DEEPWORK: A PATTERN-AWARE DEEP LEARNING FRAMEWORK FOR REMOTE PRODUCTIVITY ANALYSIS

Abstract

The Remote Work Productivity Analyzer aims to address the growing challenge of evaluating 
employee efficiency in remote work settings, where traditional productivity assessment methods often 
fall short. A 2023 Gartner survey reported that 48% of remote workers experienced reduced 
productivity due to distractions, while a 2024 McKinsey study revealed that 35% of organizations 
faced difficulties in accurately measuring remote employee performance. Existing approaches tend to 
depend on outdated or overly simplistic metrics, failing to account for the complexities of remote 
work environments. To overcome these limitations, this study introduces a novel solution that utilizes 
a context-specific dataset, meticulously preprocessed through cleaning, normalization, and feature 
scaling to ensure high data quality. A Ridge Classifier (RFC) is implemented as a baseline, against 
which a Deep Neural Network (DNN) model comprising Dense1-ReLU, Dense2-ReLU, and Dense3
Softmax layers is proposed to capture intricate productivity patterns. The integration of RFC with 
DNN enhances feature extraction and enables robust classification of employees into two categories: 
'Underperforming Workers' and 'Efficient Workers.' The performance is evaluated using key metrics 
such as accuracy, precision, recall, and F1 score, demonstrating marked improvements over 
conventional models and offering valuable insights for managing remote workforces more effectively.