ORCID Profile
0000-0001-6876-1437
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Manufacturing Engineering | Simulation And Modelling | Manufacturing Safety and Quality | Safety And Quality | Control Systems, Robotics and Automation | Robotics And Mechatronics |
ANZSRC Socio-Economic Objective (SEO)
Aerospace Equipment | Automotive Equipment | Industrial Instruments | Injury control | Wind Energy | Road safety | Automotive equipment
Related Links
Publications
A Cluster Randomized Trial of a Personalized Multi-Condition Risk Assessment in Primary Care
Publisher: Elsevier BV
Date: 2017
Zika virus-induced hyper excitation precedes death of mouse primary neuron
Publisher: Springer Science and Business Media LLC
Date: 27-04-2018
EggCountAI: A Convolutional Neural Network Based Software for Counting of Aedes Aegypti Mosquito Eggs
Publisher: Research Square Platform LLC
Date: 25-05-2023
DOI: 10.21203/RS.3.RS-2963897/V1
Abstract: 1. Mosquito-borne diseases exert a huge impact on both animal and human populations, posing substantial health risks. Established practices, such as long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS), are proving inadequate for controlling mosquito-borne diseases. The behavioural fitness traits of mosquitoes, such as locomotion and fecundity, are crucial factors that influence the spread of diseases. Technological advancements, including Artificial Intelligence (AI), have provided various methods that can be used to monitor these traits effectively. 2. This study presents EggCountAI, a Mask RCNN (Region-based Convolutional Neural Network) based free automatic egg counting tool for Aedes aegypti mosquitoes, the primary vector of several life-threatening diseases, including dengue fever. EggCountAI takes a folder containing egg strip images as input and counts eggs in all the images without any supervision. EggCountAI also provides flexible filtration, considering the possibility of unwanted impurities of different sizes on images. The performance of the EggCountAI was tested using microscopic and macroscopic images of eggs laid on a paper strip. To validate EggCountAI's capability, the results were also compared with two commonly employed tools, ICount and MECVision, confirmed by manually counting all eggs on strips. 3. The results obtained from EggCountAI highlight its remarkable performance, achieving an overall percentage accuracy of 98.88% for micro images and 96.06% for macro images. EggCountAI also significantly outperformed two currently available tools, ICount and MECVision, in performance. The overall accuracy of ICount was 81.71% for micro images and 82.22% for macro images, while the overall accuracy of MECVision was 68.01% for micro images and 51.71% for macro images. The superior performance of the EggCountAI was most evident when handling overlapping or clustered eggs. 4. The use of such tools can benefit in establishing the role of mosquito fitness changes to improve epidemiological models and implement new mosquito management strategies. Introducing such tools can also help to reduce transmission by vectors quicker, by finding the mosquitoes' preferred sites to lay their eggs. Though the focus of this AI-based tool is to count the number of eggs, this tool can be modified to track other mosquito fitness parameters such as egg sizes.
Enhanced decision fusion of semantically segmented images via local majority saliency map
Publisher: Institution of Engineering and Technology (IET)
Date: 07-2017
DOI: 10.1049/EL.2016.4709
Automatic spike sorting by unsupervised clustering with diffusion maps and silhouettes
Publisher: Elsevier BV
Date: 04-2015
Reliable Switching Mechanism for Low Cost Multi-screen Eye Tracking Devices via Deep Recurrent Neural Networks
Publisher: IEEE
Date: 10-2018
Image fusion performance metric based on mutual information and entropy driven quadtree decomposition
Publisher: Institution of Engineering and Technology (IET)
Date: 2010
DOI: 10.1049/EL.2010.1778
Neural spike representation using Cepstrum
Publisher: IEEE
Date: 06-2014
Assessment of ICount software, a precise and fast egg counting tool for the mosquito vector Aedes aegypti
Publisher: Springer Science and Business Media LLC
Date: 18-11-2016
Statistical Modelling of Artificial Neural Network for Sorting Temporally Synchronous Spikes
Publisher: Springer International Publishing
Date: 2015
Neural signal analysis by landmark-based spectral clustering with estimated number of clusters
Publisher: IEEE
Date: 07-2014
Towards autonomous image fusion
Publisher: IEEE
Date: 12-2010
Efficient neural spike sorting using data subdivision and unification
Publisher: Public Library of Science (PLoS)
Date: 10-02-2021
DOI: 10.1371/JOURNAL.PONE.0245589
Abstract: Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.
Locally Sparsified Compressive Sensing for Improved MR Image Quality
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/SMC.2013.370
Dynamical Analysis of Neural Networks with Time-Varying Delays Using the LMI Approach
Publisher: Springer International Publishing
Date: 2015
The Study of Using Eye Movements to Control the Laparoscope Under a Haptically-Enabled Laparoscopic Surgery Simulation Environment
Publisher: IEEE
Date: 10-2018
Neurotropism and behavioral changes associated with Zika infection in the vector Aedes aegypti
Publisher: Informa UK Limited
Date: 25-04-2018
Dengue virus infection changes Aedes aegypti oviposition olfactory preferences
Publisher: Springer Science and Business Media LLC
Date: 04-09-2018
DOI: 10.1038/S41598-018-31608-X
Abstract: Aedes aegypti mosquitoes, main vectors for numerous flaviviruses, have olfactory preferences and are capable of olfactory learning especially when seeking their required environmental conditions to lay their eggs. In this study, we showed that semiochemical conditions during Aedes aegypti larval rearing affected future female choice for oviposition: water-reared mosquitoes preferred to lay eggs in water or p-cresol containers, while skatole reared mosquitoes preferred skatole sites. Using two independent behavioural assays, we showed that this skatole preference was lost in mosquitoes infected with dengue virus. Viral RNA was extracted from infected female mosquito heads, and an increase of virus load was detected from 3 to 10 days post infection, indicating replication in the insect head and possibly in the central nervous system. Expression of selected genes, potentially implied in olfactory learning processes, were also altered during dengue infection. Based on these results, we hypothesise that dengue virus infection alters gene expression in the mosquito’s head and is associated with a loss of olfactory preferences, possibly modifying oviposition site choice of female mosquitoes.
Image Fusion Metrics: Evolution in a Nutshell
Publisher: IEEE
Date: 04-2013
Noise level classification for EEG using Hidden Markov Models
Publisher: IEEE
Date: 05-2015
Intuitive Haptics Interface with Accurate Force Estimation and Reflection at Nanoscale
Publisher: Springer International Publishing
Date: 15-10-2014
Non-uniform sparsity in rapid compressive sensing MRI
Publisher: IEEE
Date: 10-2012
Unified selective sorting approach to analyse multi-electrode extracellular data
Publisher: Springer Science and Business Media LLC
Date: 24-06-2016
DOI: 10.1038/SREP28533
Abstract: Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.
Use of a Web-based Risk Appraisal Tool for Assessing Family History and Lifestyle Factors in Primary Care
Publisher: Springer Science and Business Media LLC
Date: 31-01-2013
Dengue virus infection changes Aedes aegypti oviposition olfactory preferences
Publisher: Cold Spring Harbor Laboratory
Date: 24-07-2018
DOI: 10.1101/375972
Abstract: Aedes aegypti mosquitoes, main vectors for numerous flaviviruses, have olfactory preferences and are capable of olfactory learning especially when seeking their required environmental conditions to lay their eggs. In this study, we showed that semiochemical conditions during Aedes aegypti larval rearing affected future female choice for oviposition: water-reared mosquitoes preferred to lay eggs in water or p-cresol containers, while skatole reared mosquitoes preferred skatole sites. Using two independent behavioural assays, we showed that this skatole preference was lost in mosquitoes infected with dengue virus. Viral RNA was extracted from infected female mosquito heads, and an increase of virus load was detected from 3 to 10 days post infection, indicating replication in the insect head and possibly in the central nervous system. Expression of selected genes, potentially implied in olfactory learning processes, were also altered during dengue infection. Based on these results, we hypothesise that dengue virus infection alters gene expression in the mosquito’s head and is associated with a loss of olfactory preferences, possibly modifying oviposition site choice of female mosquitoes.
Genomic mutations and changes in protein secondary structure and solvent accessibility of SARS-CoV-2 (COVID-19 virus)
Publisher: Springer Science and Business Media LLC
Date: 10-02-2021
DOI: 10.1038/S41598-021-83105-3
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.
Eye behaviour as a hazard perception measure
Publisher: IEEE
Date: 04-2018
A review on cluster estimation methods and their application to neural spike data
Publisher: IOP Publishing
Date: 16-04-2018
Abstract: The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons-'spike sorting'-is an indispensable step in studying the function and the response of an in idual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.
RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian Process
Publisher: Public Library of Science (PLoS)
Date: 26-10-2016
Defining Sub-Regions in Locally Sparsified Compressive Sensing MRI
Publisher: ACTAPRESS
Date: 2013
A classifier approach to multi-screen switching based on low cost eye-trackers
Publisher: IEEE
Date: 04-2018
Force reflection distribution of haptic devices
Publisher: IEEE
Date: 12-2014
Multiclass EEG data classification using fuzzy systems
Publisher: IEEE
Date: 07-2017
Flight Traits of Dengue-Infected Aedes Aegypti Mosquitoes
Publisher: Research Square Platform LLC
Date: 09-08-2023
DOI: 10.21203/RS.3.RS-3245622/V1
Abstract: Understanding the flight behaviour of dengue-infected mosquitoes can play a vital role in various contexts, including modelling disease risks and developing effective interventions against dengue. Studies on the locomotor activity of dengue-infected mosquitoes have often faced challenges in terms of methodology. Some studies used small tubes, which impacted the natural movement of the mosquitoes, while others that used cages did not capture the three-dimensional flights, despite mosquitoes naturally flying in three dimensions. Here we performed a comprehensive analysis of the three-dimensional flight behaviour of dengue-infected Aedes aegypti mosquitoes using Mask RCNN (Region-based Convolutional Neural Network). This analysis considered varying parameters such as flight duration, number of flights, Euclidean distance, flight speed, and the volume (space) covered during flights. Results revealed that dengue infection significantly increases the flight duration and volume (space) covered during flights whilst decreasing the total number of flights compared to uninfected mosquitoes. The study did not observe any evident impact on the Euclidean distance and speed of Aedes aegypti . These results highlight the intricate relationship between dengue infection and the flight behaviour of Aedes aegypti , providing valuable insights into the virus transmission dynamics.
Age-Related Effects of Multi-screen Setup on Task Performance and Eye Movement Characteristics
Publisher: IEEE
Date: 10-2018
Electrophysiological evidence of RML12 mosquito cell line towards neuronal differentiation by 20-hydroxyecdysdone
Publisher: Springer Science and Business Media LLC
Date: 04-07-2018
DOI: 10.1038/S41598-018-28357-2
Abstract: Continuous cell lines from insect larval tissues are widely used in different research domains, such as virology, insect immunity, gene expression, and bio pharmacology. Previous study showed that introduction of 20-hydroxyecdysone to Spodoptera cell line induced a neuron-like morphology with neurite extensions. Despite some results suggesting potential presence of neuro-receptors, no study so far has shown that these neuron-induced cells were functional. Here, using microelectrode arrays, we showed that the mosquito cell line, RML12, differentiated with 20-hydroxyecdysone, displays spontaneous electrophysiological activity. Results showed that these cells can be stimulated by GABAergic antagonist as well as nicotinic agonist. These results provide new evidence of neuron-like functionality of 20-hydroxyecdysone induced differentiated mosquito cell line. Finally, we used this new model to test the effects of two insecticides, temephos and permethrin. Our analysis revealed significant changes in the spiking activity after the introduction of these insecticides with prolonged effect on the neuronal activity. We believe that this differentiated mosquito neuronal cell model can be used for high-throughput screening of new pesticides on insect nervous system instead of primary neurons or in vivo studies.
Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes
Publisher: Elsevier BV
Date: 08-2017
Human Performance Measures for Interactive Haptic-Audio-Visual Interfaces
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
DOI: 10.1109/TOH.2012.41
Wavelets/multiwavelets bases and correspondence estimation problem: An analytic study
Publisher: IEEE
Date: 12-2010
Cepstrum based unsupervised spike classification
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/SMC.2013.633
Spike sorting using hidden markov models
Publisher: Springer Berlin Heidelberg
Date: 2013
Chaotic synchronization of time-delay coupled Hindmarsh–Rose neurons via nonlinear control
Publisher: Springer Science and Business Media LLC
Date: 27-07-2016
Locally Sparsified Compressive Sensing in Magnetic Resonance Imaging
Publisher: Springer International Publishing
Date: 2015
Related Organisations
No related organisations have been discovered for Asim Bhatti.
Related Funding Activities
Linkage Projects - Grant ID: LP110200364
Start Date: 06-2012
End Date: 12-2015
Amount: $255,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Infrastructure, Equipment And Facilities - Grant ID: LE0883093
Start Date: 09-2007
End Date: 12-2008
Amount: $285,000.00
Funder: Australian Research Council
View Funded Activity