Pub Date : 2022-05-21DOI: 10.1142/s0219649222400111
Weixia Han, Sun Li
The traditional resource sharing method allocates resources according to their importance and node weight ranking, which leads to uneven distribution of node loads and resource allocation with less efficiency and high energy consumption. In order to solve the above problems, the method of resource allocation of college ideological and political education based on Internet of Things (IoT) technology is studied. The IoT technology is used for establishing the communication Internet of Things or for sharing college ideological and political education resources, and the MCTS algorithm is used to search for college ideological and political education resources. For the case of education resources in colleges and universities, according to the simple semantic reasoning for establishing the mapping relationship between education resources and distribution of nodes, we realise the allocation of resources for education. The test experiment results show that the researched resource allocation method has low allocation delay and reduces at least about 23.7% of energy consumption, which is more effective.
{"title":"The Method of Allocating Resources for Ideological and Political Education in Universities Based on IoT Technology","authors":"Weixia Han, Sun Li","doi":"10.1142/s0219649222400111","DOIUrl":"https://doi.org/10.1142/s0219649222400111","url":null,"abstract":"The traditional resource sharing method allocates resources according to their importance and node weight ranking, which leads to uneven distribution of node loads and resource allocation with less efficiency and high energy consumption. In order to solve the above problems, the method of resource allocation of college ideological and political education based on Internet of Things (IoT) technology is studied. The IoT technology is used for establishing the communication Internet of Things or for sharing college ideological and political education resources, and the MCTS algorithm is used to search for college ideological and political education resources. For the case of education resources in colleges and universities, according to the simple semantic reasoning for establishing the mapping relationship between education resources and distribution of nodes, we realise the allocation of resources for education. The test experiment results show that the researched resource allocation method has low allocation delay and reduces at least about 23.7% of energy consumption, which is more effective.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128332354","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 : 2022-05-21DOI: 10.1142/s0219649222400214
Min Xu, Dongyue Liu, Yan Zhang
Nowadays, with the continuous change and innovation of teaching methods in Colleges and universities, the curriculum system of students is also constantly enriched and developed. Therefore, people’s requirements for teaching management and teaching system are also improving. Physical education curriculum is usually based on outdoor teaching, and some schools have not established a complete teaching system. Therefore, the interactive teaching system of physical training based on artificial intelligence is designed. First of all, through the construction of the interactive teaching system of the total control circuit, determine the corresponding circuit address decoding, improve the audio control circuit, associated video connection interactive drive three parts, the intelligent sports training interactive system hardware design. Then, through the creation of intelligent training function module, the design of training database and the realisation of effective training and teaching of intelligent sports, the software design of intelligent sports training interactive system is carried out. Finally, through the test of the system, to verify the corresponding effect, further improve the relevant system, make it more safe and accurate, improve the efficiency of sports training interactive system, enhance the integrity of the teaching process.
{"title":"Design of Interactive Teaching System of Physical Training Based on Artificial Intelligence","authors":"Min Xu, Dongyue Liu, Yan Zhang","doi":"10.1142/s0219649222400214","DOIUrl":"https://doi.org/10.1142/s0219649222400214","url":null,"abstract":"Nowadays, with the continuous change and innovation of teaching methods in Colleges and universities, the curriculum system of students is also constantly enriched and developed. Therefore, people’s requirements for teaching management and teaching system are also improving. Physical education curriculum is usually based on outdoor teaching, and some schools have not established a complete teaching system. Therefore, the interactive teaching system of physical training based on artificial intelligence is designed. First of all, through the construction of the interactive teaching system of the total control circuit, determine the corresponding circuit address decoding, improve the audio control circuit, associated video connection interactive drive three parts, the intelligent sports training interactive system hardware design. Then, through the creation of intelligent training function module, the design of training database and the realisation of effective training and teaching of intelligent sports, the software design of intelligent sports training interactive system is carried out. Finally, through the test of the system, to verify the corresponding effect, further improve the relevant system, make it more safe and accurate, improve the efficiency of sports training interactive system, enhance the integrity of the teaching process.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129526143","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 : 2022-05-21DOI: 10.1142/s0219649222400196
Yan Chengcheng
Traditional database query optimisation methods use stochastic algorithms to approximate the query optimisation results by continuously adjusting the optimisation plan. Since the stochastic algorithm only performs query optimisation from a single perspective, it leads to no significant improvement of the optimised database query efficiency. To address the above problems, we studied the query optimisation method of foreign enterprises’ German language data database based on hybrid learning. By reducing the database query search space and selecting query optimisation strategy, the data query complexity is reduced. After estimating the cost of database query optimisation, the policy selection algorithm is trained using the hybrid learning theory to obtain the database query optimisation path. The simulation experimental results show that the average query response of the optimised database after applying the studied method saves about 13.6%, and the query cost is lower and the optimisation effect is better.
{"title":"Optimisation of German Language Database Query for Foreign Companies Based on Hybrid Learning","authors":"Yan Chengcheng","doi":"10.1142/s0219649222400196","DOIUrl":"https://doi.org/10.1142/s0219649222400196","url":null,"abstract":"Traditional database query optimisation methods use stochastic algorithms to approximate the query optimisation results by continuously adjusting the optimisation plan. Since the stochastic algorithm only performs query optimisation from a single perspective, it leads to no significant improvement of the optimised database query efficiency. To address the above problems, we studied the query optimisation method of foreign enterprises’ German language data database based on hybrid learning. By reducing the database query search space and selecting query optimisation strategy, the data query complexity is reduced. After estimating the cost of database query optimisation, the policy selection algorithm is trained using the hybrid learning theory to obtain the database query optimisation path. The simulation experimental results show that the average query response of the optimised database after applying the studied method saves about 13.6%, and the query cost is lower and the optimisation effect is better.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130353014","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 : 2022-05-19DOI: 10.1142/s0219649222500423
Aftab Alam Abdussami, Mohammed Faizan Farooqui
Fog computing acts as an intermediate component to reduce the delays in communication among end-users and the cloud that offer local processing of requests among end-users through fog devices. Thus, the primary aim of fog devices is to ensure the authenticity of incoming network traffic. Anyhow, these fog devices are susceptible to malicious attacks. An efficient Intrusion Detection System (IDS) or Intrusion Prevention System (IPS) is necessary to offer secure functioning of fog for improving efficiency. IDSs are a fundamental component for any security system like the Internet of things (IoT) and fog networks for ensuring the Quality of Service (QoS). Even though different machine learning and deep learning models have shown their efficiency in intrusion detection, the deep insight of managing the incremental data is a complex part. Therefore, the main intent of this paper is to implement an effective model for intrusion detection in a fog computing platform. Initially, the data dealing with intrusion are collected from diverse benchmark sources. Further, data cleaning is performed, which is to identify and remove errors and duplicate data, to create a reliable dataset. This improves the quality of the training data for analytics and enables accurate decision making. The conceptual and temporal features are extracted. Concerning reducing the data length for reducing the training complexity, optimal feature selection is performed based on an improved meta-heuristic concept termed Modified Active Electrolocation-based Electric Fish Optimization (MAE-EFO). With the optimally selected features or data, incremental learning-based detection is accomplished by Incremental Deep Neural Network (I-DNN). This deep learning model optimises the testing weight using the proposed MAE-EFO by concerning the objective as to minimise the error difference between the predicted and actual results, thus enhancing the performance of new incremental data. The validation of the proposed model on the benchmark datasets and other datasets achieves an attractive performance when compared over other state-of-the-art IDSs.
雾计算充当中间组件，减少终端用户和云之间的通信延迟，云通过雾设备为终端用户之间的请求提供本地处理。因此，雾设备的主要目的是确保传入网络流量的真实性。无论如何，这些雾装置很容易受到恶意攻击。为了提高效率，需要一个高效的入侵检测系统(IDS)或入侵防御系统(IPS)来提供安全的雾功能。ids是任何安全系统(如物联网(IoT)和雾网络)的基本组件，用于确保服务质量(QoS)。尽管不同的机器学习和深度学习模型在入侵检测中已经显示出它们的效率，但管理增量数据的深度洞察是一个复杂的部分。因此，本文的主要目的是在雾计算平台中实现一种有效的入侵检测模型。最初，处理入侵的数据是从不同的基准源收集的。此外，还执行数据清理，以识别和删除错误和重复数据，从而创建可靠的数据集。这提高了用于分析的训练数据的质量，并实现了准确的决策。提取概念特征和时间特征。在减少数据长度以降低训练复杂性方面，基于改进的元启发式概念进行了最优特征选择，称为改进的主动电定位电鱼优化(MAE-EFO)。增量深度神经网络(incremental Deep Neural Network, I-DNN)利用最优选择的特征或数据，实现基于学习的增量检测。该深度学习模型使用所提出的MAE-EFO优化测试权重，其目标是最小化预测结果与实际结果之间的误差差异，从而提高新增量数据的性能。与其他最先进的ids相比，在基准数据集和其他数据集上对所提出的模型进行了验证，获得了具有吸引力的性能。
{"title":"Optimal Feature Selection with Weight Optimised Deep Neural Network for Incremental Learning-Based Intrusion Detection in Fog Environment","authors":"Aftab Alam Abdussami, Mohammed Faizan Farooqui","doi":"10.1142/s0219649222500423","DOIUrl":"https://doi.org/10.1142/s0219649222500423","url":null,"abstract":"Fog computing acts as an intermediate component to reduce the delays in communication among end-users and the cloud that offer local processing of requests among end-users through fog devices. Thus, the primary aim of fog devices is to ensure the authenticity of incoming network traffic. Anyhow, these fog devices are susceptible to malicious attacks. An efficient Intrusion Detection System (IDS) or Intrusion Prevention System (IPS) is necessary to offer secure functioning of fog for improving efficiency. IDSs are a fundamental component for any security system like the Internet of things (IoT) and fog networks for ensuring the Quality of Service (QoS). Even though different machine learning and deep learning models have shown their efficiency in intrusion detection, the deep insight of managing the incremental data is a complex part. Therefore, the main intent of this paper is to implement an effective model for intrusion detection in a fog computing platform. Initially, the data dealing with intrusion are collected from diverse benchmark sources. Further, data cleaning is performed, which is to identify and remove errors and duplicate data, to create a reliable dataset. This improves the quality of the training data for analytics and enables accurate decision making. The conceptual and temporal features are extracted. Concerning reducing the data length for reducing the training complexity, optimal feature selection is performed based on an improved meta-heuristic concept termed Modified Active Electrolocation-based Electric Fish Optimization (MAE-EFO). With the optimally selected features or data, incremental learning-based detection is accomplished by Incremental Deep Neural Network (I-DNN). This deep learning model optimises the testing weight using the proposed MAE-EFO by concerning the objective as to minimise the error difference between the predicted and actual results, thus enhancing the performance of new incremental data. The validation of the proposed model on the benchmark datasets and other datasets achieves an attractive performance when compared over other state-of-the-art IDSs.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"63 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114040053","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 : 2022-05-18DOI: 10.1142/s0219649222400287
Yanqing Liu, Qiaoli Quan
At present, there is a lack of careful consideration in the judgment process of pronunciation errors in many English speeches. These pronunciation errors will create a great impact on personalized learning. The process of creating a data set for errors is also not an easy work. On considering the above obstacle, an artificial intelligent recognition method of pronunciation errors in English speeches for personalized learning along with big data is proposed. This method takes the average pronunciation level of standard speech as the basis of pronunciation error judgment, and judges the pronunciation and application of words such as speed, pronunciation, semantics, etc. In the Hidden Markov Model (HMM) modelling method of speech recognition, Viterbi algorithm and improved posterior probability algorithm are implemented to recognize student’s vocalization instinctively. Through the segmentation and scoring of basic units, English learners are provided with reliable pronunciation information feedback, correct pronunciation errors and give corresponding feedback according to the judgment results. The innovation outcome establishes that the intelligent recognition method for personalized learning can efficiently diminish the error rate and enhance the accuracy of error detection. Let the artificial intelligence (AI) correct English learner’s pronunciation errors intelligently.
{"title":"AI Recognition Method of Pronunciation Errors in Oral English Speech with the Help of Big Data for Personalized Learning","authors":"Yanqing Liu, Qiaoli Quan","doi":"10.1142/s0219649222400287","DOIUrl":"https://doi.org/10.1142/s0219649222400287","url":null,"abstract":"At present, there is a lack of careful consideration in the judgment process of pronunciation errors in many English speeches. These pronunciation errors will create a great impact on personalized learning. The process of creating a data set for errors is also not an easy work. On considering the above obstacle, an artificial intelligent recognition method of pronunciation errors in English speeches for personalized learning along with big data is proposed. This method takes the average pronunciation level of standard speech as the basis of pronunciation error judgment, and judges the pronunciation and application of words such as speed, pronunciation, semantics, etc. In the Hidden Markov Model (HMM) modelling method of speech recognition, Viterbi algorithm and improved posterior probability algorithm are implemented to recognize student’s vocalization instinctively. Through the segmentation and scoring of basic units, English learners are provided with reliable pronunciation information feedback, correct pronunciation errors and give corresponding feedback according to the judgment results. The innovation outcome establishes that the intelligent recognition method for personalized learning can efficiently diminish the error rate and enhance the accuracy of error detection. Let the artificial intelligence (AI) correct English learner’s pronunciation errors intelligently.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125103387","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 : 2022-05-18DOI: 10.1142/s0219649222500174
Casper Gihes Kaun Simon, N. Jhanjhi, Goh Wei Wei, Sanath Sukumaran
As new generations of technology appear, legacy knowledge management solutions and applications become increasingly out of date, necessitating a paradigm shift. Machine learning presents an opportunity by foregoing rule-based knowledge intensive systems inundating the marketplace. An extensive review was made on the literature pertaining to machine learning which common machine learning algorithms were identified. This study has analysed more than 200 papers extracted from Scopus and IEEE databases. Searches ranged with the bulk of the articles from 2018 to 2021, while some articles ranged from 1959 to 2017. The research gap focusses on implementing machine learning algorithm to knowledge management systems, specifically knowledge management attributes. By investigating and reviewing each algorithm extensively, the usability of each algorithm is identified, with its advantages and disadvantages. From there onwards, these algorithms were mapped for what area of knowledge management it may be beneficial. Based on the findings, it is evidently seen how these algorithms are applicable in knowledge management and how it can enhance knowledge management system further. Based on the findings, the paper aims to bridge the gap between the literature in knowledge management and machine learning. A knowledge management–machine learning framework is conceived based on the review done on each algorithm earlier and to bridge the gap between the two literatures. The framework highlights how machine learning algorithm can play a part in different areas of knowledge management. From the framework, it provides practitioners how and where to implement machine learning in knowledge management.
{"title":"Applications of Machine Learning in Knowledge Management System: A Comprehensive Review","authors":"Casper Gihes Kaun Simon, N. Jhanjhi, Goh Wei Wei, Sanath Sukumaran","doi":"10.1142/s0219649222500174","DOIUrl":"https://doi.org/10.1142/s0219649222500174","url":null,"abstract":"As new generations of technology appear, legacy knowledge management solutions and applications become increasingly out of date, necessitating a paradigm shift. Machine learning presents an opportunity by foregoing rule-based knowledge intensive systems inundating the marketplace. An extensive review was made on the literature pertaining to machine learning which common machine learning algorithms were identified. This study has analysed more than 200 papers extracted from Scopus and IEEE databases. Searches ranged with the bulk of the articles from 2018 to 2021, while some articles ranged from 1959 to 2017. The research gap focusses on implementing machine learning algorithm to knowledge management systems, specifically knowledge management attributes. By investigating and reviewing each algorithm extensively, the usability of each algorithm is identified, with its advantages and disadvantages. From there onwards, these algorithms were mapped for what area of knowledge management it may be beneficial. Based on the findings, it is evidently seen how these algorithms are applicable in knowledge management and how it can enhance knowledge management system further. Based on the findings, the paper aims to bridge the gap between the literature in knowledge management and machine learning. A knowledge management–machine learning framework is conceived based on the review done on each algorithm earlier and to bridge the gap between the two literatures. The framework highlights how machine learning algorithm can play a part in different areas of knowledge management. From the framework, it provides practitioners how and where to implement machine learning in knowledge management.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"14 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130634638","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 : 2022-05-18DOI: 10.1142/s0219649222500447
Cindi T. Smatt, Renée M. E. Pratt, M. Wasko
The purpose of this research was to further the understanding of knowledge exchange within organisations by examining how the dyadic relationships between individuals, in terms of the channels of communication used (structural capital), knowledge awareness (cognitive capital), and the quality of their relationships (relational capital), influence opportunities for knowledge exchange (access to advice), and ultimately individual performance. data were analysed using social network analysis to determine individual network centralities, and structural equation modelling was used to test the hypotheses at the individual level. The findings suggest (1) face-to-face channels with trusted sources are the most preferred method for exchanging sensitive knowledge, (2) knowing where expertise resides and source availability is key to research knowledge exchange, and (3) centrality in knowledge network does not result in uniform increases in individual performance. While technology has the potential to increase the efficiency of knowledge exchange by removing the barriers to same-time, same-place interactions, computer-mediated communication may actually inhibit the exchange of tacit knowledge and advice because of the lean medium of the exchange, negatively impacting performance. Using a network perspective, this study adds to the literature on intra-organisational learning networks by examining how an individual’s use of different communication channels to share knowledge is related to centrality in knowledge networks, and how this impacts individual performance.
{"title":"The Paradox of Knowledge Networks: Why More Knowledge Does Not Always Make You More Successful","authors":"Cindi T. Smatt, Renée M. E. Pratt, M. Wasko","doi":"10.1142/s0219649222500447","DOIUrl":"https://doi.org/10.1142/s0219649222500447","url":null,"abstract":"The purpose of this research was to further the understanding of knowledge exchange within organisations by examining how the dyadic relationships between individuals, in terms of the channels of communication used (structural capital), knowledge awareness (cognitive capital), and the quality of their relationships (relational capital), influence opportunities for knowledge exchange (access to advice), and ultimately individual performance. data were analysed using social network analysis to determine individual network centralities, and structural equation modelling was used to test the hypotheses at the individual level. The findings suggest (1) face-to-face channels with trusted sources are the most preferred method for exchanging sensitive knowledge, (2) knowing where expertise resides and source availability is key to research knowledge exchange, and (3) centrality in knowledge network does not result in uniform increases in individual performance. While technology has the potential to increase the efficiency of knowledge exchange by removing the barriers to same-time, same-place interactions, computer-mediated communication may actually inhibit the exchange of tacit knowledge and advice because of the lean medium of the exchange, negatively impacting performance. Using a network perspective, this study adds to the literature on intra-organisational learning networks by examining how an individual’s use of different communication channels to share knowledge is related to centrality in knowledge networks, and how this impacts individual performance.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131303461","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}
This study examines the effect of network externalities and flow on continual usage of e-banking services. A sample of 400 e-banking users was conveniently engaged using a structured questionnaire. The method of analysis used included Spearman’s correlation analysis, confirmatory factor analysis and structural equation modelling analysis. The findings indicate that referent network size does not significantly influence continuance intention of e-banking users. However, flow positively influences continuance intention of e-banking users. Stakeholders in the financial institutions will understand the driving factors behind continual usage of e-banking services. Some researchers have explored continual usage of e-banking but such studies are rare in the African context. This study will contribute to extant literature by adding a new dimension, intrinsic and extrinsic factors, of e-banking continual usage.
{"title":"E-Banking Continuance: An Integration of Network Externalities and Flow Theory","authors":"Ernestina Onyina, Kwame Owusu Kwateng, Esther Dzidzah","doi":"10.1142/s0219649222500356","DOIUrl":"https://doi.org/10.1142/s0219649222500356","url":null,"abstract":"This study examines the effect of network externalities and flow on continual usage of e-banking services. A sample of 400 e-banking users was conveniently engaged using a structured questionnaire. The method of analysis used included Spearman’s correlation analysis, confirmatory factor analysis and structural equation modelling analysis. The findings indicate that referent network size does not significantly influence continuance intention of e-banking users. However, flow positively influences continuance intention of e-banking users. Stakeholders in the financial institutions will understand the driving factors behind continual usage of e-banking services. Some researchers have explored continual usage of e-banking but such studies are rare in the African context. This study will contribute to extant literature by adding a new dimension, intrinsic and extrinsic factors, of e-banking continual usage.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134284009","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 : 2022-05-18DOI: 10.1142/s0219649222400238
Chao Wu
Blended teaching is a kind of teaching that combines online teaching with traditional teaching, which is defined as “online and offline”. Through the organic combination of these two teaching forms, students’ learning can be from shallow to deep. Therefore, based on the data mining algorithm, this paper designs the method of College English online and offline blended teaching effect. First, it collects the College English blended teaching resources, then builds the College English online and offline teaching support, debugs the College English teaching environment, and finally designs the College English blended teaching model based on the data mining algorithm, so as to realize the College English online and offline blended teaching, The experiment shows that the method designed in this paper can effectively improve the reading ability of College English, and has certain application value.
{"title":"Effect of Online and Offline Blended Teaching of College English Based on Data Mining Algorithm","authors":"Chao Wu","doi":"10.1142/s0219649222400238","DOIUrl":"https://doi.org/10.1142/s0219649222400238","url":null,"abstract":"Blended teaching is a kind of teaching that combines online teaching with traditional teaching, which is defined as “online and offline”. Through the organic combination of these two teaching forms, students’ learning can be from shallow to deep. Therefore, based on the data mining algorithm, this paper designs the method of College English online and offline blended teaching effect. First, it collects the College English blended teaching resources, then builds the College English online and offline teaching support, debugs the College English teaching environment, and finally designs the College English blended teaching model based on the data mining algorithm, so as to realize the College English online and offline blended teaching, The experiment shows that the method designed in this paper can effectively improve the reading ability of College English, and has certain application value.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127941881","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 : 2022-05-18DOI: 10.1142/s0219649222500320
Deepjyoti Roy, M. Dutta
Recommender systems are often employed in different fields such as music, travel, and movies. The recommender systems are broadly utilised nowadays due to the emergence of social activities, in which particular recommendations are offered by group recommender systems. It is a system for recommending the items to a set of users together based on their preferences. The user preferences are used from the behavioural and social aspects of group members to enhance the quality of products recommended in various groups for generating the group recommendations. These group recommender systems solve the cold start problem, which is raised in an individual recommendation system. The ultimate aim of this paper is to design and develop a new Improved Deep Ensemble Learning Model (ID-ELM) for the group recommender systems concerning different application-oriented datasets. Initially, the datasets from different applications like healthcare, e-commerce, and e-learning are gathered from benchmark sources and split the data into various groups. These data are given to the pre-processing for making it fit for further processing. The pre-processing steps like stop word removal, stemming, and punctuation removal are performed here. Then the features are extracted using the Continuous Bag of Words Model (CBOW), and Principal Component Analysis (PCA) is used for dimension reduction. These features are fed to the ID-ELM, in which the optimised Convolutional Neural Network (CNN) extracts the significant features from the pooling layer, and the fully connected layer is replaced by a set of classifiers termed Neural Networks (NN), AdaBoost, and Logistic Regression (LR). Finally, the ranking of the ensemble learning model based on the group reviews extends the recommendation outcome. The optimised CNN is proposed by the Adaptive Seeking Range-based Cat Swarm Optimisation (ASR-CSO) for attaining better results. This model is validated on the benchmark datasets to show the efficiency of the designed model with different meta-heuristic-based algorithms and classification algorithms.
{"title":"An Improved Cat Swarm Search-Based Deep Ensemble Learning Model for Group Recommender Systems","authors":"Deepjyoti Roy, M. Dutta","doi":"10.1142/s0219649222500320","DOIUrl":"https://doi.org/10.1142/s0219649222500320","url":null,"abstract":"Recommender systems are often employed in different fields such as music, travel, and movies. The recommender systems are broadly utilised nowadays due to the emergence of social activities, in which particular recommendations are offered by group recommender systems. It is a system for recommending the items to a set of users together based on their preferences. The user preferences are used from the behavioural and social aspects of group members to enhance the quality of products recommended in various groups for generating the group recommendations. These group recommender systems solve the cold start problem, which is raised in an individual recommendation system. The ultimate aim of this paper is to design and develop a new Improved Deep Ensemble Learning Model (ID-ELM) for the group recommender systems concerning different application-oriented datasets. Initially, the datasets from different applications like healthcare, e-commerce, and e-learning are gathered from benchmark sources and split the data into various groups. These data are given to the pre-processing for making it fit for further processing. The pre-processing steps like stop word removal, stemming, and punctuation removal are performed here. Then the features are extracted using the Continuous Bag of Words Model (CBOW), and Principal Component Analysis (PCA) is used for dimension reduction. These features are fed to the ID-ELM, in which the optimised Convolutional Neural Network (CNN) extracts the significant features from the pooling layer, and the fully connected layer is replaced by a set of classifiers termed Neural Networks (NN), AdaBoost, and Logistic Regression (LR). Finally, the ranking of the ensemble learning model based on the group reviews extends the recommendation outcome. The optimised CNN is proposed by the Adaptive Seeking Range-based Cat Swarm Optimisation (ASR-CSO) for attaining better results. This model is validated on the benchmark datasets to show the efficiency of the designed model with different meta-heuristic-based algorithms and classification algorithms.","PeriodicalId":127309,"journal":{"name":"J. Inf. Knowl. Manag.","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130654091","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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