Employee layoff is a phenomenon that affects both individuals and organizations in various ways. It can cause financial, psychological, and social problems for the employees who are laid off, as well as reduce the productivity, morale, and reputation of the organizations that conduct layoffs. Therefore, it is important to identify the factors that influence the likelihood of employee layoff and to develop a predictive model that can help organizations make informed decisions about workforce planning and management. In this paper, we propose a novel method for employee layoff prediction using recurrent neural networks (RNN). RNN is a type of deep learning model that can capture the sequential and contextual information in data. We use RNN to process the employee-related features, such as performance, tenure, salary, department, and job role, and to extract the hidden patterns that are relevant for layoff prediction. We then use a classifier to predict the probability of employee layoff in a given time period. We evaluate our method on a dataset of employee records from a large company and compare it with existing methods based on support vector machines (SVM) and random forest (RF). Our results show that our method achieves higher accuracy, precision, recall, and F1-score than the state-of-the-art methods. We also analyze the feature importance and the impact of different hyperparameters on the performance of our method. Our method can be a useful tool for assisting human resource managers and executives in the decision-making process of employee layoff.


  • This paper proposes a novel data-driven neural network approach for dynamically modeling the compatibility in
  • P-O fit and its meaningful relationships with two critical issues in talent management, namely talent turnover and job performance.
  •  Specifically, inspired by the practical management scenarios, we creatively propose a novel neural-network-based P-O fit model.


  • They have high computational requirements, which means that they need a lot of processing power and memory to train and run123. This can limit their scalability and efficiency for large-scale applications.
    They have difficulty with small datasets, which means that they need a lot of data to learn to recognize patterns in images12. If the dataset is too small, the CNN may overfit, meaning it becomes too specialized to the training dataset and performs poorly on new data.
    They are vulnerable to adversarial attacks, which means that they can be fooled by maliciously crafted inputs that are designed to cause misclassification2. For example, adding a small amount of noise or distortion to an image can make a CNN misidentify it as something else.
    They have limited ability to generalize, which means that they can only process images that are similar to the ones they have seen before23. They cannot handle images that have different orientations, positions, scales, or perspectives, unless they are explicitly trained on such variations.


  • Layoff prediction using Recurrent Neural Networks (RNNs) involves leveraging the temporal dependencies in historical data to forecast potential workforce reductions. RNNs are particularly suitable for sequential data, making them valuable for tasks where the order and context of information play a crucial role
  •  Specifically, inspired by the practical management scenarios, we creatively propose a novel neural-network-based P-O fit model.
  • The model is trained on these historical sequences, learning to discern patterns and temporal relationships that might precede layoffs.
  • Evaluation metrics such as accuracy, precision, and recall are employed to assess the model’s predictive performance on a separate test set. Incorporating attention mechanisms within the RNN enhances interpretability, shedding light on the specific features contributing to layoff predictions.
  • This approach offers organizations a proactive tool for
  • strategic workforce planning, allowing them to navigate  economic uncertainties and make informed decisions,  ultimately fostering resilience and employee well-being.
  • Continuous monitoring and periodic model updates ensure adaptability to evolving patterns in the workforce, making RNN-based layoff prediction
  • a valuable asset in modern HR and organizational management.


  • Recurrent neural network Explore Recurrent Neural Networks (RNN) are a type of artificial neural network that can handle sequential data, such as text, speech, and time series. Some of the advantages of RNN are:
  • They can process inputs of any length, unlike feedforward neural networks that require fixed-size inputs
    They can remember previous information through their hidden state, which is useful for capturing temporal dependencies and context
    They share weights across time steps, which reduces the number of parameters and enhances training efficiency



Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements

  • •Hard Disk: Greater than 500 GB
  • •RAM: Greater than 4 GB
  • •Processor: I3 and Above


Including Packages


  • * Base Paper
  • * Complete Source Code
  • * Complete Documentation
  • * Complete Presentation Slides
  • * Flow Diagram
  • * Database File
  • * Screenshots
  • * Execution Procedure
  • * Readme File
  • * Addons
  • * Video Tutorials
  • * Supporting Softwares

Specialization =======================

  • * 24/7 Support * Ticketing System
  • * Voice Conference
  • * Video On Demand 
  • * Remote Connectivity
  • * Code Customization
  • * Document Customization 
  • * Live Chat Support


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