Pub Date : 2023-11-09DOI: 10.1142/s0219649223500636
Choon Ling Kwek, Mary Siew Cheng Lee, Shee Ping Lee, Hwee Ling Siek, Kay Hooi Keoy, Aswani Kumar Cherukuri
Imposing the movement control order and social distancing measures during the outbreak of the COVID-19 pandemic increased the adoption of information and communication technology. The practices of remote work, virtual classes, and even entertainment were forced to be conducted remotely from home. Despite the current relaxation of restricted measurements on the COVID-19 pandemic in different nations, people still prefer online meetings via social media because of the potential threats of new recurrence of the COVID-19 pandemic and the significant saving of time, money, and effort. Therefore, the objectives of this research intend to investigate the direct and indirect relationships between attitude, perceived direct benefit, trust, and intention to use social media for virtual events. Moreover, this research will also assess the role of top management support in moderating the relationship between attitude and intention to use social media for virtual events. To accomplish the research objectives, 400 samples were collected through an online self-administered questionnaire survey. The data were analysed by using Statistical Package for Social Sciences (SPSS) and Partial Least Square–Structural Equation Modelling (PLS–SEM). Based on the generated statistical outcomes, all the direct relationships between attitude, perceived direct benefit, trust, and intention to use social media for virtual events are significantly supported. For the indirect relationships, all the mediation relationships are significantly supported. However, the findings indicated that the moderation variable of top management support does not have any moderating effect on the relationship between the attitude and intention to use in this research study.
{"title":"Antecedents of Intention to Use Social Media for Virtual Events","authors":"Choon Ling Kwek, Mary Siew Cheng Lee, Shee Ping Lee, Hwee Ling Siek, Kay Hooi Keoy, Aswani Kumar Cherukuri","doi":"10.1142/s0219649223500636","DOIUrl":"https://doi.org/10.1142/s0219649223500636","url":null,"abstract":"Imposing the movement control order and social distancing measures during the outbreak of the COVID-19 pandemic increased the adoption of information and communication technology. The practices of remote work, virtual classes, and even entertainment were forced to be conducted remotely from home. Despite the current relaxation of restricted measurements on the COVID-19 pandemic in different nations, people still prefer online meetings via social media because of the potential threats of new recurrence of the COVID-19 pandemic and the significant saving of time, money, and effort. Therefore, the objectives of this research intend to investigate the direct and indirect relationships between attitude, perceived direct benefit, trust, and intention to use social media for virtual events. Moreover, this research will also assess the role of top management support in moderating the relationship between attitude and intention to use social media for virtual events. To accomplish the research objectives, 400 samples were collected through an online self-administered questionnaire survey. The data were analysed by using Statistical Package for Social Sciences (SPSS) and Partial Least Square–Structural Equation Modelling (PLS–SEM). Based on the generated statistical outcomes, all the direct relationships between attitude, perceived direct benefit, trust, and intention to use social media for virtual events are significantly supported. For the indirect relationships, all the mediation relationships are significantly supported. However, the findings indicated that the moderation variable of top management support does not have any moderating effect on the relationship between the attitude and intention to use in this research study.","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":" 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135292522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1142/s0219649223500648
May Y. Al-Nashashibi, Nuha El-Khalili, Wael Hadi, Abedal-Kareem Al-Banna, Ghassan Issa
Objective: This paper used three feature selection methods on a Jordanian automobile drivers’ dataset to identify the most significant features for stress prediction algorithm performance. The dataset contains “stress” and “no-stress” classes with 30 features, categorised into physiological and contextual subsets. Methods: Eighteen classifiers from six prediction algorithm categories were evaluated: Rule-based, Tree-based, Ensemble-based, Function-based, Naïve Bayes-based and Lazy-based. Three Feature Subset Selection (FSS) methods were used: Gain Ratio, Chi-square and feature separation. Eight evaluation measures included [Formula: see text]1, Accuracy, Specificity, Sensitivity, Kappa Statistics, Mean Absolute Error (MAE), Area Under Curve (AUC) and Precision Recall Curve Area (PRCA). Results: Among the classifiers, Lazy-based LocalKNN performed significantly well in [Formula: see text]1, Accuracy, Kappa and MAE. Naïve Bayes-based Bayesian Network excelled in other measures. The original dataset with all features yielded the best overall performance, followed by the physiological-only subset. Gain Ratio and Chi-square FSS methods also showed promising results, though not significant. Conclusion: Four physiological (EMG, EMG Amplitude, Heart rate, Respiration Amplitude) and seven contextual (time range of driving, gender, age, driving skills, general accidents, last year’s accidents, stress frequency) features contributed to the best prediction outcomes. The study highlights the importance of proper feature selection and identifies optimal algorithms for specific measures.
{"title":"Identifying the Most Significant Features for Stress Prediction of Automobile Drivers: A Comprehensive Study","authors":"May Y. Al-Nashashibi, Nuha El-Khalili, Wael Hadi, Abedal-Kareem Al-Banna, Ghassan Issa","doi":"10.1142/s0219649223500648","DOIUrl":"https://doi.org/10.1142/s0219649223500648","url":null,"abstract":"Objective: This paper used three feature selection methods on a Jordanian automobile drivers’ dataset to identify the most significant features for stress prediction algorithm performance. The dataset contains “stress” and “no-stress” classes with 30 features, categorised into physiological and contextual subsets. Methods: Eighteen classifiers from six prediction algorithm categories were evaluated: Rule-based, Tree-based, Ensemble-based, Function-based, Naïve Bayes-based and Lazy-based. Three Feature Subset Selection (FSS) methods were used: Gain Ratio, Chi-square and feature separation. Eight evaluation measures included [Formula: see text]1, Accuracy, Specificity, Sensitivity, Kappa Statistics, Mean Absolute Error (MAE), Area Under Curve (AUC) and Precision Recall Curve Area (PRCA). Results: Among the classifiers, Lazy-based LocalKNN performed significantly well in [Formula: see text]1, Accuracy, Kappa and MAE. Naïve Bayes-based Bayesian Network excelled in other measures. The original dataset with all features yielded the best overall performance, followed by the physiological-only subset. Gain Ratio and Chi-square FSS methods also showed promising results, though not significant. Conclusion: Four physiological (EMG, EMG Amplitude, Heart rate, Respiration Amplitude) and seven contextual (time range of driving, gender, age, driving skills, general accidents, last year’s accidents, stress frequency) features contributed to the best prediction outcomes. The study highlights the importance of proper feature selection and identifies optimal algorithms for specific measures.","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"41 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135430799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1142/s021964922302001x
Peter Heisig
Journal of Information & Knowledge ManagementOnline Ready No AccessPreface: Special Issue on Knowledge, Uncertainty and RisksPeter HeisigPeter HeisigDepartment Information Sciences, University of Applied Sciences, Potsdam, Germanyhttps://doi.org/10.1142/S021964922302001XCited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Online Ready Metrics History Published: 7 November 2023 PDF download
{"title":"Preface: Special Issue on Knowledge, Uncertainty and Risks","authors":"Peter Heisig","doi":"10.1142/s021964922302001x","DOIUrl":"https://doi.org/10.1142/s021964922302001x","url":null,"abstract":"Journal of Information & Knowledge ManagementOnline Ready No AccessPreface: Special Issue on Knowledge, Uncertainty and RisksPeter HeisigPeter HeisigDepartment Information Sciences, University of Applied Sciences, Potsdam, Germanyhttps://doi.org/10.1142/S021964922302001XCited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Online Ready Metrics History Published: 7 November 2023 PDF download","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"25 98","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135541208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1142/s0219649223500594
Ahmad Saleh Shatat, Abdallah Saleh Shatat
This research seeks to examine the artificial intelligence (AI) competencies in logistics management by reviewing its capabilities, challenges and benefits. To increase the use of AI in logistics management, this study addresses the issues of the current technology in AI adoption in logistics management. This goal was accomplished using a systematic methodology. First, a detailed review was conducted to look at the advantages, challenges and current AI competencies. Using a survey instrument and a simple random sampling technique, the required data was collected from 44 businesses which effectively use AI in their logistical operations. The collected data gave insightful information on how AI is currently being used in logistics management. The outcome of this study shows that AI significantly affects logistics management. The study reveals notable competencies, significant challenges and major advantages of AI in managing logistics activities through the systematic analysis and synthesis of the obtained data. These findings demonstrate how AI has the potential to improve operational effectiveness, resource allocation, decision-making processes and supply chain operations in logistics management. A potential recommendation is to establish strategies and guidelines for efficient implementation and integration of AI technologies in logistics management based on the observed technology gap and the research’s findings. This will minimise the current gap and optimise the advantages of the industry’s use of AI, resulting in higher performance, cost savings and increased competitiveness for logistics business organisations.
{"title":"Artificial Intelligence Competencies in Logistics Management: An Empirical Insight from Bahrain","authors":"Ahmad Saleh Shatat, Abdallah Saleh Shatat","doi":"10.1142/s0219649223500594","DOIUrl":"https://doi.org/10.1142/s0219649223500594","url":null,"abstract":"This research seeks to examine the artificial intelligence (AI) competencies in logistics management by reviewing its capabilities, challenges and benefits. To increase the use of AI in logistics management, this study addresses the issues of the current technology in AI adoption in logistics management. This goal was accomplished using a systematic methodology. First, a detailed review was conducted to look at the advantages, challenges and current AI competencies. Using a survey instrument and a simple random sampling technique, the required data was collected from 44 businesses which effectively use AI in their logistical operations. The collected data gave insightful information on how AI is currently being used in logistics management. The outcome of this study shows that AI significantly affects logistics management. The study reveals notable competencies, significant challenges and major advantages of AI in managing logistics activities through the systematic analysis and synthesis of the obtained data. These findings demonstrate how AI has the potential to improve operational effectiveness, resource allocation, decision-making processes and supply chain operations in logistics management. A potential recommendation is to establish strategies and guidelines for efficient implementation and integration of AI technologies in logistics management based on the observed technology gap and the research’s findings. This will minimise the current gap and optimise the advantages of the industry’s use of AI, resulting in higher performance, cost savings and increased competitiveness for logistics business organisations.","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"26 104","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135541394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the knowledge economy, knowledge-based organisations, in particular, open a special account for their employees. Knowledge acquisition is important for organisations, because it enables them to improve their skills and creates value, credibility and competitive advantage. This research has been made to identify the motivational factors effective for knowledge acquisition and prioritise these factors, as well as providing a framework for managers to enable knowledge sharing from knowledge workers and increase their desire to overcome current problems. The organisation has been paid. The statistical population of the research is 300 managers and experts in the automotive industry, and in this research, the opinions of 20 experts have been used to analyse the results. The results were analysed using the fuzzy technique to answer the research questions. The calculations obtained by applying the proposed method show that among the six factors affecting knowledge acquisition, Behavioural factors, with a weight of 0.296, have the most impact on knowledge acquisition compared to other factors. After that, the factor of Information Technology in the organisation, with a weight of 0.17, is in second place concerning the level of influence on knowledge acquisition. Also, the Organisational Learning Criteria are ranked third with a weight of 0.165. And the factors of Organisational Culture, Reward and Structure are placed in the next priorities with weights of 0.153, 0.094 and 0.121, respectively.
{"title":"Presenting an Effective Motivational Model on the Knowledge Acquisition Process Using Fuzzy Best-Worst Method (FBWM)","authors":"Mostafa Jafari, Mohammadreza Zahedi, Shayan Naghdi Khanachah","doi":"10.1142/s0219649223500612","DOIUrl":"https://doi.org/10.1142/s0219649223500612","url":null,"abstract":"In the knowledge economy, knowledge-based organisations, in particular, open a special account for their employees. Knowledge acquisition is important for organisations, because it enables them to improve their skills and creates value, credibility and competitive advantage. This research has been made to identify the motivational factors effective for knowledge acquisition and prioritise these factors, as well as providing a framework for managers to enable knowledge sharing from knowledge workers and increase their desire to overcome current problems. The organisation has been paid. The statistical population of the research is 300 managers and experts in the automotive industry, and in this research, the opinions of 20 experts have been used to analyse the results. The results were analysed using the fuzzy technique to answer the research questions. The calculations obtained by applying the proposed method show that among the six factors affecting knowledge acquisition, Behavioural factors, with a weight of 0.296, have the most impact on knowledge acquisition compared to other factors. After that, the factor of Information Technology in the organisation, with a weight of 0.17, is in second place concerning the level of influence on knowledge acquisition. Also, the Organisational Learning Criteria are ranked third with a weight of 0.165. And the factors of Organisational Culture, Reward and Structure are placed in the next priorities with weights of 0.153, 0.094 and 0.121, respectively.","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"56 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main advantage of Agile Project Management (APM) lies in its ability to investigate and resolve issues that arise during the project period, making timely adjustments to save resources and deliver successful projects on time and at a lower cost. Customers play a crucial role in determining many of these changes, highlighting their special involvement in managing projects using an agile approach. This paper prioritizes the factors influencing challenges related to customer knowledge in APM, initially identifying three categories: individual, organizational, and technological factors. Expert opinions from the software development industry verified these factors, while the DANP method explored their causal relationships and importance. The analysis revealed organizational factors’ impact on the other two categories, with individual factors ranking highest, followed by technological factors. Notable challenges related to customer knowledge include lack of time for knowledge sharing, reluctance to adopt information technology systems, ineffective communication between knowledge-holders and seekers, inadequate training on new technology, and a lack of awareness regarding knowledge benefits for project partners. These findings are presented as suggestions to project teams for effectively managing agile projects and addressing customer knowledge challenges. By implementing these recommendations, projects can achieve greater efficiency and success.
{"title":"Identify and Prioritize the Challenges of Customer Knowledge in Successful Project Management: An Agile Project Management Approach","authors":"Mostafa Jafari, Mohammadreza Zahedi, Shayan Naghdi Khanachah","doi":"10.1142/s0219649223500600","DOIUrl":"https://doi.org/10.1142/s0219649223500600","url":null,"abstract":"The main advantage of Agile Project Management (APM) lies in its ability to investigate and resolve issues that arise during the project period, making timely adjustments to save resources and deliver successful projects on time and at a lower cost. Customers play a crucial role in determining many of these changes, highlighting their special involvement in managing projects using an agile approach. This paper prioritizes the factors influencing challenges related to customer knowledge in APM, initially identifying three categories: individual, organizational, and technological factors. Expert opinions from the software development industry verified these factors, while the DANP method explored their causal relationships and importance. The analysis revealed organizational factors’ impact on the other two categories, with individual factors ranking highest, followed by technological factors. Notable challenges related to customer knowledge include lack of time for knowledge sharing, reluctance to adopt information technology systems, ineffective communication between knowledge-holders and seekers, inadequate training on new technology, and a lack of awareness regarding knowledge benefits for project partners. These findings are presented as suggestions to project teams for effectively managing agile projects and addressing customer knowledge challenges. By implementing these recommendations, projects can achieve greater efficiency and success.","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"12 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rise of online learning has brought about a close connection between micro-credentials and lifelong learning, employability, and new models of digital education. Micro-credentials are considered instrumental in transforming higher education today. This study aims to examine the extent to which micro-credentials have been adopted in Malaysia, focussing on the viewpoint of Higher Education Providers (HEPs). It seeks to identify the challenges faced by HEPs when offering micro-credentials, encompassing technological, organisational, and people-related obstacles. By analysing empirical data, this research intends to propose a conceptual framework that can guide the successful adoption and implementation of micro-credentials within educational institutions. By addressing these recommendations, HEPs in Malaysia can successfully adopt and implement micro-credentials within their institutions. This will not only enhance the learning experience for students but also contribute to the overall transformation of higher education, keeping pace with the demands of the digital age and fostering a culture of continuous learning and skill development.
{"title":"Streamlining Micro-Credentials Implementation in Higher Education Institutions: Considerations for Effective Implementation and Policy Development","authors":"Kay Hooi Keoy, Yung Jing Koh, Javid Iqbal, Shaik Shabana Anjum, Sook Fern Yeo, Aswani Kumar Cherukuri, Wai Yee Teoh, Dayang Aidah Awang Piut","doi":"10.1142/s0219649223500697","DOIUrl":"https://doi.org/10.1142/s0219649223500697","url":null,"abstract":"The rise of online learning has brought about a close connection between micro-credentials and lifelong learning, employability, and new models of digital education. Micro-credentials are considered instrumental in transforming higher education today. This study aims to examine the extent to which micro-credentials have been adopted in Malaysia, focussing on the viewpoint of Higher Education Providers (HEPs). It seeks to identify the challenges faced by HEPs when offering micro-credentials, encompassing technological, organisational, and people-related obstacles. By analysing empirical data, this research intends to propose a conceptual framework that can guide the successful adoption and implementation of micro-credentials within educational institutions. By addressing these recommendations, HEPs in Malaysia can successfully adopt and implement micro-credentials within their institutions. This will not only enhance the learning experience for students but also contribute to the overall transformation of higher education, keeping pace with the demands of the digital age and fostering a culture of continuous learning and skill development.","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"21 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-04DOI: 10.1142/s0219649222500137
Runumi Devi, D. Mehrotra, Sana Ben Abdallah Ben Lamine
Electronic Health Record (EHR) systems in healthcare organisations are primarily maintained in isolation from each other that makes interoperability of unstructured(text) data stored in these EHR systems challenging in the healthcare domain. Similar information may be described using different terminologies by different applications that can be evaded by transforming the content into the Resource Description Framework (RDF) model that is interoperable amongst organisations. RDF requires a document’s contents to be translated into a repository of triplets (subject, predicate, object) known as RDF statements. Natural Language Processing (NLP) techniques can help get actionable insights from these text data and create triplets for RDF model generation. This paper discusses two NLP-based approaches to generate the RDF models from unstructured patients’ documents, namely dependency structure-based and constituent(phrase) structure-based parser. Models generated by both approaches are evaluated in two aspects: exhaustiveness of the represented knowledge and the model generation time. The precision measure is used to compute the models’ exhaustiveness in terms of the number of facts that are transformed into RDF representations.
{"title":"Constituent vs Dependency Parsing-Based RDF Model Generation from Dengue Patients’ Case Sheets","authors":"Runumi Devi, D. Mehrotra, Sana Ben Abdallah Ben Lamine","doi":"10.1142/s0219649222500137","DOIUrl":"https://doi.org/10.1142/s0219649222500137","url":null,"abstract":"Electronic Health Record (EHR) systems in healthcare organisations are primarily maintained in isolation from each other that makes interoperability of unstructured(text) data stored in these EHR systems challenging in the healthcare domain. Similar information may be described using different terminologies by different applications that can be evaded by transforming the content into the Resource Description Framework (RDF) model that is interoperable amongst organisations. RDF requires a document’s contents to be translated into a repository of triplets (subject, predicate, object) known as RDF statements. Natural Language Processing (NLP) techniques can help get actionable insights from these text data and create triplets for RDF model generation. This paper discusses two NLP-based approaches to generate the RDF models from unstructured patients’ documents, namely dependency structure-based and constituent(phrase) structure-based parser. Models generated by both approaches are evaluated in two aspects: exhaustiveness of the represented knowledge and the model generation time. The precision measure is used to compute the models’ exhaustiveness in terms of the number of facts that are transformed into RDF representations.","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44599407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1142/S0219649222500101
M. Kumar, P. R. Kumar
{"title":"Deep Convolutional Neural Network driven Neuro-Fuzzy System for Moving Target Detection Using the Radar Signals","authors":"M. Kumar, P. R. Kumar","doi":"10.1142/S0219649222500101","DOIUrl":"https://doi.org/10.1142/S0219649222500101","url":null,"abstract":"","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"21 1","pages":"2250010:1-2250010:24"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63903846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1142/s0219649222500125
R. R. S. Ravi Kumar, G. Appa Rao, S. Anuradha
With the emergence of e-commerce and social networking systems, the use of recommendation systems gained popularity to predict the user ratings of an item. Since the large volume of data is generated from various sources at high speed, predicting the ratings accurately in real-time adds enormous benefit to the users while choosing the correct item. So a recommendation system must be capable enough to predict the rating accurately when the data are large. Apache Spark is a distributed framework well suited for processing large datasets and real-time data streams. In this paper, we propose an efficient matrix factorisation algorithm based on Spark MLlib alternating least squares (ALS) for collaborative filtering. The optimisations used for the proposed algorithm using Tungsten improved the performance of the algorithm significantly while doing the predictions. The experimental results prove that the proposed work is significantly faster for top-N recommendations and rating predictions compared with the existing works.
{"title":"Efficient Distributed Matrix Factorization Alternating Least Squares (EDMFALS) for Recommendation Systems Using Spark","authors":"R. R. S. Ravi Kumar, G. Appa Rao, S. Anuradha","doi":"10.1142/s0219649222500125","DOIUrl":"https://doi.org/10.1142/s0219649222500125","url":null,"abstract":"With the emergence of e-commerce and social networking systems, the use of recommendation systems gained popularity to predict the user ratings of an item. Since the large volume of data is generated from various sources at high speed, predicting the ratings accurately in real-time adds enormous benefit to the users while choosing the correct item. So a recommendation system must be capable enough to predict the rating accurately when the data are large. Apache Spark is a distributed framework well suited for processing large datasets and real-time data streams. In this paper, we propose an efficient matrix factorisation algorithm based on Spark MLlib alternating least squares (ALS) for collaborative filtering. The optimisations used for the proposed algorithm using Tungsten improved the performance of the algorithm significantly while doing the predictions. The experimental results prove that the proposed work is significantly faster for top-N recommendations and rating predictions compared with the existing works.","PeriodicalId":45460,"journal":{"name":"Journal of Information & Knowledge Management","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48436921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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