Performance Predicting in Hiring Process and Performance Appraisals Using Machine Learning

Mahmoud, Ali; Shawabkeh, Tahani Al; Salameh, Walid A.; Amro, Ibrahim Al

doi:10.1109/iacs.2019.8809154

Cited by 30 publications

(17 citation statements)

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“…For example, an ANN trained on a large corpus of scientific literature predicted multiple advances in materials science before they were reported 1628 . ANNs are already used for financial asset management 1629,1630 and recruiting [1631][1632][1633][1634] , so we anticipate that artificial scientific oracle consultation will become an important part of scientific grant 1635,1636 reviews.…”

Section: Discussionmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

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Section: Discussionmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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“…Various machine learning models are analyzed to find an answer. [8]In the modern competitive world, employers are looking into machine learning models to help them fill their spot of vacancy. [2] One among the models and the most prominent ones uses recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

Turtle Score -- Similarity Based Developer Analyzer

Varshini¹,

Ponshriharini²,

Kannan³

et al. 2022

Preprint

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In day-to-day life, a highly demanding task for IT companies is to find the right candidates who fit the companies' culture. This research aims to comprehend, analyze and automatically produce convincing outcomes to find a candidate who perfectly fits right in the company. Data is examined and collected for each employee who works in the IT domain focusing on their performance measure. This is done based on various different categories which bring versatility and a wide view of focus. To this data, learner analysis is done using machine learning algorithms to obtain learner similarity and developer similarity in order to recruit people with identical working patterns. It's been proven that the efficiency and capability of a particular worker go higher when working with a person of a similar personality. Therefore this will serve as a useful tool for recruiters who aim to recruit people with high productivity. This is to say that the model designed will render the best outcome possible with high accuracy and an immaculate recommendation score.

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“…Moreover, pre-employment background screening is another talent acquisition task that has been recently handed over to AI. For instance, "FAMA" uses natural language to screen the internet, news, blogs, social media, and professional networks to investigate candidates criminal and violent history, workplace misconducts, drug abuse, as well as positive indicators such as volunteering, and other relevant information (Mahmoud et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence In Talent Acquisition, Do we Trust It?

Hmoud

Várallyai

2022

JAI

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This study aims to investigate HR leaders’ trust in AI application in talent acquisition and the role of technology trust as a predictor of HR leaders’ attitude toward its adoption. The sample was drawn from the HR professionals’ network in the Middle East using an online survey with 389 responses. The study results concluded that HR leaders have a positive attitude toward the adoption of AI applications in the talent acquisition function. Additionally, HR leaders perceived it as highly advantageous and this perception positively influenced their attitude. Further, it is concluded that. HR leaders possess high trust in AI-based talent acquisition solutions and that their perception about its reliability, credibility and technical competence are significant predictors of this trusting belief.

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Performance Predicting in Hiring Process and Performance Appraisals Using Machine Learning

Cited by 30 publications

References 1 publication

Review: Deep Learning in Electron Microscopy

Review: Deep Learning in Electron Microscopy

Turtle Score -- Similarity Based Developer Analyzer

Artificial Intelligence In Talent Acquisition, Do we Trust It?

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