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Bilal Mirza
Researcher at University of California, Los Angeles
Publications - 111
Citations - 1913
Bilal Mirza is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Extreme learning machine & Online machine learning. The author has an hindex of 17, co-authored 109 publications receiving 1568 citations. Previous affiliations of Bilal Mirza include Singapore Polytechnic & Singapore University of Technology and Design.
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The absence of reactive astrocytosis is indicative of a unique inflammatory process in Parkinson's disease.
TL;DR: The inflammatory process in Parkinson's disease is characterized by activation of resident microglia without reactive astrocytosis, suggesting that the progressive loss of dopaminergic neurons in Parkinson’s disease is an ongoing neurodegenerative process with a minimum of involvement of the surrounding nervous tissue.
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Machine Learning and Integrative Analysis of Biomedical Big Data.
Bilal Mirza,Wei Wang,Jie Wang,Howard Choi,Neo Christopher Chung,Neo Christopher Chung,Peipei Ping +6 more
TL;DR: In this article, state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.
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Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift
Bilal Mirza,Zhiping Lin,Nan Liu +2 more
TL;DR: A computationally efficient framework, referred to as ensemble of subset online sequential extreme learning machine (ESOS-ELM), is proposed for class imbalance learning from a concept-drifting data stream and a change detection mechanism to promptly detect concept drifts is proposed.
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The risk of hardware infection in deep brain stimulation surgery is greater at impulse generator replacement than at the primary procedure.
TL;DR: It is unclear why infection rates should be more than three times higher after IPG replacement surgery than after the de novo DBS procedure, but the former is a shorter and simpler procedure to conduct.
Journal ArticleDOI
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
TL;DR: WOS-ELM is a general online learning method that alleviates the class imbalance problem in both chunk-by-chunk and one- by-one learning and an appropriate weight setting for CIL is selected in a computationally efficient manner.