Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16759
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dc.contributor.authorSharma, Kuldeep-
dc.contributor.authorSukheswala, Jenish-
dc.contributor.authorYadav, Brijendra Singh-
dc.contributor.authorLondhe, Gaurav Vishnu-
dc.contributor.authorSingh, Rohit-
dc.contributor.authorPallathadka, Harikumar-
dc.date.accessioned2024-12-12T09:29:57Z-
dc.date.available2024-12-12T09:29:57Z-
dc.date.issued2024-
dc.identifier.isbn9798350389432-
dc.identifier.urihttps://doi.org/10.1109/ACCAI61061.2024.10601739-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16759-
dc.description.abstractThis paper presents a new approach exercising artificial intelligence(AI) to prognosticate hand performance dynamics throughout work hours, easing informed opinions regarding payment adaptations. using advanced AI algorithms, the proposed system integrates colorful factors similar to literal performance data, real-time criteria, and contextual variables to induce accurate performance vaticinations. By assaying patterns and trends in hand geste and productivity, the model offers precious perceptivity into implicit oscillations in performance situations during the course of the workday. also, grounded on these vaticinations, the system suggests applicable payment adaptations acclimatized to individual performance circles, thereby optimizing compensation structures for enhanced hand provocation and organizational productivity. Through empirical confirmation and case studies, the efficacity and trustability of the proposed system are demonstrated, pressing its eventuality to revise performance operation practices and foster a further indifferent and satisfying work terrain. © 2024 IEEE.en_US
dc.language.isoenen_US
dc.publisherProceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectAi Forecastingen_US
dc.subjectAi-Driven Compensation Modelingen_US
dc.subjectEmployee Performance Predictionen_US
dc.subjectPerformance Optimizationen_US
dc.subjectSalary Adjustment Recommendationen_US
dc.subjectWork Hour Analysisen_US
dc.subjectWorkplace Productivity Enhancementen_US
dc.titleA Method Leveraging AI to Forecast Employee Performance During Work Hours and Propose Appropriate Salary Adjustmentsen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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