Pub Date : 2022-07-14DOI: 10.1142/s0219649222500575
Irshad Ahmad Thukroo, Rumaan Bashir, K. Giri
Spoken language identification (LID) is the identification of language present in a speech segment despite its size (duration and speed), ambiance (topic and emotion), and moderator (gender, age, demographic region). Information Technology has touched new vistas for a couple of decades mostly to simplify the day-to-day life of humans. One of the key contributions of Information Technology is the application of Artificial Intelligence to achieve better results. The advent of artificial intelligence has given rise to a new branch of Natural Language Processing (NLP) called Computational Linguistics, which generates frameworks for intelligently manipulating spoken language knowledge and has brought human–machine into a new stage. In this context, speech has arisen to be one of the imperative forms of interfaces, which is the basic mode of communication for us, and generally the most preferred one. Recognition of the spoken language is a frontend for several technologies, like multiple languages conversation systems, expressed translation software, multilingual speech recognition, spoken word extraction, speech production systems. This paper reviews and summarises the different levels of information that can be used for language identification. A broad study of acoustic, phonetic, and prosody features has been provided and various classifiers have been used for spoken language identification specifically for Indian languages. This paper has investigated various existing spoken language identification models implemented using prosodic, phonotactic, acoustic, and deep learning approaches, the datasets used, and performance measures utilized for their analysis. It also highlights the main features and challenges faced by these models. Moreover, this review analyses the efficiency of the spoken language models that can help the researchers to propose new language identification models for speech signals.
{"title":"Spoken Language Identification Using Prosody, Phonotactics, and Acoustics: A Review","authors":"Irshad Ahmad Thukroo, Rumaan Bashir, K. Giri","doi":"10.1142/s0219649222500575","DOIUrl":"https://doi.org/10.1142/s0219649222500575","url":null,"abstract":"Spoken language identification (LID) is the identification of language present in a speech segment despite its size (duration and speed), ambiance (topic and emotion), and moderator (gender, age, demographic region). Information Technology has touched new vistas for a couple of decades mostly to simplify the day-to-day life of humans. One of the key contributions of Information Technology is the application of Artificial Intelligence to achieve better results. The advent of artificial intelligence has given rise to a new branch of Natural Language Processing (NLP) called Computational Linguistics, which generates frameworks for intelligently manipulating spoken language knowledge and has brought human–machine into a new stage. In this context, speech has arisen to be one of the imperative forms of interfaces, which is the basic mode of communication for us, and generally the most preferred one. Recognition of the spoken language is a frontend for several technologies, like multiple languages conversation systems, expressed translation software, multilingual speech recognition, spoken word extraction, speech production systems. This paper reviews and summarises the different levels of information that can be used for language identification. A broad study of acoustic, phonetic, and prosody features has been provided and various classifiers have been used for spoken language identification specifically for Indian languages. This paper has investigated various existing spoken language identification models implemented using prosodic, phonotactic, acoustic, and deep learning approaches, the datasets used, and performance measures utilized for their analysis. It also highlights the main features and challenges faced by these models. Moreover, this review analyses the efficiency of the spoken language models that can help the researchers to propose new language identification models for speech signals.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133850025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-06DOI: 10.1142/s0219649222500587
Yuanda Luo
As the subject of pollutant emissions, enterprises play an important role in environmental governance and should consciously fulfil their environmental responsibility. So, can the enterprise perform environmental responsibility to increase their investment in research and development (R&D)? Based on the data from China’s Shanghai and Shenzhen A-share listed enterprises in 2011–2019, this paper explores the relationship between corporate environmental responsibility and R&D investment and examines the dual moderating effects of public attention and intellectual property protection. It is found that the implementation of corporate environmental responsibility can promote R&D investment. Public attention moderates the positive relationship between corporate environmental responsibility and R&D investment; it seeks that the positive relationship becomes higher with public attention. With higher regional intellectual property protection levels, the moderating effect of public attention on corporate environmental responsibility and R&D investment will be more obviously higher. This research provides theoretical support for enterprises to fulfil their environmental responsibilities and a new perspective for revealing the factors of enterprises’ R&D investment, which can offer a foundation for future research and practices.
{"title":"Influence of Corporate Environmental Responsibility on R&D Investment: Dual Moderating Effects of Public Attention and Intellectual Property Protection","authors":"Yuanda Luo","doi":"10.1142/s0219649222500587","DOIUrl":"https://doi.org/10.1142/s0219649222500587","url":null,"abstract":"As the subject of pollutant emissions, enterprises play an important role in environmental governance and should consciously fulfil their environmental responsibility. So, can the enterprise perform environmental responsibility to increase their investment in research and development (R&D)? Based on the data from China’s Shanghai and Shenzhen A-share listed enterprises in 2011–2019, this paper explores the relationship between corporate environmental responsibility and R&D investment and examines the dual moderating effects of public attention and intellectual property protection. It is found that the implementation of corporate environmental responsibility can promote R&D investment. Public attention moderates the positive relationship between corporate environmental responsibility and R&D investment; it seeks that the positive relationship becomes higher with public attention. With higher regional intellectual property protection levels, the moderating effect of public attention on corporate environmental responsibility and R&D investment will be more obviously higher. This research provides theoretical support for enterprises to fulfil their environmental responsibilities and a new perspective for revealing the factors of enterprises’ R&D investment, which can offer a foundation for future research and practices.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127357710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1142/s0219649222500563
Nabil Moussaoui, Abdeslem Dennai, Khaled Benali
In the medical field, ontologies are mainly used to standardise the coding of knowledge, either during the drafting phase of documents or during subsequent processing intended to give them a format which makes them usable for automatic processing. In this sense, they have a normative role analogous to the classical medical terminologies (in particular thesauri): to set up a common vocabulary and to make use of shared representations and concepts, in order to allow the interoperability of documents and medicals information systems. Medical and biomedical data play a very important role in the medical field. They solve many of the problems encountered during the diagnosis of diseases and in the prescription medical treatments. The objective of this paper is to develop ontology for the field of medical analyses in an attempt to facilitate the tasks of the various actors in the medical field, namely: doctors, laboratory assistants and patients. It also endeavours to improve the care of patients and facilitate the various acts of health professionals, the purpose of which is to aid in decision-making in the treatment of a disease. To this end, we followed the so-called METHONTOLOGY based on the phases ranging from the needs specification to the evaluation and documentation of this ontology of medical analysis.
{"title":"OntoBa: Ontology of Biomedical Analysis","authors":"Nabil Moussaoui, Abdeslem Dennai, Khaled Benali","doi":"10.1142/s0219649222500563","DOIUrl":"https://doi.org/10.1142/s0219649222500563","url":null,"abstract":"In the medical field, ontologies are mainly used to standardise the coding of knowledge, either during the drafting phase of documents or during subsequent processing intended to give them a format which makes them usable for automatic processing. In this sense, they have a normative role analogous to the classical medical terminologies (in particular thesauri): to set up a common vocabulary and to make use of shared representations and concepts, in order to allow the interoperability of documents and medicals information systems. Medical and biomedical data play a very important role in the medical field. They solve many of the problems encountered during the diagnosis of diseases and in the prescription medical treatments. The objective of this paper is to develop ontology for the field of medical analyses in an attempt to facilitate the tasks of the various actors in the medical field, namely: doctors, laboratory assistants and patients. It also endeavours to improve the care of patients and facilitate the various acts of health professionals, the purpose of which is to aid in decision-making in the treatment of a disease. To this end, we followed the so-called METHONTOLOGY based on the phases ranging from the needs specification to the evaluation and documentation of this ontology of medical analysis.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121663112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-29DOI: 10.1142/s0219649222500551
V. Ekhosuehi
This study examines the relationship between the research performance and the hierarchical staff-mix by rank (or staff-mix categories) for academics in a research-oriented system. A supervised learning approach is employed to classify academics on the basis of their research performance and the association between this classification and the staff-mix categories is measured using the Somer’s [Formula: see text] coefficient. Although there have been other studies on research performance for such a system based on the volume-based indicators of research performance, this is the first study that assesses the researchers’ position in the academic reward structure on the basis of research performance. The Scopus database is used as a collection of individual productivity in research. A case-study is presented on a cross-section of academics in the mathematics discipline from different federal universities in Nigeria. The results show that there is a dearth of outstanding scientists in the system and that there is a weak association between research performance and the staff-mix categories. The need for scientific collaboration by way of a continuous collegial interaction between the outstanding scientists and the emerging scholars in the system is suggested.
{"title":"Research Performance and Hierarchical Staff-Mix by Rank in a Research-Oriented System: A Case Study","authors":"V. Ekhosuehi","doi":"10.1142/s0219649222500551","DOIUrl":"https://doi.org/10.1142/s0219649222500551","url":null,"abstract":"This study examines the relationship between the research performance and the hierarchical staff-mix by rank (or staff-mix categories) for academics in a research-oriented system. A supervised learning approach is employed to classify academics on the basis of their research performance and the association between this classification and the staff-mix categories is measured using the Somer’s [Formula: see text] coefficient. Although there have been other studies on research performance for such a system based on the volume-based indicators of research performance, this is the first study that assesses the researchers’ position in the academic reward structure on the basis of research performance. The Scopus database is used as a collection of individual productivity in research. A case-study is presented on a cross-section of academics in the mathematics discipline from different federal universities in Nigeria. The results show that there is a dearth of outstanding scientists in the system and that there is a weak association between research performance and the staff-mix categories. The need for scientific collaboration by way of a continuous collegial interaction between the outstanding scientists and the emerging scholars in the system is suggested.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122750419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-29DOI: 10.1142/s0219649222500502
M. Yadav
Customer Relationship Management (CRM) is a systematic way of working with current and prospective customers to manage long-term relationships and interactions between the company and customers. Recently, Big Data has become a buzzword. It consists of huge data repositories, having information collected from online and offline resources, and it is hard to process such datasets with traditional data processing tools and techniques. The presented research work tries to explore the potential of Big Data to create, optimise and transform an insightful customer relationship management system by analysing large amount of datasets for enhancing customer life cycle profitability. In this research work, a dataset, “Book Crossing” is used for Big Data processing and execution time analysis for simple and complex SQL queries. This research tries to analyse the impact of data size on the query execution time for one of the majorly used Big Data frameworks, namely Apache Spark. It is a recently developed in-memory Big Data processing framework with a SPARK SQL module for efficient SQL query execution. It has been found that Apache-Spark gives better results with large size datasets compare to small size datasets and fares better as compared to Hadoop, one of the majorly used Big Data Frameworks (based on qualitative analysis).
{"title":"Query Execution Time Analysis Using Apache Spark Framework for Big Data: A CRM Approach","authors":"M. Yadav","doi":"10.1142/s0219649222500502","DOIUrl":"https://doi.org/10.1142/s0219649222500502","url":null,"abstract":"Customer Relationship Management (CRM) is a systematic way of working with current and prospective customers to manage long-term relationships and interactions between the company and customers. Recently, Big Data has become a buzzword. It consists of huge data repositories, having information collected from online and offline resources, and it is hard to process such datasets with traditional data processing tools and techniques. The presented research work tries to explore the potential of Big Data to create, optimise and transform an insightful customer relationship management system by analysing large amount of datasets for enhancing customer life cycle profitability. In this research work, a dataset, “Book Crossing” is used for Big Data processing and execution time analysis for simple and complex SQL queries. This research tries to analyse the impact of data size on the query execution time for one of the majorly used Big Data frameworks, namely Apache Spark. It is a recently developed in-memory Big Data processing framework with a SPARK SQL module for efficient SQL query execution. It has been found that Apache-Spark gives better results with large size datasets compare to small size datasets and fares better as compared to Hadoop, one of the majorly used Big Data Frameworks (based on qualitative analysis).","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123132266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-23DOI: 10.1142/s021964922250054x
R. Enakrire, Hanlie Smuts
Knowledge retention (KR) is when ideas developed over time in the human brain are retained, for enhanced efficiency and effectiveness of job performance. KR is fundamental in every organisation. KR implies the ways through which the organisations grow, thus resulting in having a competitive advantage other their competitors. Therefore, retaining the individuals that carry diverse expertise in the organisation is important, because it helps to transform the knowledge economy. However, the issues of improper organisation of tasks, loss of experienced employees, the influx of young employees, thus resulting to transfer problem from one department/unit to another, low productivity causing a delay in operational excellence and achievement of timeous job specification, non-viability of the organisation, has made many staff members resign from their present organisation to join other institutions or organisations due to lack of KR. This study investigates KR for enhanced organisational growth in higher education institutions (HEIs). The qualitative research approach made use of the interpretive content analysis. The qualitative survey design made use of an unstructured monkey survey questionnaire in collecting data from respondents across different HEIs in Africa. The purposive and convenient sampling technique selected HEIs across Africa. The rationale behind selecting HEIs across Africa was due to the nature of activities that surrounds KR in transformative organisational growth and the ability to have a quick respondent’s response under the study being investigated. Results indicate that the understanding of KR was not uniform among respondents due to different contexts, fields of expertise, and the nature of work performed. Findings further indicate that KR has helped respondents to create new knowledge, strive to perform tasks in workplace learning, fostered and equipped individuals in their career pursuit, self-development, and deepen research drive. Different mechanism of memorising and keeping short notes, attending different courses, and helping others to solve their problem gives someone the experiences to always remember, and the tools of desktop computers, laptop, tablets, CD-ROM, emails, social media, flash drive, and YouTube are prevalent in support of KR among individuals. Diverse sets of print to electronic sources of information were used to support KR among respondents. Factors such as virus, lack of structures, no specific projects, lack of affirming organisational policy, environmental factors, electricity power supply, and lack of good reading facilities affected the individuals/staff members in their attempt to retain knowledge across sample HEIs. The study recommends attractive income, suitable provision of structure, favourable working environment, self-development opportunities, non-discriminatory treatment to staff, and opened organisational culture, which will enforce staff members to stay and be willing to retain their knowle
{"title":"Knowledge Retention for Enhanced Organisational Growth in Higher Education Institutions","authors":"R. Enakrire, Hanlie Smuts","doi":"10.1142/s021964922250054x","DOIUrl":"https://doi.org/10.1142/s021964922250054x","url":null,"abstract":"Knowledge retention (KR) is when ideas developed over time in the human brain are retained, for enhanced efficiency and effectiveness of job performance. KR is fundamental in every organisation. KR implies the ways through which the organisations grow, thus resulting in having a competitive advantage other their competitors. Therefore, retaining the individuals that carry diverse expertise in the organisation is important, because it helps to transform the knowledge economy. However, the issues of improper organisation of tasks, loss of experienced employees, the influx of young employees, thus resulting to transfer problem from one department/unit to another, low productivity causing a delay in operational excellence and achievement of timeous job specification, non-viability of the organisation, has made many staff members resign from their present organisation to join other institutions or organisations due to lack of KR. This study investigates KR for enhanced organisational growth in higher education institutions (HEIs). The qualitative research approach made use of the interpretive content analysis. The qualitative survey design made use of an unstructured monkey survey questionnaire in collecting data from respondents across different HEIs in Africa. The purposive and convenient sampling technique selected HEIs across Africa. The rationale behind selecting HEIs across Africa was due to the nature of activities that surrounds KR in transformative organisational growth and the ability to have a quick respondent’s response under the study being investigated. Results indicate that the understanding of KR was not uniform among respondents due to different contexts, fields of expertise, and the nature of work performed. Findings further indicate that KR has helped respondents to create new knowledge, strive to perform tasks in workplace learning, fostered and equipped individuals in their career pursuit, self-development, and deepen research drive. Different mechanism of memorising and keeping short notes, attending different courses, and helping others to solve their problem gives someone the experiences to always remember, and the tools of desktop computers, laptop, tablets, CD-ROM, emails, social media, flash drive, and YouTube are prevalent in support of KR among individuals. Diverse sets of print to electronic sources of information were used to support KR among respondents. Factors such as virus, lack of structures, no specific projects, lack of affirming organisational policy, environmental factors, electricity power supply, and lack of good reading facilities affected the individuals/staff members in their attempt to retain knowledge across sample HEIs. The study recommends attractive income, suitable provision of structure, favourable working environment, self-development opportunities, non-discriminatory treatment to staff, and opened organisational culture, which will enforce staff members to stay and be willing to retain their knowle","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121928074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-23DOI: 10.1142/s0219649222500526
Fenglong Wu, Chunxue Wei, Baowei Zhang
In recent years, there exist few difficulties for textile industries to predict the yarn nep index for small data and data with mutation. To fill this gap, a yarn nep prediction method combining grey correlation analysis and nearest-neighbour prediction method is proposed. In this paper, 26 indicators such as the raw cotton quality indicators and key process parameters are used as the input of the prediction model for yarn nep. The experimental results show that the relative error of the new method is lower than 10%, while the relative error of the individual data predicted by the traditional three-layer BP neural network is very large. Compared with the BP neural network, the average relative error and root-mean-square error of our proposed method are smaller, while the data are stable and the volatility is small. The prediction performance meets the user’s requirements. The effectiveness of the proposed model is proved.
{"title":"A Yarn Nep Prediction Method Combining Grey Correlation and Nearest Neighbour","authors":"Fenglong Wu, Chunxue Wei, Baowei Zhang","doi":"10.1142/s0219649222500526","DOIUrl":"https://doi.org/10.1142/s0219649222500526","url":null,"abstract":"In recent years, there exist few difficulties for textile industries to predict the yarn nep index for small data and data with mutation. To fill this gap, a yarn nep prediction method combining grey correlation analysis and nearest-neighbour prediction method is proposed. In this paper, 26 indicators such as the raw cotton quality indicators and key process parameters are used as the input of the prediction model for yarn nep. The experimental results show that the relative error of the new method is lower than 10%, while the relative error of the individual data predicted by the traditional three-layer BP neural network is very large. Compared with the BP neural network, the average relative error and root-mean-square error of our proposed method are smaller, while the data are stable and the volatility is small. The prediction performance meets the user’s requirements. The effectiveness of the proposed model is proved.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115341981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1142/s0219649222500496
V. Chellappa, U. Salve
In the construction industry, safety has always been a persistent issue. The importance of safety knowledge for construction was highlighted by literature and practices. This study aimed to understand safety knowledge management (KM) commitments, strategies, and tools being used in Indian construction organisations. A survey was conducted among safety managers/heads in eight leading Indian construction contractors operating in a global construction market. The results indicated that out of eight companies, safety KM systems were available in seven companies and one was looking to implement it. All the organisations consider safety KM as the strategic assets for their companies and were aware of safety KM’s benefits. Email, Internet, small-group meetings and brainstorming were considered the most important tools to transfer safety knowledge among these organisations. Out of eight, six contracting organisations were aware that costly errors occurred at their companies when safety knowledge was not available when and where it was needed. Hence, safety knowledge sharing culture should be cultivated to enhance the safety performance of contracting companies. The findings may be used to establish standards to facilitate safety KM as an initial point for the government. This study would serve as a foundation for companies to enhance safety performance by improving their safety KM systems.
{"title":"Safety Knowledge Management Practices in Indian Construction Companies","authors":"V. Chellappa, U. Salve","doi":"10.1142/s0219649222500496","DOIUrl":"https://doi.org/10.1142/s0219649222500496","url":null,"abstract":"In the construction industry, safety has always been a persistent issue. The importance of safety knowledge for construction was highlighted by literature and practices. This study aimed to understand safety knowledge management (KM) commitments, strategies, and tools being used in Indian construction organisations. A survey was conducted among safety managers/heads in eight leading Indian construction contractors operating in a global construction market. The results indicated that out of eight companies, safety KM systems were available in seven companies and one was looking to implement it. All the organisations consider safety KM as the strategic assets for their companies and were aware of safety KM’s benefits. Email, Internet, small-group meetings and brainstorming were considered the most important tools to transfer safety knowledge among these organisations. Out of eight, six contracting organisations were aware that costly errors occurred at their companies when safety knowledge was not available when and where it was needed. Hence, safety knowledge sharing culture should be cultivated to enhance the safety performance of contracting companies. The findings may be used to establish standards to facilitate safety KM as an initial point for the government. This study would serve as a foundation for companies to enhance safety performance by improving their safety KM systems.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"977 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116216056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1142/s0219649222500484
A. Sadabadi, S. Ramezani, K. Fartash, Iman Nikijoo
The purpose of this study is to analyse the structure of social network co-occurrence and co-authorship of scientific documents of social innovation which are indexed in Scopus database. By using scientometric and network analysis techniques, the records were retrieved and integrated. It has been used a combination of software packages, including VOSviewer, Gephi, HistCite, Publish or Perish and NodeXL, for data analysis and mapping. Analysing all keywords shows that the most important keywords, based on frequency distribution, are innovation, sustainable growth and social entrepreneurship. Thematic mapping of the keywords using co-words analysis technique indicates that the topics innovation, social services and social change had top ranking in degree centrality, closeness centrality and betweenness indicators. The analysis of the co-authorship network of the field demonstrated that it is disconnected and sparse. Moreover, the total number of citations was 8,350. Mapping the knowledge structure of social innovation papers extracted from Scopus database could help to represent and visualise the thematic structure of research in the field of Social Science and Knowledge Studies and identify more specific research focuses within this field. It should be noted that in this study, the importance of concepts such as innovation, sustainable development and social entrepreneurship has been confirmed by reviewing the literature on these issues.
本研究的目的是分析被Scopus数据库收录的社会创新科学文献的社会网络共现和合著结构。利用科学计量学和网络分析技术，对这些记录进行检索和整合。它已经结合使用软件包，包括VOSviewer, Gephi, HistCite, Publish or Perish和NodeXL，用于数据分析和绘图。对所有关键词的分析表明，基于频率分布，最重要的关键词是创新、可持续增长和社会企业家精神。利用共词分析技术对关键词进行主题映射，结果表明，创新、社会服务和社会变革在度中心性、密切中心性和中间性指标上排名最高。对该领域合著者网络的分析表明，该网络是不连贯的、稀疏的。总引用数为8350次。绘制从Scopus数据库中提取的社会创新论文的知识结构，有助于表示和可视化社会科学和知识研究领域的研究主题结构，并确定该领域更具体的研究重点。值得注意的是，在本研究中，通过回顾有关这些问题的文献，创新、可持续发展和社会企业家精神等概念的重要性得到了证实。
{"title":"Social Innovation: Drawing and Analysis with Using Research in Scientific Base","authors":"A. Sadabadi, S. Ramezani, K. Fartash, Iman Nikijoo","doi":"10.1142/s0219649222500484","DOIUrl":"https://doi.org/10.1142/s0219649222500484","url":null,"abstract":"The purpose of this study is to analyse the structure of social network co-occurrence and co-authorship of scientific documents of social innovation which are indexed in Scopus database. By using scientometric and network analysis techniques, the records were retrieved and integrated. It has been used a combination of software packages, including VOSviewer, Gephi, HistCite, Publish or Perish and NodeXL, for data analysis and mapping. Analysing all keywords shows that the most important keywords, based on frequency distribution, are innovation, sustainable growth and social entrepreneurship. Thematic mapping of the keywords using co-words analysis technique indicates that the topics innovation, social services and social change had top ranking in degree centrality, closeness centrality and betweenness indicators. The analysis of the co-authorship network of the field demonstrated that it is disconnected and sparse. Moreover, the total number of citations was 8,350. Mapping the knowledge structure of social innovation papers extracted from Scopus database could help to represent and visualise the thematic structure of research in the field of Social Science and Knowledge Studies and identify more specific research focuses within this field. It should be noted that in this study, the importance of concepts such as innovation, sustainable development and social entrepreneurship has been confirmed by reviewing the literature on these issues.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121009952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1142/s0219649222500514
Murchhana Tripathy, Santilata Champati, H. K. Bhuyan
Two famous matrix factorization techniques, the Singular Value Decomposition (SVD) and the Nonnegative Matrix Factorization (NMF), are popularly used by recommender system applications. Recommender system data matrices have many missing entries, and to make them suitable for factorization, the missing entries need to be filled. For matrix completion, we use mean, median and mode as three different cases of imputation. The natural clusters produced after factorization are used to formulate simple out-of-sample extension algorithms and methods to generate recommendation for a new user. Two cluster evaluation measures, Normalized Mutual Information (NMI) and Purity are used to evaluate the quality of clusters.
{"title":"Knowledge Discovery in a Recommender System: The Matrix Factorization Approach","authors":"Murchhana Tripathy, Santilata Champati, H. K. Bhuyan","doi":"10.1142/s0219649222500514","DOIUrl":"https://doi.org/10.1142/s0219649222500514","url":null,"abstract":"Two famous matrix factorization techniques, the Singular Value Decomposition (SVD) and the Nonnegative Matrix Factorization (NMF), are popularly used by recommender system applications. Recommender system data matrices have many missing entries, and to make them suitable for factorization, the missing entries need to be filled. For matrix completion, we use mean, median and mode as three different cases of imputation. The natural clusters produced after factorization are used to formulate simple out-of-sample extension algorithms and methods to generate recommendation for a new user. Two cluster evaluation measures, Normalized Mutual Information (NMI) and Purity are used to evaluate the quality of clusters.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130980558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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