Classification of customer call details records using Support Vector Machine (SVMs) and Decision Tree (DTs)

On a daily basis, telecom businesses create a massive amount of data. Decision-makers underlined that acquiring new customers is more difficult than maintaining current ones. Further, existing churn customers' data may be used to identify churn consumers as well as their behavior patterns. This study provides a churn prediction model for the telecom industry that employs SVMs and DTs to detect churn customers. The suggested model uses classification techniques to churn customers' data, with the Support Vector Machine (SVMs) method performing well 98.36 % properly categorized instances) and the Decision Tree (DTs) approach performing poorly 33.04 % and the decision tree algorithm deliver outstanding results

Industry 4.0 Evolutionary Framework: The Increasing Need to Include the Human Factor

Since 2011, when it appeared as a concept, Industry 4.0 has been expanding worldwide, impacting many organizations’ productivity, performance, and supply chain management, especially in some developed economies. Prior research in Industry 4.0 has focused mainly on the conceptualization, modeling, and technological and operative improvements in the supply chain. Based on a systematic literature review and considering the onset of the COVID-19 pandemic as a major milestone in the global industry, this study proposes a new framework, divided into three phases, for the analysis of the evolution of Industry 4.0, considering the importance of including the human factor as much as technology selection and change management. To achieve long-term business success, the implementation of Industry 4.0 must consider the human aspect, such as middle management leadership, the challenges of empowering the operator 4.0, and the well-being of workers. Moreover, Industry 4.0 could foster a more sustainable, inclusive, and diverse business

Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis.

Mobile phones have been used to monitor mobility changes during the COVID-19 pandemic but surprisingly few studies addressed in detail the implementation of practical applications involving whole populations. We report a method of generating a "mobility-index" and a "stay-at-home/resting-index" based on aggregated anonymous Call Detail Records of almost all subscribers in Hungary, which tracks all phones, examining their strengths and weaknesses, comparing it with Community Mobility Reports from Google, limited to smartphone data. The impact of policy changes, such as school closures, could be identified with sufficient granularity to capture a rush to shops prior to imposition of restrictions. Anecdotal reports of large scale movement of Hungarians to holiday homes were confirmed. At the national level, our results correlated well with Google mobility data, but there were some differences at weekends and national holidays, which can be explained by methodological differences. Mobile phones offer a means to analyse population movement but there are several technical and privacy issues. Overcoming these, our method is a practical and inexpensive way forward, achieving high levels of accuracy and resolution, especially where uptake of smartphones is modest, although it is not an alternative to smartphone-based solutions used for contact tracing and quarantine monitoring

EduChain: CIA-Compliant Block-chain forIntelligent Cyber Defense of Microservices inEducation Industry 4.0

This is an accepted manuscript of an article published by IEEE in IEEE Transactions on Industrial Informatics, available online: https://ieeexplore.ieee.org/document/9468408 The accepted version of the publication may differ from the final published version.Massive data handling requirement in education industry 4.0 has attracted interests in the research of microservice architectures due to their scalability, resilience and elasticity characteristics. This development has been challenged by extensive data exchange required by a set of independent microservices tobuilda complete application, which could resultin increasing risksandexposuretothe securityand privacy breaches of the data. It is imperative to see that educational data are highly sensitive, critical for ascertaining educational attainment and facilitating credentials for qualifcation verifcations. This paper puts forward a new proposal of devising a security and privacy-preserving design mechanism of data transactions in educational microservices leveraging the blockchain technology. The design comprises three phases, namely the blockchain framework, data sending-receiving and confdentiality-integrity-availability over a secured platform with each phase having detailed mechanisms for algorithm implementation. The proposal is shown to exhibit favourable performance in terms of time cost of publishing, throughput and latency, and shown to have high surveyacceptance in terms of confdentiality, integrity and availability with approximately 10% improvement from prior blockchain adoption

Industry 4.0 Evolutionary Framework: The Increasing Need to Include the Human Factor

Since 2011, when it appeared as a concept, Industry 4.0 has been expanding worldwide, impacting many organizations’ productivity, performance, and supply chain management, especially in some developed economies. Prior research in Industry 4.0 has focused mainly on the conceptualization, modeling, and technological and operative improvements in the supply chain. Based on a systematic literature review and considering the onset of the COVID-19 pandemic as a major milestone in the global industry, this study proposes a new framework, divided into three phases, for the analysis of the evolution of Industry 4.0, considering the importance of including the human factor as much as technology selection and change management. To achieve long-term business success, the implementation of Industry 4.0 must consider the human aspect, such as middle management leadership, the challenges of empowering the operator 4.0, and the well-being of workers. Moreover, Industry 4.0 could foster a more sustainable, inclusive, and diverse business