Python
Machine Learning
Sentiment Analysis and Emotion Detection on Crypto currency Related Tweets Using GPT2 Model
Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model
Smart Attendance Monitoring System Using Face Recognition for People with Disabilities
The Effect of Fake Reviews on e-Commerce during and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection
Time Series Forecasting and Modeling of Food Demand Supply Chain Based on Regressors Analysis
Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm
Twitter Spam Detection Using Natural Language Processing by Encoder Decoder Model
WIPE: A Novel Web-Based Intelligent Packaging Evaluation via Machine Learning and Association Mining
Our study aims to introduce the Web-Based Intelligent Packaging Evaluation (WIPE) platform, which uses machine learning and association rule mining to assess packaging performance in e-commerce. By analyzing customer reviews, WIPE identifies packaging defects, their causes, and effects, offering a dynamic, real-world alternative to traditional laboratory methods.
By using a pre-trained BERT, it ensures precise predictions even with varying data quality. Additionally, the system captures the full context of customer feedback by generating dynamic word clouds, which visually represent common issues and sentiments, offering deeper insights into customer concerns.