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Article
Publication date: 28 January 2025

Valdecy Pereira, Marcio Pereira Basilio and Carlos Henrique Tarjano Santos

This paper presents pyBibX, a Python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Scopus, Web of Science and…

71

Abstract

Purpose

This paper presents pyBibX, a Python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Scopus, Web of Science and PubMed, seamlessly integrating state-of-the-art artificial intelligence (AI) capabilities into its core functionality.

Design/methodology/approach

The library executes a comprehensive exploratory data analysis (EDA), presenting outcomes via visually appealing graphical illustrations. Network capabilities have been deftly integrated, encompassing citation, collaboration and similarity analysis. Furthermore, the library incorporates AI capabilities, including embedding vectors, topic modeling, text summarization and other general natural language processing tasks, employing models such as sentence-BERT, BerTopic, BERT, chatGPT and PEGASUS.

Findings

As a demonstration, we have analyzed 184 documents associated with “multiple-criteria decision analysis” published between 1984 and 2023. The EDA emphasized a growing fascination with decision-making and fuzzy logic methodologies. Next, network analysis further accentuated the significance of central authors and intra-continental collaboration, identifying Canada and China as crucial collaboration hubs. Finally, AI analysis distinguished two primary topics and chatGPT’s preeminence in text summarization. It also proved to be an indispensable instrument for interpreting results, as our library enables researchers to pose inquiries to chatGPT regarding bibliometric outcomes. Even so, data homogeneity remains a daunting challenge due to database inconsistencies.

Originality/value

PyBibX is the first application integrating cutting-edge AI capabilities for analyzing scientific publications, enabling researchers to examine and interpret these outcomes more effectively. pyBibX is freely available at https://bit.ly/442wD5z.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 14 November 2008

Lieven Degroote, Lieven Vandevelde, Bert Renders and Johan Gyselinck

The aim is to develop a nonlinear transformer model to achieve an accurate model to obtain the frequency components of the magnetizing current based on the harmonic voltages at…

724

Abstract

Purpose

The aim is to develop a nonlinear transformer model to achieve an accurate model to obtain the frequency components of the magnetizing current based on the harmonic voltages at the primary and secondary side. So, it can easily be implemented in a harmonic load‐flow program.

Design/methodology/approach

The transformer model is based on the harmonic balance method. The electric and magnetic equations of the transformer are derived from the electric and magnetic equivalent circuits.

Findings

The transformer model can be easily implemented in a harmonic load‐flow program. The accuracy of the model has been shown by comparing it with a finite element simulation. The transformer model can be used with asymmetrical supply voltages, because different saturation levels of the phases can occur. There is a coupling between the phases which can be concluded out of the asymmetrical currents in the transformer under symmetrical supply voltages.

Research limitations/implications

The transformer model does not consider the iron losses and the interharmonics. In future work the transformer model will be used to study the harmonic losses in distribution networks, so the transformer losses due to these harmonics have to be considered. This can be achieved with a postcalculation process where the magnetic flux density is used to calculate the eddy current losses and the magnetic field intensity will be applied in a static Preisach model to quantify the hysteresis losses.

Practical implications

The model can be used in a harmonic load‐flow program in order to obtain more accurate simulations for the power system analysis and design.

Originality/value

The model presented in this paper is more detailed than similar papers found in literature (saturation of the yokes, coupling between the phases, interaction between different harmonics) and still it takes a brief simulation time.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 27 no. 6
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 August 2002

François Bry and Michael Kraus

While the World Wide Web (WWW or Web) is steadily expanding, electronic books (e‐books) remain a niche market. In this article, it is first postulated that specialized contents…

1153

Abstract

While the World Wide Web (WWW or Web) is steadily expanding, electronic books (e‐books) remain a niche market. In this article, it is first postulated that specialized contents and device independence can make Web‐based e‐books compete with paper prints; and that adaptive features that can be implemented by client‐side computing are relevant for e‐books, while more complex forms of adaptation requiring server‐side computations are not. Then, enhancements of the WWW standards (specifically of XML, XHTML, of the style‐sheet languages CSS and XSL, and of the linking language XLink) are proposed for a better support of client‐side adaptation and device independent content modeling. Finally, advanced browsing functionalities desirable for e‐books as well as their implementation in the WWW context are described.

Details

The Electronic Library, vol. 20 no. 4
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 14 November 2023

Shaodan Sun, Jun Deng and Xugong Qin

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained…

168

Abstract

Purpose

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

Design/methodology/approach

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

Findings

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Originality/value

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

Details

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

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Article
Publication date: 13 August 2024

Samia Nawaz Yousafzai, Hooria Shahbaz, Armughan Ali, Amreen Qamar, Inzamam Mashood Nasir, Sara Tehsin and Robertas Damaševičius

The objective is to develop a more effective model that simplifies and accelerates the news classification process using advanced text mining and deep learning (DL) techniques. A…

34

Abstract

Purpose

The objective is to develop a more effective model that simplifies and accelerates the news classification process using advanced text mining and deep learning (DL) techniques. A distributed framework utilizing Bidirectional Encoder Representations from Transformers (BERT) was developed to classify news headlines. This approach leverages various text mining and DL techniques on a distributed infrastructure, aiming to offer an alternative to traditional news classification methods.

Design/methodology/approach

This study focuses on the classification of distinct types of news by analyzing tweets from various news channels. It addresses the limitations of using benchmark datasets for news classification, which often result in models that are impractical for real-world applications.

Findings

The framework’s effectiveness was evaluated on a newly proposed dataset and two additional benchmark datasets from the Kaggle repository, assessing the performance of each text mining and classification method across these datasets. The results of this study demonstrate that the proposed strategy significantly outperforms other approaches in terms of accuracy and execution time. This indicates that the distributed framework, coupled with the use of BERT for text analysis, provides a robust solution for analyzing large volumes of data efficiently. The findings also highlight the value of the newly released corpus for further research in news classification and emotion classification, suggesting its potential to facilitate advancements in these areas.

Originality/value

This research introduces an innovative distributed framework for news classification that addresses the shortcomings of models trained on benchmark datasets. By utilizing cutting-edge techniques and a novel dataset, the study offers significant improvements in accuracy and processing speed. The release of the corpus represents a valuable contribution to the field, enabling further exploration into news and emotion classification. This work sets a new standard for the analysis of news data, offering practical implications for the development of more effective and efficient news classification systems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 17 no. 4
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 24 November 2022

Nao Li, Xiaoyu Yang, IpKin Anthony Wong, Rob Law, Jing Yang Xu and Binru Zhang

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a…

466

Abstract

Purpose

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a neural network model.

Design/methodology/approach

This study constructs an aspect-oriented sentiment classification model using an integrated four-layer neural network: the bidirectional encoder representation from transformers (BERT) word vector model, long short-term memory, interactive attention-over-attention (IAOA) mechanism and a linear output layer. The model was trained, tested and validated on an open training data set and 92,905 reviews extrapolated from restaurants in Tokyo.

Findings

The model achieves significantly better performance compared with other neural networks. The findings provide empirical evidence to validate the suitability of this new approach in the tourism-hospitality domain.

Research limitations/implications

More sentiments should be identified to measure more fine-grained tourism-hospitality experience, and new aspects are recommended that can be automatically added into the aspect set to provide dynamic support for new dining experiences.

Originality/value

This study provides an update to the literature with respect to how a neural network could improve the performance of aspect-oriented sentiment classification for tourism-hospitality online reviews.

研究目的

本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。

研究设计/方法/途径

本研究使用集成的四层神经网络构建面向方面的情感分类模型:BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。

研究发现

与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。

研究原创性

该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。

研究研究局限

应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。

Details

Journal of Hospitality and Tourism Technology, vol. 14 no. 1
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 8 July 2022

Chuanming Yu, Zhengang Zhang, Lu An and Gang Li

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of…

581

Abstract

Purpose

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

Design/methodology/approach

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

Findings

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

Originality/value

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

Details

Aslib Journal of Information Management, vol. 75 no. 3
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 11 October 2024

Reema Nayyar, Pratyush Yadav, Rupashree Baral, Mahima Raina and Lalatendu Kesari Jena

This study aims to explore the emergence of workplace spirituality (WPS) in Indian organisations through a systematic literature review by unwrapping the past, present and future…

174

Abstract

Purpose

This study aims to explore the emergence of workplace spirituality (WPS) in Indian organisations through a systematic literature review by unwrapping the past, present and future state of WPS research in the Indian context. The data was covered for 15 years (2008–2023) and spread across 116 studies screened from Scopus, Web of Science and EBSCO.

Design/methodology/approach

Theory-context-characteristics-methods (TCCM) framework analysis and topic modelling (bidirectional encoder representations from transformers [BERT] analysis) techniques were adopted for a systematic exploration of theoretical underpinnings, contextual relevance, characteristic features and methodological rigour within the domain of WPS and analysis of the literature’s emerging trends and thematic patterns, respectively.

Findings

Using the TCCM framework, this study analysed the dominant theories applied in WPS literature within the Indian context, including social exchange theory and self-determination theory. In addition, this review highlights the key industries, variables and methodologies that have been the focus of prior research. Using BERT, this study clustered the textual data and identified three thematic patterns in the literature. By analysing past and current studies, this study identified potential gaps that future research could address, as guided by the TCCM framework.

Originality/value

To the best of the authors’ knowledge, this is one of the initial literature reviews focused on country-level studies adopting two techniques to bring more rigour: TCCM and BERT analysis.

Details

International Journal of Organizational Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1934-8835

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Article
Publication date: 25 January 2023

Ashutosh Kumar and Aakanksha Sharaff

The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

127

Abstract

Purpose

The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

Design/methodology/approach

In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.

Findings

The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.

Research limitations/implications

As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.

Practical implications

As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.

Social implications

During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.

Originality/value

In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

Details

Data Technologies and Applications, vol. 57 no. 2
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 21 April 2020

Ciarán McFadden

This paper discusses the factors to consider when designing studies to measure hiring discrimination against transgender job applicants.

2278

Abstract

Purpose

This paper discusses the factors to consider when designing studies to measure hiring discrimination against transgender job applicants.

Design/methodology/approach

The paper builds on academic literature related to hiring discrimination and transgender employment to build a detailed discussion of the numerous factors and issues inherent in hiring discrimination against transgender job applicants. By isolating and describing a number of relevant considerations, the paper aims to act as a guide for future studies to build upon.

Findings

Three types of hiring discrimination studies are discussed: correspondence tests, in-person experiments and student cohort experiments. Three main categories of factors relevant to an experiment’s design are then discussed: the legal context, industry/role factors and transgender population-specific factors. A flow-chart detailing the research design decision-making process is provided.

Research limitations/implications

The discussion within this paper will act as a reference and a guide for researchers seeking to address the dearth of empirical studies in the literature. The list is not exhaustive; while a number of factors relevant to transgender-specific studies are identified, there may be more that could affect an experiment's design.

Originality/value

Hiring discrimination against transgender people has been recorded in many surveys, but there is little empirical measurement of this discrimination. To the author's knowledge, this paper is the first to examine the experimental design decisions related to transgender hiring discrimination. In doing so, it provides contributions for two primary audiences: those researching transgender employment issues but who have never conducted a study measuring hiring discrimination; and those who have previously conducted studies on hiring discrimination, but have not done so with reference to transgender job applicants.

Details

International Journal of Manpower, vol. 41 no. 6
Type: Research Article
ISSN: 0143-7720

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