Search
Search Results
-
Unsupervised and Semi-supervised Bias Benchmarking in Face Recognition
We introduce Semi-supervised Performance Evaluation for Face Recognition (SPE-FR). SPE-FR is a statistical method for evaluating the performance and...
-
Nonparametric Bayesian Deep Visualization
Visualization methods such as t-SNE [1] have helped in knowledge discovery from high-dimensional data; however, their performance may degrade when...
-
Transfer Learning Fusion and Stacked Auto-encoders for Viral Lung Disease Classification
The objective of this research endeavor is to identify an effective model for the classification of multiple viral respiratory diseases, encompassing...
-
Empowering Interpretable, Explainable Machine Learning Using Bayesian Network Classifiers
Even before the deep learning era, the machine learning (ML) community commonly believed that while decision trees, neural networks (NNs), support...
-
Prostate classification network (PC-Net) for automated classification of Prostate cancer in Magnetic resonance imaging
Prostate cancer (PCa) is found to be the second most common cause of death in men after lung cancer, making it necessary to diagnose as early as...
-
Autism Spectrum Disorder Classification Based on Interpersonal Neural Synchrony: Can Classification be Improved by Dyadic Neural Biomarkers Using Unsupervised Graph Representation Learning?
Research in machine learning for autism spectrum disorder (ASD) classification bears the promise to improve clinical diagnoses. However, recent...
-
Unsupervised anomaly detection for network traffic using artificial immune network
In the existing approaches of multifarious knowledge based anomaly detection for network traffic, the priori knowledge labelled by human experts has...
-
Cyber Physical System for Distributed Network Using DoS Based Hierarchical Bayesian Network
The Cyber Physical System (CPS) is a prime target for cyber attacks due to its heterogeneity and connectivity with physical equipment. This paper...
-
TaxoSBERT: Unsupervised Taxonomy Expansion Through Expressive Semantic Similarity
Knowledge graphs are crucial resources for a large set of document management tasks, such as text retrieval and classification as well as natural...
-
A multi-label classification system for anomaly classification in electrocardiogram
Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label...
-
An efficient Bayesian network model (BNM) for software risk prediction in design phase development
The primary purpose of a software risk assessment is to predict risks and vulnerabilities that may exist in each phase of the software development...
-
A Hybrid Model Integrating Improved Fuzzy c-means and Optimized Mixed Kernel Relevance Vector Machine for Classification of Coal and Gas Outbursts
The class labels of collected coal and gas outbursts sample data may be wrong, if these collected sample data are directly used for outbursts...
-
SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection
There have been multiple attempts to tackle the problem of identifying abnormal instances that have inconsistent behaviors in multi-view data (i.e.,...
-
Unsupervised Graph Neural Networks for Source Code Similarity Detection
In this paper, we propose a novel unsupervised approach for code similarity and clone detection that is based on Graph Neural Networks. We propose a...
-
A credit risk assessment on borrowers classification using optimized decision tree and KNN with bayesian optimization
Credit risk evaluation is extremely important in the peer-to-peer lending model for finding out the defaulters. In this proposed work, a machine...
-
Deep Convolutional Neural Network for Knowledge-Infused Text Classification
Deep neural networks are extensively used in text mining and Natural Language Processing is to enable computers to understand, analyze, and generate...
-
Graph Convolution Networks for Unsupervised Learning
In recent years, graph convolution networks (GCN) have been proposed as semi-supervised learning approaches. In this paper, we introduce a new...
-
Auto focusing of in-Line Holography based on Stacked Auto Encoder with Sparse Bayesian Regression and Compressive Sensing
In recent years, Digital holography has emerged as an exceptional imaging technology for tracking high-contrast object particles and, interestingly,...
-
A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption
Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However,...
-
Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks
Unsupervised image transfer enables intra- and inter-modality image translation in applications where a large amount of paired training data is not...