63 citations · 144 across the 6 of their papers we have counts for
6 papers
ChoiceNet: CNN learning through choice of multiple feature map representations
Farshid Rayhan, Aphrodite Galata, Timothy F. Cootes
We introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network…
FRnet-DTI: Deep Convolutional Neural Networks with Evolutionary and Structural Features for Drug-Target Interaction
Farshid Rayhan, Sajid Ahmed, Zaynab Mousavian +2
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto encoder and a…
MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
Farshid Rayhan, Sajid Ahmed, Asif Mahbub +4
Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machin…
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Farshid Rayhan, Sajid Ahmed, Asif Mahbub +3
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing lea…
LIUBoost : Locality Informed Underboosting for Imbalanced Data Classification
Sajid Ahmed, Farshid Rayhan, Asif Mahbub +4
The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinal…
iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting
Farshid Rayhan, Sajid Ahmed, Swakkhar Shatabda +4
Prediction of new drug-target interactions is extremely important as it can lead the researchers to find new uses for old drugs and to realize the therapeutic profiles or side effe…