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Found 8 papers, 2 papers with code
PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification
Using the Inception score as a measure, our experiments show the better quality and realisticness of the images created by PotatoGANs, emphasizing their capacity to resemble real disease images closely.
Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification
Lung and colon cancer are serious worldwide health challenges that require early and precise identification to reduce mortality risks.
Bengali Fake Reviews: A Benchmark Dataset and Detection System
This paper introduces the Bengali Fake Review Detection (BFRD) dataset, the first publicly available dataset for identifying fake reviews in Bengali.
Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks
This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data.
Utilizing Technical Data to Discover Similar Companies in Dhaka Stock Exchange
In other words, a relation between technical data and fundamental data was discovered from the conducted experiment.
Crime Prediction using Machine Learning with a Novel Crime Dataset
This dataset is expected to serve as the foundation for crime incidence prediction systems for Bangladesh and other countries.
An Approach of Adjusting the Switch Probability based on Dimension Size: A Case Study for Performance Improvement of the Flower Pollination Algorithm
This research concentrates on the Flower Pollination algorithm, which is one of several bio-inspired algorithms.
Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market
In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors.
