33 citations · 110 across the 19 of their papers we have counts for
19 papers
Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)
Mohsen Soltanpour, Muhammad Yousefnezhad, Russ Greiner +2
As one of the leading causes of mortality and disability worldwide, Acute Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly interrupted because of a block…
Shared Space Transfer Learning for analyzing multi-site fMRI data
Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang +2
Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing diff…
Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset
Muhammad Yousefnezhad, Jeffrey Sawalha, Alessandro Selvitella +1
Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by differen…
Supervised Hyperalignment for multi-subject fMRI data alignment
Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han +1
Hyperalignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Reson…
Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data
Xiaoliang Sheng, Muhammad Yousefnezhad, Tonglin Xu +2
Representational Similarity Analysis (RSA) aims to explore similarities between neural activities of different stimuli. Classical RSA techniques employ the inverse of the covarianc…
Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis
Muhammad Yousefnezhad, Daoqiang Zhang
In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standa…
Gradient Hyperalignment for multi-subject fMRI data alignment
Tonglin Xu, Muhammad Yousefnezhad, Daoqiang Zhang
Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make…
Deep Hyperalignment
Muhammad Yousefnezhad, Daoqiang Zhang
This paper proposes Deep Hyperalignment (DHA) as a regularized, deep extension, scalable Hyperalignment (HA) method, which is well-suited for applying functional alignment to fMRI…
Anatomical Pattern Analysis for decoding visual stimuli in human brains
Muhammad Yousefnezhad, Daoqiang Zhang
Background: A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVP…
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains
Muhammad Yousefnezhad, Daoqiang Zhang
Multivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of c…
WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory
Muhammad Yousefnezhad, Sheng-Jun Huang, Daoqiang Zhang
The Wisdom of Crowds (WOC), as a theory in the social science, gets a new paradigm in computer science. The WOC theory explains that the aggregate decision made by a group is often…
Local Discriminant Hyperalignment for multi-subject fMRI data alignment
Muhammad Yousefnezhad, Daoqiang Zhang
Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results acro…
A new selection strategy for selective cluster ensemble based on Diversity and Independency
Muhammad Yousefnezhad, Ali Reihanian, Daoqiang Zhang +1
This research introduces a new strategy in cluster ensemble selection by using Independency and Diversity metrics. In recent years, Diversity and Quality, which are two metrics in…
Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images
Muhammad Yousefnezhad, Daoqiang Zhang
A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a crit…
Wisdom of Crowds cluster ensemble
Hosein Alizadeh, Muhammad Yousefnezhad, Behrouz Minaei Bidgoli
The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, th…
Evaluating the effect of topic consideration in identifying communities of rating-based social networks
Ali Reihanian, Behrouz Minaei-Bidgoli, Muhammad Yousefnezhad
Finding meaningful communities in social network has attracted the attentions of many researchers. The community structure of complex networks reveals both their organization and h…
A New Approach in Persian Handwritten Letters Recognition Using Error Correcting Output Coding
Maziar Kazemi, Muhammad Yousefnezhad, Saber Nourian
Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable resul…
A Wised Routing Protocols for Leo Satellite Networks
Saeid Aghaei Nezhad Firouzja, Muhammad Yousefnezhad, Masoud Samadi +1
This Study proposes a routing strategy of combining a packet scheduling with congestion control policy that applied for LEO satellite network with high speed and multiple traffic.…
Weighted Spectral Cluster Ensemble
Muhammad Yousefnezhad, Daoqiang Zhang
Clustering explores meaningful patterns in the non-labeled data sets. Cluster Ensemble Selection (CES) is a new approach, which can combine individual clustering results for increa…