ORCID Profile
0000-0001-6488-1052
Current Organisation
Swinburne University of Technology
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Pattern Recognition and Data Mining | Database Management | Artificial Intelligence and Image Processing | Information Systems | Global Information Systems
Information Processing Services (incl. Data Entry and Capture) |
Publisher: Wiley
Date: 05-07-2020
DOI: 10.1002/CPE.5435
Abstract: In iduals' right to privacy includes control over access to their location information. With the advent of location‐based services and personal transport services (such as ridesharing), the risk of location privacy breaches is increased greatly. The potential negative effects of location privacy leakages include spam location‐based service flooding, threats to personal safety (such as physical attacks), and intrusion related to access to private places (such as homes and hospitals). Therefore, protecting the privacy of users' real locations is becoming increasingly important. This is often achieved using a pseudo‐location near the real location, but existing pseudo‐location generators, such as NRand and the uniform random method, suffer from statistical inference, which can infer the obfuscation domain to cover the real location. In this paper, we propose an intelligent pseudo‐location recommendation (IPLR) method to reduce the risk of a statistical inference attack. In IPLR, we generate a random substitute of the real location to attract the adversary and thus hide the real location. Then, the pseudo‐location is generated in the neighborhood of the random substitute location following a normal distribution the random substitute location is changed frequently to confuse attackers. In particular, we define three levels of location privacy, ie, address level, street level, and district level, to evaluate the effectiveness of the IPLR method. Our experimental study using simulation data demonstrates that the proposed IPLR method achieves lower risk of location privacy leakage and higher probabilities of safety in all three levels of location privacy than NRand and the random method. It also demonstrates the effectiveness of the proposed IPLR to balance location privacy and service quality.
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 31-10-2006
Publisher: Elsevier BV
Date: 06-2015
Publisher: Springer New York
Date: 06-08-2013
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
DOI: 10.1109/TKDE.2010.43
Publisher: Elsevier BV
Date: 11-2013
Publisher: IEEE
Date: 10-2007
Publisher: IEEE
Date: 08-2010
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 02-12-2017
Publisher: Elsevier BV
Date: 03-2016
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2018
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/SCC.2015.41
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.NEUNET.2022.10.017
Abstract: Compared with relatively easy feature creation or generation in data analysis, manual data labeling needs a lot of time and effort in most cases. Even if automated data labeling seems to make it better in some cases, the labeling results still need to be checked and verified by manual. The High Dimension and Low S le Size (HDLSS) data are therefore very common in data mining and machine learning. For classification problems with the HDLSS data, due to data piling and approximate equidistance between any two input points in high-dimension space, some traditional classifiers often give poor predictive performance. In this paper, we propose a Maximum Decentral Projection Margin Classifier (MDPMC) in the framework of a Support Vector Classifier (SVC). In the MDPMC model, the constraints of maximizing the projection distance between decentralized input points and their supporting hyperplane are integrated into the SVC model in addition to maximizing the margin of two supporting hyperplanes. On ten real HDLSS datasets, the experiment results show that the proposed MDPMC approach can deal well with data piling and approximate equidistance problems. Compared with SVC with Linear Kernel (SVC-LK) and Radial Basis Function Kernel (SVC-RBFK), Distance Weighted Discrimination (DWD), weighted DWD (wDWD), Distance-Weighted Support Vector Machine (DWSVM), Population-Guided Large Margin Classifier (PGLMC), and Data Maximum Dispersion Classifier (DMDC), MDPMC obtains better predictive accuracy and lower classification errors than the other seven classifiers on the HDLSS data.
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/SCC.2015.43
Publisher: Springer Science and Business Media LLC
Date: 11-07-2020
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11758549_71
Publisher: Elsevier BV
Date: 2018
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 11-2014
DOI: 10.1109/BIBE.2014.41
Publisher: Springer Science and Business Media LLC
Date: 03-2006
Publisher: Elsevier BV
Date: 2014
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 05-09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Springer Singapore
Date: 2017
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: ACM
Date: 03-07-2014
Publisher: Springer Science and Business Media LLC
Date: 03-2016
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 26-02-2019
DOI: 10.1213/XAA.0000000000001017
Abstract: Giant ovarian cystadenoma is a rare and often late presentation. The mass effect of such tumors can lead to profound respiratory and cardiovascular compromise, predominately through inferior vena cava compression, but also restriction of normal lung function through transdiaphragmatic mass effect and, as is seen in this case, cardiac t onade. In this article, the authors outline an expedited program of preoperative optimization and a choreographed physiological assessment strategy to functionally assess the hemodynamic consequences of a giant ovarian mucinous neoplasm, thereby informing the safe conduct of anesthesia and surgery.
Publisher: Springer International Publishing
Date: 2016
Publisher: Wiley
Date: 07-01-2020
DOI: 10.1002/CPE.5659
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11758525_142
Publisher: Springer Science and Business Media LLC
Date: 21-05-2016
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 12-2012
Publisher: MDPI AG
Date: 06-08-2016
DOI: 10.3390/S16081245
Publisher: MDPI AG
Date: 06-08-2016
DOI: 10.3390/S16081244
Publisher: Elsevier BV
Date: 10-2016
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/ICWS.2016.11
Publisher: Wiley
Date: 09-01-2018
DOI: 10.1002/MRC.4707
Publisher: Elsevier BV
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Elsevier BV
Date: 07-2013
Publisher: ACM
Date: 31-01-2017
Publisher: MDPI AG
Date: 31-10-2014
DOI: 10.3390/S141120562
Publisher: Elsevier BV
Date: 11-2015
Publisher: ACM
Date: 31-01-2017
Publisher: Elsevier BV
Date: 2019
Publisher: Springer International Publishing
Date: 2016
Publisher: Public Library of Science (PLoS)
Date: 29-04-2014
Publisher: Wiley
Date: 27-09-2021
DOI: 10.1002/CPE.6599
Publisher: Elsevier BV
Date: 2019
Publisher: Association for Computing Machinery (ACM)
Date: 2012
Abstract: Combining the Semantic Web and the Ubiquitous Web, Web 3.0 is for things . The Semantic Web enables human knowledge to be machine-readable and the Ubiquitous Web allows Web services to serve any thing, forming a bridge between the virtual world and the real world. By using context, Web services can become smarter—that is, aware of the target things' or applications' physical environments, or situations and respond proactively and intelligently. Existing methods for implementing context-aware Web services on Web 2.0 mainly enumerate different implementations corresponding to different attribute values of the context, in order to improve the Quality of Services (QoS). However, things in the physical world are extremely erse, which poses new problems for Web services: it is difficult to unify the context of things and to implement a flexible smart Web service for things. This article proposes a novel smart Web service based on the context of things, which is implemented using a REpresentational State Transfer for Things (Thing-REST) style, to tackle the two problems. In a smart Web service, the user's description (semantic context) and sensor reports (sensing context) are two channels for acquiring the context of things which are then employed by ontology services to make the context of things machine-readable. With guidance of domain knowledge services, event detection services can analyze things' needs particularly, well through the context of things. We then propose a Thing-REST style to manage the context of things and user context, and to mashup Web services through three structures (i.e., chain, select, and merge) to implement smart Web services. A smart plant watering-service application demonstrates the effectiveness of our method.
Publisher: Elsevier BV
Date: 2014
Publisher: IEEE
Date: 12-2018
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2017
DOI: 10.1161/STROKEAHA.117.018450
Abstract: ECP (eosinophil cationic protein) is a marker of eosinophil activity and degranulation, which has been linked to atherosclerosis and cardiovascular disease. We examined the relationship between ECP, carotid plaque, and incidence of stroke in a prospective population-based cohort. The subjects participated in the Malmö Diet and Cancer Study between 1991 and 1994. A total of 4706 subjects with no history of stroke were included (40% men mean age, 57.5 years). Carotid plaque was determined by B-mode ultrasound of the right carotid artery. Incidence of stroke was followed up during a mean period of 16.5 years in relation to plasma ECP levels. Subjects in the third tertile (versus first tertile) of ECP tended to have higher prevalence of carotid plaque (odds ratio: 1.18 95% confidence interval: 1.003–1.39 P =0.044 after multivariate adjustments). A total of 258 subjects were diagnosed with ischemic stroke (IS) during follow-up. ECP was associated with increased incidence of IS after risk factor adjustment (hazard ratio, 1.57 95% confidence interval: 1.13–2.18 for third versus first tertile P =0.007). High ECP was associated with increased risk of IS in subjects with carotid plaque. The risk factor–adjusted hazard ratio for IS was 1.86 (95% confidence interval: 1.32–2.63) in subjects with carotid plaque and ECP in the top tertile, compared with those without plaque and ECP in the first or second tertiles. High ECP is associated with increased incidence of IS. The association between ECP and IS was also present in the subgroup with carotid plaque.
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 06-2008
Publisher: Wiley
Date: 23-11-2018
DOI: 10.1111/COIN.12195
Publisher: Hindawi Limited
Date: 02-03-2020
DOI: 10.1155/2020/4365191
Abstract: When multiple Wireless Body Area Networks (WBANs) are aggregated, the overlapping region of their communications will result in internetwork interference, which could impose severe impacts on the reliability of WBAN performance. Therefore, how to mitigate the internetwork interference becomes the key problem to be solved urgently in practical applications of WBAN. However, most of the current researches on internetwork interference focus on traditional cellular networks and large-scale wireless sensor networks. In this paper, an Optimal Backoff Time Interference Mitigation Algorithm (OBTIM) is proposed. This method performs rescheduling or channel switching when the performance of the WBANs falls below tolerance, utilizing the cell neighbour list established by the beacon method. Simulation results show that the proposed method improves the channel utilization and the network throughput, and in the meantime, reduces the collision probability and energy consumption, when compared with the contention-based beacon schedule scheme.
Publisher: Elsevier BV
Date: 2018
Publisher: Association for Computing Machinery (ACM)
Date: 18-01-2016
DOI: 10.1145/2806889
Abstract: Witnessing the wide spread of malicious information in large networks, we develop an efficient method to detect anomalous diffusion sources and thus protect networks from security and privacy attacks. To date, most existing work on diffusion sources detection are based on the assumption that network snapshots that reflect information diffusion can be obtained continuously. However, obtaining snapshots of an entire network needs to deploy detectors on all network nodes and thus is very expensive. Alternatively, in this article, we study the diffusion sources locating problem by learning from information diffusion data collected from only a small subset of network nodes. Specifically, we present a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay. We theoretically analyze the strength of the model and derive performance bounds. We empirically test and compare the model using both synthetic and real-world networks to demonstrate its performance.
Publisher: Oxford University Press (OUP)
Date: 11-03-2011
Publisher: World Scientific Pub Co Pte Lt
Date: 2020
DOI: 10.1142/S0219622019500469
Abstract: To improve the quality of service and network performance for the Flash P2P video-on-demand, the prediction Flash P2P network traffic flow is beneficial for the control of the network video traffic. In this paper, a novel prediction algorithm to forecast the traffic rate of Flash P2P video is proposed. This algorithm is based on the combination of the ensemble local mean decomposition (ELMD) and the generalized autoregressive conditional heteroscedasticity (GARCH). The ELMD is used to decompose the original long-related flow into the summation of the short-related flow. Then, the GRACH is utilized to predict the short-related flow. The developed algorithm is tested in a university’s c us network. The predicted results show that our proposed method can further achieve higher accuracy than those obtained by existing algorithms, such as GARCH and Local Mean Decomposition and Generalized AutoRegressive Conditional Heteroskedasticity (LMD-GARCH) while keeping lower computational complexity.
Publisher: Springer Science and Business Media LLC
Date: 04-06-2014
Publisher: IEEE
Date: 04-2013
Publisher: Wiley
Date: 17-12-2020
DOI: 10.1002/CPE.5599
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF). In the financial environment, we design a four-layer architecture for this model. By employing the external trusted or-acle group and a financial regulator agency, a variety types of data can be effectively stored in the blockchain, such that the data type extension based on a single-chain is realized. The experimental results indicate that the proposed model can improve the efficiency of simplified payment verifi-cation.
Publisher: Wiley
Date: 14-04-2020
DOI: 10.1002/CPE.5751
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/SCC.2016.66
Publisher: IEEE
Date: 12-2008
Publisher: Elsevier BV
Date: 07-2017
DOI: 10.1016/J.CMPB.2017.05.009
Abstract: Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection. The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features.
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2016
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 2020
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 21-09-2021
DOI: 10.1161/CIRCULATIONAHA.121.055340
Abstract: Early detection of coronary atherosclerosis using coronary computed tomography angiography (CCTA), in addition to coronary artery calcification (CAC) scoring, may help inform prevention strategies. We used CCTA to determine the prevalence, severity, and characteristics of coronary atherosclerosis and its association with CAC scores in a general population. We recruited 30 154 randomly invited in iduals age 50 to 64 years to SCAPIS (the Swedish Cardiopulmonary Bioimage Study). The study includes in iduals without known coronary heart disease (ie, no previous myocardial infarctions or cardiac procedures) and with high-quality results from CCTA and CAC imaging performed using dedicated dual-source CT scanners. Noncontrast images were scored for CAC. CCTA images were visually read and scored for coronary atherosclerosis per segment (defined as no atherosclerosis, 1% to 49% stenosis, or ≥50% stenosis). External validity of prevalence estimates was evaluated using inverse probability for participation weighting and Swedish register data. In total, 25 182 in iduals without known coronary heart disease were included (50.6% women). Any CCTA-detected atherosclerosis was found in 42.1% any significant stenosis (≥50%) in 5.2% left main, proximal left anterior descending artery, or 3-vessel disease in 1.9% and any noncalcified plaques in 8.3% of this population. Onset of atherosclerosis was delayed on average by 10 years in women. Atherosclerosis was more prevalent in older in iduals and predominantly found in the proximal left anterior descending artery. Prevalence of CCTA-detected atherosclerosis increased with increasing CAC scores. Among those with a CAC score , all had atherosclerosis and 45.7% had significant stenosis. In those with 0 CAC, 5.5% had atherosclerosis and 0.4% had significant stenosis. In participants with 0 CAC and intermediate 10-year risk of atherosclerotic cardiovascular disease according to the pooled cohort equation, 9.2% had CCTA-verified atherosclerosis. Prevalence estimates had excellent external validity and changed marginally when adjusted to the age-matched Swedish background population. Using CCTA in a large, random s le of the general population without established disease, we showed that silent coronary atherosclerosis is common in this population. High CAC scores convey a significant probability of substantial stenosis, and 0 CAC does not exclude atherosclerosis, particularly in those at higher baseline risk.
Publisher: MDPI AG
Date: 18-04-2019
DOI: 10.3390/S19081863
Abstract: Facial Expression Recognition (FER) can be widely applied to various research areas, such as mental diseases diagnosis and human social hysiological interaction detection. With the emerging advanced technologies in hardware and sensors, FER systems have been developed to support real-world application scenes, instead of laboratory environments. Although the laboratory-controlled FER systems achieve very high accuracy, around 97%, the technical transferring from the laboratory to real-world applications faces a great barrier of very low accuracy, approximately 50%. In this survey, we comprehensively discuss three significant challenges in the unconstrained real-world environments, such as illumination variation, head pose, and subject-dependence, which may not be resolved by only analysing images/videos in the FER system. We focus on those sensors that may provide extra information and help the FER systems to detect emotion in both static images and video sequences. We introduce three categories of sensors that may help improve the accuracy and reliability of an expression recognition system by tackling the challenges mentioned above in pure image/video processing. The first group is detailed-face sensors, which detect a small dynamic change of a face component, such as eye-trackers, which may help differentiate the background noise and the feature of faces. The second is non-visual sensors, such as audio, depth, and EEG sensors, which provide extra information in addition to visual dimension and improve the recognition reliability for ex le in illumination variation and position shift situation. The last is target-focused sensors, such as infrared thermal sensors, which can facilitate the FER systems to filter useless visual contents and may help resist illumination variation. Also, we discuss the methods of fusing different inputs obtained from multimodal sensors in an emotion system. We comparatively review the most prominent multimodal emotional expression recognition approaches and point out their advantages and limitations. We briefly introduce the benchmark data sets related to FER systems for each category of sensors and extend our survey to the open challenges and issues. Meanwhile, we design a framework of an expression recognition system, which uses multimodal sensor data (provided by the three categories of sensors) to provide complete information about emotions to assist the pure face image/video analysis. We theoretically analyse the feasibility and achievability of our new expression recognition system, especially for the use in the wild environment, and point out the future directions to design an efficient, emotional expression recognition system.
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Hindawi Limited
Date: 07-07-2019
DOI: 10.1155/2019/5173589
Abstract: Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) for extraction of representative and discriminatory information from epileptic EEG data. As the multichannel EEG signals are highly correlated and are in large volumes, the DP algorithm is applied to extract the most representative s les from EEG data. The PCA is utilised to produce uncorrelated variables and to reduce the dimensionality of the DP s les for better recognition. To verify the robustness of the proposed method, four machine learning techniques, random forest classifier (RF), k -nearest neighbour algorithm ( k -NN), support vector machine (SVM), and decision tree classifier (DT), are employed on the obtained features. Furthermore, we assess the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that the DP technique effectively extracts the representative s les from EEG signals compressing up to over 47% s le points of EEG signals. The results also indicate that the proposed feature method with the RF classifier achieves the best performance and yields 99.85% of the overall classification accuracy ( OCA ). The proposed method outperforms the most recently reported methods in terms of OCA in the same epileptic EEG database.
Publisher: Elsevier BV
Date: 09-2015
Publisher: Elsevier BV
Date: 2019
Publisher: Wiley
Date: 16-08-2019
DOI: 10.1002/CPE.5484
Publisher: Elsevier BV
Date: 2018
Publisher: Wiley
Date: 08-07-2009
DOI: 10.1002/CPE.1445
Publisher: Elsevier BV
Date: 11-2020
Publisher: ACM
Date: 14-10-2019
Publisher: ACM
Date: 14-10-2019
Publisher: Elsevier BV
Date: 06-2016
Publisher: IEEE
Date: 12-2011
Publisher: World Scientific Pub Co Pte Lt
Date: 11-2011
DOI: 10.1142/S0219622011004750
Abstract: Multiple criteria linear programming and multiple criteria quadratic programming classification models have been applied in some field in financial risk analysis and credit risk control such as credit cardholders' behavior analysis. In this paper, a fuzzy linear programming classification method with soft constraints and criteria was proposed based on the previous findings from other researchers. In this method, the satisfied result can be obtained through selecting constraint and criteria boundary variable d i *, respectively. A general framework of this method is also constructed. Two real-life datasets, one from a major USA bank and the other from a database of KDD 99, are used to test the accurate rate of the proposed method. And the result shows the feasibility of this method.
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 12-2010
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 05-2015
Publisher: IEEE
Date: 06-2016
Publisher: Hindawi Limited
Date: 16-11-2019
DOI: 10.1155/2019/3108950
Abstract: With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating. With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges. FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read. Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement. For ex le, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc. Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology. In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed. HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences. It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding. A large number of experiments fully evaluated the performance of HRCM. Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression. Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.
Publisher: IEEE
Date: 07-2020
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 20-03-2018
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 09-2012
Publisher: Springer Science and Business Media LLC
Date: 03-2014
Publisher: Springer Science and Business Media LLC
Date: 13-08-2014
Publisher: ACM
Date: 07-04-2014
Publisher: Springer Science and Business Media LLC
Date: 03-11-2016
Publisher: Springer Singapore
Date: 2020
Publisher: IEEE
Date: 12-2017
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2007
DOI: 10.1142/S0219622007002538
Abstract: Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively.
Publisher: Wiley
Date: 11-05-2022
DOI: 10.1002/CPE.7003
Abstract: QoS‐aware based web service recommendation is one of the crucial solutions to help users find high‐quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is ided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection.
Publisher: Mathematical Sciences Publishers
Date: 27-05-2020
Publisher: MDPI AG
Date: 02-06-2020
DOI: 10.3390/ELECTRONICS9060925
Abstract: Research on electroencephalography (EEG) signals and their data analysis have drawn much attention in recent years. Data mining techniques have been extensively applied as efficient solutions for non-invasive brain–computer interface (BCI) research. Previous research has indicated that human brains produce recognizable EEG signals associated with specific activities. This paper proposes an optimized data s ling model to identify the status of the human brain and further discover brain activity patterns. The s ling methods used in the proposed model include the segmented EEG graph using piecewise linear approximation (SEGPA) method, which incorporates optimized data s ling methods and the EEG-based weighted network for EEG data analysis, which can be used for machinery control. The data s ling and segmentation techniques combine normal distribution approximation (NDA), Poisson distribution approximation (PDA), and related s ling methods. This research also proposes an efficient method for recognizing human thinking and brain signals with entropy-based frequent patterns (FPs). The obtained recognition system provides a foundation that could to be useful in machinery or robot control. The experimental results indicate that the NDA–PDA segments with less than 10% of the original data size can achieve 98% accuracy, as compared with original data sets. The FP method identifies more than 12 common patterns for EEG data analysis based on the optimized s ling methods.
Publisher: IEEE
Date: 09-2008
Publisher: IEEE
Date: 07-2014
Publisher: Springer Science and Business Media LLC
Date: 29-07-2020
Publisher: Elsevier BV
Date: 04-2019
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 22-04-2008
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 09-2012
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 10-2007
Publisher: Springer International Publishing
Date: 2017
Start Date: 2013
End Date: 12-2016
Amount: $270,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2013
End Date: 12-2019
Amount: $339,434.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2011
End Date: 06-2016
Amount: $345,000.00
Funder: Australian Research Council
View Funded Activity