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Saudi Airlines Cabin Crew Performance Appraisal Using Artificial Intelligence – The Adaptive Neuro-Fuzzy Inference System

机译:沙特航空公司机组人员使用人工智能绩效评估 - 自适应神经模糊推理系统

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This paper targets the application of artificial intelligence in improving and developing performance through the reduction of human error and the development of fair judgment in the performance of different activities. It focuses on Adaptive Neural Fuzzy Inference System (ANFIS) for the Saudi Airlines cabin crew. Among the findings of the research was that the training error is that the difference between the training data output value and the output of the fuzzy inference system and that the fuzzy linguistic variables are not clear when applied in the performance evaluation system. The study applied a case study to analyze an adaptive neural inference system and rate it to the impact it could create on the cabin crew. The study concluded that in conducting the performance appraisal of the employees some factors such as work goals, and information on the effectiveness of goals must be considered. Also, the lack of a formal reasoning mechanism supporting the inference and evaluations is a critical challenge in performance appraisal; hence, the application of artificial intelligence in the evaluation of the performance of employees in an organization could significantly impact the employees in working towards a common goal.
机译:本文通过减少人类误差和不同活动表现的公平判断,实现人工智能在提高和开发性能方面的应用。它侧重于沙特航空公司机组人员的自适应神经模糊推理系统(ANFIS)。研究中的研究结果是训练误差是训练数据输出值与模糊推理系统的输出之间的差异,并且模糊语言变量在性能评估系统中应用时不清楚。该研究应用了案例研究,分析了自适应神经推理系统,并将其速率降至其在机舱上产生的影响。该研究得出结论,在雇员审判员工的绩效评估时,必须考虑工作目标等因素和有关目标有效性的信息。此外,缺乏支持推理和评估的正式推理机制是绩效评估中的一个关键挑战;因此,人工智能在组织中雇员表现评估中的应用可能会影响员工致力于共同目标。

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