ORCID Profile
0000-0002-6603-3257
Current Organisation
University of Western Australia
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Publisher: IEEE
Date: 12-2015
DOI: 10.1109/ICCV.2015.26
Publisher: Cold Spring Harbor Laboratory
Date: 21-12-2022
DOI: 10.1101/2022.12.20.22283735
Abstract: Millions of patients attend emergency departments (EDs) around the world every year. Patients are triaged on arrival by a trained nurse who collects structured data and an unstructured free-text history of presenting complaint. Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at ED triage to predict various outcomes. The objective of this systematic review is to evaluate how NLP has been applied to ED triage, assess if NLP based models outperform humans or current risk stratification techniques, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data. All English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion. We excluded studies focusing solely on disease surveillance, and studies that used information obtained after triage. We searched the electronic databases MEDLINE, Embase, Cochrane Database of Systematic Reviews, Web of Science, and Scopus for medical subject headings and text keywords related to NLP and triage. Databases were last searched on 01/01/2022. Risk of bias in studies was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Due to the high level of heterogeneity between studies, a metanalysis was not conducted. Instead, a narrative synthesis is provided. In total, 3584 studies were screened, and 19 studies were included. The population size varied greatly between studies ranging from 1.8 million patients to 762 simulated encounters. The most common primary outcomes assessed were prediction of triage score, prediction of admission, and prediction of critical illness. NLP models achieved high accuracy in predicting need for admission, critical illness, and mapping free-text chief complaints to structured fields. Overall, NLP models predicted admission with greater accuracy than emergency physicians, outperformed abnormal vital sign trigger and triage score at predicting critical illness, and were more accurate than nurses at assigning triage scores in two out of three papers. Incorporating both structured data and free-text data improved results when compared to models that used only structured data. The majority of studies were (79%) were assessed to have a high risk of bias, and only one study reported the deployment of an NLP model into clinical practice. Unstructured free-text triage notes contain valuable information that can be used by NLP models to predict clinically relevant outcomes. The use of NLP at ED triage appears feasible and could allow for early and accurate prediction of multiple important patient-oriented outcomes. However, there are few ex les of implementation of into clinical practice, most research in retrospective, and the potential benefits of NLP at triage are yet to be realised.
Publisher: Elsevier BV
Date: 2012
Publisher: IEEE
Date: 06-2013
Publisher: Springer Science and Business Media LLC
Date: 28-07-2020
Publisher: IEEE
Date: 1999
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Elsevier BV
Date: 12-2002
Publisher: SPIE
Date: 30-05-2000
DOI: 10.1117/12.386708
Publisher: Elsevier BV
Date: 03-2013
Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 2016
Publisher: Elsevier BV
Date: 12-2019
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 09-2013
Publisher: IEEE
Date: 11-2014
Publisher: MDPI AG
Date: 24-09-2020
DOI: 10.3390/RS12193137
Abstract: In this paper, we propose a high performance Two-Stream spectral-spatial Residual Network (TSRN) for hyperspectral image classification. The first spectral residual network (sRN) stream is used to extract spectral characteristics, and the second spatial residual network (saRN) stream is concurrently used to extract spatial features. The sRN uses 1D convolutional layers to fit the spectral data structure, while the saRN uses 2D convolutional layers to match the hyperspectral spatial data structure. Furthermore, each convolutional layer is preceded by a Batch Normalization (BN) layer that works as a regularizer to speed up the training process and to improve the accuracy. We conducted experiments on three well-known hyperspectral datasets, and we compare our results with five contemporary methods across various sizes of training s les. The experimental results show that the proposed architecture can be trained with small size datasets and outperforms the state-of-the-art methods in terms of the Overall Accuracy, Average Accuracy, Kappa Value, and training time.
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 06-2011
Publisher: Elsevier BV
Date: 04-2017
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: Elsevier BV
Date: 12-1992
Publisher: IEEE
Date: 09-2016
Publisher: IEEE
Date: 06-2011
Publisher: IEEE Comput. Soc
Date: 1998
Publisher: IEEE
Date: 2000
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: SCITEPRESS - Science and and Technology Publications
Date: 2014
Publisher: SPIE
Date: 30-05-2000
DOI: 10.1117/12.386613
Publisher: SPIE
Date: 26-08-1999
DOI: 10.1117/12.360305
Publisher: Elsevier BV
Date: 07-2011
Publisher: Elsevier BV
Date: 10-2023
Publisher: IEEE
Date: 2008
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 30-11-2022
Publisher: Elsevier BV
Date: 04-2023
Publisher: Springer Singapore
Date: 30-08-2019
Publisher: Springer Singapore
Date: 30-08-2019
Publisher: Springer Singapore
Date: 30-08-2019
Publisher: Springer Singapore
Date: 30-08-2019
Publisher: Springer Singapore
Date: 30-08-2019
Publisher: Springer Singapore
Date: 30-08-2019
Publisher: IEEE
Date: 10-2020
Publisher: IEEE
Date: 2013
Publisher: Informa UK Limited
Date: 2008
Publisher: IEEE
Date: 05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: IEEE
Date: 2013
Publisher: ACM
Date: 05-06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE Comput. Soc. Press
Date: 1994
Publisher: Springer Science and Business Media LLC
Date: 17-07-2021
Publisher: IEEE
Date: 02-2013
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2012
Publisher: Emerald
Date: 06-2005
DOI: 10.1108/02602280510585745
Abstract: In model‐based recognition the 3D models of objects are stored in a model library during an offline phase. During the online recognition phase, a view of the scene is matched with the model library to identify the location and pose of certain library objects in the scene. Aims to focus on the process of 3D modeling and model‐based recognition. This paper discusses the process of 3D modeling and model‐based recognition along with their potential applications in industry with a particular emphasis on robot grasp analysis. The paper also emphasises the main challenges in these areas and give a brief literature review. In order to develop an automatic 3D model‐based object recognition system it is necessary to automate the process of 3D modeling and recognition. The challenge in automating the 3D modeling process is to develop an automatic correspondence technique. The core of recognition is the representation scheme. Recognition is an online process. Therefore, representation and matching must be very fast in order to facilitate real time recognition. There are numerous applications of 3D modeling in a variety of areas ranging from the entertainment industry to industrial automation. Some of its applications include computer graphics, virtual reality, medical imaging, reverse engineering, and 3D terrain construction. Provides information on 3D modeling which constitutes an important part of computer vision or robot vision.
Publisher: Springer Science and Business Media LLC
Date: 28-02-2017
Publisher: IEEE Comput. Soc
Date: 2000
Publisher: IEEE
Date: 12-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2013
Publisher: Springer Science and Business Media LLC
Date: 08-04-2022
DOI: 10.1186/S12870-022-03559-Z
Abstract: Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean in iduals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11919476_86
Publisher: Springer Science and Business Media LLC
Date: 25-09-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE Comput. Soc
Date: 1998
Publisher: Elsevier BV
Date: 11-2022
DOI: 10.1016/J.COMPBIOMED.2022.106126
Abstract: Appropriate anticoagulant therapy for patients with atrial fibrillation (AF) requires assessment of stroke and bleeding risks. However, risk stratification schemas such as CHA This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The outcomes were ischemic stroke (167), major bleeding (430) admissions, all-cause death (1912) and event-free survival (7387). Discrimination and calibration of ML models were compared with clinical risk scores by area under the curve (AUC). Risk stratification was assessed using net reclassification index (NRI). Multilabel gradient boosting classifier chain provided the best AUCs for stroke (0.685 95% CI 0.676, 0.694), major bleeding (0.709 95% CI 0.703, 0.716) and death (0.765 95% CI 0.763, 0.768) compared to multi-layer neural networks and classifier chain using support vector machine. It provided modest performance improvement for stroke compared to AUC of CHA Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.
Publisher: IEEE
Date: 2005
Publisher: Elsevier BV
Date: 03-2018
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 02-03-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: ACM
Date: 07-02-2010
Publisher: Springer Science and Business Media LLC
Date: 28-09-2000
Publisher: Elsevier BV
Date: 02-2015
Publisher: Springer Science and Business Media LLC
Date: 19-05-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 1998
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 16-12-2021
Publisher: Springer Science and Business Media LLC
Date: 20-09-2009
Publisher: IEEE
Date: 08-2010
Publisher: Wiley
Date: 15-06-2015
Abstract: Face recognition is a popular research topic with a number of applications in several industrial sectors including security, surveillance, entertainment, virtual reality, and human–machine interaction. Both 2D images and 3D data can now be easily acquired and used for face recognition. For any 2D/3D face recognition system, feature extraction and selection play a significant role. Currently, both holistic and local features have been intensively investigated in the literature. In this article, fundamental background knowledge of face recognition, including 2D/3D data acquisition, data preprocessing, feature extraction, classification, and performance evaluation, is presented. The state‐of‐the‐art feature extraction algorithms, including 2D holistic feature, 2D local feature, 3D holistic feature, and 3D local feature extraction algorithms, are then described in detail. Finally, feature selection and fusion techniques are presented. The article covers the complete related aspects of feature selection for 2D and 3D face recognition.
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: British Machine Vision Association
Date: 2010
DOI: 10.5244/C.24.51
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 10-2021
Publisher: OMICS Publishing Group
Date: 2016
Publisher: IEEE
Date: 1996
Publisher: Springer Science and Business Media LLC
Date: 24-06-2022
DOI: 10.1007/S11063-022-10925-3
Abstract: A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only a single kernel. In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base rimary kernels. In this paper, we propose a novel multi-kernel RBFNN in which every base kernel has its own (local) weight. This novel flexibility in the network provides better performance such as faster convergence rate, better local minima and resilience against stucking in poor local minima. These performance gains are achieved at a competitive computational complexity compared to the contemporary multi-kernel RBF algorithms. The proposed algorithm is thoroughly analysed for performance gain using mathematical and graphical illustrations and also evaluated on three different types of problems namely: (i) pattern classification, (ii) system identification and (iii) function approximation. Empirical results clearly show the superiority of the proposed algorithm compared to the existing state-of-the-art multi-kernel approaches.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
Publisher: IEE
Date: 1999
DOI: 10.1049/CP:19990276
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 12-2008
Publisher: FapUNIFESP (SciELO)
Date: 04-2015
Publisher: Elsevier BV
Date: 09-2016
Publisher: Public Library of Science (PLoS)
Date: 31-08-2023
DOI: 10.1371/JOURNAL.PONE.0290642
Abstract: Surveys conducted internationally have found widespread interest in artificial intelligence (AI) amongst medical students. No similar surveys have been conducted in Western Australia (WA) and it is not known how medical students in WA feel about the use of AI in healthcare or their understanding of AI. We aim to assess WA medical students’ attitudes towards AI in general, AI in healthcare, and the inclusion of AI education in the medical curriculum. A digital survey instrument was developed based on a review of available literature and consultation with subject matter experts. The survey was piloted with a group of medical students and refined based on their feedback. We then sent this anonymous digital survey to all medical students in WA (approximately 1539 students). Responses were open from the 7 th of September 2021 to the 7 th of November 2021. Students’ categorical responses were qualitatively analysed, and free text comments from the survey were qualitatively analysed using open coding techniques. Overall, 134 students answered one or more questions (8.9% response rate). The majority of students (82.0%) were 20–29 years old, studying medicine as a postgraduate degree (77.6%), and had started clinical rotations (62.7%). Students were interested in AI (82.6%), self-reported having a basic understanding of AI (84.8%), but few agreed that they had an understanding of the basic computational principles of AI (33.3%) or the limitations of AI (46.2%). Most students (87.5%) had not received teaching in AI. The majority of students (58.6%) agreed that AI should be part of medical training and most (72.7%) wanted more teaching focusing on AI in medicine. Medical students appeared optimistic regarding the role of AI in medicine, with most (74.4%) agreeing with the statement that AI will improve medicine in general. The majority (56.6%) of medical students were not concerned about the impact of AI on their job security as a doctor. Students selected radiology (72.6%), pathology (58.2%), and medical administration (44.8%) as the specialties most likely to be impacted by AI, and psychiatry (61.2%), palliative care (48.5%), and obstetrics and gynaecology (41.0%) as the specialties least likely to be impacted by AI. Qualitative analysis of free text comments identified the use of AI as a tool, and that doctors will not be replaced as common themes. Medical students in WA appear to be interested in AI. However, they have not received education about AI and do not feel they understand its basic computational principles or limitations. AI appears to be a current deficit in the medical curriculum in WA, and most students surveyed were supportive of its introduction. These results are consistent with previous surveys conducted internationally.
Publisher: Elsevier BV
Date: 11-2017
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 12-2018
Publisher: Wiley
Date: 30-07-2016
DOI: 10.1002/CPE.3600
Publisher: IEEE
Date: 2022
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 08-06-2007
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 06-2012
DOI: 10.1109/HSI.2012.4
Publisher: Springer Science and Business Media LLC
Date: 05-03-2023
Publisher: British Machine Vision Association
Date: 2007
DOI: 10.5244/C.21.34
Publisher: Elsevier BV
Date: 09-2021
Publisher: ACM
Date: 20-04-2020
Publisher: IEEE
Date: 14-11-2022
Publisher: Institution of Engineering and Technology (IET)
Date: 03-2018
Publisher: IEEE
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: ACM
Date: 07-07-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: World Scientific Pub Co Pte Lt
Date: 11-2003
DOI: 10.1142/S0218001403002800
Abstract: Image registration plays a crucial role in the computer vision and medical imaging field where it is used to develop a spatial mapping between different sets of data. These transformations can range from simple rigid registrations to complex nonrigid deformations. Mutual information (MI) is a popular entropy-based similarity measure which has recently experienced a prolific expansion in a number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it only assumes a statistical dependence between images. This paper provides a thorough introduction to the MI measure and its use in rigid medical image registration. A look at the extensions proposed to the original measure will also be provided. These were developed to improve the robustness of the measure and to avoid certain cases when maximizing MI does not lead to the correct spatial alignment.
Publisher: Springer International Publishing
Date: 2019
Publisher: MDPI AG
Date: 13-01-2020
DOI: 10.3390/S20020447
Abstract: Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, such as kelps. This paper presents an automatic hierarchical classification method local binary classification as opposed to the conventional flat classification to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs. 57.6% and 77.2% vs. 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys.
Publisher: IEEE
Date: 10-2020
Publisher: Springer Science and Business Media LLC
Date: 16-01-2022
Publisher: IEEE
Date: 2021
Publisher: IEEE
Date: 06-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 10-2016
Publisher: Springer Science and Business Media LLC
Date: 19-07-2023
DOI: 10.1007/S00521-023-08826-0
Abstract: Weakly supervised semantic segmentation (WSSS) commonly relies on Class Activation Mapping (CAM) to produce pseudo semantic labels using image-level annotations. However, because CAM maps often form sparse object regions with poor boundaries, they cannot provide sufficient segmentation supervision. Because off-the-shelf saliency maps can provide rich object boundaries that can be leveraged to improve semantic segmentation, we propose to jointly learn semantic segmentation and class-agnostic masks by using image-level annotations and off-the-shelf saliency maps as supervision. We also propose a cross-task label refinement mechanism, which takes advantage of the learned class-agnostic masks and semantic segmentation masks, to refine the pseudo labels and provide more accurate supervision to both tasks. Moreover, we introduce a new normalization method for CAM to generate more complete class-specific localization maps. The improved CAM maps complement our learned class-agnostic masks, leading to high-quality pseudo semantic segmentation labels. Extensive experiments demonstrate the effectiveness of the proposed approach, with state-of-the-art WSSS results established on PASCAL VOC 2012 and MS COCO.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2017
Publisher: Quintessence Publishing
Date: 07-2015
DOI: 10.11607/PRD.2285
Abstract: The aim of this study was to evaluate the impact of conventional and digital diagnostic wax-up on the axial tooth contour. Dental models of 15 patients were collected. Each model received conventional wax-up and digital wax-up. The conventional wax-up was based on tooth modification with dental wax on actual models. The digital wax-up involved fitting an average tooth form on virtual pretreatment models. Each wax-up model was digitally superimposed on the corresponding pretreatment model. For each modified tooth, analysis planes were extracted at three locations: mesial line angle, midtooth, and distal line angle. The impact of the following variables was evaluated: interarch location (maxilla vs mandible), intra-arch location (anterior vs posterior), tooth category (incisors, canines, premolars, and molars), and tooth location (midtooth vs line angle). The axial contour of the modified teeth increased after each wax-up, and this increase was directly proportional to the distance from the gingival margin. There is a clear tendency for the digital wax-up to cause a greater contour increase than the conventional wax-up. The anterior teeth were associated with a greater tooth contour increase than posterior teeth and the contour of the molars was the least affected. Although the conventional wax-up contour alteration was significantly less than for the digital wax-up, the actual difference was minimal.
Publisher: MDPI AG
Date: 12-11-2020
DOI: 10.3390/S20226450
Abstract: Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than ersified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.
Publisher: IEEE
Date: 04-2018
Publisher: MDPI AG
Date: 04-10-2021
DOI: 10.3390/RS13193976
Abstract: Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.
Publisher: Elsevier BV
Date: 09-2016
Publisher: IEEE
Date: 08-2018
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 09-2011
Publisher: IEEE
Date: 1995
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 07-2019
Publisher: Elsevier BV
Date: 2016
Publisher: Elsevier BV
Date: 10-2023
Publisher: IEEE
Date: 09-2010
Publisher: Oxford University Press (OUP)
Date: 28-06-2021
Abstract: High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.NEUNET.2018.06.005
Abstract: By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization-minimization (MM) algorithms. We prove that the proposed two-hidden-layer SCRNs, which exhibit negative weights in the second hidden layer and negative weights in the output layer, are capable of separating any number of disjoint pattern sets. Furthermore, the proposed two-hidden-layer SCRNs can decompose the patterns of each class into several clusters so that each cluster is convexly separable from all the patterns from the other classes. This provides a means to learn the pattern structures and analyse the discriminant factors between different classes of patterns. Experimental results are provided to show the benefits of sign constraints in improving classification performance and the efficiency of the proposed MM algorithm.
Publisher: Elsevier BV
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 08-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: Springer Science and Business Media LLC
Date: 21-04-2021
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 2016
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 08-2010
Publisher: Springer Science and Business Media LLC
Date: 04-2011
Publisher: Association for Computing Machinery (ACM)
Date: 08-2012
Abstract: Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: IEEE
Date: 06-2020
Publisher: Elsevier BV
Date: 07-2020
Publisher: IEEE
Date: 2000
Publisher: Cold Spring Harbor Laboratory
Date: 10-02-2022
DOI: 10.1101/2022.02.09.479647
Abstract: Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, being involved in both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning and deep learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species ( Zea mays, Arabidopsis thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Glycine max and Oryza sativa ) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences, and that genome assembly quality affects the accuracy of lncRNA identification. Furthermore, we demonstrated that the NLP models are applicable for cross-species prediction as they could predict lncRNAs from a species not used to train the model, with an average of 61% accuracy. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important for lncRNA prediction and that these motifs were frequently present flanking the lncRNA sequence. We demonstrate for the first time the identification of lncRNAs from genomic sequences, instead of transcriptome sequences, allowing the identification of lowly expressed lncRNAs. A deep learning model (natural language processing) was employed for the prediction of lncRNAs in two monocot and five dicot plant species. We used explainable machine learning to extract the genomic motifs associated with lncRNA identification, highlighting potentially conserved structures present in more than one plant species.
Publisher: Journal of Artificial Societies and Social Simulation
Date: 2019
DOI: 10.18564/JASSS.3997
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/829893
Abstract: Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its in idual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.
Publisher: Elsevier BV
Date: 11-2023
Publisher: Wiley
Date: 18-03-2022
Abstract: Deep learning has been shown to be effective for classification of coral in benthic imagery, and is becoming a tool for use in many monitoring programs around the world. Although deep learning accuracy for coral reef classification has been well published for studies where validation metrics are generated from data within surveys, little research has been done on the transferability and generalisability of deep learned models to data never seen by the trained model. Ex les include data across multiple capture methods, camera systems, habitat types, water quality conditions and temporal studies. In this paper we investigate the use of deep ensembling to measure the reliability of predictions in new or unseen environments. In this paper we show that ensemble methods are more stable in their calibration across dataset shifts compared with other approaches. Ensembles show more robust uncertainty quantification in unseen environments compared to alternative methods, thus providing more confidence in the use of pre‐trained models in unconstrained environments. These results show that ensembles should be the de facto standard for any practitioner using deep learning for benthic image automation, and applying deep learning approaches to coral classification.
Publisher: Springer Science and Business Media LLC
Date: 11-02-2022
DOI: 10.1007/S00521-022-06958-3
Abstract: Missing data is a major problem in real-world datasets, which hinders the performance of data analytics. Conventional data imputation schemes such as univariate single imputation replace missing values in each column with the same approximated value. These univariate single imputation techniques underestimate the variance of the imputed values. On the other hand, multivariate imputation explores the relationships between different columns of data, to impute the missing values. Reinforcement Learning (RL) is a machine learning paradigm where the agent learns by taking actions and receiving rewards in response, to achieve its goal. In this work, we propose an RL-based approach to impute missing data by learning a policy to impute data through an action-reward-based experience. Our approach imputes missing values in a column by working only on the same column (similar to univariate single imputation) but imputes the missing values in the column with different values thus keeping the variance in the imputed values. We report superior performance of our approach, compared with other imputation techniques, on a number of datasets.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: IEEE
Date: 07-2017
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: Elsevier BV
Date: 02-2013
Abstract: The purpose of this study was to evaluate the combination of panitumumab and irinotecan in patients with KRAS wild-type metastatic colorectal cancer refractory to standard chemotherapy (oxaliplatin, fluoropyrimidines-irinotecan and bevacizumab). KRAS status was first determined locally but subsequent validation of KRAS status and additional screenings (rare KRAS, NRAS, BRAF mutations and EGFR copy number) were centrally assessed. Patients received panitumumab (6 mg/kg) and irinotecan (180 mg/m²) every 2 weeks. Sixty-five eligible patients were analyzed. The objective response rate (ORR) was 29.2% [95% confidence interval (95% CI) 18.2-40.3]. Median progression-free and overall survivals were 5.5 and 9.7 months, respectively. Most frequent grade 3/4 toxic effects were skin 32.3%, diarrhea 15.4% and neutropenia 12.3%. Tissue s les were available for 60 patients. For the confirmed KRAS wild-type population codon 12 or 13 mutation (n = 54), ORR was 35.2% (95% CI 22.4.1-47.9). Thirteen patients had a NRAS, a BRAF or a rare KRAS mutation, and no tumor response was observed in this subgroup when compared with 46.3% (95% CI 31.1-61.6) ORR in the subgroup of 41 patients with no identified mutation. Panitumumab and irinotecan is an active third-line regimen in a well-defined population based on biomarkers. ClinicalTrials.gov Identifier NCT00655499.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 24-04-2013
Publisher: IEEE Comput. Soc
Date: 1997
Publisher: Elsevier BV
Date: 02-2023
Publisher: SciTePress - Science and and Technology Publications
Date: 2006
Publisher: ACM
Date: 26-11-2012
Publisher: IEEE
Date: 07-2017
Publisher: SciTePress - Science and and Technology Publications
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: MDPI AG
Date: 30-08-2022
DOI: 10.3390/RS14174288
Abstract: Biotic and abiotic plant stress (e.g., frost, fungi, diseases) can significantly impact crop production. It is thus essential to detect such stress at an early stage before visual symptoms and damage become apparent. To this end, this paper proposes a novel deep learning method, called Spectral Convolution and Channel Attention Network (SC-CAN), which exploits the difference in spectral responses of healthy and stressed crops. The proposed SC-CAN method comprises two main modules: (i) a spectral convolution module, which consists of dilated causal convolutional layers stacked in a residual manner to capture the spectral features (ii) a channel attention module, which consists of a global pooling layer and fully connected layers that compute inter-relationship between feature map channels before scaling them based on their importance level (attention score). Unlike standard convolution, which focuses on learning local features, the dilated convolution layers can learn both local and global features. These layers also have long receptive fields, making them suitable for capturing long dependency patterns in hyperspectral data. However, because not all feature maps produced by the dilated convolutional layers are important, we propose a channel attention module that weights the feature maps according to their importance level. We used SC-CAN to classify salt stress (i.e., abiotic stress) on four datasets (Chinese Spring (CS), Aegilops columnaris (co(CS)), Ae. speltoides auchery (sp(CS)), and Kharchia datasets) and Fusarium head blight disease (i.e., biotic stress) on Fusarium dataset. Reported experimental results show that the proposed method outperforms existing state-of-the-art techniques with an overall accuracy of 83.08%, 88.90%, 82.44%, 82.10%, and 82.78% on CS, co(CS), sp(CS), Kharchia, and Fusarium datasets, respectively.
Publisher: Wiley
Date: 05-06-2013
Publisher: Springer International Publishing
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: MDPI AG
Date: 20-06-2022
Abstract: Gene models are regions of the genome that can be transcribed into RNA and translated to proteins, or belong to a class of non-coding RNA genes. The prediction of gene models is a complex process that can be unreliable, leading to false positive annotations. To help support the calling of confident conserved gene models and minimize false positives arising during gene model prediction we have developed Truegene, a machine learning approach to classify potential low confidence gene models using 14 gene and 41 protein-based characteristics. Amino acid and nucleotide sequence-based features were calculated for conserved (high confidence) and non-conserved (low confidence) annotated genes from the published Pisum sativum Cameor genome. These features were used to train eXtreme Gradient Boost (XGBoost) classifier models to predict whether a gene model is likely to be real. The optimized models demonstrated a prediction accuracy ranging from 87% to 90% and an F-1 score of 0.91–0.94. We used SHapley Additive exPlanations (SHAP) and feature importance plots to identify the features that contribute to the model predictions, and we show that protein and gene-based features can be used to build accurate models for gene prediction that have applications in supporting future gene annotation processes.
Publisher: Association for Computational Linguistics
Date: 2018
DOI: 10.18653/V1/P18-4002
Publisher: IEEE
Date: 09-2016
Publisher: Elsevier BV
Date: 2016
DOI: 10.1016/J.PROSDENT.2015.07.005
Abstract: Improving dental esthetics is a main objective of prosthodontic treatment. Recently, digital diagnostic waxing has been proposed as an alternative to conventional diagnostic waxing however, the impact on esthetics has not been evaluated. The purpose of this study was to evaluate the impact of diagnostic waxing on biometric esthetic variables and to compare the esthetic outcome achieved by digital waxing with conventional waxing. Three biometric variables were evaluated: perceived frontal proportion (PFP), width/height (W:H) ratio, and symmetry. Maxillary casts of 13 patients were collected. All of them had maxillary anterior teeth that required prosthodontic treatment. Two forms of diagnostic waxing were executed: conventional and digital waxing. Measurements of the esthetic variables were conducted digitally. For the PFP, a frontal image was made and the width of each tooth was measured. Subsequently, the PFP values of the lateral incisor to central incisor and of the canine to central incisor were calculated. In addition, the height and width of each tooth was measured to calculate the W:H ratio. Using the previous measurements, the symmetry between the right and left sides was determined. No consistent or recurrent PFP was detected for any cast. The diagnostic waxing did not alter the PFP of the pretreatment casts. The diagnostic waxing had restored the W:H ratio to what is assumed to be a natural ratio. An improvement in symmetry was detected after the diagnostic waxing and was more prominent after the digital waxing. However, no significant difference was found between the 2 diagnostic waxing methods. The 2 diagnostic waxing methods influenced the esthetic variables of the anterior maxillary teeth and yielded similar outcomes. Digital waxing appears to be a reasonable alternative, but further investigations are needed to ensure its practicality.
Publisher: IEEE
Date: 11-2010
Publisher: Elsevier BV
Date: 04-2016
Publisher: Springer Science and Business Media LLC
Date: 15-09-2021
DOI: 10.1038/S41598-021-97643-3
Abstract: Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.
Publisher: IEEE
Date: 2001
Publisher: Elsevier BV
Date: 12-2013
Publisher: IEEE
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: IEEE
Date: 03-2017
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11919476_10
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 07-07-2010
Publisher: IEEE
Date: 25-11-2020
Publisher: Emerald
Date: 06-2004
DOI: 10.1108/02602280410525995
Abstract: In this paper, we review the process of “3D modeling” and “model‐based recognition” along with their potential industrial applications. We put a particular emphasis on the case scenario of robot grasp analysis for which 3D model‐based object recognition seems to be a more palpable choice compared with the conventional tactile sensors solutions. We also put a particular emphasis on the main challenges in the areas of 3D modeling and model‐based recognition and give a brief literature review of the latest research that was carried out to respond to these challenges.
Publisher: IEEE
Date: 2004
Publisher: Springer Science and Business Media LLC
Date: 2006
Publisher: Institution of Engineering and Technology
Date: 30-09-2017
DOI: 10.1049/PBSE003E_CH4
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11556121_8
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: IEEE
Date: 06-2011
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Association for Computing Machinery (ACM)
Date: 06-2012
Abstract: Biometric-based human recognition is rapidly gaining popularity due to breaches of traditional security systems and the lowering cost of sensors. The current research trend is to use 3D data and to combine multiple traits to improve accuracy and robustness. This article comprehensively reviews unimodal and multimodal recognition using 3D ear and face data. It covers associated data collection, detection, representation, and matching techniques and focuses on the challenging problem of expression variations. All the approaches are classified according to their methodologies. Through the analysis of the scope and limitations of these techniques, it is concluded that further research should investigate fast and fully automatic ear-face multimodal systems robust to occlusions and deformations.
Publisher: IEEE
Date: 03-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 06-2014
Publisher: Elsevier BV
Date: 04-0309
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: IEEE
Date: 06-2023
Publisher: Springer Science and Business Media LLC
Date: 22-05-2012
Publisher: Elsevier BV
Date: 2002
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 2015
Publisher: IEEE
Date: 2013
Publisher: World Scientific Pub Co Pte Lt
Date: 11-2003
Publisher: IEEE
Date: 1997
Publisher: Oxford University Press (OUP)
Date: 22-11-2019
Abstract: Underwater imaging is being extensively used for monitoring the abundance of lobster species and their bio ersity in their local habitats. However, manual assessment of these images requires a huge amount of human effort. In this article, we propose to automate the process of lobster detection using a deep learning technique. A major obstacle in deploying such an automatic framework for the localization of lobsters in erse environments is the lack of large annotated training datasets. Generating synthetic datasets to train these object detection models has become a popular approach. However, the current synthetic data generation frameworks rely on automatic segmentation of objects of interest, which becomes difficult when the objects have a complex shape, such as lobster. To overcome this limitation, we propose an approach to synthetically generate parts of the lobster. To handle the variability of real-world images, these parts were inserted into a set of erse background marine images to generate a large synthetic dataset. A state-of-the-art object detector was trained using this synthetic parts dataset and tested on the challenging task of Western rock lobster detection in West Australian seas. To the best of our knowledge, this is the first automatic lobster detection technique for partially visible and occluded lobsters.
Publisher: Elsevier
Date: 2017
Publisher: IEEE
Date: 1998
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 1998
Publisher: Elsevier BV
Date: 02-2001
Publisher: World Scientific Pub Co Pte Lt
Date: 18-07-2021
DOI: 10.1142/S021812662150016X
Abstract: Geometric analysis of three-dimensional (3D) surfaces with local deformations is a challenging task, required by mobile devices. In this paper, we propose a new local feature-based method derived from diffusion geometry, including a keypoint detector named persistence-based Heat Kernel Signature (pHKS), and a feature descriptor named Heat Propagation Strips (HeaPS). The pHKS detector first constructs a scalar field using the heat kernel signature function. The scalar field is generated at a small scale to capture fine geometric information of the local surface. Persistent homology is then computed to extract all the local maxima from the scalar field, and to provide a measure of persistence. Points with a high persistence are selected as pHKS keypoints. In order to describe a keypoint, an intrinsic support region is generated by the diffusion area. This support region is more robust than its geodesic distance counterpart, and provides a local surface with adaptive scale for subsequent feature description. The HeaPS descriptor is then developed by encoding the information contained in both the spatial and temporal domains of the heat kernel. We conducted several experiments to evaluate the effectiveness of the proposed method. On the TOSCA Dataset, the HeaPS descriptor achieved a high performance in terms of descriptiveness. The feature detector and descriptor were then tested on the SHREC 2010 Feature Detection and Description Dataset, and produced results that were better than the state-of-the-art methods. Finally, their application to shape retrieval was evaluated. The proposed pHKS detector and HeaPS descriptor achieved a notable improvement on the SHREC 2014 Human Dataset.
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 06-2022
Publisher: Elsevier BV
Date: 11-2021
Publisher: IEEE
Date: 1997
Publisher: American Society of Clinical Oncology (ASCO)
Date: 20-05-2010
Publisher: Elsevier BV
Date: 09-2015
Publisher: Elsevier BV
Date: 04-2022
Publisher: Association for Computing Machinery (ACM)
Date: 05-01-2023
DOI: 10.1145/3522714
Abstract: The vulnerability of re-identification (re-ID) models under adversarial attacks is of significant concern as criminals may use adversarial perturbations to evade surveillance systems. Unlike a closed-world re-ID setting (i.e., a fixed number of training categories), a reliable re-ID system in the open world raises the concern of training a robust yet discriminative classifier, which still shows robustness in the context of unknown ex les of an identity. In this work, we improve the robustness of open-world re-ID models by proposing a generative metric learning approach to generate adversarial ex les that are regularized to produce robust distance metric. The proposed approach leverages the expressive capability of generative adversarial networks to defend the re-ID models against feature disturbance attacks. By generating the target people variants and s ling the triplet units for metric learning, our learned distance metrics are regulated to produce accurate predictions in the feature metric space. Experimental results on the three re-ID datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17 demonstrate the robustness of our method.
Publisher: IEEE
Date: 1997
Publisher: Wiley
Date: 09-07-2018
DOI: 10.1111/SUM.12426
Publisher: IEEE
Date: 2000
Publisher: Springer Science and Business Media LLC
Date: 26-03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: Springer International Publishing
Date: 2020
Publisher: Institution of Engineering and Technology (IET)
Date: 23-09-2022
DOI: 10.1049/CVI2.12141
Publisher: Elsevier BV
Date: 10-2019
Publisher: IEEE
Date: 10-2017
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 04-09-2016
Publisher: Elsevier BV
Date: 03-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 2008
Publisher: IEEE
Date: 1994
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Springer Science and Business Media LLC
Date: 22-09-2010
Publisher: IEEE
Date: 1997
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/WCSE.2013.55
Publisher: IEEE
Date: 1996
Publisher: SPIE-Intl Soc Optical Eng
Date: 23-04-2021
Publisher: IEEE
Date: 10-2017
Publisher: Hindawi Limited
Date: 2014
DOI: 10.1155/2014/783948
Abstract: In prosthodontics, conventional methods of fabrication of oral and facial prostheses have been considered the gold standard for many years. The development of computer-aided manufacturing and the medical application of this industrial technology have provided an alternative way of fabricating oral and facial prostheses. This narrative review aims to evaluate the different streams of computer-aided manufacturing in prosthodontics. To date, there are two streams: the subtractive and the additive approaches. The differences reside in the processing protocols, materials used, and their respective accuracy. In general, there is a tendency for the subtractive method to provide more homogeneous objects with acceptable accuracy that may be more suitable for the production of intraoral prostheses where high occlusal forces are anticipated. Additive manufacturing methods have the ability to produce large workpieces with significant surface variation and competitive accuracy. Such advantages make them ideal for the fabrication of facial prostheses.
Publisher: Elsevier BV
Date: 05-2007
Publisher: Elsevier BV
Date: 11-2023
Publisher: IEEE
Date: 2004
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11595755_82
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 18-07-2022
Publisher: Queensland Univ. Technol
Date: 1999
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 12-2013
Abstract: HER2 is overexpressed in 10 to 20% of gastro-esophageal adenocarcinoma (GE-ADK), and is a target for trastuzumab in metastatic patients. We conducted a study to compare HER2 expression between diagnostic biopsies (DBs) and surgical specimens (SSs) of GE-ADK, and to determine the influence of non-trastuzumab containing neoadjuvant chemotherapy (NAC) on this expression. Pathological specimens from biopsies of 228 patients operated on between 2004 and 2011 were collected. Two cohorts treated (n = 141) or not (n = 87) with a NAC were constituted. Two blind independent pathological HER2 analyses on DB and on SS were carried out using immunohistochemistry (IHC) and colorimetric in situ hybridization (CISH). HER-2 overexpression (HER2+) was defined by a score 3+ in IHC, or 2+ with a positive CISH test, according to the specific HER2 scoring guidelines for GE-ADK. Paired HER2 status could be determined for 218 out of the 228 patients (95.6%). HER2+ rates were 13.3% on DB (29/218) and 14.7% on SS (32/218). HER2+ tumors were mainly cardial or esophageal adenocarcinomas, with a well-differentiated, intestinal histological type. HER2 status differed between DB and SS in 6% of cases. When DB analyses were added to SS analyses, the relative increase in HER2+ cases was 13.5% (17.1% for patients with NAC and 23.5% for patients with histological response to NAC, versus 7.1% for patients without NAC, P = 0.4, NS). Differences between DB and SS HER2 expression could be explained by intratumoral heterogeneity and by a HER2 expression decrease in SS after NAC in responding patients possibly due to a higher chemosensitivity of HER2-positive clones. The determination of HER2 status on DB provides results that complete those obtained with SS. Combining the analysis of DB and of SS enables to optimize the selection of trastuzumab-eligible patients in case of metastatic relapse, and particularly in previously NAC-responding patients.
Publisher: IEEE
Date: 11-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: Elsevier BV
Date: 10-2023
Publisher: Springer Science and Business Media LLC
Date: 18-01-2011
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-1997
DOI: 10.1109/3477.650052
Abstract: This paper presents the results of the integration of a proposed part-segmentation-based vision system. The first stage of this system extracts the contour of the object using a hybrid first- and second-order differential edge detector. The object defined by its contour is then decomposed into its constituent parts using the part segmentation algorithm given by Bennamoun (1994). These parts are then isolated and modeled with 2D superquadrics. The parameters of the models are obtained by the minimization of a best-fit cost function. The object is then represented by its structural description which is a set of data structures whose predicates represent the constituent parts of the object and whose arguments represent the spatial relationship between these parts. This representation allows the recognition of objects independently of their positions, orientations, or sizes. It is also insensitive to objects with partially missing parts. In this paper, ex les illustrating the acquired images of objects, the extraction of their contours, the isolation of the parts, and their fitting with 2D superquadrics are reported. The reconstruction of objects from their structural description is illustrated and improvements are suggested.
Publisher: IEEE
Date: 2004
Publisher: Wiley
Date: 27-02-2019
DOI: 10.1002/EHF2.12419
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Publisher: ACM
Date: 08-12-2015
Publisher: IGI Global
Date: 2011
Publisher: Elsevier
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Ubiquity Press, Ltd.
Date: 29-10-2019
DOI: 10.5334/JORS.244
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Public Library of Science (PLoS)
Date: 24-08-2021
DOI: 10.1371/JOURNAL.PONE.0252612
Abstract: Chest pain is amongst the most common reason for presentation to the emergency department (ED). There are many causes of chest pain, and it is important for the emergency physician to quickly and accurately diagnose life threatening causes such as acute myocardial infarction (AMI). Multiple clinical decision tools have been developed to assist clinicians in risk stratifying patients with chest. There is growing recognition that machine learning (ML) will have a significant impact on the practice of medicine in the near future and may assist with diagnosis and risk stratification. This systematic review aims to evaluate how ML has been applied to adults presenting to the ED with undifferentiated chest pain and assess if ML models show improved performance when compared to physicians or current risk stratification techniques. We conducted a systematic review of journal articles that applied a ML technique to an adult patient presenting to an emergency department with undifferentiated chest pain. Multiple databases were searched from inception through to November 2020. In total, 3361 articles were screened, and 23 articles were included. We did not conduct a metanalysis due to a high level of heterogeneity between studies in both their methods, and reporting. The most common primary outcomes assessed were diagnosis of acute myocardial infarction (AMI) (12 studies), and prognosis of major adverse cardiovascular event (MACE) (6 studies). There were 14 retrospective studies and 5 prospective studies. Four studies reported the development of a machine learning model retrospectively then tested it prospectively. The most common machine learning methods used were artificial neural networks (14 studies), random forest (6 studies), support vector machine (5 studies), and gradient boosting (2 studies). Multiple studies achieved high accuracy in both the diagnosis of AMI in the ED setting, and in predicting mortality and composite outcomes over various timeframes. ML outperformed existing risk stratification scores in all cases, and physicians in three out of four cases. The majority of studies were single centre, retrospective, and without prospective or external validation. There were only 3 studies that were considered low risk of bias and had low applicability concerns. Two studies reported integrating the ML model into clinical practice. Research on applications of ML for undifferentiated chest pain in the ED has been ongoing for decades. ML has been reported to outperform emergency physicians and current risk stratification tools to diagnose AMI and predict MACE but has rarely been integrated into practice. Many studies assessing the use of ML in undifferentiated chest pain in the ED have a high risk of bias. It is important that future studies make use of recently developed standardised ML reporting guidelines, register their protocols, and share their datasets and code. Future work is required to assess the impact of ML model implementation on clinical decision making, patient orientated outcomes, and patient and physician acceptability. International Prospective Register of Systematic Reviews registration number: CRD42020184977 .
Publisher: IEEE
Date: 06-2012
DOI: 10.1109/HSI.2012.16
Publisher: Elsevier BV
Date: 05-2015
DOI: 10.1016/J.COMPBIOMED.2015.03.007
Abstract: Adequate occlusal contacts are critical for masticatory function. The aim of this study is to evaluate the intercuspal occlusal contacts following conventional and digital wax-ups. Stone casts of 15 patients undergoing prosthodontic treatment were gathered. Each cast was duplicated twice, so that conventional and digital wax-ups could be performed. To assess the occlusion, the following variables were evaluated: contact number per tooth (CNT), contact area per tooth (CAT) and contact accuracy. Further, the impact of tooth location in the arch was assessed. The CNT and CAT after the wax-ups increased significantly following each wax-up, and this increase was more prominent for the posterior teeth than the anterior teeth. The conventional wax-up was associated with lower CNT than the digital wax-up, especially for the posterior teeth. On the other hand, the CAT was greater for the conventional wax-up than the digital wax-up for the anterior and posterior teeth. In terms of accuracy, the two wax-ups showed greater discrepancies than the pre-treatment casts, however, the magnitude of discrepancy was greater for the digital wax-up. The two wax-ups improved the contact number and area. Despite the statistical variation between the wax-ups, the actual difference was minimal. Therefore, it could be speculated that the two wax-ups produced a similar outcome.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 09-2013
Publisher: Informa UK Limited
Date: 11-2003
Publisher: Elsevier BV
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Elsevier BV
Date: 04-2015
Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 22-05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Elsevier BV
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Association for Computing Machinery (ACM)
Date: 05-10-2023
DOI: 10.1145/3615863
Abstract: 3D face recognition has been extensively investigated in the last two decades due to its wide range of applications in many areas such as security and forensics. Numerous methods have been proposed to deal with the challenges faced by 3D face recognition such as facial expressions, pose variations and occlusions. These methods have achieved superior performances on several small-scale datasets including FRGC v2.0, Bosphorus, BU-3DFE, and Gavab. However, deep learning based 3D face recognition methods are still in their infancy due to the lack of large-scale 3D face datasets. To stimulate future research in this area, we present a comprehensive review of the progress achieved by both traditional and deep learning based 3D face recognition methods in the last two decades. Moreover, comparative results on several publicly available datasets under different challenges of facial expressions, pose variations and occlusions are also presented.
Publisher: MDPI AG
Date: 05-09-2023
DOI: 10.3390/LIFE13091870
Publisher: British Machine Vision Association
Date: 2005
DOI: 10.5244/C.19.33
Publisher: Elsevier BV
Date: 02-2014
Publisher: Elsevier BV
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-61520-991-0.CH009
Abstract: In this chapter, the authors discuss the problem of face recognition using sparse representation classification (SRC). The SRC classifier has recently emerged as one of the latest paradigm in the context of view-based face recognition. The main aim of the chapter is to provide an insight of the SRC algorithm with thorough discussion of the underlying “Compressive Sensing” theory. Comprehensive experimental evaluation of the approach is conducted on a number of standard databases using exemplary evaluation protocols to provide a comparative index with the benchmark face recognition algorithms. The algorithm is also extended to the problem of video-based face recognition for more realistic applications.
Publisher: Springer Science and Business Media LLC
Date: 13-08-2019
Publisher: IEEE
Date: 2000
Publisher: ACM
Date: 12-07-2011
Publisher: Springer Science and Business Media LLC
Date: 30-07-2016
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: IEEE
Date: 09-2012
Publisher: Elsevier BV
Date: 11-2016
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: IEEE
Date: 09-2015
Publisher: Springer Science and Business Media LLC
Date: 16-04-2015
Publisher: Wiley
Date: 02-02-2011
DOI: 10.1002/SEC.240
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2007
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE Comput. Soc
Date: 2000
Publisher: IEEE
Date: 02-2007
Publisher: Springer Science and Business Media LLC
Date: 05-09-2021
Publisher: IEEE Comput. Soc
Date: 2000
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 07-2005
Publisher: American Society of Clinical Oncology (ASCO)
Date: 20-05-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Elsevier BV
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 2005
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 04-2015
Publisher: American Society of Clinical Oncology (ASCO)
Date: 20-05-2011
Publisher: Elsevier BV
Date: 06-2017
Publisher: IEEE
Date: 1997
Publisher: Elsevier BV
Date: 07-2017
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2018
DOI: 10.1142/S0219691318500406
Abstract: In this paper, we propose a consolidated framework for the automatic selection of the most discriminant subbands for the problem of face recognition. Essentially, the face images are transformed into textures using the linear binary pattern (LBP) approach, these texturized-faces undergo the wavelet packet decomposition resulting in several subband images. We propose to use the energy features to effectively represent these subband images. The underlying statistical patterns of the data are harnessed in form of information-theoretic metrics to select the most discriminant subbands. The proposed algorithms are extensively evaluated on several standard databases and are shown to always pick the most significant subbands resulting in better performance. The proposed algorithms are entirely generic and do not depend on the selection of features or/and classifiers.
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: IEEE
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2015
Publisher: Elsevier BV
Date: 03-2008
Publisher: Public Library of Science (PLoS)
Date: 08-06-2023
DOI: 10.1371/JOURNAL.PONE.0286460
Abstract: Hajj, the Muslim pilgrimage, is a large mass gathering event that involves performing rituals at several sites on specific days and times in a fixed order, thereby requiring transport of pilgrims between sites. For the past two decades, Hajj transport has relied on conventional and shuttle buses, train services, and pilgrims walking along pedestrian routes that link these sites. To ensure smooth and efficient transport during Hajj, specific groups of pilgrims are allocated with the cooperation of Hajj authorities to specific time windows, modes, and routes. However, the large number of pilgrims, delays and changes in bus schedules/timetables, and occasional lack of coordination between transport modes have often caused congestion or delays in pilgrim transfer between sites, with a cascading effect on transport management. This study focuses on modelling and simulating the transport of pilgrims between the sites using a discrete event simulation tool called “ExtendSim”. Three transport modules were validated, and different scenarios were developed. These scenarios consider changes in the percentages of pilgrims allocated to each transport mode and the scheduling of various modes. The results can aid authorities to make informed decisions regarding transport strategies for managing the transport infrastructure and fleets. The proposed solutions could be implemented with judicious allocation of resources, through pre-event planning and real-time monitoring during the event.
Publisher: Elsevier BV
Date: 11
Publisher: Springer Science and Business Media LLC
Date: 13-02-2018
Publisher: IEEE
Date: 02-2013
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 2001
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11744078_27
Publisher: Springer Science and Business Media LLC
Date: 28-04-2022
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2010
Publisher: Elsevier BV
Date: 11-2021
Publisher: IEEE
Date: 12-2016
Publisher: Institution of Engineering and Technology (IET)
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 05-2016
Publisher: Elsevier BV
Date: 08-2017
Publisher: Springer Singapore
Date: 2019
Publisher: Public Library of Science (PLoS)
Date: 26-06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: ACM
Date: 07-07-2021
Publisher: Association for Computing Machinery (ACM)
Date: 10-2011
Abstract: Geometric distortion measurement and the associated metrics involved are integral to the Rate Distortion (RD) shape coding framework, with importantly the efficacy of the metrics being strongly influenced by the underlying measurement strategy. This has been the catalyst for many different techniques with this article presenting a comprehensive review of geometric distortion measurement, the erse metrics applied, and their impact on shape coding. The respective performance of these measuring strategies is analyzed from both a RD and complexity perspective, with a recent distortion measurement technique based on arc-length-parameterization being comparatively evaluated. Some contemporary research challenges are also investigated, including schemes to effectively quantify shape deformation.
Publisher: Springer Science and Business Media LLC
Date: 20-04-2018
Publisher: Elsevier BV
Date: 11-2018
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: MDPI AG
Date: 29-03-2023
DOI: 10.3390/RS15071817
Abstract: Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 02-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 03-2018
Publisher: IEEE
Date: 2011
Publisher: Bentham Science Publishers Ltd.
Date: 22-09-2015
Publisher: Institution of Engineering and Technology (IET)
Date: 2010
Publisher: Frontiers Media SA
Date: 18-05-2022
DOI: 10.3389/FGENE.2022.822173
Abstract: Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 11-2022
Publisher: IEEE
Date: 2004
Publisher: IEEE Comput. Soc
Date: 1998
Publisher: IEEE
Date: 09-2018
Publisher: IEEE
Date: 1998
Publisher: SPIE
Date: 06-07-1998
DOI: 10.1117/12.316426
Publisher: ACM
Date: 17-02-2023
Publisher: Bentham Science Publishers Ltd.
Date: 11-07-2017
Publisher: ACM Press
Date: 2006
Publisher: IEEE
Date: 23-05-2022
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 12-2016
Publisher: Elsevier BV
Date: 2001
Publisher: IEEE
Date: 10-0002
Publisher: Springer Netherlands
Date: 2008
Publisher: IEEE
Date: 11-2009
Publisher: Springer Science and Business Media LLC
Date: 03-07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Elsevier BV
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: Institution of Engineering and Technology (IET)
Date: 07-2014
DOI: 10.1049/EL.2014.1927
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2022
DOI: 10.1145/3503043
Abstract: Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are conceptually similar in operation to traditional neural networks (and neural network variants), other elements represent a departure from traditional deep learning techniques. This tutorial exposes the power and novelty of GNNs to AI practitioners by collating and presenting details regarding the motivations, concepts, mathematics, and applications of the most common and performant variants of GNNs. Importantly, we present this tutorial concisely, alongside practical ex les, thus providing a practical and accessible tutorial on the topic of GNNs.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE Comput. Soc
Date: 2000
Publisher: IEEE
Date: 05-2015
Publisher: Springer Science and Business Media LLC
Date: 13-01-2023
No related grants have been discovered for Mohammed Bennamoun.