Papers by A/Prof Abeer Alsadoon
University students’ intention to use search engines for research purposes: A structural equation modeling approach
Teaching empirical educational research in higher education involves implementing a very useful w... more Teaching empirical educational research in higher education involves implementing a very useful web tool for bibliography/scientific literature research, a search engine specifically employed for scientific papers, such as “Google Scholar”. The aim of this study is to examine undergraduate students’ acceptance of technology, through their intention to adopt and use a specific search engine for research purposes. To accomplish this goal, a questionnaire was administered to 225 students from two Universities in Greece. The study was based on TAM (Technology Acceptance Model), reinforced by four external determinants (perceived self-efficacy, subjective norms, facilitating conditions and technological complexity), that contributed to the indirect prediction of the behavioral intention to use the particular search engine. The results of the survey confirm that the main factors of TAM, perceived ease of use and perceived usefulness are significant determinants of students’ behavioral int...
springer, 2021
Accurate food image classification is often critical to accurately monitor the dietary assessment... more Accurate food image classification is often critical to accurately monitor the dietary assessment to reduce the risk of different heart-related diseases, obesity, diabetes, and other related health conditions. The accuracy and efficiency of image classification results when using traditional deep learning methods were less than optimal. This research aimed at enhancing the classification and prediction accuracy of food images and reducing the processing time by using the Deep Convolutional Neural Network (DCN) algorithm. The solution starts by using the Modified Loss function, the images are fed into the DCN for features extraction through alternating between convolutional layers and pooling layers, then this is followed by a fully connected layer. Finally, the Softmax function is used to classify the images. The result was compared during the classification phase in the DCN. The proposed solution enhanced the accuracy of the classification by using the regularized loss function and lowered the processing time by decreasing the weights of the neurons in the neural network. Probability score is used as the evaluation metric for the accuracy, and total execution time is used as the evaluation metric for the speed of the algorithm. The combination of deep neural network with regularized cross entropy cost function has improved the fast-food images classification by ahcieving better processing time by 40~50s and accuracy by 5% in average.
Springer, 2021
In recent years, Augmented Reality (AR) has gained more attention as an effective tool in medical... more In recent years, Augmented Reality (AR) has gained more attention as an effective tool in medical surgeries. The potentials of using AR in the medical field can change conventional medical procedures. However, the technology still facing fundamental challenges, especially hidden organs, for example, the organs behind the bowel and liver. The surgeries in these areas lack accuracy in the visualization of the soft tissues behind the bowel and liver like the uterus and gall bladder. This research aims to improve the accuracy of visualisation and the processing time of the augmented video. The proposed system consists of an enhanced super-pixel algorithm with variance weight adaptation and subsampling method. The simulation studies show significant improvements in visualization accuracy and a reduction in processing time. The results show reduced visualisation error by 0.23 mm. It provides better accuracy of the video in terms of visualization error from 1.58~1.83 mm to 1.35~1.60 mm, and the processing time decreases from 50~58 ms/frames to 40~48 ms/frames. The proposed system \ focused on the pixel refinement for the 3d reconstruction of the soft tissue, which helps solve the issue of visualising the bowel and liver in an augmented video.
Wiley, 2021
Background and Aim: Most of the mixed reality models used in the surgical tele
This paper works on one of the most recent pedestrian crowd evacuation models-i.e., "a simulation... more This paper works on one of the most recent pedestrian crowd evacuation models-i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques"-which was developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it into the model. This method enables the developed model to find a more appropriate evacuation area design regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output-i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees' evacuation times of each exit door location; then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs.
springer, 2020
Your article is protected by copyright and all rights are held exclusively by Springer Science+Bu... more Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media, LLC, part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to selfarchive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".
Your article is protected by copyright and all rights are held exclusively by Springer Science+Bu... more Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media, LLC, part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to selfarchive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".
Security threats are crucial challenges that deter Mixed reality (MR) communication in medical te... more Security threats are crucial challenges that deter Mixed reality (MR) communication in medical telepresence. This research aims to improve the security by reducing the chances of types of various attacks occurring during the real-time data transmission in surgical telepresence as well as reduce the time of the cryptographic algorithm and keep the quality of the media used. The proposed model consists of an enhanced RC6 algorithm in combination. Dynamic keys are generated from the RC6 algorithm mixed with RC4 to create dynamic S-box and permutation table, preventing various known attacks during the real-time data transmission. For every next session, a new key is created, avoiding possible reuse of the same key from the attacker. The results obtained from our proposed system are showing better performance compared to the state of art. The resistance to the tested attacks is measured throughout the entropy, Pick to Signal Noise Ratio (PSNR) is decreased for the encrypted image than the state of art, structural similarity index (SSIM) closer to zero. The execution time of the algorithm is decreased for an average of 20%. The proposed system is focusing on preventing the brute force attack occurred during the surgical telepresence data transmission. The paper proposes a framework that enhances the security related to data transmission during surgeries with acceptable performance. Keywords Mixed reality. Surgical Telepresence. Secure data transmission. Hybrid RC6 and Rubik's cube algorithm. Brute force attack Multimedia Tools and Applications https://doi.
Your article is protected by copyright and all rights are held exclusively by Springer Science+Bu... more Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media, LLC, part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to selfarchive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".
International Journal of Medical Robotic and computer assisted surgery, 2020
Background and aim: Most of the Mixed Reality models used in the surgical telepresence are suffer... more Background and aim: Most of the Mixed Reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. Methodology: The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discoloration artefacts around the blending region. Results: As compared to the state of art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds. Conclusion: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video.
Background and aim: Image registration and alignment are the main limitations of augmented realit... more Background and aim: Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. Methodology: markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction. Results: This study has improved video precision to 0.57 mm ~ 0.61 mm alignment error. Furthermore, with the use of bidirectional points, i.e. Forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improved the processing time as well. The processing time of video frames was improved to 7.4 ~11.74 fps. Conclusions: It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc.
Your article is protected by copyright and all rights are held exclusively by Springer Science+Bu... more Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media, LLC, part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to selfarchive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".
Your article is protected by copyright and all rights are held exclusively by Springer Science+Bu... more Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media, LLC, part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to selfarchive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".
Background: Mixed reality (MR) visualization is gaining popularity in image-guided
Extensive use of surveillance cameras for human tracking and observation have been fostering the ... more Extensive use of surveillance cameras for human tracking and observation have been fostering the research on face recognition technique for individual identification in an unconstrained environment. However, face recognition is a challenging task in an unconstrained environment, where the captured images are affected by illumination effect, varying poses, noise and occlusion. The main objective of this research is to improve the accuracy and processing time in extracting facial features by using the fusion of deep learning and handcrafted architecture for recognizing individuals in uncon-strained conditions, thereby providing accurate information about the individuals to security systems. The proposed system consists of Multi-Block Local Binary Pattern (MB-LBP) modules for extracting the handcrafted features and Convolutional Neural Network (CNN) for extracting the high-level distinctive features. The features from both modules are fused and passed through fully connected layer with Softmax classifier to identify individuals. The results show that the enhanced algorithm based on Softmax loss function aided classifier with regularization improves the accuracy and processing time for face recognition. The proposed model improves accuracy by 94.37% against 90.01% for the state-of-the-art solution. In addition to that, it improves the processing time of 307 ms against 357 ms. The proposed system focuses on fusing hand-crafted and deep learned features to extract face features accurately and thus improving the accuracy and overall performance of the proposed system in an unconstrained environment. Keywords Deep learning. Facial image recognition. Multi-block local binary pattern .
Mixed Reality (MR) surgery has not been effectively implemented in telemedicine due to strict req... more Mixed Reality (MR) surgery has not been effectively implemented in telemedicine due to strict requirements of security and delay minimization during real-time video transmission. Hence, this paper aims to propose a novel solution for Surgical Telepresence with highly secured and faster real-time video transmission. The proposed system consists of three components: Authentication (Pre-surgery), Data transmission (During-Surgery), and Storage (Post-Surgery). For Authentication, Pass-Matrix technique is used at both ends to provide graphical passwords. During the surgery, a hybrid system is used to provide highly secured and faster real-time video transmission. This system includes a Feistel Encryption System (FES), Modified Scaled Zhongtang Chaotic System (M-SCZS), and Modified Advanced Encryption System (M-AES) algorithm. After Surgery, the transmitted data are stored using the Information Accountability Framework (IAF) for future purposes. The results are obtained from the during-surgery stage for jaw, breast, and bowel surgery. Both solutions are simulated in MATLAB on a personal computer with average processing capability. The proposed solution improves the entropy from 7.733~7.782 to 7.798-7.996 and reduces the processing time from 8.642~9.911 s/frames to 5.071~6.563 s/frames. The proposed focus on reducing the total processing time for the encryption and decryption process with improving security during the surgery process. Finally, this solution provides a fast security system for surgical telepresence that helps both local and remote surgeons for secure real-time communication. The complexity for this work need to know the used chaotic method, the values of the chaotic parameters and for which this method was used, in addition to the complexity of state of the art. Multimedia Tools and Applications https://doi.
Journal of Ambient Intelligence and Humanized Computing, 1-29, 2020. Q2 Journal.
Brain tumour identification with traditional magnetic resonance imaging (MRI) tends to be time-co... more Brain tumour identification with traditional magnetic resonance imaging (MRI) tends to be time-consuming and in most cases, reading of the resulting images by human agents is prone to error, making it desirable to use automated image segmentation. This is a multi-step process involving: (a) collecting data in the form of raw processed or raw images, (b) removing bias by using pre-processing, (c) processing the image and locating the brain tumour, and (d) showing the tumour affected areas on a computer screen or projector. Several systems have been proposed for medical image segmentation but have not been evaluated in the field. This may be due to ongoing issues of image clarity, grey and white matter present in a scan image, lack of knowledge of the end user and constraints arising from MRI imaging systems. This makes it imperative to develop a comprehensive technique for the accurate diagnosis of brain tumors in MRI images. In this paper, we introduce a taxonomy consisting of 'Data, Image segmentation processing, and View' (DIV) which are the major components required to develop a high-end system for brain tumour diagnosis based on deep learning neural networks. The DIV taxonomy is evaluated based on system completeness and acceptance. The utility of the DIV taxonomy is demonstrated by classifying 30 state-of-the-art publications in the domain of medFical image segmentation systems based on deep neural networks. The results demonstrate that few components of medical image segmentation systems have been validated although several have been evaluated by identifying role and efficiency of the components in this domain. Keywords Taxonomy · Medical image segmentation · Magnetic resonance imaging (MRI) · Brain tumour · Deep neural networks (DNN) · Diagnosis · Image contrast · Image clustering · Re-clustering · Image pixels · Tumour boundaries Abbreviations MRI Magnetic resonance imaging MCFM Modified fuzzy C-means CLE Confocal laser endomicroscopy CNN Convolutional neural networks DCNN Deep conventional neural network ACM Active contour models CRFs Conditional random fields FCNN Fully convolutional neural network LHNPSO Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight KFECSB Kernelized fuzzy entropy clustering with spatial information and bias correction RF Classifier Random forests classifier
Haritha Sallepalli Venkata, Abeer Alsadoon, P.W.C. Prasad, Omar Hisham Alsadoon, Sami Haddad, Anand Deva, Jeremy Hsu, " A novel mixed reality in breast and constructive jaw surgical tele-presence", Computer-methods-and-programs-in-biomedicine Journal,DOI: 10.1016/j.cmpb.2019.05.025, 2019 Q1 Journal Computer-methods-and-programs-in-biomedicine Journal, 2019
Background and aim: Surgical telepresence has been implemented using Mixed reality (MR) but, MR i... more Background and aim: Surgical telepresence has been implemented using Mixed reality (MR) but, MR is theory based and only used for investigating research. The Aim of this paper is to propose and implement a new solution by merging augmented video (generating in local site) and virtual expertise surgeon hand (remote site). This system is to improve the visualization of surgical area, overlay accuracy in the merged video without having any discoloured patterns on hand, smudging artefacts on surgeon hand boundary and occluded areas of surgical area. Methodology: The Proposed system consists of an Enhanced Multi-Layer Mean Value Cloning (EMLMV) algorithm that improves the overlay accuracy, visualization accuracy and the processing time. This proposed algorithm includes trimap and alpha matting as a pre-processing stage of merging process, which helps to remove the smudging and discoloured artefacts surrounded by remote surgeon hand. Results: Results showing that the proposed system improved the accuracy by reducing the overlay error of merging image from 1.3 mm (Millimeter) to 0.9 mm. Furthermore, it improves the visibility of surgeon hand in the final merged image from 98.4% (visibility of pixels) to 99.1% (visibility of pixels). Similarly, the processing time in our proposed solution is reduced, which is computed as 10 s to produce 50 frames, whilst, the state of art solution computes 11 s for the same number of frames. Conclusion: The proposed system focuses on the merging of augmented reality video (local site), and the virtual reality video (remote site) with the accurate visualization. we consider discoloured areas, smudging artefacts and occlusion as the main aspects to improve the accuracy of merged video in terms of overlay error and visualization error. So, the proposed system would produce the merged video with the removal of artefacts around the expert surgeon hand.
Transactions on Emerging Telecommunications Technologies, 2019
Three-dimensional tele-immersion systems for tele-collaboration in surgical medical education (SM... more Three-dimensional tele-immersion systems for tele-collaboration in surgical medical education (SME) have not been implemented due to issues of variable bandwidth availability and delay requirements for high real-time video quality. The proposed solution in this paper aims to implement a three-dimensional tele-immersion system for tele-collaboration in SME with improved video quality, minimized end-to-end delay, and an adaptation ratio, to increase tele-collaboration experience. The proposed system is based on an algorithm that enhances compression, creates bandwidth adaptivity, and leads to low latency (ECBAaLL), to make efficient use of limited network bandwidth and reduce end-to-end delay, by introducing a raptor code and an enhanced algorithm based on segmentation. The results achieved in terms of video quality , end-to-end delay, and adaptation ratio of the video streams reflect that the proposed new algorithm improves the quality per stream of the real-time videos by 8% and reduces the end-to-end delay of the video packets by 6%-7% and that, with the new algorithm, the adaptation ratio of the bandwidth-adapted videos is increased by 4%-5%. This work focuses on reducing transmission delays and implementing an improved compression algorithm with reduced end-to-end delay and high video adaptation. Hence, the outcome is the quality of video streams for available bandwidth budget with minimal end-to-end and processing time delays on a real-time tele-collaboration environment in SME.
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Papers by A/Prof Abeer Alsadoon