R
Rafsanjani Muhammod
Researcher at United International University
Publications - 4
Citations - 99
Rafsanjani Muhammod is an academic researcher from United International University. The author has contributed to research in topics: Cluster analysis & Support vector machine. The author has an hindex of 3, co-authored 4 publications receiving 50 citations.
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Journal ArticleDOI
PyFeat: A Python-based Effective Feature Generation Tool for DNA, RNA, and Protein Sequences.
Rafsanjani Muhammod,Sajid Ahmed,Dewan Md. Farid,Swakkhar Shatabda,Alokanand Sharma,Alokanand Sharma,Abdollah Dehzangi +6 more
TL;DR: PyFeat is presented as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs, and RNAs and is able to extract features from 13 different techniques and represent context free combination of effective features.
Posted ContentDOI
ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides
Sajid Ahmed,Rafsanjani Muhammod,Sheikh Adilina,Zahid Hossain Khan,Swakkhar Shatabda,Abdollah Dehzangi +5 more
TL;DR: A new multi headed deep convolutional neural network model called ACP-MHCNN is proposed, for extracting and combining discriminative features from different information sources in an interactive way and outperforms other models for anticancer peptide identification by a substantial margin.
Posted ContentDOI
Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications
TL;DR: A clustering-based ensemble technique is presented and a developed brain game that distinguishes different human thoughts is developed employing the suggested ensemble technique to improve the classification performance of real-time BCI applications.
Journal ArticleDOI
CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data
Ochiuddin Miah,Rafsanjani Muhammod,Khondaker Abdullah Al Mamun,Dewan Md. Farid,Shiu Kumar,Alok Sharma,Abdollah Dehzangi +6 more
TL;DR: Miah et al. as discussed by the authors presented a new clustering-based ensemble technique called CluSem to mitigate the high dimensionality and dynamic behaviors of the real-time EEG data, which is able to improve the classification accuracy between 5% and 15% compared with the existing methods on their collected as well as the publicly available EEG datasets.