JournalTOCs

Journal of Information & Knowledge Management
Journal Prestige (SJR): 0.19

Citation Impact (citeScore): 1
Number of Followers: 151

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0219-6492 - ISSN (Online) 1793-6926
Published by World Scientific

[120 journals]

Social Innovation: Drawing and Analysis with Using Research in Scientific
Base
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Ali Asghar Sadabadi, Saeed Ramezani, Kiarash Fartash, Iman Nikijoo
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-06-22T07:00:00Z
  DOI: 10.1142/S0219649222500484
Safety Knowledge Management Practices in Indian Construction Companies
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Vigneshkumar Chellappa, Urmi Ravindra Salve
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-06-22T07:00:00Z
  DOI: 10.1142/S0219649222500496
Knowledge Discovery in a Recommender System: The Matrix Factorization
Approach
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Murchhana Tripathy, Santilata Champati, Hemanta Kumar Bhuyan
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-06-22T07:00:00Z
  DOI: 10.1142/S0219649222500514
U-Shaped Relation between Negative Emotions and Customer Creativity in
Corporate Innovation Context
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Mengfei Lin, Depeng Zhang, Si Liu, Yanpin Huang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Purpose — Emotion is one of the key factors affecting creativity. In the field of marketing research, researchers generally begin to explore how to make rational use of customers” negative emotions to contribute to companies’ innovation process. However, the existing views are still divergent. Design/methodology/approach — To explore the relationship between customers’ negative emotions and creativity, we construct a research model from the perspective of self-determination Theory and Resource Preservation Theory, Based on this model, we conducted an empirical study with 401 participants. Findings: — We found that there is an inverse U-shaped relation between negative emotion and creativity. And we further verified the mediating role of customer intrinsic motivation and the moderating role of innovation self-efficacy. Originality/value — The understanding of the nonlinear relationship between emotion and creativity may provide valuable theoretical contributions to the research of creativity, and provide practical guidance for the design of innovative activities.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-06-22T07:00:00Z
  DOI: 10.1142/S0219649222500538
A Review of Collaboration and Secure Information-Sharing for Supply Chain
Management
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Abdullah Ali Salamai
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Over the last decade, collaboration and secure information-sharing (SIS) have been studied in the context of supply chain management (SCM) to determine their influence on improving a business’s performance and profitability. Collaboration refers to the firms working together to accomplish a particular objective, whereas SIS is a vital technology which permits the firms and the enablers of a supply chain to be integrated. In this paper, these aspects and their impacts on SCM are reviewed. A conceptual model with a set of hypotheses for measuring the effects of collaboration and information-sharing on SCs, which demonstrate their effective roles in SCM, is proposed.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-06-20T07:00:00Z
  DOI: 10.1142/S0219649222500472
The Quality Evaluation Method of Sci-Tech English Translation for
Intercultural Communication
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Ying Xu
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  English for Science and Technology (EST), as a special language style, is widely used in the field of Science and Technology. For this kind of articles, the requirements of translation quality are relatively high. Therefore, this paper studies a quality evaluation method of Sci-Tech English translation for cross-cultural communication. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This paper also describes the evaluation of machine translation quality with and automatic evaluation process with machine learning technology. The evaluation index of EST translation quality is selected according to the selection principle and expert consultation method. Then, the weight of the index is calculated by using the analytic hierarchy process. Finally, the translation quality evaluation is given by using the fuzzy comprehensive evaluation, glass-box and black-box evaluation with machine learning method. The results show that under the application of the research method, the evaluation results are completely corresponding to the actual competition results of four competitors, which proves the effectiveness of the research method.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-06-02T07:00:00Z
  DOI: 10.1142/S0219649222400275
An Approach Using E-Khool User Log Data for E-Learning Recommendation
System
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: P. Vijaya, M. Selvi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The personalised learning is growing rapidly with the help of mobile and online technology. The e-learning recommendation scheme provides the suggestion concerning the courses to the students from numerous countries without past information of the courses online. The accuracy is an important issue in the e-learning course recommendation method. Hence, in this paper, Fuzzy-c-means clustering (FCM) and collaborative filtering are applied in the E-Khool user log data for effective e-learning recommendation system. The training phase and testing phase are the two phases of the devised method. During training, the relationship among the data in clustering is determined using the weighted cosine similarity and the data clustering is carried out with the help of FCM. During testing, the rating of the course is calculated using collaborative filtering. At last, the deep RNN classifier is used to evaluate prediction measure of the course recommendation. The devised e-learning recommendation method based on FCM and collaborative filtering offered a higher accuracy of 0.97 and less mean square error of 0.00115, respectively.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-30T07:00:00Z
  DOI: 10.1142/S0219649222500411
Exploring the Contributing Factors of the Continuance Intention to Use the
Mobile Government-to-Employees Services
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Ashraf Ahmed Fadelelmoula
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The purpose of this paper is to evaluate the effects of certain motivational factors on driving the continuance usage intention of mobile government-to-employees services (MG2ES). These services have been frequently overlooked by the extant IT adoption literature in determining the predictors that drive the user’s continuance intention to adopt them. To respond to this lack, an integrated model incorporating factors from several IT adoption theories was developed. These factors were divided into two categories, namely, m-service-centric and user-centric ones. Both categories were specified as direct antecedents of the MG2ES continuance intention. A structured questionnaire-based survey was carried out to empirically examine the hypothesised relationships between the model constructs. The target population of this survey was employees of Saudi’s public sector. The analysis of the collected data (i.e. 194 valid responses) was conducted using the structural equation modelling (SEM) approach. The results demonstrated that only two m-service-centric factors (i.e. m-service strength and effort expectancy) and one user-centric factor (i.e. attitude towards the MG2ES usage) are having positive impacts on the continuance intention to use MG2ES. These findings provide valuable insights and clarifications to the key MG2ES stakeholders about the aspects that motivate such intention, including augmenting the MG2ES strength, implementing effective design mechanisms to reduce the MG2ES usage efforts, delivering more compatible services, and acquiring effective tools for improving information sharing.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-28T07:00:00Z
  DOI: 10.1142/S0219649222500344
Automated Fake News Detection by LSTM Enabled with Optimal Feature
Selection
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: S. Hannah Nithya, Arun Sahayadhas
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Fake news plays a major role by broadcasting misinformation, which influences people’s knowledge or perceptions and distorts their decision-making and awareness. Online forums and social media have stimulated the broadcast of fake news by embedding it with truthful information. Thus, fake news has evolved into the main challenge of better impact in the information-driven community for intense fakesters. The detection of fake news articles that is generally found by considering the quality of the information in their news feeds under uncertain authenticity calls for automated tools. However, designing such tools is a major problem because of the multiple faces of fakesters. This paper offers a new text-analytics-driven method for detecting fake news to reduce the risks impacted by the consumption of fake news. The methodology for improved fake news detection focusses on four phases: (a) pre-processing, (b) feature extraction, (c) optimal feature selection and (d) classification. The pre-processing of the text data will be initially done by stop word removal, blank space removal and stemming. Further, the feature extraction is performed by term frequency-inverse document frequency, and grammatical analysis is done using mean, Q25, Q50, Q75, Max, Min and standard deviation. Then, the optimal feature selection is developed, which minimises the number of input variables. It is intended to reduce the number of input variables to improve the model’s performance by minimising the computational cost of modelling. An improved meta-heuristic algorithm called successive position-based barnacles mating optimisation is used for optimal feature selection and classification. As the main contribution, the influence of deep learning is employed, which employs optimised long short-term memory. Finally, the result shows the superiority in terms of different significant measures by the proposed model over other methods for fake news detection experimentally done on a publicly available benchmark dataset.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-28T07:00:00Z
  DOI: 10.1142/S0219649222500368
Automated Dual-Channel Speech Enhancement using Adaptive Coherence
Function with Optimised Discrete Wavelet Transform
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Vanita Raj Tank, Shrinivas Padmakar Mahajan
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Voice quality enhancement is a significant method for any speech communication model. Speech Enhancement (SE) and noise reduction approaches can significantly improve the perceptual voice quality of a hands-free communication system and increase the recognition rates of automatic speech recognition systems. Speech communications in real-world cases require high-performance enhancement techniques for addressing the distortions, which can corrupt the intelligibility and quality of the speech signal. Recent portable devices generally incorporate several microphones that can be easily used for improving signal quality. This paper plans to present a novel dual-channel SE model using the coherence function and heuristic concepts. The adaptive coherence function relates to the dual-microphone SE approach suitable for smartphones with primary and reference microphones. With this improved signal, the enhancement is performed by optimising denoising using Discrete Wavelet Transform (DWT) by Adaptive wind speed-based Deer Hunting Optimization Algorithm (AWS-DHOA). The considered objective function depends on the quality measure called Perceptual Evaluation of Speech Quality (PESQ) score. From the results, the RMSE of the proposed model using AWS-DHOA is 39.8%, 45.5%, 53.8% and 45.5% minimised than GWO-CFD, WOA-CFD, CSA-CFD, and RDA-CFD, respectively, on considering the babble noise. Finally, the comparative analysis confirmed that the proposed method improves speech quality and intelligibility by comparing diverse algorithms when different noise types corrupt the speech.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-27T07:00:00Z
  DOI: 10.1142/S021964922250037X
An Empirical Study of Factors Influencing the Intention to Use
Robo-Advisors
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Donghwan Kwon, Pilwon Jeong, Doohee Chung
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Artificial intelligence-based investment services (robo-advisors) are becoming increasingly commercialized. Robo-advisors are expected to expand further due to the enhancement of accessibility to investment for general investors through customized portfolio selection and automated transactions established upon the artificial intelligence-based algorithm. This study comprehensively investigates factors that influence acceptance intention of and resistance to robo-advisors using a combined model of technology acceptance model and innovation resistance model. The model was examined through conducting a choice-based conjoint analysis of 158 users with investment experience and age ranging from 20s to 60s. The independent variables of the research for robo-advisors are transparency, customization, social presence, and user control. The effects of the independent variables on acceptance intention and innovation resistance are analyzed, respectively, through mediator variables of perceived usefulness, perceived complexity, and perceived safety. This study indicates the fundamental factors for the promotion of the domestic robo-advisor market based on the analysis of further advanced overseas robo-advisor markets. The significance of this study derives from providing implications on the direction of development for companies or financial institutions in the sphere of robo-advisors.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-27T07:00:00Z
  DOI: 10.1142/S0219649222500393
Studies on the Role of Knowledge Management in Performance Enhancement and
Promotion of Renewable Energy Industries in India
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: K. Jeykishan Kumar, Richa Sharma
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Energy industries are the pioneers in exploiting the knowledge management (KM) for meeting the challenges. Renewable energy industries are emerging to meet the energy security and climate changes and challenges. Therefore, it was of interest to study how the Indian renewable energy (RE) industries are able to exploit the KM practices to boost their organisation performance. Pilot study was undertaken to study the prevalence of the knowledge management (KM) practices in Indian renewable energy industries through the questionnaire and the measurement of Knowledge Management Performance Index (KMPI) value. The questionnaire was modified based on the outcomes of pilot study. The same qualitative analysis and quantitative analysis were done for the pilot study, and the KMPI value was also determined. The relation of all KM concepts, viz. KM creation, KM storage, KM transfer, KM exploitation and KM dissemination was the construct. This study provides one of first insights of KM performance in promoting new and renewable energy technologies. The clarity on the knowledge and technological gap, process of extracting and disseminating information, difficulty in accessing skilled labour, lack of collaborative R and D and research activities and storage of knowledge were found to be major issues in the exploitation of KM in RE industries in India.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-26T07:00:00Z
  DOI: 10.1142/S021964922250040X
Evaluation Model of College English Education Effect Based on Big Data
Analysis
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yan Jing, Zhou Mingfang, Chen Yafang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The evaluation system of education effect is an important part of the whole teaching process, and the establishment of the evaluation system of college English teaching effect is an important work to test the effect of college English teaching. The traditional evaluation model is widely used and cannot be applied to a variety of teaching situations. Therefore, this paper proposes an evaluation model of college English education effect based on big data analysis. This paper determines the selection principle of the evaluation index of college English education effect, and on this basis, selects the evaluation index factors of college English education effect (experts, students and teachers), calculates the weight and membership matrix of the evaluation index, and outputs the comprehensive evaluation results of college English education effect, which realizes the construction of the evaluation model of college English education effect. The results show that: under the background of the experimental subjects (senior one and senior two), the evaluation errors of English education effect meet the needs of colleges and universities, which proves that the construction model is effective and feasible, and provides the basis and support for the reform of college English education. The range of assessment errors is between 0.78% and 1.44%, all consistent with the demands of the evaluation of the English education effect which demonstrates that the model is successful.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-25T07:00:00Z
  DOI: 10.1142/S0219649222500460
The Method of Allocating Resources for Ideological and Political Education
in Universities Based on IoT Technology
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Weixia Han, Sun Li
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-21T07:00:00Z
  DOI: 10.1142/S0219649222400111
Optimisation of German Language Database Query for Foreign Companies Based
on Hybrid Learning
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yan Chengcheng
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-21T07:00:00Z
  DOI: 10.1142/S0219649222400196
Design of Interactive Teaching System of Physical Training Based on
Artificial Intelligence
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Min Xu, DongAo Liu, Yan Zhang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-21T07:00:00Z
  DOI: 10.1142/S0219649222400214
Optimal Feature Selection with Weight Optimised Deep Neural Network for
Incremental Learning-Based Intrusion Detection in Fog Environment
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Aftab Alam Abdussami, Mohammed Faizan Farooqui
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-19T07:00:00Z
  DOI: 10.1142/S0219649222500423
Effect of Online and Offline Blended Teaching of College English based on
Data Mining Algorithm
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Chaoqun Wu
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-18T07:00:00Z
  DOI: 10.1142/S0219649222400238
AI Recognition Method of Pronunciation Errors in Oral English Speech with
the Help of Big Data for Personalized Learning
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yanqing Liu, Qiaoli Quan
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-18T07:00:00Z
  DOI: 10.1142/S0219649222400287
Applications of Machine Learning in Knowledge Management System: A
Comprehensive Review
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Casper Gihes Kaun Simon, Noor Zaman Jhanjhi, Goh Wei Wei, Sanath Sukumaran
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-18T07:00:00Z
  DOI: 10.1142/S0219649222500174
An Improved Cat Swarm Search-Based Deep Ensemble Learning Model for Group
Recommender Systems
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Deepjyoti Roy, Mala Dutta
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-18T07:00:00Z
  DOI: 10.1142/S0219649222500320
E-Banking Continuance: An Integration of Network Externalities and Flow
Theory
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Ernestina Onyina, Kwame Owusu Kwateng, Esther Dzidzah
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-18T07:00:00Z
  DOI: 10.1142/S0219649222500356
A Digital Platform to Facilitate the Resilience of Rural Territories
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Jorge Martinez-Gil, Mario Pichler, Gianluca Lentini, Valerio Mazzeschi, Guillaume Doukhan, Claire Belet
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This work shows how we have designed and implemented a digital platform for Smart Villages, which can serve as a knowledge management and decision support system for rural stakeholders, including planners, administrative staff, and decision-makers. Our platform is intended to help pilot a smooth transition into a sustainable administration, help to boost rural services through digital innovation, and, in general, reduce the gap concerning the city’s world. Although the platform has arisen around a community belonging to the European Alpine Space, the lessons learned can be easily transferred to other scenarios in which determining and improving the degree of smartness maturity of the rural communities is a critical challenge that can facilitate undertaking public or private investments. Our goal is to provide the necessary tools for collaboration and sharing experiences and data collection and analysis to enable the best possible informed decision-making concerning the promotion of smarter villages.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-18T07:00:00Z
  DOI: 10.1142/S0219649222500435
The Paradox of Knowledge Networks: Why More Knowledge Does Not Always Make
You More Successful
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Cindi T. Smatt, Renée M. E. Pratt, Molly Wasko
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  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.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-18T07:00:00Z
  DOI: 10.1142/S0219649222500447
Intelligent Robot English Speech Recognition Method Based on Online
Database
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yong Wu, Guicang Li
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In order to solve the problem of low accuracy of traditional English speech recognition, an intelligent robot English speech recognition method based on online database is proposed. A speech recognition device is installed on the intelligent robot as the hardware support for running the speech recognition method. The online English speech standard database is constructed to provide reference data for speech recognition. The real-time speech information is collected, and the speech signal is preprocessed by pre-emphasis, framing, windowing and other steps. According to the principle of speech signal generation, the features of speech signal are extracted, and the results of English speech recognition are obtained by similarity calculation and matching. Compared with the traditional recognition method, the experimental results show that the recognition rate of the optimised speech recognition method is improved by 1.3%, i.e. the recognition accuracy is improved.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222400123
Intelligent Correction System of Students’ English Pronunciation Errors
Based on Speech Recognition Technology
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Meili Dai
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  With the increasingly frequent international exchanges, English has become a common language for communication between countries. Under this research background, in order to correct students’ wrong English pronunciation, an intelligent correction system for students’ English pronunciation errors based on speech recognition technology is designed. In order to provide a relatively stable hardware correction platform for voice data information, the sensor equipment is optimised and combined with the processor and intelligent correction circuit. On this basis, the MLP (Multi-layer Perceptron) error correction function is defined; with the help of the known recognition confusion calculation results, the actual input speech error is processed by gain mismatch; and the software execution environment of the system is built. Combined with the related hardware structure, the intelligent correction system of students’ English pronunciation error based on speech recognition technology is successfully applied, and the comparative experiment is designed and the practical application value of the system is highlighted.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222400135
Immersion Teaching Method of Business English Based on Virtual Reality
Technology
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Ting Xu
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In order to solve the problem of students’ low performance under the traditional business English immersion teaching method, the business English immersion teaching method is designed based on virtual reality technology. Based on the virtual reality technology, the virtual reality teaching courseware of business English is constructed, and the teaching virtual page is designed to simulate the negotiation of business trade English, and its virtual reality teaching function can be realised. At the same time, the teaching evaluation mechanism is established, the teaching content and method are adjusted in real time, and the method design is completed. The results show that the final scores of the students in the experimental class are generally higher than those in the ordinary class, which can solve the problem of students’ low scores under the traditional teaching methods.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222400159
A Method of Digital English Teaching Resource Sharing based on Artificial
Intelligence
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Chengxiong Chen
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In order to better improve the quality of English teaching, this paper studies the sharing method of English digital teaching resources combined with modern digital technology. Through the collection and management of English digital teaching resources, it constructs the evaluation index system of teaching resources, so as to comprehensively and objectively evaluate the digital teaching resources and promote the construction and development of digital teaching resource and to effectively promote and improve the teaching effect, and realise the research requirements of effective sharing of massive teaching resources in complex environment.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222400184
Feasibility Analysis of the Application of Virtual Reality Technology in
College English Culture Teaching
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yan Jing, Zhou Mingfang, Chen Yafang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Although the traditional English teaching mode has changed qualitatively, it still has not broken through the two-dimensional limitation, which limits the students’ creative thinking to a certain extent. The application of virtual reality technology in teaching is a qualitative leap in the development of educational informatisation. Based on the data collected from questionnaires and in-depth interviews, this paper studies the feasibility of applying virtual reality immersion teaching in primary and secondary school English teaching. On this basis, this paper attempts to combine virtual reality technology with immersion teaching, and make use of its diversity, flexibility, interactivity and other characteristics to prospect the realisation of virtual reality in English learning, and strive to explore a virtual reality immersion English teaching mode suitable for primary and secondary school children, so as to make up for the shortcomings of traditional language education and optimise the learning effect, It provides a feasible reference for the application of virtual reality technology in English teaching in the future.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222400202
Corpus Design of Chinese Medicine English Vocabulary Translation Teaching
System based on Python
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Chongya Liu
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The current corpus has the problem of imperfect span retrieval function, which leads to a large classification noise. This paper designs a Python-based corpus of Chinese medicine English vocabulary translation teaching system. Here, we select the script material of web crawler, extract topic tags in the form of tag window, calculate the amount of information carried by words, use Python to extract the characteristics of Chinese medicine English vocabulary, and according to the observation value of exploration strategy, use instant time difference learning algorithm to construct the translation mode of teaching system, limit the scope of key words, and design the cross-range retrieval function of corpus. Experimental results: the average classification noise of the designed corpus and the other two corpora is 25.007[math]dB, 33.877[math]dB and 32.166[math]dB, which proves that the integrated Python corpus has higher comprehensive value.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222400226
Understanding Open Collaboration of Wikipedia Good Articles with Factor
Analysis
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Huichen Chou, Donghui Lin, Toru Ishida
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This research aims at understanding the open collaboration involved in producing Wikipedia Good Articles (GA). To achieve this goal, it is necessary to analyse who contributes to the collaborative creation of GA and how they are involved in the collaboration process. We propose an approach that first employs factor analysis to identify editing abilities and then uses these editing abilities scores to distinguish editors. Then, we generate sequence of editors participating in the work process to analyse the patterns of collaboration. Without loss of generality, we use GA of three Wikipedia categories covering two general topics and a science topic to demonstrate our approach. The result shows that we can successfully generate editor abilities and identify different types of editors. Then we observe the sequence of different editor involved in the creation process. For the three GA categories examined, we found that GA exhibited the characteristic of highly scored content-shaping ability editors involved in the later stage of the collaboration process. The result demonstrates that our approach provides a clearer understanding of how Wikipedia GA are created through open collaboration.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222500307
The Directional Knowledge Sharing Paradigm: A Mixed-Methods Case Study of
the Interaction Between Organisational Culture and Intra-Organisational
Knowledge Sharing
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Ali M. Baker
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  A partially mixed-methods case study in a Fortune 50 technology company was conducted to delineate the interaction between organisational culture (OC) types (competitive, bureaucratic and clan) and intra-organisational knowledge sharing (KS). This study provided empirical evidence that show differences in KS horizontally (peer-to-peer) and vertically (between direct-report and manager) within an organisation. By focussing on “socialization” adopted from the organisational knowledge creation theory, the iceberg theory and the competing values framework, we addressed an unexamined area within the body of knowledge. Survey data of 82 employees and interview data of 23 employees were analysed. Multivariate analysis of covariance (MANCOVA) was used to analyse the quantitative survey data. The qualitative interview data were analysed through content analysis. A triangulation design was then followed to merge the data through an equivalent status ([math]) interpretation to derive meta-inferences. MANCOVA displayed a statistically significant interaction between OC and KS via socialisation. The triangulated results showed that OC types distinctly impacted KS via socialisation with differences between seeking, contributing and the direction of knowledge flow (vertical and horizontal). The empirical evidence shows that organisations must consider the direction of knowledge flow (vertical or horizontal) when enforcing cultural values to drive KS via socialisation. Similarly, researchers should not ignore the directional knowledge sharing paradigm, nor the organisational knowledge creation theory, when examining intra-organisational KS.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222500319
Reflections of Community Engagement and Wisdom in the Works of Information
Professionals
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Muhammad Asim Qayyum, Arif Khan, Sarah Redshaw
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Goal/purpose: This study focused on information professionals working in the GLAM (galleries, libraries, archives and museums) sector, and how information was sought and used by them for community engagement and to attain wiser outcomes. The primary purpose was to investigate the information collection, use, reflection and values of professionals in the GLAM sector to determine if wise actions occur that may potentially benefit the community. Methodology: A qualitative approach was used to conduct this research using the wise action model’s (WAM) wisdom characteristics. Data were collected from information professionals working in managerial positions in the GLAM sector using in-depth interviews. Thematic analysis was used to analyse the data. Results: The findings indicate that while most participants exhibit some elements of wisdom, there are gaps that need to be addressed before wise functioning is deemed applicable in their roles. While knowledgeable information acquisition and community engagement were very visible, more emphasis on values and stakeholder well-being is recommended for wiser considerations. Originality/Value: Study of wisdom certainly deserves more attention in knowledge management research as previous studies have indicated. With increasing stresses in the lives of professionals, it is now more important than ever to gain an understanding of how much wisdom prevails in organisational functioning to improve the works of individuals and consequently improve the well-being of impacted communities.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-17T07:00:00Z
  DOI: 10.1142/S0219649222500459
Information-Seeking Behaviour and Knowledge Transfer: A Case Study of
Family Business Owners in Kuwait
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Bedour H. AlShakhs, Naresh Kumar Agarwal
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Information-seeking behaviour is the process of acquiring information from sources in response to a need. The acquired information can then be transferred into knowledge for use, archiving, or sharing. This study investigates the information-seeking behaviour and knowledge transfer among food industry business (fbiz) owners in Kuwait, and how these differ across generations of fbiz owners. Eighteen interviews with 10 large fbiz owners from different generations were carried out. The study found that the information needs of fbiz owners changed at different stages of their business. For information seeking, fbiz members consult a variety of information sources, with the newer generation relying more on online sources than word-of-mouth. Both paper-and-computer-based archiving is used, but information loss remains a threat. Effective knowledge transfer requires better communication between generations and appropriate archiving systems. Each fbiz in Kuwait, irrespective of generation, is required to follow Islamic law. This study brings together the related but differing fields of information behaviour and knowledge management. Most importantly, it contributes two models—a family business information behaviour model and a family business knowledge retention and transfer model that would be useful to the research and practice of information behaviour and knowledge management in family businesses.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-14T07:00:00Z
  DOI: 10.1142/S0219649222500381
An Interactive English–Chinese Translation System Based on GLA
Algorithm
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Guoxi Chai, Qiaozhi Wen
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In view of the longtime interactive English–Chinese translation system, an interactive English–Chinese translation system based on Griffin–Lim algorithm (GLA) is proposed. The hardware design of the system is completed by the hardware structure design, the interactive English–Chinese translation memory design and the interactive English–Chinese translation retrieval system design. Through analysing the semantic characteristics of interactive English–Chinese translation, constructing interactive English–Chinese translation database and designing interactive English–Chinese translation process, the system software design is completed and interactive English–Chinese translation is realised. The results of the system test show that the interactive English–Chinese translation system based on the GLA algorithm cannot only shorten the time of interactive English–Chinese translation, but also accelerate the response speed of the translation system, and greatly improve the overall performance of the interactive English–Chinese translation system.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-13T07:00:00Z
  DOI: 10.1142/S0219649222400147
The Cognitive Force Equations
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Theodore J. Randles, Lisa A. Gardner, Lee Allison
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The knowledge spectrum provides information about pragmatic knowledge types, which are placed on a spectrum based on their potential cognitive force. Based on the relationship between insight and Dretske’s three knowledge types, a conceptual framework for the equations is provided. These equations provide a shorthand method for determining the cognitive force of a specific pragmatic knowledge type. Together, the graphical and mathematical techniques of knowledge microanalysis, which is based on the knowledge chemistry approach, will be calibrated and refined. Two tools will be used to estimate cognitive force. These tools will enable organisations to develop models of their existing and proposed knowledge-intensive processes for analysis and refinement.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-12T07:00:00Z
  DOI: 10.1142/S0219649222500332
An Analysis Method of the Supply-side Influencing Factors of “Vocational
Master of Education” Based on ISM
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Bing Xiao, Weibin Liu, Xiaolin Wu, Yanyong Lan
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In order to explore new approaches of training postgraduates of taught postgraduate programs, this paper proposed an analysis method to discuss the supply-side influencing factors of “vocational master of education” using the interpretative structural model (ISM). The research results showed that the quality of postgraduates, recruitment plan and the number of graduated students are the direct influencing factors of the supply side of “vocational master of education”. Teaching with practices, dissertation, postgraduate management, employment policy and the actual recruitment are the necessary influencing factors. Training program and courses, faculty, basic environment, entrance examination, program promotion, professional direction and planned enrolment are the indirect influencing factors. The strategic environment, the profession of secondary vocational schools and the demand of teachers in these schools are the fundamental influencing factors. Therefore, when studying the supply side of “vocational master of education”, we analysed top-down with specific emphasis on its hierarchy to strengthen the synergy effect of the subject.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-11T07:00:00Z
  DOI: 10.1142/S0219649222400160
Reforming the Activities of Leaders for Professional Level Employee
Engagement: A Blue Ocean Leadership Approach
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: S. Porkodi, Bassam Khalil Hamdan Tabash
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  For an organisation, it is noteworthy to engage employees, where the organisations look to their workers’ inventiveness, activities and are proactive with the solutions for the current requirements. Achieving employees’ engagement is similar to the new Blue Ocean leadership approach (BOL) that gives an entirely new system as worker’s points of view are considered in building a new leadership profile. The key objective of this research paper is to analyse the impact of BOL on professional-level employee engagement through Kim and Mauborgne’s BOL grid four-action framework variables, particularly in the private sector. To test the developed hypothesis, the researcher applied simple linear regression and multiple regression analysis methods. The result of this research study showed that all the four-action BOL grid variables are significant in employee engagement. Further, to develop the new leadership profile, the researcher also used multiple regression analysis to conduct a detailed analysis on the impact of the BOL approach through BOL grid four-action framework variables separately. From the outcomes of the evaluation, it is accomplished that the new leadership blue ocean methodology creates a higher influence on employee engagement via the detected acts along with activities in the new BOL grid since the Create variables have extremely higher significance with 87% ([math] and [math]), the Eliminate variables have next-level higher significance with 84% (([math] and [math]), followed by the Raise variables that have the next higher importance with 72% ([math] and [math]) which displays the consequence of BOL grid (all four-action) variables on employee engagement. In view of impending private sectors in Oman Vision 2040, the results of this research could be an important pointer to be considered in Oman’s private sector development.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-11T07:00:00Z
  DOI: 10.1142/S0219649222400251
Evaluation of a Conceptualised Learning Design for the Development of
Techno-Pedagogic Competencies among Pre-Service Teachers
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Kritika Gosain, M. Rajendran
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The present study aims to evaluate a conceptualised learning design for the growth of techno-pedagogic competencies among Pre-Service Teachers (PST). A teaching experiment is conducted for assessing the effectiveness of the conceptualised learning design by implementing it in a two-year teacher training programme for one year on 36 PST. The progress in the techno-pedagogic competencies’ growth is measured through the self-reporting rating scale, assessment of lesson activities prepared by the PST and semi-structured interview. The result of repeated measure ANOVA statistically signifies the growth of techno-pedagogic competencies amongst PST who participated in the teaching experiment. The assessment of lesson activities further illustrated the growth of techno-pedagogic competencies. PST also reported that they have learned new ways of integrating technology into teaching and have become a more confident user of computers.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-11T07:00:00Z
  DOI: 10.1142/S0219649222400263
Preface: Special Issue “Adaptive Technologies and Personalised
Learning”
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: K. Shankar, Gyanendra Prasad Joshi, Bassam A. Y. Alqaralleh
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-07T07:00:00Z
  DOI: 10.1142/S0219649222020026
Statistically Empirical Integrated Approach for Knowledge Refined Text
Classification
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: N. Venkata Sailaja, L. Padma Sree, N. Mangathayaru
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Automated text mining is an especially important task in modern data analysis, both from theoretical and experimental points of view. This particular problem has a major interest in the digital age that is related to “Artificial Intelligence, Machine learning and Information Retrieval”. Feature selection and classification of high dimensionality of text data are challenging tasks. In this paper, we adopted an optimal method for dealing with high dimensionality of data. Later, we chose an appropriate strategy (learning algorithm) for an effcient model training. Our empirical evaluation and experimental analysis show that the proposed method performs better compared with other variable selection-based dimension reduction and further text categorisation methods. We exploited several systematic and careful experimentation scenarios in this work to discover what architecture works best for this BBC news dataset. We used 3 hidden layers, each layer with 128 neurons. We observed this architecture optimal as per our specific problem experimentation. Moreover, our proposed method can be useful for improving efficiency and speed-up the calculations on certain datasets.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-07T07:00:00Z
  DOI: 10.1142/S0219649222500277
Design of Personalised English Distance Teaching Platform Based on
Artificial Intelligence
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yabin Huang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In the application of the existing distance education platform, the server load is often unbalanced, which increases the response time of the platform. This paper designs a personalised English distance teaching platform based on artificial intelligence. Put forward the overall architecture of the platform, and design the platform on this basis. In order to realise the personalised course resource recommendation function, a collaborative filtering personalised recommendation algorithm is designed to get the best course recommendation results. Using artificial intelligence technology to set up multipoint control unit, the task balance is allocated to multiple operation units, and the reasonable allocation of curriculum resources is realised. According to the functional requirements of the platform design, the composition of functional modules is further set to realise the scheduling and management of curriculum resources. The experimental results show that, under the condition of the same number of concurrent users, the average response time of this design platform is less than that of the existing distance teaching platform, which shows that it has certain advantages in server load balancing and resource scheduling, which can improve the rapid response ability of the platform and enhance the stability of the platform.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-05T07:00:00Z
  DOI: 10.1142/S0219649222400172
An Enhanced Term Weighted Question Embedding for Visual Question Answering
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Sruthy Manmadhan, Binsu C Kovoor
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Visual Question Answering (VQA) is a multi-modal AI-complete task of answering natural language questions about images. Literature solved VQA with a three-phase pipeline: image and question featurisation, multi-modal feature fusion and answer generation or prediction. Most of the works have given attention to the second phase, where multi-modal features get combined ignoring the effect of individual input features. This work investigates VQA’s natural language question embedding phase by proposing a new question featurisation framework based on Supervised Term Weighting (STW) schemes. In addition, two new STW schemes integrating text semantics, qf.cos and tf.rf.sim, have been introduced to boost the framework’s performance. A series of tests on the DAQUAR VQA dataset is used to compare the new system to conventional pre-trained word embedding. Over the past few years, STW schemes have been commonly used in text classification research. In light of this, tests are carried out to verify the effectiveness of the two newly proposed STW schemes in the general text classification task.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-05-05T07:00:00Z
  DOI: 10.1142/S0219649222500289
Entrepreneurs’ Level of Awareness on Knowledge Management for
Promoting Tourism in Nepal
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Saramsh Kharel, K C Anup, Niranjan Devkota, Udaya Raj Paudel
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Managing knowledge in the field of tourism and the hospitality industry will carry significant importance as most people are not aware of knowledge management (KM) implications. As the importance of knowledge management is not well captured in the tourism sector of Nepal, this study aims to identify the awareness level of knowledge management among the tourism entrepreneurs in Nepal and suggest managerial implications for the same. The primary data were collected through 276[math]questionnaire surveys. Tourism entrepreneurs saw the benefits of KM for tourism development despite the costs and challenges it poses. Awareness of knowledge management of entrepreneurs differs according to the people, process, technology, organization structure, and the organization culture dimension. It was further influenced by the demographic characteristics of the tourism entrepreneurs. Enterprises are in more need of knowledge management awareness and several amendments in tourism development policies and programs. Therefore, this study recommends increasing entrepreneurs’ awareness of knowledge management by the joint effort of tourism enterprises and the Nepal Tourism Board. Various knowledge management seminars (integrating tourism experts with tourism entrepreneurs) and training programs should be conducted to manage knowledge effectively.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-30T07:00:00Z
  DOI: 10.1142/S021964922250023X
Investigating Interdependencies Between Key Features of Lessons Learned:
An Integral Approach for Knowledge Sharing
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yawar Abbas, Alberto Martinetti, Lex Frunt, Jeroen Klinkers, Mohammad Rajabalinejad, Leo A. M. van Dongen
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  While there is a clear consensus in the literature on the need to share lessons learned, it remains unclear how to properly do so. This paper addresses this point and offers insight into how best to incorporate tacitly held social preferences for developing knowledge-sharing strategies. A descriptive survey was conducted to analyse the knowledge sharing practices for lessons learned within the railway sector. Eight variables are investigated that are derived from the four LEAF features: learnability, embraceability, applicability, and findability. This study revealed that for learnability, storytelling and discussion with colleagues are preferred ways to share personal experiences. Trust and the creation of a learning culture emerged as key aspects of embraceability. With regard to applicability, a process-related knowledge-sharing focus for intraorganisational and a content-related focus for interorganisational knowledge domains are preferred. Better technological findability is identified as a key area of improvement. Finally, novel dependencies are established using the chi-square test between key LEAF features.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-29T07:00:00Z
  DOI: 10.1142/S0219649222500198
A Novel Multilayer Model for Link Prediction in Online Social Networks
Based on Reliable Paths
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Fariba Sarhangnia, Nona Ali Asgharzadeholiaee, Milad Boshkani Zadeh
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Link Prediction (LP) is one of the critical problems in Online Social Networks (OSNs) analysis. LP is a technique for predicting forthcoming or missing links based on current information in the OSN. Typically, modelling an OSN platform is done in a single-layer scheme. However, this is a limitation which might lead to incorrect descriptions of some real-world details. To overcome this limitation, this paper presents a multilayer model of OSN for the LP problem by analysing Twitter and Foursquare networks. LP in multilayer networks involves performing LP on a target layer benefitting from the structural information of the other layers. Here, a novel criterion is proposed, which calculates the similarity between users by forming intralayer and interlayer links in a two-layer network (i.e. Twitter and Foursquare). Particularly, LP in the Foursquare layer is done by considering the two-layer structural information. In this paper, according to the available information from the Twitter and Foursquare OSNs, a weighted graph is created and then various topological features are extracted from it. Based on the extracted features, a database with two classes of link existence and no link has been created, and therefore the problem of LP has become a two-class classification problem that can be solved by supervised learning methods. To prove the better performance of the proposed method, Katz and FriendLink indices as well as SEM-Path algorithm have been used for comparison. Evaluations results show that the proposed method can predict new links with better precision.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-29T07:00:00Z
  DOI: 10.1142/S0219649222500253
A Blockchain-Based Security Model for IoT Systems
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Bing Chen, Ding Liu, Ting Zhang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  The current Internet of Things (IoT) technology has entered a relatively mature development stage, and more and more IoT devices can readily access the Internet. However, along with this, the IoT system still faces fragile security of device nodes, easy data tampering, and low system stability. To this end, this paper proposes a smart contract-based security model for IoT systems. The proposal is based on the super ledger Fabric blockchain platform having decentralised, tamper-proof, and programmable features. These features achieve credible authentication of IoT device nodes on the one hand and tamper-proof data storage on the other hand. Further, with these features, we gain a trustworthy environment for enhancing the security of the whole IoT system.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-28T07:00:00Z
  DOI: 10.1142/S0219649222500046
Blockchain Token Model for Supply Chain Financing of SMMEs
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Yanfang Ma, Xuezhen Liu, Xiaofei Deng
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Financing difficulties are common among the small-, medium- and micro-enterprises (SMMEs). Although supply chain financing alleviates the problems of SMMEs, such as narrow financing channels, intractable financing and expensive financing, however, due to the centralised storage and management of data, the authenticity of data cannot be guaranteed. The credit of the core enterprises in the supply chain cannot penetrate the SMMEs in upstream and downstream. This paper establishes a blockchain pass-through model for supply chain financing by improving the PBFT consensus algorithm based on blockchain’s decentralised and tamper-evident characteristics and the pass-through of SMMEs’ assets in the supply chain. The model improves the circulation efficiency of the supply chain; moreover, it enables the credit of core enterprises to the upstream and downstream, solving the financing dilemma of SMMEs.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-28T07:00:00Z
  DOI: 10.1142/S0219649222500150
Stuttering Disfluency Detection Using Machine Learning Approaches
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Abedal-Kareem Al-Banna, Eran Edirisinghe, Hui Fang, Wael Hadi
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Stuttering is a neurodevelopmental speech disorder wherein people suffer from disfluency in speech generation. Recent research has applied machine learning and deep learning approaches to stuttering disfluency recognition and classification. However, these studies have focussed on small datasets, generated by a limited number of speakers and within specific tasks, such as reading. This paper rigorously investigates the effective use of eight well-known machine learning classifiers, on two publicly available datasets (FluencyBank and SEP-28k) to automatically detect stuttering disfluency using multiple objective metrics, i.e. prediction accuracy, recall, precision, F1-score, and AUC measures. Our experimental results on the two datasets show that the Random Forest classifier achieves the best performance, with an accuracy of 50.3% and 50.35%, a recall of 50% and 42%, a precision of 42% and 46%, and an F1 score of 42% and 34%, against the FluencyBank and SEP-28K datasets, respectively. Moreover, we show that the machine learning-based approaches may not be effective in accurate stuttering disfluency evaluation, due to diverse variations in speech rate, and differences in vocal tracts between children and adults. We argue that the use of deep learning approaches and Automatic Speech Recognition (ASR) with language models may improve outcomes, specifically for large scale and imbalanced datasets.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-28T07:00:00Z
  DOI: 10.1142/S0219649222500204
Application of Quality Function Deployment as an Integrative Method to
Knowledge Management Implementation
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Luciana Paula Reis, June Marques Fernandes, Sérgio Evangelista Silva, Alana Deusilan Sester Pereira
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Knowledge Management Implementation (KMI) can be analysed from three perspectives: Knowledge Management System (KMS) implementation, Knowledge Management Processes (KMPs) implementation and Organisational Outcomes (OO). Quality Function Deployment (QFD), conceived within the scope of quality, represents a method capable of bringing significant contributions to the knowledge field of KMI. The QFD method stands out as a comprehensive approach to improve the quality of products and services, focussing on customer requirements. The literature presents a scarcity of studies that discuss the KMI implementation process, addressing these three perspectives. Furthermore, studies that address QFD in the context of KM are more focussed on the KMS implementation perspective. In this context, this research proposes an innovative approach adopting QFD in order to structure the KMI, encompassing the three perspectives presented (KMS implementation, KMP implementation and OO). In this context, QFD is seen as a method of integrating and operationalising KMI activities. To validate this approach, we applied the case study in an academic support department at a Brazilian public university. As a result, it was possible to verify that the QFD helps in the operationalisation of the KMS and KMP and also improves OO. Still, it was observed that the approach to knowledge management seems to be easy to apply in the academic setting and has produced good results in the service offered in the department where it was applied.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-28T07:00:00Z
  DOI: 10.1142/S0219649222500228
Building Self-Healing Feature Based on Faster R-CNN Deep Learning
Technique in Web Data Extraction Systems
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Sudhir Kumar Patnaik, C. Narendra Babu
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Web data extraction has evolved over the years with extracting data from documents to today’s World Wide Web (WWW). The WWW growth has placed data at the centre of this ecosystem and benefited society at large, businesses and consumers. The proposed system uses deep learning technique, Faster region convolutional neural network (R-CNN) for automated navigation, extraction of data and self-healing of data extraction engine to adapt to dynamic changes in website layout. The proposed system trains the Faster R-CNN model for detection of product in the web page using bounding box image detection technique and extracts product details with high extraction accuracy. Deep learning technique has advanced rapidly in the different fields for image detection, but its application in data extraction makes this paper unique. An ecommerce retail website is used as real-world example to prove the self-healing capability of the proposed automated web data extraction system.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-28T07:00:00Z
  DOI: 10.1142/S0219649222500290
Evaluating the Impacts of COVID-19 on Operations and Management of
Community Centres: An Auckland, New Zealand Case Study
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Alexander Petrov, Olufemi Muibi Omisakin
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  This study evaluates the impact of COVID-19 on the operations and management of Community Centres (CCs) in Auckland. This Coronavirus posed unprecedented challenges to the communities and public facilities here and in many other countries. To mitigate the pandemic outbreak, the New Zealand government adopted the so-called “go hard, go early” strategy, which put all of the country to strict self-isolation for 52 days [Baker et al. (2020). The Medical Journal of Australia, 213(5), 198–200.e1]. This research aims to explore what challenges some of Auckland’s CCs faced during COVID-19 lockdown and what management decisions were taken to operate the CCs during these periods effectively. The study was based principally on primary data collected from the managers and visitors of eight CCs across Auckland via semi-structured interviews and anonymous online surveys. With all the information gathered and generated through thematic, correlation and deductive analysis, the study produced a “road map” for management actions. This framework should enable CCs to better articulate decisions for more effective and safer operating during future pandemic outbreaks.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-25T07:00:00Z
  DOI: 10.1142/S0219649222500186
Multi-Response Optimisation of Process Parameters in Pocket Milling Using
Artificial Neural Networks and Genetic Algorithms
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: M. Rajyalakshmi, M. Venkateswara Rao
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  In the plastic industry for mold making, pocket milling is applied. The surface finish of the mold affects the quality of the plastic product, especially for toys. This can be achieved by minimising the surface roughness of the mold. To get a good quality product with a better production rate, the selection of the best combination of parameters in pocket milling is necessary. Multi-response optimisation can be applied for selecting such parameters which are suited for fulfilling the objective. In this study, one of the toy mold designs is selected as a pocket profile on which, two tool trajectories, viz Follow Periphery (FP) and Zigzag (ZZ), are applied for generation of pocket by varying Speed (S), Feed (F) and Step Over (SO). Box–Behnken Response Surface Methodology is applied to find the experimental runs. Two conflicting objectives minimising Surface Roughness (SR) and maximising Material Removal Rate (MRR) are obtained by applying Artificial Neural Networks (ANN) and Multi-Objective Genetic Algorithm (MOGA). Conformational experiments were conducted for the random set of Pareto results obtained from MOGA for both the tool trajectories to validate the model. From the analysis, it is observed that the FP tool path strategy is well suited to generate the pocket to get minimum SR and maximum MRR as the error percentage between the predicted and test results observed is 0.8085% for SR and 0.9236% for MRR.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-23T07:00:00Z
  DOI: 10.1142/S0219649222500265
Credit Risk Early Warning of Small and Medium-Sized Enterprises based on
Blockchain Trusted Data
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Authors: Shekun Tong, Ting Zhang, Zhigang Zhang
  Abstract: Journal of Information & Knowledge Management, Ahead of Print.
  Small and medium-sized enterprises (SMEs) are now growing rapidly and playing an important role in the development of the national economy. As the economy grows, the contradiction between the credit risk of SMEs and the credit risk early warning mechanism of traditional supply chain financing has become increasingly important. In response to the issues of a single source of business information, the high investment cost of the existing early risk early warning mechanism, etc., from a commercial bank credit risk management perspective, this paper proposes to build an SMEs credit risk early warning system based on reliable blockchain data. The reliability of the data obtained is assessed utilising a hierarchical analysis and a vague overall judgement method. The results show that the use of blockchain technology can enhance the credibility and accuracy of the data, which provides a data guarantee for more rapid risk alert.
  Citation: Journal of Information & Knowledge Management
  PubDate: 2022-04-18T07:00:00Z
  DOI: 10.1142/S0219649222500149