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Decentralized content sharing in mobile ad-hoc networks:A survey

Digital Communications and Networks 2023年第6期9卷 1363-1398页

作者： shahriar kaisar Joarder Kamruzzaman Gour Karmakar Md Mamunur Rashid Department of Information Systems and Business Analytics RMIT UniversityAustralia School of Engineering IT and Physical SciencesFederation University AustraliaVictoriaAustralia School of Engineering and Technology Central Queensland UniversityAustralia

The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet *** address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising *** content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content *** articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content *** survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.

关键词： Decentralized content sharing Mobile ad-hoc networks Delay-tolerant networks Flying ad hoc networks Message forwarding Content caching Incentive Group formation Misbehavior detection

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and Analytics 2024年第2期7卷 294-314页

作者： shahriar kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business Analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia Centre for Smart Analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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