Khalid Mahmood Malik

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Associate Professor

Oakland University

United States

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About

Dr. Khalid Mahmood Malik is an Associate Professor at Oakland University in the United States. His research areas include medical image classification, cybersecurity in smart grid systems, and deepfake detection methodologies. He has published extensively on topics such as multimodal approaches for explainable AI and the application of machine learning in healthcare, particularly in prognosis prediction and risk assessment. Dr. Malik's work emphasizes innovative frameworks for automated knowledge generation and risk factor identification.

Recent Grants

Grant: Close

Deep Forgery Detector

Open Date: 2022-02-01

Close Date: 2023-01-01

Grant: Close

A Fully Automated AI-based Personalized Subarachnoid Hemorrhage Prediction Tool

Open Date: 2021-09-01

Close Date: 2022-08-01

Grant: Close

SaTC: CORE: Small: Collaborative: ForensicExaminer: Testbed for Benchmarking Digital Audio Forensic Algorithms

Open Date: 2018-09-01

Close Date: 2021-08-01

Grant: Close

NeuroAssist: An Intelligent Decision Support System for Prediction of Brain Aneurysm Rupture

Open Date: 2018-09-01

Close Date: 2019-05-01

Articles (15)

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2025

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2023

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2022

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2022

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2022

Collaborators (4)

Mozaherul Hoque Abul Hasanat

SAUDI ARABIA

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Khan Muhammad

Assistant Professor (Tenure-Track)

Sungkyunkwan University

SOUTH KOREA

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Hafiz Malik

University of Michigan

UNITED STATES

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Abdul Khader Jilani Saudagar

SAUDI ARABIA

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Khalid Mahmood Malik

Research Interests

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About

Recent Grants

Articles (15)

One-shot learning for generalization in medical image classification across modalities

Cybersecurity Challenges in Smart Grid Systems: Current and Emerging Attacks, Opportunities, and Recommendations

Generalized Deepfake Detection Using Identity, Behavioral, and Geometric Signatures

Multimodal Neurosymbolic Approach for Explainable Deepfake Detection

ExplaNET: A Descriptive Framework for Detecting Deepfakes With Interpretable Prototypes

Automated clinical knowledge graph generation framework for evidence based medicine

Context-aggregator: An approach of loss- and class imbalance-aware aggregation in federated learning

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype

Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning

Toward Grading Subarachnoid Hemorrhage Risk Prediction: A Machine Learning-Based Aneurysm Rupture Score

AVFakeNet: A unified end-to-end Dense Swin Transformer deep learning model for audio–visual deepfakes detection

Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward

COVID-19 Genome Sequence Analysis for New Variant Prediction and Generation

Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification

Voice spoofing detector: A unified anti-spoofing framework

Collaborators (4)

Mozaherul Hoque Abul Hasanat

Khan Muhammad

Hafiz Malik

Abdul Khader Jilani Saudagar

Make your next move.

Khalid Mahmood Malik

Research Interests

Explore related searches

Contact this professor

About

Recent Grants

Articles (15)

One-shot learning for generalization in medical image classification across modalities

Cybersecurity Challenges in Smart Grid Systems: Current and Emerging Attacks, Opportunities, and Recommendations

Generalized Deepfake Detection Using Identity, Behavioral, and Geometric Signatures

Multimodal Neurosymbolic Approach for Explainable Deepfake Detection

ExplaNET: A Descriptive Framework for Detecting Deepfakes With Interpretable Prototypes

Automated clinical knowledge graph generation framework for evidence based medicine

Context-aggregator: An approach of loss- and class imbalance-aware aggregation in federated learning

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype

Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning

Toward Grading Subarachnoid Hemorrhage Risk Prediction: A Machine Learning-Based Aneurysm Rupture Score

AVFakeNet: A unified end-to-end Dense Swin Transformer deep learning model for audio–visual​ deepfakes detection

Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward

COVID-19 Genome Sequence Analysis for New Variant Prediction and Generation

Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification

Voice spoofing detector: A unified anti-spoofing framework

Collaborators (4)

Mozaherul Hoque Abul Hasanat

Khan Muhammad

Hafiz Malik

Abdul Khader Jilani Saudagar

Make your next move.

AVFakeNet: A unified end-to-end Dense Swin Transformer deep learning model for audio–visual deepfakes detection