ORCID Profile
0000-0002-8465-0996
Current Organisations
Victoria University
,
University of Southern Queensland
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Artificial Intelligence and Image Processing | Pattern Recognition and Data Mining | Information Storage, Retrieval And Management | Data Security | Computer Software | Information Systems | Computer System Security | Database Management | Library and Information Studies | Information Systems Development Methodologies | Data and information privacy | Pattern recognition | Graph social and multimedia data | Web Technologies (excl. Web Search) | Database Management | Decision Support and Group Support Systems | Computer vision and multimedia computation
ANZSRC Socio-Economic Objective (SEO)
Information processing services | Application Tools and System Utilities | Application tools and system utilities | Application packages | Information Processing Services (incl. Data Entry and Capture) | Electronic Information Storage and Retrieval Services |
Related Links
Publications
Neighbor-aware review helpfulness prediction
Publisher: Elsevier BV
Date: 09-2021
Privacy preserving access control policy and algorithms for conflicting problems
Publisher: IEEE
Date: 11-2011
Feature selection for helpfulness prediction of online product reviews: An empirical study
Publisher: Public Library of Science (PLoS)
Date: 23-12-2019
Early Detection of Diabetic Eye Disease from Fundus Images with Deep Learning
Publisher: Springer International Publishing
Date: 2020
Multi-level delegations with trust management in access control systems
Publisher: Springer Science and Business Media LLC
Date: 09-05-2012
D-ECG: A Dynamic Framework for Cardiac Arrhythmia Detection from IoT-Based ECGs
Publisher: Springer International Publishing
Date: 2018
Web Information Systems Engineering – WISE 2018
Publisher: Springer International Publishing
Date: 2018
A New Data Mining Scheme for Analysis of Big Brain Signal Data
Publisher: Springer International Publishing
Date: 2017
Preserving Data Privacy and Security in Australian My Health Record System: A Quality Health Care Implication
Publisher: Springer International Publishing
Date: 2018
Recovery of nickel and cobalt from laterite leach tailings through resin-in-pulp scavenging and selective ammoniacal elution
Publisher: Elsevier BV
Date: 12-2013
Exploring Human Mobility Patterns in Melbourne Using Social Media Data
Publisher: Springer International Publishing
Date: 2018
From logs to Stories: Human-Centred Data Mining for Cyber Threat Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
In-Situ XRD investigation of bauxite dehydroxylation
Publisher: Springer International Publishing
Date: 2018
Selective elution of nickel and cobalt from iminodiacetic acid cation exchange resin using ammoniacal solutions
Publisher: Elsevier BV
Date: 2014
Discriminative Regularization with Conditional Generative Adversarial Nets for Semi-Supervised Learning
Publisher: IEEE
Date: 07-2019
Distributed Differential Evolution for Anonymity-Driven Vertical Fragmentation in Outsourced Data Storage
Publisher: No publisher found
Date: 2020
Automatic and Efficient Framework for Identifying Multiple Neurological Disorders From EEG Signals
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
A Framework for Early Detection of Antisocial Behavior on Twitter Using Natural Language Processing
Publisher: Springer International Publishing
Date: 21-06-2019
Privacy-Preserving Task Recommendation Services for Crowdsourcing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Gradient Correlation: Are Ensemble Classifiers More Robust Against Evasion Attacks in Practical Settings?
Publisher: Springer International Publishing
Date: 2018
Unsupervised Multi-label Text Classification Using a World Knowledge Ontology
Publisher: No publisher found
Date: 2012
Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs
Publisher: Elsevier BV
Date: 12-2018
A Topic Model Based on Poisson Decomposition
Publisher: ACM
Date: 06-11-2017
An approximate microaggregation approach for microdata protection
Publisher: Elsevier BV
Date: 02-2012
A Framework for the Development of Living Practice Guidelines in Health Care.
Publisher: American College of Physicians
Date: 08-2022
DOI: 10.7326/M22-0514
Health Information Science
Publisher: Springer International Publishing
Date: 2015
Alcoholic Brain State Identification from Brain Signals Using Support Vector Machine-Based Algorithm
Publisher: Springer Singapore
Date: 04-07-2020
In situ AFM investigation of gibbsite growth in high ionic strength, highly alkaline, aqueous media
Publisher: Elsevier BV
Date: 05-2016
Effectively Delivering XML Information in Periodic Broadcast Environments
Publisher: No publisher found
Date: 2013
Deep learning for Antisocial Behaviour Analysis on Social Media
Publisher: IEEE
Date: 09-2020
Systematic Clustering-Based Microaggregation for Statistical Disclosure Control
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/NSS.2010.66
An adaptive image smoothing technique based on localization
Publisher: WORLD SCIENTIFIC
Date: 13-08-2020
The Devil is in the Detail: SDP-Driven Malformed Message Attacks and Mitigation in SIP Ecosystems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
A Global Ticket-Based Access Scheme for Mobile Users
Publisher: Springer Science and Business Media LLC
Date: 03-2004
Enhanced deep learning algorithm development to detect pain intensity from facial expression images
Publisher: Elsevier BV
Date: 07-2020
Detection of motor imagery EEG signals employing Naïve Bayes based learning process
Publisher: Elsevier BV
Date: 05-2016
A Joint Deep Neural Network Model for Pain Recognition from Face
Publisher: IEEE
Date: 02-2019
A Minority Class Boosted Framework for Adaptive Access Control Decision-Making
Publisher: Springer International Publishing
Date: 2021
A Novel Blockchain Based Smart Contract System for eReferral in Healthcare: HealthChain
Publisher: No publisher found
Date: 2020
An Advanced Two-Step DNN-Based Framework for Arrhythmia Detection
Publisher: Springer International Publishing
Date: 2020
Neural Sparse Topical Coding
Publisher: Association for Computational Linguistics
Date: 2018
DOI: 10.18653/V1/P18-1217
Diagnosis of autism spectrum disorder from EEG using a time–frequency spectrogram image‐based approach
Publisher: Institution of Engineering and Technology (IET)
Date: 08-10-2020
DOI: 10.1049/EL.2020.2646
Solubility of tricalcium aluminate in synthetic spent Bayer liquor
Publisher: American Chemical Society (ACS)
Date: 24-10-2014
DOI: 10.1021/IE502693P
Data-Driven Hierarchical Neural Network Modeling for High-Pressure Feedwater Heater Group
Publisher: Springer International Publishing
Date: 2020
Mining Actionable Knowledge Using Reordering Based Diversified Actionable Decision Trees
Publisher: No publisher found
Date: 2016
Exploring sampling in the detection of multicategory EEG signals
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/576437
Abstract: The paper presents a structure based on s lings and machine leaning techniques for the detection of multicategory EEG signals where random s ling (RS) and optimal allocation s ling (OS) are explored. In the proposed framework, before using the RS and OS scheme, the entire EEG signals of each class are partitioned into several groups based on a particular time period. The RS and OS schemes are used in order to have representative observations from each group of each category of EEG data. Then all of the selected s les by the RS from the groups of each category are combined in a one set named RS set. In the similar way, for the OS scheme, an OS set is obtained. Then eleven statistical features are extracted from the RS and OS set, separately. Finally this study employs three well-known classifiers: k -nearest neighbor ( k -NN), multinomial logistic regression with a ridge estimator (MLR), and support vector machine (SVM) to evaluate the performance for the RS and OS feature set. The experimental outcomes demonstrate that the RS scheme well represents the EEG signals and the k -NN with the RS is the optimum choice for detection of multicategory EEG signals.
Improving Distant Supervision of Relation Extraction with Unsupervised Methods
Publisher: No publisher found
Date: 2016
A role-involved purpose-based access control model
Publisher: Springer Science and Business Media LLC
Date: 03-05-2012
Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
A deep learning based framework for diagnosis of mild cognitive impairment
Publisher: Elsevier BV
Date: 07-2022
In situ AFM investigation of heterogeneous nucleation and growth of sodium oxalate on industrial gibbsite surfaces in concentrated alkaline solution
Publisher: Elsevier BV
Date: 04-2015
Parameter-Free Voronoi Neighborhood for Evolutionary Multimodal Optimization
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Classification of Alcoholic EEG Signals Using a Deep Learning Method
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Towards Secure XML Document with Usage Control
Publisher: Springer Berlin Heidelberg
Date: 2005
Health Information Science
Publisher: Springer International Publishing
Date: 2017
Agromining: Farming for Metals in the Future?
Publisher: American Chemical Society (ACS)
Date: 04-03-2015
DOI: 10.1021/ES506031U
Abstract: Phytomining technology employs hyperaccumulator plants to take up metal in harvestable plant biomass. Harvesting, drying and incineration of the biomass generates a high-grade bio-ore. We propose that "agromining" (a variant of phytomining) could provide local communities with an alternative type of agriculture on degraded lands farming not for food crops, but for metals such as nickel (Ni). However, two decades after its inception and numerous successful experiments, commercial phytomining has not yet become a reality. To build the case for the minerals industry, a large-scale demonstration is needed to identify operational risks and provide "real-life" evidence for profitability.
Morphological investigation of sodium oxalate crystals grown in aqueous sodium hydroxide solution
Publisher: Wiley
Date: 22-02-2013
Corrosion of Ti-2 and Ti-7 relevant to nickel acid leach chemistry
Publisher: Elsevier BV
Date: 03-2010
Web Information Systems Engineering – WISE 2016
Publisher: Springer International Publishing
Date: 2016
Exclusionary visual depiction of disabled persons in Malaysian news photographs
Publisher: SAGE Publications
Date: 09-04-2018
Abstract: Disability has been perceived as a social conditioning phenomenon and a sign system marking the body and mind. Accordingly, photographs of disability could shape our cultural perceptions about disability and disabled persons. In response to this position, we engage in a critical semiotic inquiry into press photographs of disability ( N = 1002) from The Star, a Malaysian mainstream English newspaper. We adapted Van Leeuwen’s (2008) social and visual actor networks to understand the visual techniques employed in depicting disabled actors in these images. The depiction is examined in relation to their absence and/or presence in these published photographs. If absent, the inclusion of non-disabled is analyzed. When present, the social categorizations of roles, grouping and specific/generic depictions are investigated. Findings reveal disabled persons have been symbolically excluded and thus, socially othered. These exclusionary strategies imply disabling journalistic practices which should be cautioned as they could could potentially undermine the advocacy for an inclusive society.
A novel JSON based regular expression language for pattern matching in the internet of things
Publisher: Springer Science and Business Media LLC
Date: 25-05-2018
Role-Based Delegation with Negative Authorization
Publisher: No publisher found
Date: 2006
DOI: 10.1007/11610113\_28
A Coevolutionary Estimation of Distribution Algorithm for Group Insurance Portfolio
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Special issue on Security, Privacy and Trust in network-based Big Data
Publisher: Elsevier BV
Date: 10-2015
Mining Event-Oriented Topics in Microblog Stream with Unsupervised Multi-View Hierarchical Embedding
Publisher: Association for Computing Machinery (ACM)
Date: 11-04-2018
DOI: 10.1145/3173044
Abstract: This article presents an unsupervised multi-view hierarchical embedding (UMHE) framework to sufficiently reveal the intrinsic topical knowledge in social events. Event-oriented topics are highly related to such events as it can provide explicit descriptions of what have happened in social community. In many real-world cases, however, it is difficult to include all attributes of microblogs, more often, textual aspects only are available. Traditional topic modelling methods have failed to generate event-oriented topics with the textual aspects, since the inherent relations between topics are often overlooked in these methods. Meanwhile, the metrics in original word vocabulary space might not effectively capture semantic distances. Our UMHE framework overcomes the severe information deficiency and poor feature representation. The UMHE first develops a multi-view Bayesian rose tree to preliminarily generate prior knowledge for latent topics and their relations. With such prior knowledge, we design an unsupervised translation-based hierarchical embedding method to make a better representation of these latent topics. By applying self-adaptive spectral clustering on the embedding space and the original space concomitantly, we eventually extract event-oriented topics in word distributions to express social events. Our framework is purely data-driven and unsupervised, without any external knowledge. Experimental results on TREC Tweets2011 dataset and Sina Weibo dataset demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics therefore, it can derive event-oriented topics with meaningful descriptions.
Crystallisation of tricalcium aluminate from sodium aluminate solution using slaked lime
Publisher: Elsevier BV
Date: 06-2016
A Blockchain based model for Curbing Doctors Shopping and Ensuring Provenance Management
Publisher: IEEE
Date: 12-2020
Visualising Korea: Critical Moments in History, Society and Politics
Publisher: Informa UK Limited
Date: 03-07-2021
Development of a nickel extraction-mineral carbonation process: Analysis of leaching mechanisms using regenerated acid
Publisher: Elsevier BV
Date: 11-2020
Ubiquitous computing environments and its usage access control
Publisher: ACM Press
Date: 2006
Nickel complexation as an innovative approach for nickel-cobalt selective recovery using sulfate-reducing bacteria
Publisher: Elsevier BV
Date: 2021
Health Information Science
Publisher: Springer International Publishing
Date: 2016
Building a consumer scalable anonymity payment protocol for Internet purchases
Publisher: IEEE Comput. Soc
Date: 2002
Adaptive Online Learning for Vulnerability Exploitation Time Prediction
Publisher: No publisher found
Date: 2020
Neural attention with character embeddings for hay fever detection from twitter
Publisher: Springer Science and Business Media LLC
Date: 12-10-2019
DOI: 10.1007/S13755-019-0084-2
Abstract: The paper aims to leverage the highly unstructured user-generated content in the context of pollen allergy surveillance using neural networks with character embeddings and the attention mechanism. Currently, there is no accurate representation of hay fever prevalence, particularly in real-time scenarios. Social media serves as an alternative to extract knowledge about the condition, which is valuable for allergy sufferers, general practitioners, and policy makers. Despite tremendous potential offered, conventional natural language processing methods prove limited when exposed to the challenging nature of user-generated content. As a result, the detection of actual hay fever instances among the number of false positives, as well as the correct identification of non-technical expressions as pollen allergy symptoms poses a major problem. We propose a deep architecture enhanced with character embeddings and neural attention to improve the performance of hay fever-related content classification from Twitter data. Improvement in prediction is achieved due to the character-level semantics introduced, which effectively addresses the out-of-vocabulary problem in our dataset where the rate is approximately 9%. Overall, the study is a step forward towards improved real-time pollen allergy surveillance from social media with state-of-art technology.
A conditional purpose-based access control model with dynamic roles
Publisher: Elsevier BV
Date: 03-2011
Topic-Net Conversation Model
Publisher: Springer International Publishing
Date: 2018
Scalable Architecture for Personalized Healthcare Service Recommendation Using Big Data Lake
Publisher: Springer International Publishing
Date: 2018
Secure k-NN Query on Encrypted Cloud Data with Multiple Keys
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Microaggregation Sorting Framework for K-Anonymity Statistical Disclosure Control in Cloud Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Does zonation and accessibility of wetlands influence human presence and mediate wildlife disturbance?
Publisher: Informa UK Limited
Date: 24-09-2019
Anomaly Detection in Quasi-Periodic Time Series Based on Automatic Data Segmentation and Attentional LSTM-CNN
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Access Control Management in Cloud Environments
Publisher: Springer International Publishing
Date: 2020
Domestic Violence Crisis Identification From Facebook Posts Based on Deep Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
A role-involved conditional purpose-based access control model
Publisher: Springer Berlin Heidelberg
Date: 2010
Mining Actionable Knowledge Using Reordering Based Diversified Actionable Decision Trees
Publisher: Springer International Publishing
Date: 2016
Role-Based Delegation with Negative Authorization
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11610113_28
Improving Distant Supervision of Relation Extraction with Unsupervised Methods
Publisher: Springer International Publishing
Date: 2016
Multi-criteria differential evolution
Publisher: ACM
Date: 07-07-2021
Analyzing EEG Signal Data for Detection of Epileptic Seizure: Introducing Weight on Visibility Graph with Complex Network Feature
Publisher: No publisher found
Date: 2016
SODIT: An innovative system for outlier detection using multiple localized thresholding and interactive feedback
Publisher: IEEE
Date: 04-2013
Ionic liquid electrodeposition of reactive metals
Publisher: Informa UK Limited
Date: 06-2008
Epilepsy Detection From EEG Using Complex Network Techniques: A Review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Emerging Topic Detection from Microblog Streams Based on Emerging Pattern Mining
Publisher: IEEE
Date: 05-2018
Guest Editorial Introduction to the Special Issue on Cognitive Networking for Intelligent Transportation Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Dilated Convolutional Networks Incorporating Soft Entity Type Constraints for Distant Supervised Relation Extraction
Publisher: IEEE
Date: 07-2019
Distributed Individuals for Multiple Peaks: A Novel Differential Evolution for Multimodal Optimization Problems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Editorial: Special Issue on Security and Privacy of IoT
Publisher: Springer Science and Business Media LLC
Date: 19-08-2017
Antisocial Behaviour Analyses Using Deep Learning
Publisher: Springer International Publishing
Date: 2020
Guest Editorial: Introduction to the Special Section on Advanced Networking Technologies in the Battle Against the Outbreak of Epidemic Diseases
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Developing a Deep Learning Based Approach for Anomalies Detection from EEG Data
Publisher: Springer International Publishing
Date: 2021
Microdata protection method through microaggregation: A median-based approach
Publisher: Informa UK Limited
Date: 11-02-2011
Indexing linked data in a wireless broadcast system with 3D hilbert space-filling curves
Publisher: ACM
Date: 03-11-2014
Supervised anomaly detection in uncertain pseudoperiodic data streams
Publisher: Association for Computing Machinery (ACM)
Date: 22-01-2016
DOI: 10.1145/2806890
Abstract: Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this article, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts the wavelet soft-thresholding method to remove the noises or errors in data streams. Based on the refined data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on several real datasets.
Students as Partners in the Academic Library: Co-Designing for Transformation
Publisher: Informa UK Limited
Date: 10-2020
Health Information Science - 8th International Conference, HIS 2019, Xi'an, China, October 18-20, 2019, Proceedings
Publisher: Springer International Publishing
Date: 2019
MDDE: multitasking distributed differential evolution for privacy-preserving database fragmentation
Publisher: Springer Science and Business Media LLC
Date: 04-01-2022
Enhanced Branch-and-Bound Framework for a Class of Sequencing Problems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Security and Privacy in Big Data Environment
Publisher: Springer International Publishing
Date: 2018
Selective removal of radioactive 210Pb(II) and nonradioactive Pb(II) isotopes from Cu(II)-rich acidic chloride solution by a new polyamine anion exchanger
Publisher: Elsevier BV
Date: 11-2020
Web Technologies and Applications
Publisher: Springer Berlin Heidelberg
Date: 2012
Multi-window based ensemble learning for classification of imbalanced streaming data
Publisher: Springer Science and Business Media LLC
Date: 08-03-2017
Incorporating word embeddings into topic modeling of short text
Publisher: Springer Science and Business Media LLC
Date: 18-12-2018
Empowering Vulnerability Prioritization: A Heterogeneous Graph-Driven Framework for Exploitability Prediction
Publisher: Springer Nature Singapore
Date: 2023
Optimal Privacy-Aware Path in Hippocratic Databases
Publisher: Springer Berlin Heidelberg
Date: 2009
The Effect of Regional Economic Clusters on Housing Price
Publisher: Springer International Publishing
Date: 2021
Health Information Science - 7th International Conference, HIS 2018, Cairns, QLD, Australia, October 5-7, 2018, Proceedings
Publisher: Springer International Publishing
Date: 2018
A Role-Involved Conditional Purpose-Based Access Control Model
Publisher: No publisher found
Date: 2010
Image Preprocessing in Classification and Identification of Diabetic Eye Diseases
Publisher: Springer Science and Business Media LLC
Date: 17-08-2021
DOI: 10.1007/S41019-021-00167-Z
Abstract: Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.
An Attention-based Hybrid LSTM-CNN Model for Arrhythmias Classification
Publisher: IEEE
Date: 07-2019
Authorization approaches for advanced permission-role assignments
Publisher: IEEE
Date: 04-2008
Delegating revocations and authorizations in collaborative business environments
Publisher: Springer Science and Business Media LLC
Date: 23-05-2008
Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation
Publisher: Springer Science and Business Media LLC
Date: 21-08-2021
DOI: 10.1007/S41019-021-00170-4
Abstract: By breaking sensitive associations between attributes, database fragmentation can protect the privacy of outsourced data storage. Database fragmentation algorithms need prior knowledge of sensitive associations in the tackled database and set it as the optimization objective. Thus, the effectiveness of these algorithms is limited by prior knowledge. Inspired by the anonymity degree measurement in anonymity techniques such as k -anonymity, an anonymity-driven database fragmentation problem is defined in this paper. For this problem, a set-based adaptive distributed differential evolution (S-ADDE) algorithm is proposed. S-ADDE adopts an island model to maintain population ersity. Two set-based operators, i.e., set-based mutation and set-based crossover, are designed in which the continuous domain in the traditional differential evolution is transferred to the discrete domain in the anonymity-driven database fragmentation problem. Moreover, in the set-based mutation operator, each in idual’s mutation strategy is adaptively selected according to the performance. The experimental results demonstrate that the proposed S-ADDE is significantly better than the compared approaches. The effectiveness of the proposed operators is verified.
Towards Secure XML Document with Usage Control
Publisher: No publisher found
Date: 2005
A Long Short-Term Memory Based Framework for Early Detection of Mild Cognitive Impairment From EEG Signals
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Mechanisms of Polyacrylate Modified Sodium Oxalate Crystallization from Highly Alkaline Solutions
Publisher: American Chemical Society (ACS)
Date: 17-02-2016
Automatic Niching Differential Evolution With Contour Prediction Approach for Multimodal Optimization Problems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Specifying Role-Based Access Constraints with Object Constraint Language
Publisher: No publisher found
Date: 2004
A flexible payment scheme and its role-based access control
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2005
DOI: 10.1109/TKDE.2005.35
An Interactive Network for End-to-End Review Helpfulness Modeling
Publisher: Springer Science and Business Media LLC
Date: 26-06-2020
DOI: 10.1007/S41019-020-00133-1
Abstract: Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness prediction. However, star ratings are used in a way that has either little representation capacity or limited interaction with review texts. As a result, rating information has yet to be fully exploited during the combination. This paper aims to overcome the two drawbacks. A deep interactive architecture is proposed to learn the text–rating interaction (TRI) for helpfulness modeling. TRI enlarges the representation capacity of star ratings while enhancing the influence of rating information on review texts. TRI is evaluated on six real-world domains of the Amazon 5-Core dataset. Extensive experiments demonstrate that TRI can better predict review helpfulness and beat the state of the art. Ablation studies and qualitative analysis are provided to further understand model behaviors and the learned parameters.
A LSTM and CNN Based Assemble Neural Network Framework for Arrhythmias Classification
Publisher: IEEE
Date: 05-2019
Editorial: Special issue on security and privacy in network computing
Publisher: Springer Science and Business Media LLC
Date: 25-07-2019
A framework for cardiac arrhythmia detection from IoT-based ECGs
Publisher: Springer Science and Business Media LLC
Date: 28-02-2020
Central Topic Model for Event-oriented Topics Mining in Microblog Stream
Publisher: ACM
Date: 17-10-2015
Comparative study of symmetric cryptographic algorithms
Publisher: IEEE
Date: 10-2021
Coordinated Charging Scheduling of Electric Vehicles: A Mixed-Variable Differential Evolution Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
MSReNet: Multi-step Reformulation for Open-Domain Question Answering
Publisher: No publisher found
Date: 2020
Web Information Systems Engineering – WISE 2015
Publisher: Springer International Publishing
Date: 2015
Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization
Publisher: Springer Science and Business Media LLC
Date: 22-02-2022
DOI: 10.1007/S40747-022-00650-8
Abstract: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problem (MO-MTOP) using evolutionary computation. However, most existing methods tend to directly treat the multiple multi-objective tasks as different problems and optimize them by different populations, which face the difficulty in designing good knowledge transferring strategy among the tasks opulations. Different from existing methods that suffer from the difficult knowledge transfer, this paper proposes to treat the MO-MTOP as a multi-objective multi-criteria optimization problem (MO-MCOP), so that the knowledge of all the tasks can be inherited in a same population to be fully utilized for solving the MO-MTOP more efficiently. To be specific, the fitness evaluation function of each task in the MO-MTOP is treated as an evaluation criterion in the corresponding MO-MCOP, and therefore, the MO-MCOP has multiple relevant evaluation criteria to help the in idual selection and evolution in different evolutionary stages. Furthermore, a probability-based criterion selection strategy and an adaptive parameter learning method are also proposed to better select the fitness evaluation function as the criterion. By doing so, the algorithm can use suitable evaluation criteria from different tasks at different evolutionary stages to guide the in idual selection and population evolution, so as to find out the Pareto optimal solutions of all tasks. By integrating the above, this paper develops a multi-objective multi-criteria evolutionary algorithm framework for solving MO-MTOP. To investigate the proposed algorithm, extensive experiments are conducted on widely used MO-MTOPs to compare with some state-of-the-art and well-performing algorithms, which have verified the great effectiveness and efficiency of the proposed algorithm. Therefore, treating MO-MTOP as MO-MCOP is a potential and promising direction for solving MO-MTOP.
Exploring Data Mining Techniques in Medical Data Streams
Publisher: No publisher found
Date: 2016
Quantum distance to uncontrollability and quantum speed limits
Publisher: American Physical Society (APS)
Date: 04-04-2022
A Niching Memetic Algorithm for Multi-Solution Traveling Salesman Problem
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Child Abuse and Domestic Abuse: Content and Feature Analysis from Social Media Disclosures
Publisher: Springer International Publishing
Date: 2018
Protecting outsourced data in cloud computing through access management
Publisher: Wiley
Date: 21-04-2016
DOI: 10.1002/CPE.3286
Antisocial Behaviour Analyses Using Deep Learning
Publisher: No publisher found
Date: 2020
Text Mining and Real-Time Analytics of Twitter Data: A Case Study of Australian Hay Fever Prediction
Publisher: Springer International Publishing
Date: 2018
Automated epilepsy detection techniques from electroencephalogram signals: a review study
Publisher: Springer Science and Business Media LLC
Date: 12-10-2020
Adaptive Online Learning for Vulnerability Exploitation Time Prediction
Publisher: Springer International Publishing
Date: 2020
An Efficient Framework for the Analysis of Big Brain Signals Data
Publisher: Springer International Publishing
Date: 2018
Distributed Differential Evolution for Anonymity-Driven Vertical Fragmentation in Outsourced Data Storage
Publisher: Springer International Publishing
Date: 2020
MSReNet: Multi-step Reformulation for Open-Domain Question Answering
Publisher: Springer International Publishing
Date: 2020
Security and Privacy-Preserving Challenges of e-Health Solutions in Cloud Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Pattern Filtering Attention for Distant Supervised Relation Extraction via Online Clustering
Publisher: Springer International Publishing
Date: 2019
Achieving secure and flexible M-services through tickets
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2003
Scalable Architecture for Personalized Healthcare Service Recommendation Using Big Data Lake
Publisher: No publisher found
Date: 2017
Web Information Systems Engineering – WISE 2015
Publisher: Springer International Publishing
Date: 2015
A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Dissecting contact tracing apps in the Android platform
Publisher: Public Library of Science (PLoS)
Date: 14-05-2021
DOI: 10.1371/JOURNAL.PONE.0251867
Abstract: Contact tracing has historically been used to retard the spread of infectious diseases, but if it is exercised by hand in large-scale, it is known to be a resource-intensive and quite deficient process. Nowadays, digital contact tracing has promptly emerged as an indispensable asset in the global fight against the coronavirus pandemic. The work at hand offers a meticulous study of all the official Android contact tracing apps deployed hitherto by European countries. Each app is closely scrutinized both statically and dynamically by means of dynamic instrumentation. Depending on the level of examination, static analysis results are grouped in two axes. The first encompasses permissions, API calls, and possible connections to external URLs, while the second concentrates on potential security weaknesses and vulnerabilities, including the use of trackers, in-depth manifest analysis, shared software analysis, and taint analysis. Dynamic analysis on the other hand collects data pertaining to Java classes and network traffic. The results demonstrate that while overall these apps are well-engineered, they are not free of weaknesses, vulnerabilities, and misconfigurations that may ultimately put the user security and privacy at risk.
Analysing Big Brain Signal Data for Advanced Brain Computer Interface System
Publisher: Springer International Publishing
Date: 2022
A graph empowered insider threat detection framework based on daily activities
Publisher: Elsevier BV
Date: 10-2023
A Novel Evolutionary Algorithm with Column and Sub-Block Local Search for Sudoku Puzzles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Sentence level topic models for associated topics extraction
Publisher: Springer Science and Business Media LLC
Date: 18-10-2018
Block Bayesian Sparse Topical Coding
Publisher: IEEE
Date: 05-2018
Distributed Differential Evolution With Adaptive Resource Allocation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
A Novel Incremental Dictionary Learning Method for Low Bit Rate Speech Streaming
Publisher: Springer International Publishing
Date: 2018
The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space
Publisher: Elsevier BV
Date: 12-2020
Cloning for privacy protection in multiple independent data publications
Publisher: ACM
Date: 24-10-2011
Access control management with provenance in healthcare environments
Publisher: IEEE
Date: 05-2016
Web service QoS prediction using improved software source code metrics
Publisher: Public Library of Science (PLoS)
Date: 15-01-2020
Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks
Publisher: Elsevier BV
Date: 2022
Access control management for ubiquitous computing
Publisher: Elsevier BV
Date: 10-2008
On Secure Wireless Communications for IoT Under Eavesdropper Collusion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Decision-Based Evasion Attacks on Tree Ensemble Classifiers
Publisher: Springer Science and Business Media LLC
Date: 20-04-2020
Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction
Publisher: Springer International Publishing
Date: 2019
Selective precipitation of mixed nickel-cobalt hydroxide
Publisher: Elsevier BV
Date: 2011
Automatic Breast Lesion Segmentation Using Continuous Max-Flow Algorithm in Phase Preserved DCE-MRIs
Publisher: Springer International Publishing
Date: 2021
When things matter: A survey on data-centric internet of things
Publisher: Elsevier BV
Date: 04-2016
Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach
Publisher: MDPI AG
Date: 11-07-2023
DOI: 10.3390/INFORMATICS10030060
Abstract: This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, user-friendliness, and legal compliance. To address these challenges, the study developed a universal privacy model designed to efficiently manage and share patients’ personal and sensitive data across different platforms, such as MHR and NHS systems. The research employed various BERT techniques to differentiate between legitimate and illegitimate privacy policies. Among them, Distil BERT emerged as the most accurate, demonstrating the potential of our ML-based approach to effectively identify inadequate privacy policies. This paper outlines future research directions, emphasizing the need for comprehensive evaluations, testing in real-world case studies, the investigation of adaptive frameworks, ethical implications, and fostering stakeholder collaboration. This research offers a pioneering approach towards enhancing healthcare information privacy, providing an innovative foundation for future work in this field.
EEG Sleep Stages Analysis and Classification Based on Weighed Complex Network Features
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Efficient algorithms for scheduling XML data in a mobile wireless broadcast environment
Publisher: IEEE
Date: 12-2008
Corrosion of Titanium and Its Alloys in Sulfuric Acid in the Presence of Chlorides
Publisher: The Electrochemical Society
Date: 2006
DOI: 10.1149/1.2126580
Automated detection of COVID-19 through convolutional neural network using chest x-ray images
Publisher: Public Library of Science (PLoS)
Date: 21-01-2022
DOI: 10.1371/JOURNAL.PONE.0262052
Abstract: The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.
Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Automatic breast lesion segmentation in phase preserved DCE-MRIs
Publisher: Springer Science and Business Media LLC
Date: 20-05-2022
DOI: 10.1007/S13755-022-00176-W
Abstract: We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method.
Historical Information-based Differential Evolution for Dynamic Optimization Problem
Publisher: IEEE
Date: 28-06-2021
Data privacy against composition attack
Publisher: Springer Berlin Heidelberg
Date: 2012
Optimal Privacy-Aware Path in Hippocratic Databases
Publisher: No publisher found
Date: 2009
A pyramid-like model for heartbeat classification from ECG recordings
Publisher: Public Library of Science (PLoS)
Date: 14-11-2018
Delegating Revocations and Authorizations
Publisher: No publisher found
Date: 2007
Cloud Service Description Model: An Extension of USDL for Cloud Services
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2018
A probabilistic method for emerging topic tracking in Microblog stream
Publisher: Springer Science and Business Media LLC
Date: 21-04-2016
A knowledge graph empowered online learning framework for access control decision-making
Publisher: Springer Science and Business Media LLC
Date: 23-06-2022
DOI: 10.1007/S11280-022-01076-5
Abstract: Knowledge graph, as an extension of graph data structure, is being used in a wide range of areas as it can store interrelated data and reveal interlinked relationships between different objects within a large system. This paper proposes an algorithm to construct an access control knowledge graph from user and resource attributes. Furthermore, an online learning framework for access control decision-making is proposed based on the constructed knowledge graph. Within the framework, we extract topological features to represent high cardinality categorical user and resource attributes. Experimental results show that topological features extracted from knowledge graph can improve the access control performance in both offline learning and online learning scenarios with different degrees of class imbalance status.
Web Information Systems Engineering – WISE 2016
Publisher: Springer International Publishing
Date: 2016
Determination of total mercury in bauxite and bauxite residue by flow injection cold vapour atomic absorption spectrometry
Publisher: Elsevier BV
Date: 03-2014
Parallelization of Massive Textstream Compression Based on Compressed Sensing
Publisher: Association for Computing Machinery (ACM)
Date: 21-08-2017
DOI: 10.1145/3086702
Abstract: Compressing textstreams generated by social networks can both reduce storage consumption and improve efficiency such as fast searching. However, the compression process is a challenge due to the large scale of textstreams. In this article, we propose a textstream compression framework based on compressed sensing theory and design a series of matching parallel procedures. The new approach uses a linear projection technique in the textstream compression process, achieving fast compression speed and low compression ratio. Two processes are executed by designing elaborated parallel procedures for efficient compressing and decompressing of large-scale textstreams. The decompression process is implemented for approximate solutions of underdetermined linear systems. Experimental results show that the new method can efficiently achieve the compression and decompression tasks on a large amount of text generated by social networks.
Effect of key parameters on the selective acid leach of nickel from mixed nickel-cobalt hydroxide
Publisher: Author(s)
Date: 2017
DOI: 10.1063/1.4974412
Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Data Privacy against Composition Attack
Publisher: No publisher found
Date: 2012
Properties of Lewatit® TP272, a commercial solvent impregnated cation exchange resin for cobalt recovery
Publisher: Elsevier BV
Date: 03-2016
Microdata protection method through microaggregation: A systematic approach
Publisher: International Academy Publishing (IAP)
Date: 11-2012
A real-time dynamic concept adaptive learning algorithm for exploitability prediction
Publisher: Elsevier BV
Date: 02-2022
Cyberpulse: A Machine Learning Based Link Flooding Attack Mitigation System for Software Defined Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
A Study on Securing Software Defined Networks
Publisher: Springer International Publishing
Date: 2017
Web Information Systems Engineering – WISE 2018
Publisher: Springer International Publishing
Date: 2018
DSGA: A Distributed Segment-Based Genetic Algorithm for Multi-Objective Outsourced Database Partitioning
Publisher: Elsevier BV
Date: 10-2022
Assessing the Utilisation of TELedentistry from Perspectives of Early Career Dental Practitioners
Publisher: Springer Nature Switzerland
Date: 2022
Electrodeposition of Aluminum from Aluminum Chloride–Trihexyl(tetradecyl) Phosphonium Chloride
Publisher: The Electrochemical Society
Date: 2008
DOI: 10.1149/1.2804422
Bi-Directional Feature Fixation-Based Particle Swarm Optimization for Large-Scale Feature Selection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
A Pulmonary Vascular Segmentation Algorithm of Chest CT Images Based on Fast Marching Method
Publisher: IEEE
Date: 12-2018
Correction to: Web Information Systems Engineering – WISE 2020
Publisher: Springer International Publishing
Date: 18-10-2021
A novel statistical technique for intrusion detection systems
Publisher: Elsevier BV
Date: 02-2018
Effective Collaboration with Information Sharing in Virtual Universities
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2009
Cryptographic Access Control in Electronic Health Record Systems: A Security Implication
Publisher: Springer International Publishing
Date: 2017
Deep learning for pollen allergy surveillance from twitter in Australia
Publisher: Springer Science and Business Media LLC
Date: 08-11-2019
DOI: 10.1186/S12911-019-0921-X
Abstract: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches. The data was extracted from Twitter based on pre-defined keywords (i.e. ’hayfever’ OR ’hay fever’) throughout the period of 6 months, covering the high pollen season in Australia. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers. Standard evaluation metrics (i.e. Accuracy, Precision and Recall) were calculated for the results validation. Finally, visual correlation with weather variables was performed. The neural networks-based approach was able to correctly identify the implicit mentions of the symptoms and treatments, even unseen previously (accuracy up to 87.9% for GRU with GloVe embeddings of 300 dimensions). The system addresses the shortcomings of the conventional machine learning techniques with manual feature-engineering that prove limiting when exposed to a wide range of non-standard expressions relating to medical concepts. The case-study presented demonstrates an application of ’black-box’ approach to the real-world problem, along with its internal workings demonstration towards more transparent, interpretable and reproducible decision-making in health informatics domain.
Movie Recommendation System Using Deep Learning
Publisher: IEEE
Date: 16-12-2021
Selective reductive leaching of oxidised cobalt containing residue
Publisher: Elsevier BV
Date: 12-2013
Potentiodynamic polarization of platinum and aluminum in AlCl 3-[P14,6,6,6]Cl melts
Publisher: The Electrochemical Society
Date: 29-08-2008
DOI: 10.1149/1.3159344
Abstract: Ionic liquids (ILs) are being considered for a wide range of applications including as a medium for the electrowinning and refining of reactive metals such as aluminium. The electrochemical characteristics of the aluminum chloride - trihexyl(tetradecyl) phosphonium chloride (AlCl3-[P14,6,6,6]Cl) system are explored in this paper. Cyclic voltammograms using a platinum working electrode are presented over a wide range of AlCl3 concentrations (XAlCl3 = 0, 0.3, 0.5, 0.6, 0.67 mole fraction). The potentiodynamic polarization of aluminum were measured in the XAlCl3 = 0.67 melt at 323, 355, 375, 402 and 423 K. The [P14,6,6,6]Cl used was of commercial purity one of the main impurities is HCl. The presence of HCl in the melt detracts from the current efficiency of aluminum electrodeposition.
Deep Learning for Multi-Class Identification From Domestic Violence Online Posts
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Graph Intelligence Enhanced Bi-Channel Insider Threat Detection
Publisher: Springer Nature Switzerland
Date: 2022
A benefit-driven genetic algorithm for balancing privacy and utility in database fragmentation
Publisher: ACM
Date: 13-07-2019
Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining
Publisher: MDPI AG
Date: 27-03-2019
DOI: 10.3390/S19071489
Abstract: Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For ex le, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.
An Electronic Cash Scheme and its Management
Publisher: SAGE Publications
Date: 09-2004
Abstract: A secure electronic cash scheme and its role-based access control (RBAC) management are proposed in this paper. The scheme uses electronic cash for payment transactions. In this protocol, from the viewpoint of banks, consumers can improve anonymity if they are worried about disclosure of their identities. A new role called anonymity provider agent (AP) provides a highly anonymous certificate for consumers during a payment processing. The role AP certifies re-encrypted data after verifying the validity of the content from consumers, but with no private information of the consumers required. Each consumer can get a required anonymity level through this new method, depending on the available time, computation and cost. Consumers can also use different banks (cross-payment) and anonymity provider agents (multiple APs) without double-spending problems. Role-based access control is widely used in system management and products since its advantages such as reducing administration cost and complexity. However, conflicting problems may arise between roles when RBAC is applied for system management. For ex le, mutually exclusive roles may be granted to a user with RBAC and the user may have or derive a high level of authority. To solve these problems, we analyze the duty separation constraints of the roles and role hierarchies in the scheme, then discuss how to grant a role to a user.
A computer aided analysis scheme for detecting epileptic seizure from EEG data
Publisher: Springer Science and Business Media LLC
Date: 2018
Welcome message from the Editor-in-Chief and Co-Editor-in-Chief
Publisher: European Alliance for Innovation n.o.
Date: 25-09-2017
Discriminative Regularized Deep Generative Models for Semi-Supervised Learning
Publisher: IEEE
Date: 11-2019
Arrhythmias Classification by Integrating Stacked Bidirectional LSTM and Two-Dimensional CNN
Publisher: Springer International Publishing
Date: 2019
Satisfying Privacy Requirements Before Data Anonymization
Publisher: Oxford University Press (OUP)
Date: 17-03-2011
On Efficient and Robust Anonymization for Privacy Protection on Massive Streaming Categorical Information
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Privacy-aware access control with trust management in web service
Publisher: Springer Science and Business Media LLC
Date: 03-02-2011
A consumer scalable anonymity payment scheme with role based access control
Publisher: IEEE Comput. Soc
Date: 2001
Selective leaching of nickel from mixed nickel cobalt hydroxide precipitate
Publisher: Elsevier BV
Date: 06-2013
Securing Electronic Medical Record and Electronic Health Record Systems Through an Improved Access Control
Publisher: No publisher found
Date: 2015
A Protocol for Untraceable Electronic Cash
Publisher: No publisher found
Date: 2000
Healthchain: A novel framework on privacy preservation of electronic health records using blockchain technology
Publisher: Public Library of Science (PLoS)
Date: 09-12-2020
DOI: 10.1371/JOURNAL.PONE.0243043
Abstract: The privacy of Electronic Health Records (EHRs) is facing a major hurdle with outsourcing private health data in the cloud as there exists danger of leaking health information to unauthorized parties. In fact, EHRs are stored on centralized databases that increases the security risk footprint and requires trust in a single authority which cannot effectively protect data from internal attacks. This research focuses on ensuring the patient privacy and data security while sharing the sensitive data across same or different organisations as well as healthcare providers in a distributed environment. This research develops a privacy-preserving framework viz Healthchain based on Blockchain technology that maintains security, privacy, scalability and integrity of the e-health data. The Blockchain is built on Hyperledger fabric, a permissioned distributed ledger solutions by using Hyperledger composer and stores EHRs by utilizing InterPlanetary File System (IPFS) to build this healthchain framework. Moreover, the data stored in the IPFS is encrypted by using a unique cryptographic public key encryption algorithm to create a robust blockchain solution for electronic health data. The objective of the research is to provide a foundation for developing security solutions against cyber-attacks by exploiting the inherent features of the blockchain, and thus contribute to the robustness of healthcare information sharing environments. Through the results, the proposed model shows that the healthcare records are not traceable to unauthorized access as the model stores only the encrypted hash of the records that proves effectiveness in terms of data security, enhanced data privacy, improved data scalability, interoperability and data integrity while sharing and accessing medical records among stakeholders across the healthchain network.
Brain Data Mining Framework Involving Entropy Topography and Deep Learning
Publisher: Springer International Publishing
Date: 2022
Neurological abnormality detection from electroencephalography data: a review
Publisher: Springer Science and Business Media LLC
Date: 09-2021
Specifying Role-Based Access Constraints with Object Constraint Language
Publisher: Springer Berlin Heidelberg
Date: 2004
A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
Publisher: Public Library of Science (PLoS)
Date: 25-06-2021
DOI: 10.1371/JOURNAL.PONE.0253094
Abstract: Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.
Integrating microorganism and macroorganism dispersal: modes, techniques and challenges with particular focus on co-dispersal
Publisher: Informa UK Limited
Date: 02-10-2015
Building access control policy model for privacy preserving and testing policy conflicting problems
Publisher: Elsevier BV
Date: 12-2014
Child Abuse and Domestic Abuse: Content and Feature Analysis from Social Media Disclosures
Publisher: No publisher found
Date: 2018
Challenges in Electroencephalography Data Processing Using Machine Learning Approaches
Publisher: Springer International Publishing
Date: 2022
Enhancing System Security by Intrusion Detection Using Deep Learning
Publisher: Springer International Publishing
Date: 2022
Effectively delivering XML information in periodic broadcast environments
Publisher: Springer Berlin Heidelberg
Date: 2013
An Integrated MCI Detection Framework Based on Spectral-temporal Analysis
Publisher: Springer Science and Business Media LLC
Date: 08-10-2019
Effects of inorganic anions on the morphology of sodium oxalate crystallized from highly alkaline solutions
Publisher: American Chemical Society (ACS)
Date: 19-03-2014
DOI: 10.1021/CG5000952
MicroThings: A Generic IoT Architecture for Flexible Data Aggregation and Scalable Service Cooperation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
MtMR: Ensuring MapReduce Computation Integrity with Merkle Tree-Based Verifications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Global maps of soil temperature.
Publisher: Wiley
Date: 11-02-2022
DOI: 10.1111/GCB.16060
Abstract: Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km
A Protocol for Untraceable Electronic Cash
Publisher: Springer Berlin Heidelberg
Date: 2000
Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results
Publisher: MDPI AG
Date: 31-01-2022
DOI: 10.3390/EN15031061
Abstract: We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors.
Understanding housing market behaviour from a microscopic perspective
Publisher: IEEE
Date: 07-2018
Depression detection from social network data using machine learning techniques
Publisher: Springer Science and Business Media LLC
Date: 27-08-2018
New feature extraction for automated detection of epileptic seizure using complex network framework
Publisher: Elsevier BV
Date: 09-2021
A pairwise-systematic microaggregation for statistical disclosure control
Publisher: IEEE
Date: 12-2010
Apply transfer learning to cybersecurity: Predicting exploitability of vulnerabilities by description
Publisher: Elsevier BV
Date: 12-2020
On the identity anonymization of high-dimensional rating data
Publisher: Wiley
Date: 24-03-2011
DOI: 10.1002/CPE.1724
Improving accessibility of the Australian My Health Records while preserving privacy and security of the system
Publisher: Springer Science and Business Media LLC
Date: 08-10-2020
Unsupervised feature selection with adaptive residual preserving
Publisher: Elsevier BV
Date: 11-2019
Warming shortens flowering seasons of tundra plant communities
Publisher: Springer Science and Business Media LLC
Date: 10-12-2018
DOI: 10.1038/S41559-018-0745-6
Abstract: Advancing phenology is one of the most visible effects of climate change on plant communities, and has been especially pronounced in temperature-limited tundra ecosystems. However, phenological responses have been shown to differ greatly between species, with some species shifting phenology more than others. We analysed a database of 42,689 tundra plant phenological observations to show that warmer temperatures are leading to a contraction of community-level flowering seasons in tundra ecosystems due to a greater advancement in the flowering times of late-flowering species than early-flowering species. Shorter flowering seasons with a changing climate have the potential to alter trophic interactions in tundra ecosystems. Interestingly, these findings differ from those of warmer ecosystems, where early-flowering species have been found to be more sensitive to temperature change, suggesting that community-level phenological responses to warming can vary greatly between biomes.
A Novel Blockchain Based Smart Contract System for eReferral in Healthcare: HealthChain
Publisher: Springer International Publishing
Date: 2020
Exploring Data Mining Techniques in Medical Data Streams
Publisher: Springer International Publishing
Date: 2016
Aspects of the mechanism of nucleation and intergrowth of gibbsite crystals on sodium oxalate surfaces in concentrated alkaline solutions
Publisher: American Chemical Society (ACS)
Date: 26-11-2015
DOI: 10.1021/CG501465V
Finding an Optimum Set of Roles in a CPAC Model
Publisher: IEEE
Date: 10-2019
Injecting purpose and trust into data anonymisation
Publisher: Elsevier BV
Date: 07-2011
Securing Electronic Medical Record and Electronic Health Record Systems Through an Improved Access Control
Publisher: Springer International Publishing
Date: 2015
Web-Age Information Management
Publisher: Springer Berlin Heidelberg
Date: 2000
A survey of graph based complex brain network analysis using functional and diffusional MRI
Publisher: Science Publications
Date: 12-2017
DTC: Transfer learning for commonsense machine comprehension
Publisher: Elsevier BV
Date: 07-2020
Knowledge transfer-based distributed differential evolution for dynamic database fragmentation
Publisher: Elsevier BV
Date: 10-2021
Personalized app recommendation based on app permissions
Publisher: Springer Science and Business Media LLC
Date: 17-04-2017
Distributed Memetic Algorithm for Outsourced Database Fragmentation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Auto-correlation Based Feature Extraction Approach for EEG Alcoholism Identification
Publisher: Springer International Publishing
Date: 2021
Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising
Publisher: Elsevier BV
Date: 02-2017
On Secure Wireless Communications for Service Oriented Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2018
Bayesian Sparse Topical Coding
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
A conditional role-involved purpose-based access control model
Publisher: Informa UK Limited
Date: 31-01-2011
The Australian PCEHR System: Ensuring Privacy and Security through an Improved Access Control Mechanism
Publisher: European Alliance for Innovation n.o.
Date: 09-08-2016
Formal authorization allocation approaches for role-based access control based on relational algebra operations
Publisher: IEEE Comput. Sci
Date: 2002
CyberPulse++: A machine learning‐based security framework for detecting link flooding attacks in software defined networks
Publisher: Hindawi Limited
Date: 02-05-2021
DOI: 10.1002/INT.22442
Knowledge-Driven Cybersecurity Intelligence: Software Vulnerability Coexploitation Behavior Discovery
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Guest Editorial: WWWJ Special Issue of the 17th International Conference on Web Information Systems Engineering (WISE 2016)
Publisher: Springer Science and Business Media LLC
Date: 06-11-2017
Cryptographic Access Control in Electronic Health Record Systems: A Security Implication
Publisher: No publisher found
Date: 2017
Unsupervised multi-label text classification using a world knowledge ontology
Publisher: Springer Berlin Heidelberg
Date: 2012
Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Dynamic optimisation based fuzzy association rule mining method
Publisher: Springer Science and Business Media LLC
Date: 20-03-2018
Convolutional Neural Network for Multi-class Classification of Diabetic Eye Disease
Publisher: European Alliance for Innovation n.o.
Date: 13-07-2018
A New Data Mining Scheme for Analysis of Big Brain Signal Data
Publisher: No publisher found
Date: 2017
A New Design of Mental State Classification for Subject Independent BCI Systems
Publisher: Elsevier BV
Date: 10-2019
Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning
Publisher: Springer Science and Business Media LLC
Date: 07-2021
Analyzing EEG Signal Data for Detection of Epileptic Seizure: Introducing Weight on Visibility Graph with Complex Network Feature
Publisher: Springer International Publishing
Date: 2016
DRAM-Net: A Deep Residual Alzheimer’s Diseases and Mild Cognitive Impairment Detection Network Using EEG Data
Publisher: Springer Nature Switzerland
Date: 2022
Diversity-driven Multi-population Particle Swarm Optimization for Dynamic Optimization Problem
Publisher: ACM
Date: 15-07-2023
Automated detection of mild and multi-class diabetic eye diseases using deep learning
Publisher: Springer Science and Business Media LLC
Date: 08-10-2020
Evolutionary Dynamic Database Partitioning Optimization for Privacy and Utility
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Computing and Combinatorics, First Annual International Conference, COCOON '95, Xi'an, China, August 24-26, 1995, Proceedings
Publisher: Springer Berlin Heidelberg
Date: 1995
DOI: 10.1007/BFB0030813
Morphological investigation of sodium oxalate crystals grown in aqueous sodium hydroxide solution
Publisher: Springer International Publishing
Date: 2016
Selectivity of commercial and novel mixed functionality cation exchange resins in mildly acidic sulfate and mixed sulfate-chloride solution
Publisher: Elsevier BV
Date: 06-2012
Distributed Cooperative Coevolution of Data Publishing Privacy and Transparency
Publisher: Association for Computing Machinery (ACM)
Date: 07-08-2023
DOI: 10.1145/3613962
Abstract: Data transparency is beneficial to data participants’ awareness, users’ fairness, and research work’s reproducibility. However, when addressing transparency requirements, we cannot ignore data privacy. This paper defines the multi-objective data publishing (MODP) problem, optimizing data privacy and transparency at the same time. Accordingly, we propose a distributed cooperative coevolutionary genetic algorithm (DCCGA) to optimize the MODP problem. In the population of DCCGA, each in idual represents an anonymization solution to MODP. Three modules in DCCGA, i.e., grouping module, cooperative coevolutionary module, and evolving module, are proposed for distributed sub-population update and evaluation, improving DCCGA’s optimization performance and parallel efficiency. Moreover, a matrix-based crossover operator and a matrix-based mutation operator are designed to exchange and adjust anonymization information in the in iduals efficiently. Experimental results demonstrate that the proposed DCCGA outperforms the competitors with respect to solution accuracy, convergence speed, and scalability. Besides, we verify the effectiveness of all the proposed components in DCCGA.
Exploring Human Mobility Patterns in Melbourne Using Social Media Data
Publisher: No publisher found
Date: 2018
A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Delegating Revocations and Authorizations
Publisher: Springer Berlin Heidelberg
Date: 2008
Generation of topic evolution graphs from short text streams
Publisher: Elsevier BV
Date: 03-2020
A Study on Securing Software Defined Networks
Publisher: No publisher found
Date: 2017
Guest editorial: WWWJ special issue of the 21th international Conference on Web Information Systems Engineering (WISE 2020)
Publisher: Springer Science and Business Media LLC
Date: 05-11-2021
Causal dependencies of provenance data in healthcare environment
Publisher: IEEE
Date: 05-2015
Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
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Geological Survey Austria / Geologische Bundesanstalt
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