Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping

Kosylo, Nathan; Smith, John; Conover, Matthew; Chan, Leong; Zhang, Hongtao; Mei, Hanfei; Cao, Renzhi

doi:10.23919/picmet.2018.8481823

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…Scientists are tackling these problems. Kosylo, Smith, Conover, Chan, Zhang, Mei, and Cao proposed the prediction of jobhopping patterns where many profiles are incomplete based on novel AI technology, Sequential Optimization of Naive Bayesian (SONB) [18].…”

Section: Discussionmentioning

confidence: 99%

Application of artificial intelligence in human resource management in the agricultural sector

Evseeva

Burmistrov

et al. 2021

E3S Web Conf.

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Digitalization of all spheres of life has become one of the main directions of development in many countries of the world. Artificial intelligence technology has a wide range of applications in various fields. The study aims to identify artificial intelligence usage in human resource management by analysing functions. Qualitative methods are used in the study. As the result, artificial intelligence tools were analysed by matching them on human resource management functions: recruitment, onboarding, assessment, training and development, talent management, salary system, culture and engagement management, management and leadership, misconduct and compliance. Artificial intelligence technologies provide many opportunities for modern companies. Study in detail companies using AI in HR functions and possible changes in the HR manager profession and role of HR in business are future research directions.

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Section: Discussionmentioning

confidence: 99%

Application of artificial intelligence in human resource management in the agricultural sector

Evseeva

Burmistrov

et al. 2021

E3S Web Conf.

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“…Pimentel (2020) explores the job-hopping behavior specific to salespersons. With several factors explored in the extant literature, a scale has also been proposed for measuring the motivation for job switching (Lake et al , 2018), while Kosylo et al (2018) propose an “artificial intelligence” based algorithm to predict job-hopping behavior. Knowledge-intensive industries report relatively more job-hopping (Kawabe, 1991), and this might become a reason for the knowledge acquired in one firm is applied in another, also called as knowledge spillover, which might cause the companies to refrain from investing in human capital (Fallick et al , 2006).…”

Section: Job Switching/hopping Behaviourmentioning

confidence: 99%

Job switching and communities of practice: mapping facilitators and dampeners to life cycle

Lanke

Nath

2021

VJIKMS

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Purpose This paper aims to understand the impact of the job switching behavior on different stages of the communities of practice’s life cycle. Job switching has been viewed from both positive and negative point of views, and its impact on certain organizational factors might be found in literature. Job switching/job hopping behavior of an individual might be fueled by socio-economic factors as well as fun, but it has serious implication for the companies. But an understanding of how this new employee might influence the communities of practice, given which stage is the community in, is something that has not been studied yet. This work is an attempt in that direction. Design/methodology/approach Using integrative review technique, this paper forwards a conceptual framework based on the literature reviewed and builds a model using an understanding of the nuances of each stage of the life cycle of communities of practice. Findings The model proposes the impact of switching on each stage of the life cycle of communities of practice. It is observed that at each stage a new entrant who is a “job hopper” might either help or hinder the progress of a community of practice. Research limitations/implications This paper gives a new impetus to the research on communities of practice in contemporary perspective. The model proposed could be tested using data from real communities of practice. This paper limits itself to the proposal of the model and does not engage in testing it. Practical implications Organizations and managers may use the model to understand how a new entrant to the organization will complement the existing life cycle phase of the communities of practice within. Originality/value The conceptual model proposed is unique in its context of job switching behavior and its effect on communities of practice. Research on communities of practice from this contemporary perspective might bring important research directions in future.

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“…NB is a useful supervised classification algorithm based on Bayes' theorem under the ''naive'' assumption [59]- [61], which can be defined as…”

Section: ) Nbmentioning

confidence: 99%

iRNA-m5C_NB: A Novel Predictor to Identify RNA 5-Methylcytosine Sites Based on the Naive Bayes Classifier

Dou

Ding

et al. 2020

IEEE Access

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As one of the widespread RNA post-transcriptional modifications (PTCMs), 5-Methylcytosine (m5C) plays vital roles in better understanding of basic biological mechanisms and major disease treatments. In experiments, traditional high-throughput approaches to find m5C sites are usually expensive and laborious. Additionally, facing with a large number of RNA sequences, developing accurate computational methods to distinguish m5C and non-m5C sites is an efficient solution. Here we introduced a novel predictor, called iRNA-m5C_NB, to identify m5C sites in Home sapiens using Naive Bayes (NB) algorithm. In this method, unbalanced dataset Met935 is firstly analyzed using efficient hybrid-sampling strategy SMOTEEEN. Then top 57 features are selected by the ANOVA F-value from four kinds of well-performance feature extraction techniques, including Bi-profile Bayes (BPB), enhanced Nucleic Acid Composition (ENAC), electron-ion interaction pseudopotentials (EIIP) and mMGap_1. Based on the jackknife test, the evaluated recall for the unbalanced training dataset Met935 is up to 82.81% with MCC of 0.63. And for the independent dataset Test1157, the predictor still shows high recall of 70.06% and MCC of 0.34. It is the first m5C predictor constructed using the unbalanced dataset, and the recall scores are increased by 19.82% and 59.23% for jackknife and independent tests compared with the latest tool RNAm5CPred, respectively. We demonstrate that the proposed predictor iRNA-m5C_NB outperforms other state-of-art models, which hopes to be an efficient and reliable method to identify m5C sites. INDEX TERMS 5-Methylcytosine, bi-profile Bayes, naive Bayes, unbalanced data, feature selection.

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Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping

Cited by 12 publications

References 9 publications

Application of artificial intelligence in human resource management in the agricultural sector

Application of artificial intelligence in human resource management in the agricultural sector

Job switching and communities of practice: mapping facilitators and dampeners to life cycle

iRNA-m5C_NB: A Novel Predictor to Identify RNA 5-Methylcytosine Sites Based on the Naive Bayes Classifier

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