Amir Gandomi

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Professor of Data Science

University of Sydney

Australia

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About

Amir Gandomi is a Professor of Data Science at the University of Technology Sydney, Australia. His research interests encompass various domains, including machine learning applications in healthcare, predictive modeling, and structural optimization design. Recent publications include works on adaptive strategy management, semantic-preserving feature partitioning, and advancements in robust machine learning frameworks for diverse real-world applications.

Recent Grants

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Genetic Programming for Big Data Analytics

Open Date: 2021-05-01

Close Date: 2024-05-01

Grant: Close

BEACON Distinguished Postdoctoral Fellowship

Open Date: 2015-06-01

Close Date: 2017-08-01

Grant: Close

Doctoral Consortium Grant

Open Date: 2014-12-01

Close Date: 2014-12-01

Grant: Close

Summer Graduate Fellowships

Open Date: 2014-06-01

Close Date: 2014-08-01

Amir Gandomi

Research Interests

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About

Recent Grants

Articles (30)

Adaptive Strategy Management: A new framework for large-scale structural optimization design

Semantic-Preserving Feature Partitioning for multi-view ensemble learning

Effective Predictive Modelling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting

Machine learning approaches for RCPT modeling of concrete

Improved leukocyte classification in bone marrow cytology using convolutional neural network with contrast enhancement.

Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning.

Developing a robust machine learning framework for predicting the behavior of large-scale structure

Mutation adaptive cuckoo search hybridized naked mole rat algorithm for industrial engineering problems.

An approach for crop recommendation with uncertainty quantification based on machine learning for sustainable agricultural decision-making

Role of deep learning in cognitive healthcare: Wearable signal analysis, algorithms, benefits, and challenges

Advancements in optimizing wave energy converter geometry utilizing metaheuristic algorithms

An analysis of causative factors for road accidents using partition around medoids and hierarchical clustering techniques

An enhanced discrete particle swarm optimization for structural k-Anonymity in social networks

Deep learning for enhanced brain Tumor Detection and classification

An Agent-based Framework for Transition from Traditional to Advanced Water Supply Systems in Arid Regions

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Optimization of power take-off system settings and regional site selection procedure for a wave energy converter

Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis

Enhancing load prediction for structures with concrete overlay using transfer learning of time–frequency feature-based deep models

Levenberg-Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning.

A stacking classifiers model for detecting heart irregularities and predicting Cardiovascular Disease

Formulation of Constitutive Viscoelastic Properties of Modified Bitumen Mastic Using Genetic Programming

Revolutionizing sustainable supply chain management: A review of metaheuristics

A Three-Factor-Based Authentication Scheme of 5G Wireless Sensor Networks for IoT System

A comparative study on evolutionary multi-objective algorithms for next release problem

A multi-model data fusion methodology for reservoir water quality based on machine learning algorithms and bayesian maximum entropy

A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization

Internet-of-Things-Based Sensor Module for Respiratory Tracking System

Self-Adaptive Predictive Passenger Flow Modeling for Large-Scale Railway Systems

Spline Search for Slip Surfaces in 3D Slopes

Collaborators (4)

Malik Al-Wardy

Ulrike Dackermann

Ting Guo

Yang Wang

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