
Design/methodology/approach
A total of 524 children participated in the study. Anthropometric and dietary characteristics were recorded for all subjects.
Findings
The prevalence of overweight and obesity was 21.1 per cent and 8.4 per cent for boys and 17.6 and 7.3 per cent for girls, respectively. Dietary intakes of energy, fat, protein, lipids and sugar were higher in overweight and obese children compared with the normal ones ( p < 0.001), while fibre intake was lower in the overweight and obese group ( p < 0.001) than in the normal group. The current study gives an estimation of overweight and obesity in children from Northern Greece. The composition of diet, especially low in fibre, vitamin D and high in energy and fat may play a role in the etiology of obesity.
Originality/value
The paper presents information on obesity prevalence in a Mediterranean country as well as integration of some nutrients in the etiology of obesity.