Search Results for author:
Found 14 papers, 6 papers with code
A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts
In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts.
Towards Automated Recipe Genre Classification using Semi-Supervised Learning
Sharing cooking recipes is a great way to exchange culinary ideas and provide instructions for food preparation.
Evaluating the Reliability of CNN Models on Classifying Traffic and Road Signs using LIME
The objective of this investigation is to evaluate and contrast the effectiveness of four state-of-the-art pre-trained models, ResNet-34, VGG-19, DenseNet-121, and Inception V3, in classifying traffic and road signs with the utilization of the GTSRB public dataset.
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.
Contrastive Learning for API Aspect Analysis
For impact analysis, we performed empirical and developer study.
Rank Your Summaries: Enhancing Bengali Text Summarization via Ranking-based Approach
This paper aims to identify the most accurate and informative summary for a given text by utilizing a simple but effective ranking-based approach that compares the output of four different pre-trained Bengali text summarization models.
Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive Learning
Gastrointestinal diseases pose significant healthcare chall-enges as they manifest in diverse ways and can lead to potential complications.
Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models
In this paper, we propose a methodology consisting of four distinct approaches to classify fake news articles in Bengali using summarization and augmentation techniques with five pre-trained language models.
Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning
This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to.
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.
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning
In this study, we present a novel dataset of two million culinary recipes labeled in respective categories leveraging the knowledge of food experts and an active learning technique.
Spam Review Detection Using Deep Learning
In order to achieve that we have worked with both labeled and unlabeled data and proposed deep learning methods for spam review detection which includes Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and a variant of Recurrent Neural Network (RNN) that is Long Short-Term Memory (LSTM).
Can Transformer Models Effectively Detect Software Aspects in StackOverflow Discussion?
Through extensive experimentation, we have found that transformer models improve the performance of baseline SVM for most of the aspects, i. e., `Performance', `Security', `Usability', `Documentation', `Bug', `Legal', `OnlySentiment', and `Others'.
Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions
However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms.
