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Found 13 papers, 3 papers with code
Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers
In this paper, we analyze the effect on fairness in the context of graph data (node attributes) imputation using different embedding and neural network methods.
Computing Graph Descriptors on Edge Streams
Operating on edge streams allows us to avoid storing the entire graph in memory, and controlling the sample size enables us to keep the runtime of our algorithms within desired bounds.
Effective and scalable clustering of SARS-CoV-2 sequences
Using a $k$-mer based feature vector generation and efficient feature selection methods, our approach is effective in identifying variants, as well as being efficient and scalable to millions of sequences.
A k-mer Based Approach for SARS-CoV-2 Variant Identification
With the rapid spread of the novel coronavirus (COVID-19) across the globe and its continuous mutation, it is of pivotal importance to design a system to identify different known (and unknown) variants of SARS-CoV-2.
Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal
In this paper, we investigate the effect of the analysis window and feature selection on classification accuracy of different hand and wrist movements using time-domain features.
Fair Allocation Based Soft Load Shedding
However, these solutions are not fair in terms of electricity distribution.
Estimating Descriptors for Large Graphs
State-of-the-art algorithms for computing descriptors require the entire graph to be in memory, entailing a huge memory footprint, and thus do not scale well to increasing sizes of real-world networks.
Databases
Short-Term Load Forecasting Using AMI Data
A prominent feature of \textsc{fmf} is that it works at any level of user-specified granularity, both in the temporal (from a single hour to days) and spatial dimensions (a single household to groups of consumers).
Predicting Attributes of Nodes Using Network Structure
Predicting node attributes in such graphs is an important problem with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement.
A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
In this work, we present a data augmentation strategy and a multi-cascaded model for improved paraphrase detection in short texts.
Efficient Data Analytics on Augmented Similarity Triplets
Data analysis require a pairwise proximity measure over objects.
Language Independent Sentiment Analysis
These methods, however apply to texts written in a specific language.
A Multi-cascaded Deep Model for Bilingual SMS Classification
Our model achieves high accuracy for classification on this dataset and outperforms the previous model for multilingual text classification, highlighting language independence of McM.
