ARC Linkage: AI for electrification (AGL, AusNet, SpendWatt)
ARC Linkage LP230100439 ($150K, 2023) — AI for the electrification of Australian households, with AGL, AusNet, and SpendWatt.
Posts by Collection
portfolio
Cornerstone Solutions: IoT anomaly detection for construction sites
Embedded AI systems for construction site safety at Cornerstone Solutions Pty Ltd (2018–2019).
CSIRO Next Generation AI Graduates Program
CSIRO Next Generation AI Graduates Program ($1.4M, 2023) — AI for energy systems. Role: Investigator.
iMOVE CRC: AI for connected and automated transport
iMOVE CRC project (2022) on AI for connected and automated transport. Role: Investigator.
NexusCharge: intelligent EV charging
Australian Economic Accelerator project ($4M, 2024) translating ML research into a commercial intelligent EV charging product with ABB Australia.
Portfolio item number 1
Short description of portfolio item number 1
AI systems for electrical network inspection at Powercor
Production AI systems at Powercor Australia — computer vision for network inspection, Vision Language Models for document understanding, and MLOps infrastructure.
Random Finite Set framework for anomaly detection
PhD research on point-pattern learning and Random Finite Set (RFS) theory for defect and video anomaly detection.
Aerial solar-panel segmentation on AWS
Deep learning pipeline for solar panel segmentation from aerial imagery, deployed as an AWS Lambda service with SpendWatt.
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
An improved chaotic sequence based on neural network and its application to image encryption
Published in Journal of Kufa for Mathematics and Computer, 2018
A neural-network-based chaotic sequence generator applied to image encryption.
Recommended citation: Kamoona A.M., Alsaad S.N. (2018). "An improved chaotic sequence based on neural network and its application to image encryption." Journal of Kufa for Mathematics and Computer.
Particle swarm optimisation-based approach for multi-objective design
Published in Applied Soft Computing (Q1), 2019
A particle swarm optimisation approach for multi-objective engineering design problems.
Recommended citation: Kamoona A.M., Prayag M.B. (2019). "Particle swarm optimisation-based approach for multi-objective design." Applied Soft Computing.
Video anomaly detection using deep temporal encoding-decoding with multi-head attention
Published in Expert Systems with Applications (Q1), 2022
Deep temporal encoding-decoding with multi-head attention for video anomaly detection.
Recommended citation: Kamoona A.M., Bab-Hadiashar A., Hoseinnezhad R. (2022). "Video anomaly detection using deep temporal encoding-decoding with multi-head attention." Expert Systems with Applications, 205, 117699.
Prediction of photovoltaic power generation
Published in Energy Reports (Q1), 2023
Machine learning models for short-term photovoltaic power generation forecasting.
Recommended citation: Al Khafaf N., Kamoona A.M., Zhu J.G., Ali S.M.N., Jalili M. (2023). "Prediction of photovoltaic power generation." Energy Reports, 9.
Machine learning for energy demand prediction
Published in Energy Reports (Q1), 2023
A machine learning approach to short- and medium-term energy demand prediction.
Recommended citation: Al Khafaf N., Jalili M., Kamoona A.M., Zhu J.G. (2023). "Machine learning for energy demand prediction." Energy Reports, 9.
Multiple instance-based video anomaly detection using deep temporal encoding-decoding
Published in Expert Systems with Applications (Q1), 2023
A multiple-instance learning approach with deep temporal encoding-decoding for video anomaly detection.
Recommended citation: Kamoona A.M., Bab-Hadiashar A., Hoseinnezhad R. (2023). "Multiple instance-based video anomaly detection using deep temporal encoding-decoding." Expert Systems with Applications, 214, 119079.
Anomaly detection of defect using energy of point pattern features within random finite set framework
Published in Engineering Applications of Artificial Intelligence (Q1), 2024
A Random Finite Set framework using the energy of point-pattern features for defect anomaly detection.
Recommended citation: Kamoona A.M., Bab-Hadiashar A., Hoseinnezhad R. (2024). "Anomaly detection of defect using energy of point pattern features within random finite set framework." Engineering Applications of Artificial Intelligence, 132, 107857.
Online electric vehicle charging detection based on a memory-augmented transformer architecture
Published in Applied Energy (Q1), 2025
Memory-augmented transformer for online detection of EV charging events from smart-meter time series.
Recommended citation: Kamoona A.M., Lazarevic L., Al Khafaf N., Ali S.M.N., Jalili M., Razzaghi R. (2025). "Online electric vehicle charging detection based on a memory-augmented transformer architecture." Applied Energy, 377, 124549.
Impact of electric vehicle adoption on residential load profiles: An Australian case study
Published in Sustainable Energy, Grids and Networks (Q1), 2026
Australian case study on how growing EV adoption reshapes residential load profiles.
Recommended citation: Kamoona A.M., Al Khafaf N., Ali S.M.N., Jalili M., Razzaghi R., Yu X. (2026). "Impact of electric vehicle adoption on residential load profiles: An Australian case study." Sustainable Energy, Grids and Networks, 102481.
Electricity consumption forecasting for residential sector using temporal co-learning
Published in Applied Energy (Q1), 2026
A multi-task temporal co-learning framework for residential electricity consumption forecasting.
Recommended citation: Song H., ..., Kamoona A.M. et al. (2026). "Electricity consumption forecasting for residential sector using temporal co-learning." Applied Energy, 395, 125856.
talks
Particle swarm optimisation for multi-objective engineering design
Published:
Conference presentation at the IEEE Congress on Evolutionary Computation (CEC 2018). The work introduced a particle swarm optimisation approach for multi-objective engineering design and studied its convergence on standard benchmark problems. Related paper: Applied Soft Computing (2019).
IoT anomaly detection for construction site safety
Published:
Presenter at the RMIT IoT Research Forum. The talk covered embedded AI systems for construction site safety, based on my work as AI and Embedded Systems Engineer at Cornerstone Solutions Pty Ltd. It described how low-power devices can run anomaly detection models close to the sensor to flag unsafe events in real time.
Deep learning for visual anomaly detection and defect recognition
Published:
Invited speaker at the ANZAAS Science Talk series. The talk summarised my PhD research at RMIT University on deep learning methods for visual anomaly detection and defect recognition, including work on multi-head attention networks for video anomaly detection and Random Finite Set frameworks for point-pattern learning.
teaching
Digital Circuit Design
Undergraduate course, University of Kufa, Department of Electrical Engineering, 2016
Full course coordinator for 60+ students. Designed the syllabus, lectures, and lab program from scratch. Topics: combinational logic design, sequential circuits, finite state machines, VHDL modelling, and FPGA implementation. Hands-on laboratories used FPGA boards to build and test digital designs.
Engineering Electromagnetic Fields
Undergraduate course, University of Kufa, Department of Electrical Engineering, 2016
Independent delivery of a core electrical engineering course. Topics: vector calculus review, electrostatics and magnetostatics, Maxwell’s equations, plane wave propagation, and transmission line theory. Delivered lectures, set homework and exams, and supervised student projects.
Modelling and Simulation of Engineering Systems
Undergraduate course, RMIT University, School of Engineering, 2019
Lab demonstrator and teaching assistant (2019–2021). Course covered computational modelling of engineering systems, numerical methods (root finding, integration, ODE solvers), and simulation in MATLAB and Python. Supported over 40 students per semester in labs and tutorials, ran assessment reviews, and gave one-on-one help on modelling projects.
Digital Fundamentals
Undergraduate course, RMIT University, School of Engineering, 2019
Lab demonstrator for the Digital Fundamentals course at RMIT University. Topics: number systems, Boolean algebra, combinational and sequential logic, finite state machines, and the hardware-software interface. Ran weekly labs and guided students through logic-design assignments.
HDR and capstone supervision
Research supervision, RMIT University, 2022
Associate supervisor for one ongoing PhD candidate at RMIT University. Also supervised four industry-connected capstone projects in applied AI with Powercor Australia, iMOVE CRC, and Cornerstone Solutions. Supervision covers problem framing, dataset design, model development, and technical writing.
Intelligent Systems (EEET2171)
Undergraduate course, RMIT University, School of Engineering, 2025
Undergraduate course on machine learning and intelligent systems for engineering students at RMIT University. Topics: supervised and unsupervised learning, neural networks, backpropagation, gradient-based optimisation, model evaluation, and practical implementation in Python. Delivered lectures, ran labs, and set assessments. Course Experience Survey (CES) score: 3.4.