Search Results for author:
Found 20 papers, 6 papers with code
A Hybrid Local Search for Simplified Protein Structure Prediction
Protein structure prediction based on Hydrophobic-Polar energy model essentially becomes searching for a conformation having a compact hydrophobic core at the center.
CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval
Due to the advancements in technology number of entries in the structural database of proteins are increasing day by day.
GreMuTRRR: A Novel Genetic Algorithm to Solve Distance Geometry Problem for Protein Structures
In this paper, we propose a new genetic algorithm for solving the Euclidean distance geometry problem for protein structure prediction given sparse NMR data.
FGPGA: An Efficient Genetic Approach for Producing Feasible Graph Partitions
Graph partitioning, a well studied problem of parallel computing has many applications in diversified fields such as distributed computing, social network analysis, data mining and many other domains.
GraATP: A Graph Theoretic Approach for Automated Theorem Proving in Plane Geometry
Automated Theorem Proving (ATP) is an established branch of Artificial Intelligence.
Stochastic Local Search for Pattern Set Mining
Local search methods can quickly find good quality solutions in cases where systematic search methods might take a large amount of time.
An Efficient Genetic Algorithm for Discovering Diverse-Frequent Patterns
Working with exhaustive search on large dataset is infeasible for several reasons.
A novel and effective scoring scheme for structure classification and pairwise similarity measurement
The effectiveness of our method is tested on standard benchmark structures.
LIUBoost : Locality Informed Underboosting for Imbalanced Data Classification
The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning.
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 13 imbalance binary and multi-class datasets with various imbalance ratios.
MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
The performance of MEBoost has been evaluated on 12 benchmark imbalanced datasets with state of the art ensemble methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost.
FRnet-DTI: Deep Convolutional Neural Networks with Evolutionary and Structural Features for Drug-Target Interaction
In this paper, we present FRnet-DTI, an auto encoder and a convolutional classifier for feature manipulation and drug target interaction prediction.
A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model
In this work, we have developed a visual interactive web application, namely d-DeVIS, which helps to visualize the internal reasoning of the learning model which is trained on the audio data.
iPromoter-BnCNN: a Novel Branched CNN Based Predictor for Identifying and Classifying Sigma Promoters
We present iPromoter-BnCNN for identification and accurate classification of six types of promoters - sigma24, sigma28, sigma32, sigma38, sigma54, sigma70.
i6mA-CNN: a convolution based computational approach towards identification of DNA N6-methyladenine sites in rice genome
DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification and is responsible for many biological functions.
Text2Chart: A Multi-Staged Chart Generator from Natural Language Text
Firstly, it identifies the axis elements of a chart from the given text known as x and y entities.
DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic Languages
Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods which we found rather inefficient.
DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Images
In addition, we introduce a novel triple attention gate module and a hybrid triple attention module to encourage selective modeling of relevant medical image features.
iResSENet: An Accurate Convolutional Neural Network for Retinal Blood Vessel Segmentation
In this paper, we propose iResSENet, a novel deep learning-based image segmentation model based on U-Net architecture.
FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs
Furthermore, any publication that utilizes this resource should acknowledge the original paper, and the authors are encouraged to share their code and models to assist the research community in replicating the experiments and promoting the field of medical imaging.
