D.A. Karras, S.A. Karkanis and B.G. Mertzios
This paper suggests a novel methodology for building robust information processing systems based on wavelets and artificial neural networks (ANN) to be applied either in…
Abstract
This paper suggests a novel methodology for building robust information processing systems based on wavelets and artificial neural networks (ANN) to be applied either in decision‐making tasks based on image information or in signal prediction and modeling tasks. The efficiency of such systems is increased when they simultaneously use input information in its original and wavelet transformed form, invoking ANN technology to fuse the two different types of input. A quality control decision‐making system as well as a signal prediction system have been developed to illustrate the validity of our approach. The first one offers a solution to the problem of defect recognition for quality control systems. The second application improves the quality of time series prediction and signal modeling in the domain of NMR. The accuracy obtained shows that the proposed methodology deserves the attention of designers of effective information processing systems.
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Matjaž Kragelj and Mirjana Kljajić Borštnar
The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.
Abstract
Purpose
The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.
Design/methodology/approach
The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.
Findings
Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.
Research limitations/implications
The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.
Practical implications
The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.
Social implications
The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.
Originality/value
These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.
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Manuel Ferreira, Cristina Santos and Joao Monteiro
The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrial textures. One of the main purposes…
Abstract
Purpose
The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrial textures. One of the main purposes was to deal with a high diversity of textures, including structural and highly random patterns.
Design/methodology/approach
The global system includes a texture segmentation phase and a classification phase. The approach for image texture segmentation is based on features extracted from wavelets transform, fuzzy spectrum and interaction maps. The classification architecture uses a fuzzy grammar inference system.
Findings
The classifier uses the aggregation of features from the several segmentation techniques, resulting in high flexibility concerning the diversity of industrial textures. The resulted system allows on‐line learning of new textures. This approach avoids the need for a global re‐learning of the all textures each time a new texture is presented to the system.
Practical implications
These achievements demonstrate the practical value of the system, as it can be applied to different industrial sectors for quality control operations.
Originality/value
The global approach was integrated in a cork vision system, leading to an industrial prototype that has already been tested. Similarly, it was tested in a textile machine, for a specific fabric inspection, and gave results that corroborate the diversity of possible applications. The segmentation procedure reveals good performance that is indicated by high classification rates, revealing good perspectives for full industrialization.
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Jialiang Xie, Shanli Zhang, Honghui Wang and Mingzhi Chen
With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent…
Abstract
Purpose
With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.
Design/methodology/approach
Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.
Findings
The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.
Originality/value
A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.
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Francisco J. Veredas, Héctor Mesa and Laura Morente
Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, and friction. Diagnosis, treatment and care of pressure…
Abstract
Purpose
Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, and friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task to optimize the efficacy of treatments and health‐care. Clinicians evaluate the pressure ulcers by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. The purpose of this paper is to use a hybrid learning approach based on neural and Bayesian networks to design a computational system to automatic tissue identification in wound images.
Design/methodology/approach
A mean shift procedure and a region‐growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multi‐layer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes determined by clinical experts. This training procedure is driven by a k‐fold cross‐validation method. Finally, a Bayesian committee machine is formed by training a Bayesian network to combine the classifications of the k neural networks (NNs).
Findings
The authors outcomes show high efficiency rates from a two‐stage cascade approach to tissue identification. Giving a non‐homogeneous distribution of pattern classes, this hybrid approach has shown an additional advantage of increasing the classification efficiency when classifying patterns with relative low frequencies.
Practical implications
The methodology and results presented in this paper could have important implications to the field of clinical pressure ulcer evaluation and diagnosis.
Originality/value
The novelty associated with this work is the use of a hybrid approach consisting of NNs and Bayesian classifiers which are combined to increase the performance of a pattern recognition task applied to the real clinical problem of tissue detection under non‐controlled illumination conditions.
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Madjid Tavana and Vahid Hajipour
Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems…
Abstract
Purpose
Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.
Design/methodology/approach
The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.
Findings
The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.
Originality/value
Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.
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Ping Huang, Haitao Ding, Hong Chen, Jianwei Zhang and Zhenjia Sun
The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs…
Abstract
Purpose
The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.
Design/methodology/approach
According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.
Findings
The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.
Originality/value
This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.
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Salman Khan and Safeer Ullah Khan
As smart technologies rapidly permeate the hospitality industry, understanding their impact on guest experiences is crucial. Existing research often treats smart technology as a…
Abstract
Purpose
As smart technologies rapidly permeate the hospitality industry, understanding their impact on guest experiences is crucial. Existing research often treats smart technology as a peripheral factor, without fully exploring the essence of “smartness.” This study addresses that gap by leveraging the Hedonic Information Systems Acceptance Model (HISAM) to differentiate between the utilitarian and hedonic motivations that influence tourists' intentions to stay in smart hotels. Additionally, we introduce technology readiness as a moderating factor, examining how individual traits affect behavior in smart hospitality environments.
Design/methodology/approach
Using SmartPLS 3.2.8, we conducted structural equation modeling (SEM) on 311 valid samples to empirically test our hypotheses.
Findings
Our results reveal that HISAM factors significantly influence tourists’ intentions to engage with smart hotel services. Notably, the perception of smartness emerges as a key driver of perceived ease of use, usefulness and enjoyment. These insights pave the way for both theoretical advancements and practical applications, with recommendations for future research outlined.
Practical implications
This study not only advances theoretical understanding but also provides actionable insights for the hospitality industry. By identifying the factors that enhance user experience in smart hotels, industry professionals can better meet evolving guest expectations and preferences, thereby improving service quality and customer satisfaction.
Originality/value
This pioneering study is the first to integrate the concept of smartness within the HISAM framework, establishing a robust foundation for future research in the tourism and hospitality sectors. Furthermore, the introduction of technology readiness as a moderating variable offers a fresh perspective on individual differences in the adoption of smart technologies.
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Usman Farooq, Khuram Shahzad, ZhenZhong Guan and Abdul Rauf
This study aims to identify the essential elements impacting the adoption of blockchain technology (BCT) in supply chain management (SCM) by integrating the technology acceptance…
Abstract
Purpose
This study aims to identify the essential elements impacting the adoption of blockchain technology (BCT) in supply chain management (SCM) by integrating the technology acceptance and information system success (ISS) models.
Design/methodology/approach
Questionnaire-based data was collected from 236 supply chain professionals from Beijing. The proposed research framework was evaluated using structural equation modeling (SEM) by using SPSS 23 and AMOS 24 software.
Findings
The empirical findings specify the positive influence of total quality on perceived usefulness and compatibility. Further, perceived ease of use positively influences perceived usefulness, compatibility and behavioral intention. Moreover, perceived usefulness positively impacts compatibility and behavioral intention. Compatibility positively influences behavioral intention. Finally, technology trust was found to be a significant moderator between perceived usefulness and behavioral intention and between perceived ease of use and adoption intention to use BCT in SCM.
Originality/value
This study empirically develops the second-order construct of total quality, representing the ISS model. Furthermore, this study established how the ISS and technology acceptance models influence behavioral intention through compatibility. Finally, this study confirmed the moderating role of technology trust among perceived ease of use, perceived usefulness and behavioral intention.
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Imen Ben Ammar, Abderrahim El Mahi, Chafik Karra, Rachid El Guerjouma and Mohamed Haddar
The purpose of this paper is to study the mechanical behavior in fatigue tensile mode of different cross-ply laminates constituted of unidirectional carbon fibers, hybrid fibers…
Abstract
Purpose
The purpose of this paper is to study the mechanical behavior in fatigue tensile mode of different cross-ply laminates constituted of unidirectional carbon fibers, hybrid fibers and glass fibers in an epoxy matrix; and to identify and characterize the local damage in the laminated materials with the use of the acoustic emission (AE) technique.
Design/methodology/approach
The tests in the fatigue mode permitted the determination of the effect of the stacking sequences, thickness of 90° oriented layers and reinforcement types on the fatigue mechanical behavior of the laminated materials. The damage investigation in those materials is reached with the analysis of AE signals collected from fatigue tensile tests.
Findings
The results show the effects of reinforcement type, stacking sequences and thicknesses ratio of 90° and 0° layers on the mechanical behavior. A cluster analysis of AE data is achieved and the resulting clusters are correlated with the damage mechanism of specimens under loading tests.
Originality/value
The analysis of AE signals collected from tensile tests of the fatigue failure load allows the damage investigation in different types of cross-ply laminates which are differentiated by the reinforcement type, stacking sequences and thicknesses ratio of 90° and 0° layers.