614 citations · 1.3k across the 16 of their papers we have counts for
17 papers
Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection
Reza Basiri, Milos R. Popovic, Shehroz S. Khan
Diabetic Foot Ulcer (DFU) is a condition requiring constant monitoring and evaluations for treatment. DFU patient population is on the rise and will soon outpace the available heal…
Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction
Zakary Georgis-Yap, Milos R. Popovic, Shehroz S. Khan
Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abr…
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions
Pratik K. Mishra, Alex Mihailidis, Shehroz S. Khan
The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raise…
Multi Visual Modality Fall Detection Dataset
Stefan Denkovski, Shehroz S. Khan, Brandon Malamis +3
Falls are one of the leading cause of injury-related deaths among the elderly worldwide. Effective detection of falls can reduce the risk of complications and injuries. Fall detect…
FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks
Ali Abedi, Shehroz S. Khan
Federated Learning (FL) and Split Learning (SL) are privacy-preserving Machine-Learning (ML) techniques that enable training ML models over data distributed among clients without r…
Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays
Shehroz S. Khan, Faraz Khoshbakhtian, Ahmed Bilal Ashraf
The current COVID-19 pandemic is now getting contained, albeit at the cost of morethan2.3million human lives. A critical phase in any pandemic is the early detection of cases to de…
Spatio-Temporal Adversarial Learning for Detecting Unseen Falls
Shehroz S. Khan, Jacob Nogas, Alex Mihailidis
Fall detection is an important problem from both the health and machine learning perspective. A fall can lead to severe injuries, long term impairments or even death in some cases.…
initKmix -- A Novel Initial Partition Generation Algorithm for Clustering Mixed Data using k-means-based Clustering
Amir Ahmad, Shehroz S. Khan
Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms u…
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies
Ahmed Ashraf, Shehroz Khan, Nikhil Bhagwat +2
Medical imaging machine learning algorithms are usually evaluated on a single dataset. Although training and testing are performed on different subsets of the dataset, models built…
Survey of state-of-the-art mixed data clustering algorithms
Amir Ahmad, Shehroz S. Khan
Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied…
Bootstrapping and Multiple Imputation Ensemble Approaches for Missing Data
Shehroz S. Khan, Amir Ahmad, Alex Mihailidis
Presence of missing values in a dataset can adversely affect the performance of a classifier. Single and Multiple Imputation are normally performed to fill in the missing values. I…
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders
Shehroz S. Khan, Babak Taati
A fall is an abnormal activity that occurs rarely, so it is hard to collect real data for falls. It is, therefore, difficult to use supervised learning methods to automatically det…
Review of Fall Detection Techniques: A Data Availability Perspective
Shehroz S. Khan, Jesse Hoey
A fall is an abnormal activity that occurs rarely; however, missing to identify falls can have serious health and safety implications on an individual. Due to the rarity of occurre…
Relationship between Variants of One-Class Nearest Neighbours and Creating their Accurate Ensembles
Shehroz S. Khan, Amir Ahmad
In one-class classification problems, only the data for the target class is available, whereas the data for the non-target class may be completely absent. In this paper, we study o…
Detecting Falls with X-Factor Hidden Markov Models
Shehroz S. Khan, Michelle E. Karg, Dana Kulic +1
Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being. However, falling is a short term activity th…
One-Class Classification: Taxonomy of Study and Review of Techniques
Shehroz S. Khan, Michael G. Madden
One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation con…
Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model
Marek Grzes, Jesse Hoey, Shehroz Khan +4
Assistive systems for persons with cognitive disabilities (e.g. dementia) are difficult to build due to the wide range of different approaches people can take to accomplishing the…