Pub Date : 2023-06-01DOI: 10.1142/s0219649223500351
Nguyen Ngoc-Tan
This paper aims at increasing awareness of knowledge management (KM), its challenges as well as benefits in Higher Education (HE) system of Vietnam. An exploratory qualitative research design was deployed using semi-structured interviews. Nine senior institutional leaders from nine Vietnamese universities participated in the study. Thematic analysis, informed by the literature, was undertaken on English translated transcripts of the interviews. The findings shared senior HE leaders’ perspectives on how KM in higher education institutions (HEI) of Vietnam was being conceptualised and operationalised, as well as insights into how KM associates with six dimensions of HEIs’ performance so as to gear up KM’s benefits and anticipate challenges when a HEI embarking on the KM journey. Further research of different methods on the topic to enlighten the role of KM in HE system of Vietnam, and beyond, is recommended. The role and importance of KM is wildly recognised in business communities. However, studies exploring KM application in HE system remain scarce especially HE of developing countries. In Vietnam, no qualitative studies of KM in HEIs have been located. Vietnam is a nation on its way to transform from a state-based to market-driven economy; and a comprehensive education reform is deemed to shoulder the key task. KM deployment in the whole HE system is essential to comprehensive education reform and then to the global integration of its HE system. Besides, the study helps enrich the literature of KM in HE sector and provide insights of KM to campus chiefs, KM officers, administrator in HEIs.
{"title":"Knowledge Management in Higher Education in Vietnam: Insights from Higher Education Leaders - An Exploratory Study","authors":"Nguyen Ngoc-Tan","doi":"10.1142/s0219649223500351","DOIUrl":"https://doi.org/10.1142/s0219649223500351","url":null,"abstract":"This paper aims at increasing awareness of knowledge management (KM), its challenges as well as benefits in Higher Education (HE) system of Vietnam. An exploratory qualitative research design was deployed using semi-structured interviews. Nine senior institutional leaders from nine Vietnamese universities participated in the study. Thematic analysis, informed by the literature, was undertaken on English translated transcripts of the interviews. The findings shared senior HE leaders’ perspectives on how KM in higher education institutions (HEI) of Vietnam was being conceptualised and operationalised, as well as insights into how KM associates with six dimensions of HEIs’ performance so as to gear up KM’s benefits and anticipate challenges when a HEI embarking on the KM journey. Further research of different methods on the topic to enlighten the role of KM in HE system of Vietnam, and beyond, is recommended. The role and importance of KM is wildly recognised in business communities. However, studies exploring KM application in HE system remain scarce especially HE of developing countries. In Vietnam, no qualitative studies of KM in HEIs have been located. Vietnam is a nation on its way to transform from a state-based to market-driven economy; and a comprehensive education reform is deemed to shoulder the key task. KM deployment in the whole HE system is essential to comprehensive education reform and then to the global integration of its HE system. Besides, the study helps enrich the literature of KM in HE sector and provide insights of KM to campus chiefs, KM officers, administrator in HEIs.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121448257","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 : 2023-03-24DOI: 10.1142/s0219649223500181
F. Koster
Design: The paper relies on a quantitative analysis of 707 Dutch companies. Purpose: Prior research focussed on the positive relationship between organisation’s size and its innovation performance. This study investigates the role of human resource development mechanisms, in particular organisational learning and renewal of human resource management practices in this relationship. Findings: The analysis of survey data revealed that, taking into account several background variables, smaller organisations have lower innovation performance than larger ones. The differences between organisations disappear after organisational learning practices and renewal of human resource management are taken into account. These two human resource development mechanisms are, in turn, positively related to innovation performance. Originality: Prior research focussed on the direct relationship between organisations’ size and innovation performance. This paper examines this relationship more closely by focussing on intermediating mechanisms.
{"title":"The Organisation's Size-Innovation Performance Relationship: The Role of Human Resource Development Mechanisms","authors":"F. Koster","doi":"10.1142/s0219649223500181","DOIUrl":"https://doi.org/10.1142/s0219649223500181","url":null,"abstract":"Design: The paper relies on a quantitative analysis of 707 Dutch companies. Purpose: Prior research focussed on the positive relationship between organisation’s size and its innovation performance. This study investigates the role of human resource development mechanisms, in particular organisational learning and renewal of human resource management practices in this relationship. Findings: The analysis of survey data revealed that, taking into account several background variables, smaller organisations have lower innovation performance than larger ones. The differences between organisations disappear after organisational learning practices and renewal of human resource management are taken into account. These two human resource development mechanisms are, in turn, positively related to innovation performance. Originality: Prior research focussed on the direct relationship between organisations’ size and innovation performance. This paper examines this relationship more closely by focussing on intermediating mechanisms.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"10 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128901352","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 : 2023-03-18DOI: 10.1142/s021964922350003x
Archana Nagelli, B. Saleena
The sentiment data provides vital information about the feedback of the user’s opinion, attitude and emotions. The business of product development and digital marketing teams entirely depends upon the outcome of these sentiments and they apply various Data Mining techniques, Machine Learning and Deep Learning approaches to analyse the depth of the dataset. The Sentiment Analysis provides the automatic data mining of reviews, comments, opinions and suggestions, received from various input methods, including text, audio notes, images and emoticons, through Natural Language Processing. The analysis assists in the classification of reviewer feedback in terms of positive, negative and neutral categories. In this study, the opinions shared by individuals over various social networking sites in the case of any big event, the release of any new product or show and political events were analysed. Machine Learning and Deep Learning techniques are discussed and used dominantly to illustrate the outcome of opinions and events. The accurate analysis of vast information shared by individuals free of cost and without any influence can provide vital information for organisations and management authorities. This review analyses various techniques in the field of Aspect-Based Sentiment Analysis along with their features and research scopes and thus, it helps researchers to focus on more precise works in the future. Among the machine learning algorithms, Random Forest performed much better as compared to other methods, and among the Deep Learning approaches, Multichannel CNN outperformed with the highest accuracy of 96.23%. The paper includes the comparative study of multiple Machine Learning and Deep Learning techniques for the evaluation of sentiment data and concludes with the challenges and scope of Sentiment Analysis.
情绪数据提供了关于用户意见、态度和情绪反馈的重要信息。产品开发和数字营销团队的业务完全取决于这些情绪的结果，他们应用各种数据挖掘技术、机器学习和深度学习方法来分析数据集的深度。情感分析通过自然语言处理(Natural Language Processing)，对各种输入方法(包括文本、音频注释、图像和表情符号)收到的评论、评论、意见和建议进行自动数据挖掘。该分析有助于将审稿人的反馈分为积极、消极和中立三类。在这项研究中，分析了个人在各种社交网站上对任何重大事件、任何新产品或节目的发布以及政治事件的看法。讨论了机器学习和深度学习技术，并主要使用它们来说明观点和事件的结果。对个人免费共享的大量信息进行准确分析，不受任何影响，可以为组织和管理当局提供重要信息。本文分析了基于方面的情感分析领域的各种技术及其特点和研究范围，从而帮助研究人员在未来关注更精确的工作。在机器学习算法中，Random Forest的表现比其他方法要好得多，而在Deep learning方法中，Multichannel CNN的准确率最高，达到96.23%。本文包括对情感数据评估的多种机器学习和深度学习技术的比较研究，并总结了情感分析的挑战和范围。
{"title":"A Comparative Review of Sentimental Analysis Using Machine Learning and Deep Learning Approaches","authors":"Archana Nagelli, B. Saleena","doi":"10.1142/s021964922350003x","DOIUrl":"https://doi.org/10.1142/s021964922350003x","url":null,"abstract":"The sentiment data provides vital information about the feedback of the user’s opinion, attitude and emotions. The business of product development and digital marketing teams entirely depends upon the outcome of these sentiments and they apply various Data Mining techniques, Machine Learning and Deep Learning approaches to analyse the depth of the dataset. The Sentiment Analysis provides the automatic data mining of reviews, comments, opinions and suggestions, received from various input methods, including text, audio notes, images and emoticons, through Natural Language Processing. The analysis assists in the classification of reviewer feedback in terms of positive, negative and neutral categories. In this study, the opinions shared by individuals over various social networking sites in the case of any big event, the release of any new product or show and political events were analysed. Machine Learning and Deep Learning techniques are discussed and used dominantly to illustrate the outcome of opinions and events. The accurate analysis of vast information shared by individuals free of cost and without any influence can provide vital information for organisations and management authorities. This review analyses various techniques in the field of Aspect-Based Sentiment Analysis along with their features and research scopes and thus, it helps researchers to focus on more precise works in the future. Among the machine learning algorithms, Random Forest performed much better as compared to other methods, and among the Deep Learning approaches, Multichannel CNN outperformed with the highest accuracy of 96.23%. The paper includes the comparative study of multiple Machine Learning and Deep Learning techniques for the evaluation of sentiment data and concludes with the challenges and scope of Sentiment Analysis.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114279211","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 : 2023-03-10DOI: 10.1142/s0219649223500089
P. C. Padhy, Remya Lathabhavan
This study investigates the role of Knowledge Management (KM) in integrating corporate sustainability practices in the post-pandemic context. It also examines the current literature on KM and sustainable development and develops a sustainable conceptual model. Based on a survey of contemporary literature and KM and corporate sustainability approach, this study proposes a conceptual framework with KM and corporate sustainability strategy as fundamental constructs to attain organisational excellence (OE) in the post-pandemic era. The research adds conceptual and situational elements such as the interaction between KM and sustainability strategy, creative approaches for developing a structural framework, and the right direction for boosting efficiency. The research is one of the first to present a comprehensive framework for achieving OE in the post-pandemic era. Furthermore, by focussing on COVID-19 and the post-pandemic environment, this research provides a new perspective on KM and corporate sustainability literature.
{"title":"Redesigning Knowledge Management Through Corporate Sustainability Strategy in the Post-Pandemic Era","authors":"P. C. Padhy, Remya Lathabhavan","doi":"10.1142/s0219649223500089","DOIUrl":"https://doi.org/10.1142/s0219649223500089","url":null,"abstract":"This study investigates the role of Knowledge Management (KM) in integrating corporate sustainability practices in the post-pandemic context. It also examines the current literature on KM and sustainable development and develops a sustainable conceptual model. Based on a survey of contemporary literature and KM and corporate sustainability approach, this study proposes a conceptual framework with KM and corporate sustainability strategy as fundamental constructs to attain organisational excellence (OE) in the post-pandemic era. The research adds conceptual and situational elements such as the interaction between KM and sustainability strategy, creative approaches for developing a structural framework, and the right direction for boosting efficiency. The research is one of the first to present a comprehensive framework for achieving OE in the post-pandemic era. Furthermore, by focussing on COVID-19 and the post-pandemic environment, this research provides a new perspective on KM and corporate sustainability literature.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133163001","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 : 2023-03-10DOI: 10.1142/s0219649223500077
Peng Jiang
With the rapid development of China’s economic development, the demand for technical talents in all walks of life is becoming more and more urgent. Therefore, the research on the intelligent method of vocational education information is becoming more and more important. In this research, the cross-attention fusion module and attention mechanism are introduced into the knowledge map recommendation algorithm to build an algorithm model. The attention mechanism is used to give corresponding attention to each neighbour node of the head node in the knowledge map, and a weight matrix is established to represent different importances of the additional information contained by each neighbour node, which further improves the interpretability of the recommendation. Finally, the model is analysed experimentally. The results show that CAF is superior to other algorithms in Recall and NDCG, which further verifies that attention mechanism plays a significant role in communication. It can be seen that CAF optimisation model is superior to other algorithms in many tests, and is superior to a similar algorithm MKR, which further verifies the effectiveness and superiority of cross-attention fusion module. The CAF model can still maintain its stability in the case of sparse data.
{"title":"Vocational Education Information Technology Based on Cross-Attention Fusion Knowledge Map Recommendation Algorithm","authors":"Peng Jiang","doi":"10.1142/s0219649223500077","DOIUrl":"https://doi.org/10.1142/s0219649223500077","url":null,"abstract":"With the rapid development of China’s economic development, the demand for technical talents in all walks of life is becoming more and more urgent. Therefore, the research on the intelligent method of vocational education information is becoming more and more important. In this research, the cross-attention fusion module and attention mechanism are introduced into the knowledge map recommendation algorithm to build an algorithm model. The attention mechanism is used to give corresponding attention to each neighbour node of the head node in the knowledge map, and a weight matrix is established to represent different importances of the additional information contained by each neighbour node, which further improves the interpretability of the recommendation. Finally, the model is analysed experimentally. The results show that CAF is superior to other algorithms in Recall and NDCG, which further verifies that attention mechanism plays a significant role in communication. It can be seen that CAF optimisation model is superior to other algorithms in many tests, and is superior to a similar algorithm MKR, which further verifies the effectiveness and superiority of cross-attention fusion module. The CAF model can still maintain its stability in the case of sparse data.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124820363","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 : 2023-03-03DOI: 10.1142/s0219649223500016
Bassam Hasan
Computer self-efficacy (CSE) is well recognised as a significant and reliable determinant of enterprise resource planning (ERP) adoption and utilisation. However, CSE is a multifaceted concept that can be applied at a general computing level or an application-specific level, most past studies of CSE in ERP settings failed to make this distinction or examine CSE towards ERP systems and focused predominantly on CSE as a general computing construct. Furthermore, past research has focused on investigating the consequences of CSE in ERP-related behaviours and little or no attention has been given to exploring factors that can influence CSE at the ERP level. This study attempts to address these two issues and fill this void in the literature. First, this study seeks to address and examine CSE at the ERP system level. Second, this study will focus on investigating external factors affecting ERP self-efficacy beliefs. The external factors examined in this study are the ERP system characteristics of user interface, complexity, and learnability on. The results provide strong support for the effects of ERP user interface, complexity, and learnability on ERP self-efficacy beliefs. ERP user interface also demonstrated a significant impact on perceived complexity and learnability of ERP systems. Several practical and research contributions can be drawn from the findings reported in this study.
{"title":"An Empirical Investigation of ERP System Self-Efficacy Beliefs: Examining the Effects of ERP System Characteristics","authors":"Bassam Hasan","doi":"10.1142/s0219649223500016","DOIUrl":"https://doi.org/10.1142/s0219649223500016","url":null,"abstract":"Computer self-efficacy (CSE) is well recognised as a significant and reliable determinant of enterprise resource planning (ERP) adoption and utilisation. However, CSE is a multifaceted concept that can be applied at a general computing level or an application-specific level, most past studies of CSE in ERP settings failed to make this distinction or examine CSE towards ERP systems and focused predominantly on CSE as a general computing construct. Furthermore, past research has focused on investigating the consequences of CSE in ERP-related behaviours and little or no attention has been given to exploring factors that can influence CSE at the ERP level. This study attempts to address these two issues and fill this void in the literature. First, this study seeks to address and examine CSE at the ERP system level. Second, this study will focus on investigating external factors affecting ERP self-efficacy beliefs. The external factors examined in this study are the ERP system characteristics of user interface, complexity, and learnability on. The results provide strong support for the effects of ERP user interface, complexity, and learnability on ERP self-efficacy beliefs. ERP user interface also demonstrated a significant impact on perceived complexity and learnability of ERP systems. Several practical and research contributions can be drawn from the findings reported in this study.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130986485","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 : 2023-02-23DOI: 10.1142/s0219649223500028
M. Haque, Aimin Qian, Suraiea Akter Lucky
Online social networks (OSNs) are a terrifically emerging platform for information dissemination around the world. Like other settings, acceptance and adoption of OSNs among the individual capital market investors are extensive. The study developed a conceptual model for behavioural finance integrating a technology acceptance model (TAM) and valence framework from the information systems and marketing disciplines, respectively. The integrated model added some persuasive constructs from social capital and diffusion innovation theory with a view to explore the key factors swaying investors’ intention to adopt and use the OSN’s services. By using an online and offline structured questionnaire, 510 data were collected from individual capital market investors in Bangladesh. Structural Equation Modelling (SEM) was used for data analysis. The study determined that the proposed integrated model with additional constructs outperformed other models. Perceived usefulness (PU), perceived enjoyment (PE), trust and personal innovativeness in IT (PIIT) had a substantial sway on the investor’s intention to use OSNs. Hedonic value is more robust predictor of intention to use OSNs than utilitarian value. Intention to use properly mediated the relationships and had strong significant impact on investor’s investment decision. But perceived ease of use (PEOU) and perceived risk had no direct significant effect on intention to use. PEOU had significant impact on intention to use through PU and PE. Gender moderated the relationships of different constructs with the intention to use OSNs for investment decisions in the capital market. It contributes knowledge by including the integration of different models in stock market perspectives and the inclusion of technological aspect in the behavioural finance literature. The findings of the study will also succor different firms and regulatory authorities to adopt OSNs as an information dissemination platform.
{"title":"Exploring the Factors of Online Social Networks (OSNs) on Individual Investors' Capital Market Investment Decision: An Integrated Approach","authors":"M. Haque, Aimin Qian, Suraiea Akter Lucky","doi":"10.1142/s0219649223500028","DOIUrl":"https://doi.org/10.1142/s0219649223500028","url":null,"abstract":"Online social networks (OSNs) are a terrifically emerging platform for information dissemination around the world. Like other settings, acceptance and adoption of OSNs among the individual capital market investors are extensive. The study developed a conceptual model for behavioural finance integrating a technology acceptance model (TAM) and valence framework from the information systems and marketing disciplines, respectively. The integrated model added some persuasive constructs from social capital and diffusion innovation theory with a view to explore the key factors swaying investors’ intention to adopt and use the OSN’s services. By using an online and offline structured questionnaire, 510 data were collected from individual capital market investors in Bangladesh. Structural Equation Modelling (SEM) was used for data analysis. The study determined that the proposed integrated model with additional constructs outperformed other models. Perceived usefulness (PU), perceived enjoyment (PE), trust and personal innovativeness in IT (PIIT) had a substantial sway on the investor’s intention to use OSNs. Hedonic value is more robust predictor of intention to use OSNs than utilitarian value. Intention to use properly mediated the relationships and had strong significant impact on investor’s investment decision. But perceived ease of use (PEOU) and perceived risk had no direct significant effect on intention to use. PEOU had significant impact on intention to use through PU and PE. Gender moderated the relationships of different constructs with the intention to use OSNs for investment decisions in the capital market. It contributes knowledge by including the integration of different models in stock market perspectives and the inclusion of technological aspect in the behavioural finance literature. The findings of the study will also succor different firms and regulatory authorities to adopt OSNs as an information dissemination platform.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128629531","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 : 2023-02-18DOI: 10.1142/s0219649222500976
Chenyang Dai, Bo Shen, Fengxiao Yan
Target-oriented opinion word extraction and aspect-level sentiment classification are two highly relevant tasks in aspect-based sentiment analysis. Previous studies tend to separate them and focus on one of the tasks, which ignore the connection between opinion word extraction and sentiment classification, and result in the waste of useful connection information. In this paper, we propose a co-extraction model, in which the two tasks are formulated as a sequence labeling problem. The model involves two stacked Bi-LSTM modules and an information interaction component to generate all opinion-polarity pairs of the input sentences simultaneously. The experimental results show that our model achieves advanced results in target opinion word-polarity co-extraction. The performance of both tasks is stronger than the baseline, and the model is of low complexity and high operational efficiency.
{"title":"A Joint Model for Target-Oriented Opinion Words Extraction and Sentiment Classification","authors":"Chenyang Dai, Bo Shen, Fengxiao Yan","doi":"10.1142/s0219649222500976","DOIUrl":"https://doi.org/10.1142/s0219649222500976","url":null,"abstract":"Target-oriented opinion word extraction and aspect-level sentiment classification are two highly relevant tasks in aspect-based sentiment analysis. Previous studies tend to separate them and focus on one of the tasks, which ignore the connection between opinion word extraction and sentiment classification, and result in the waste of useful connection information. In this paper, we propose a co-extraction model, in which the two tasks are formulated as a sequence labeling problem. The model involves two stacked Bi-LSTM modules and an information interaction component to generate all opinion-polarity pairs of the input sentences simultaneously. The experimental results show that our model achieves advanced results in target opinion word-polarity co-extraction. The performance of both tasks is stronger than the baseline, and the model is of low complexity and high operational efficiency.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129752954","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 : 2023-02-15DOI: 10.1142/s0219649223500041
Xinyan Yu
In order to solve the problems of poor channel balance control ability and unable to effectively reduce the output bit error rate in the traditional Internet of things link load balance control methods, a new Internet of things (IoT) link load balance control algorithm based on non-parametric regression model is proposed in this paper. The transmission model of IoT link channel is constructed, and the sparse random cluster analysis method is used to extract the load characteristics of IoT link. According to the load feature extraction results, through the estimated regression function of known data features, a non-parametric regression model is constructed, and the fuzzy cyclic iterative control is used to realize the load balancing control of the Internet of things link. The experimental results show that this method has strong channel balance control ability, low output bit error rate, the maximum average link utilisation can reach 1, and the maximum output bit error rate is only 102, which improves the stability of the Internet of things.
{"title":"Load Balancing Control Algorithm of Internet of Things Link Based on Non-Parametric Regression Model","authors":"Xinyan Yu","doi":"10.1142/s0219649223500041","DOIUrl":"https://doi.org/10.1142/s0219649223500041","url":null,"abstract":"In order to solve the problems of poor channel balance control ability and unable to effectively reduce the output bit error rate in the traditional Internet of things link load balance control methods, a new Internet of things (IoT) link load balance control algorithm based on non-parametric regression model is proposed in this paper. The transmission model of IoT link channel is constructed, and the sparse random cluster analysis method is used to extract the load characteristics of IoT link. According to the load feature extraction results, through the estimated regression function of known data features, a non-parametric regression model is constructed, and the fuzzy cyclic iterative control is used to realize the load balancing control of the Internet of things link. The experimental results show that this method has strong channel balance control ability, low output bit error rate, the maximum average link utilisation can reach 1, and the maximum output bit error rate is only 102, which improves the stability of the Internet of things.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116750874","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 : 2023-02-08DOI: 10.1142/s021964922250099x
Esra’a Alshabeeb, M. Aljabri, R. Mohammad, Fatemah S. Alqarqoosh, Aseel A. Alqahtani, Zainab T. Alibrahim, Najd Y. Alawad, Mashael A. Alzeer
The stock market is an exciting field of interest to many people regardless of their occupational background. It is a market where individuals with adequate knowledge can join and earn an additional income. Nowadays, life expenses have increased. Hence, the number of people investing in stocks is increasing dramatically. Anyone may indeed start participating in the stock market at any time, yet it is not ensured that they will profit from this investment. The stock market is a risky field of investment, given that it is unknown whether the stock will rise or fall. Stock market prediction using Artificial Intelligence techniques is a possible way to help people anticipate stock market directions. Current research showed that many factors aid in changing the stock market value in general and specifically in the Saudi stock market. To our knowledge, most research studies only consider historical data in predicting stock market trends. However, this research aims to enhance the accuracy of the daily closing price for three Saudi stock market sectors by considering historical and sentimental data. Several intelligent algorithms are considered, and their performance indicators are discussed and contrasted against each other. This research concluded that more accurate stock market prediction models could be produced by employing historical and sentimental data.
{"title":"Intelligent Techniques for Predicting Stock Market Prices: A Critical Survey","authors":"Esra’a Alshabeeb, M. Aljabri, R. Mohammad, Fatemah S. Alqarqoosh, Aseel A. Alqahtani, Zainab T. Alibrahim, Najd Y. Alawad, Mashael A. Alzeer","doi":"10.1142/s021964922250099x","DOIUrl":"https://doi.org/10.1142/s021964922250099x","url":null,"abstract":"The stock market is an exciting field of interest to many people regardless of their occupational background. It is a market where individuals with adequate knowledge can join and earn an additional income. Nowadays, life expenses have increased. Hence, the number of people investing in stocks is increasing dramatically. Anyone may indeed start participating in the stock market at any time, yet it is not ensured that they will profit from this investment. The stock market is a risky field of investment, given that it is unknown whether the stock will rise or fall. Stock market prediction using Artificial Intelligence techniques is a possible way to help people anticipate stock market directions. Current research showed that many factors aid in changing the stock market value in general and specifically in the Saudi stock market. To our knowledge, most research studies only consider historical data in predicting stock market trends. However, this research aims to enhance the accuracy of the daily closing price for three Saudi stock market sectors by considering historical and sentimental data. Several intelligent algorithms are considered, and their performance indicators are discussed and contrasted against each other. This research concluded that more accurate stock market prediction models could be produced by employing historical and sentimental data.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419990","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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