Search Results for author:
Found 7 papers, 4 papers with code
How effective is incongruity? Implications for code-mixed sarcasm detection
The presence of sarcasm in conversational systems and social media like chatbots, Facebook, Twitter, etc.
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment
This research introduces a sophisticated transfer learning model based on Google's MobileNetV2 for breast cancer tumor classification into normal, benign, and malignant categories, utilizing a dataset of 1576 ultrasound images (265 normal, 891 benign, 420 malignant).
Retrieval-based Text Selection for Addressing Class-Imbalanced Data in Classification
We integrate vector search, semantic search, and machine learning classifiers to yield a good solution.
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (e. g., support, refute or not enough information), and to generate a statement to summarize and explain the reasoning and ruling process.
How Effective is Incongruity? Implications for Code-mix Sarcasm Detection
The presence of sarcasm in conversational systems and social media like chatbots, Facebook, Twitter, etc.
FiLMing Multimodal Sarcasm Detection with Attention
We propose a novel architecture that uses the RoBERTa model with a co-attention layer on top to incorporate context incongruity between input text and image attributes.
Vector Quantized Spectral Clustering applied to Soybean Whole Genome Sequences
We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a combination of Spectral Clustering (SC) and Vector Quantization (VQ) sampling for grouping Soybean genomes.
