Search Results for author:
Found 9 papers, 1 papers with code
CSECU-DSG at SemEval-2022 Task 11: Identifying the Multilingual Complex Named Entity in Text Using Stacked Embeddings and Transformer based Approach
Recognizing complex and ambiguous named entities (NEs) is one of the formidable tasks in the NLP domain.
CSECU-DSG at SemEval-2022 Task 3: Investigating the Taxonomic Relationship Between Two Arguments using Fusion of Multilingual Transformer Models
We employed two fine-tuned multilingual transformer models including XLM-RoBERTa and mBERT to train our model.
An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection
Epilepsy is one of the most common neurological diseases globally, affecting around 50 million people worldwide.
Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression
Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases.
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data
In this research, we have proposed a unified multimodal transformer-based framework with image-text pair settings to identify human desire, sentiment, and emotion.
Audio signal based danger detection using signal processing and deep learning
Android device users can use this application without any cost, which will pave the way to ensure the safety of women and children.
Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation
As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data.
CSECU-DSG at SemEval-2021 Task 1: Fusion of Transformer Models for Lexical Complexity Prediction
Lexical complexity prediction (LCP) conveys the anticipation of the complexity level of a token or a set of tokens in a sentence.
Graph Learning: A Survey
In this survey, we present a comprehensive overview on the state-of-the-art of graph learning.
