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Article
Publication date: 15 October 2021

Rangayya Rangayya, Virupakshappa Virupakshappa and Nagabhushan Patil

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past…

Abstract

Purpose

One of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.

Design/methodology/approach

The proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.

Findings

Experimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.

Originality/value

The good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 10 September 2021

Shathees Baskaran, Nomahaza Mahadi and Siti Zaleha Abd Rasid

This paper aims to clarify the relationship between multiple intelligence (MI) and entrepreneurial opportunity recognition. It discusses to what extent each dimension of…

Abstract

Purpose

This paper aims to clarify the relationship between multiple intelligence (MI) and entrepreneurial opportunity recognition. It discusses to what extent each dimension of MI is considered as an activation driver of entrepreneurial opportunities recognition. This paper also aims to expand the domain of entrepreneurial opportunities recognition via MI by considering the mediating role of neuromarketing perception, adopting a combined perspective of intelligence, entrepreneurship and also neuromarketing to provide a future direction for the creation of interdisciplinary insights in the area of entrepreneurship.

Design/methodology/approach

This paper opted for literature synthesis to define key concepts surrounding MI and entrepreneurial opportunities recognition. Besides, it also attempted to identify an influential mediator in explaining the entrepreneurial opportunities recognition phenomenon. Consequently, this paper identified the gaps in current research to draw upon a more holistic conceptual framework. The rationale for the research was justified within the body of research.

Findings

This paper suggested research propositions based on the literature synthesis in view of MI and entrepreneurial opportunities recognition. More specifically, it has proposed a conceptual framework, explaining the relationship between a multi-dimensional view of both MI and entrepreneurial opportunities recognition. It is envisaged that the mediating role of neuromarketing perception incorporated in this conceptual work will improve the predictive value of the proposed framework and offer additional insights about factors that advance entrepreneurial opportunities recognition.

Research limitations/implications

This paper suffers from the obvious limitation of lacking empirical investigation. However, it does provide a theoretical rationale for the argument that entrepreneurial opportunities recognition can be advanced if MI are identified and associated with neuromarketing dimensions. Perhaps the most important direction for future research is further extension and validation of this framework by performing an empirical investigation to produce newer insights into this phenomenon.

Originality/value

This conceptual work is different from previous studies on the grounds it has considered unexplored issues in explaining entrepreneurial opportunities recognition. To bridge the critical knowledge gap of the entrepreneurial opportunities recognition phenomenon, a mediating effect of neuromarketing perception is also integrated within the model. The proposed model was neither formulated nor tested empirically in previous studies locally or perhaps globally, therefore it stands out as an original contribution incorporating MI and entrepreneurial opportunities recognition phenomenon while considering the brain activity through neuromarketing perception.

Details

Journal of Research in Marketing and Entrepreneurship, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1471-5201

Keywords

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Article
Publication date: 26 July 2021

Pengcheng Li, Qikai Liu, Qikai Cheng and Wei Lu

This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant…

Abstract

Purpose

This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain.

Design/methodology/approach

Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities.

Findings

In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition.

Originality/value

This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

Details

The Electronic Library , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0264-0473

Keywords

Content available
Article
Publication date: 26 July 2021

Wesley R. Teter and Libing Wang

The impacts of the COVID-19 pandemic have transformed the global outlook for international higher education. Given the rapid shift to online learning, the Tokyo Convention…

Abstract

Purpose

The impacts of the COVID-19 pandemic have transformed the global outlook for international higher education. Given the rapid shift to online learning, the Tokyo Convention in the Asia-Pacific entrusted to UNESCO has become an important policy framework to facilitate regional collaboration, authoritative information sharing and recognition of qualifications across diverse modes of learning. This paper examines the role of the Tokyo Convention to establish an inclusive platform for monitoring and collaborative governance of mobility and internationalization based on fair and transparent recognition policies and practices in the Asia-Pacific.

Design/methodology/approach

In August 2019, a standardized survey instrument was sent by the Secretariat of the Tokyo Convention Committee at UNESCO Bangkok to competent recognition authorities in 46 countries in the Asia-Pacific, including the eight State Parties to the Tokyo Convention that ratified the Convention as of the reporting period. In total, qualitative data from n = 27 countries/states was received and analyzed to assess implementation of the Tokyo Convention throughout the region. The research design illustrates how normative instruments such as the Tokyo Convention are monitored and assessed over time.

Findings

A multi-stakeholder approach based on collaborative governance is needed to effectively monitor implementation and implications of the Tokyo Convention for diverse higher education stakeholders in the Asia-Pacific region.

Research limitations/implications

Implications include establishing baseline data and methods for monitoring implementation of the Tokyo Convention. Based on collaborative governance theory, the paper explores potential for a multi-stakeholder approach to promote mutual accountability in the Asia-Pacific and to develop mechanisms for inclusive participation in the governance of the forthcoming Global Convention on recognition.

Originality/value

As the first systematic review of its kind, this paper includes a unique dataset and insights into UNESCO's methodology to monitor implementation of standard-setting instruments for qualifications recognition in the Asia-Pacific.

Details

International Journal of Comparative Education and Development, vol. 23 no. 3
Type: Research Article
ISSN: 2396-7404

Keywords

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Article
Publication date: 10 June 2014

Ping Bao and Suoling Zhu

The purpose of this paper is to present a system for recognition of location names in ancient books written in languages, such as Chinese, in which proper names are not…

Abstract

Purpose

The purpose of this paper is to present a system for recognition of location names in ancient books written in languages, such as Chinese, in which proper names are not signaled by an initial capital letter.

Design/methodology/approach

Rule-based and statistical methods were combined to develop a set of rules for identification of product-related location names in the local chronicles of Guangdong. A name recognition system, with functions of document management, information extraction and storage, rule management, location name recognition, and inquiry and statistics, was developed using Microsoft's .NET framework, SQL Server 2005, ADO.NET and XML. The system was evaluated with precision ratio, recall ratio and the comprehensive index, F.

Findings

The system was quite successful at recognizing product-related location names (F was 71.8 percent), demonstrating the potential for application of automatic named entity recognition techniques in digital collation of ancient books such as local chronicles.

Research limitations/implications

Results suffered from limitations in initial digitization of the text. Statistical methods, such as the hidden Markov model, should be combined with an extended set of recognition rules to improve recognition scores and system efficiency.

Practical implications

Electronic access to local chronicles by location name saves time for chorographers and provides researchers with new opportunities.

Social implications

Named entity recognition brings previously isolated ancient documents together in a knowledge base of scholarly and cultural value.

Originality/value

Automatic name recognition can be implemented in information extraction from ancient books in languages other than English. The system described here can also be adapted to modern texts and other named entities.

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Article
Publication date: 1 July 2021

Navid Mohammadi and Saeed Heshmati

Entrepreneurship is the driving force of countries for sustainable economic development. The importance of this issue is to the extent that in recent years, countries have…

Abstract

Purpose

Entrepreneurship is the driving force of countries for sustainable economic development. The importance of this issue is to the extent that in recent years, countries have made great efforts to develop their entrepreneurial ecosystem. But the starting point for entrepreneurship is when an opportunity is identified and the entrepreneur rises to use it. Accordingly, opportunity recognition will be the foundation of entrepreneurship and ultimately sustainable development. Given the importance of this topic, this paper attempts to provide a large picture of the studies conducted in this field.

Design/methodology/approach

Given the importance of this topic, this paper attempts to provide a large picture of the studies conducted in this field by reviewing 868 articles published on the Web of Science database in the field of opportunity recognition. Accordingly, using statistical descriptions of articles, analyzing the communication network among elements such as authors, countries, institutions, keyword analysis in articles and examining their trends over time, identifying the most important articles using co-citation analysis and finally this macroimage has been mapped, clustered and identified in leading articles in the last decade by co-citation clustering.

Findings

The results of the clustering show that the five main clusters of recent decades have included entrepreneurial characteristics and opportunity recognition, macroeconomic opportunity recognition cluster (community and impact on economic development of the country), opportunity recognition process cluster, opportunity recognition cluster in serial and intra-entrepreneurship and opportunity recognition cluster in new venture internationalization.

Originality/value

Using a bibliometric analysis and co-citation analysis in the field of opportunity recognition and making a big picture of studies in this field of study is a contribution that can be used for future studies and researchers and managers in this field.

Details

World Journal of Science, Technology and Sustainable Development, vol. 18 no. 3
Type: Research Article
ISSN: 2042-5945

Keywords

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Book part
Publication date: 22 February 2010

Steven E. Abraham, Adrienne E. Eaton and Paula B. Voos

We present evidence regarding how a card check recognition process affects the labor relations climate during the period preceding recognition and that which immediately…

Abstract

We present evidence regarding how a card check recognition process affects the labor relations climate during the period preceding recognition and that which immediately follows. Interviews with managers, interviews with union representatives, and surveys of workers indicate that card check typically results in a less prolonged, costly, and stressful recognition and negotiations process. Although the resulting contracts are often similar to those in other parts of a heavily unionized corporation, sometimes they reflect a different business context – and hence are somewhat more favorable to employers without being substantially less favorable to employees. This reality is reflected in the positive reaction of the U.S. stock markets to union recognition by an employer through a card check process. Employers make card check agreements primarily for business reasons, and investors respect their judgment as to the impact of such agreements on the bottom line.

Details

Advances in Industrial and Labor Relations
Type: Book
ISBN: 978-1-84950-932-9

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Book part
Publication date: 12 June 2018

Douglas NeJaime

This chapter uncovers the destabilizing and transformative dimensions of a legal process commonly described as assimilation. Lawyers working on behalf of a marginalized…

Abstract

This chapter uncovers the destabilizing and transformative dimensions of a legal process commonly described as assimilation. Lawyers working on behalf of a marginalized group often argue that the group merits inclusion in dominant institutions, and they do so by casting the group as like the majority. Scholars have criticized claims of this kind for affirming the status quo and muting significant differences of the excluded group. Yet, this chapter shows how these claims may also disrupt the status quo, transform dominant institutions, and convert distinctive features of the excluded group into more widely shared legal norms. This dynamic is observed in the context of lesbian, gay, bisexual, and transgender (LGBT) rights, and specifically through attention to three phases of LGBT advocacy: (1) claims to parental recognition of unmarried same-sex parents, (2) claims to marriage, and (3) claims regarding the consequences of marriage for same-sex parents. The analysis shows how claims that appeared assimilationist – demanding inclusion in marriage and parenthood by arguing that same-sex couples are similarly situated to their different-sex counterparts – subtly challenged and reshaped legal norms governing parenthood, including marital parenthood. While this chapter focuses on LGBT claims, it uncovers a dynamic that may exist in other settings.

Details

Special Issue: Law and the Imagining of Difference
Type: Book
ISBN: 978-1-78756-030-7

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Article
Publication date: 30 April 2021

Tushar Jain

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are…

Abstract

Purpose

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Design/methodology/approach

Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

Findings

One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.

Originality/value

This paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

Details

International Journal of Intelligent Unmanned Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-6427

Keywords

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Abstract

Details

Philosophy of Management and Sustainability: Rethinking Business Ethics and Social Responsibility in Sustainable Development
Type: Book
ISBN: 978-1-78973-453-9

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