scispace - formally typeset
Search or ask a question
Author

Dhiah Al-Shammary

Other affiliations: RMIT University
Bio: Dhiah Al-Shammary is an academic researcher from Information Technology University. The author has contributed to research in topics: SOAP & Web service. The author has an hindex of 7, co-authored 20 publications receiving 160 citations. Previous affiliations of Dhiah Al-Shammary include RMIT University.
Topics: SOAP, Web service, Cluster analysis, XML, Web server

Papers
More filters
Journal ArticleDOI
TL;DR: It is found that the ECG signal self-similarity characteristic can be used efficiently to achieve high compression ratios and the proposed technique can achieve a higher compression ratio of 40 with lower Percentage Residual Difference (PRD) Value less than 1%.

35 citations

Proceedings ArticleDOI
11 Nov 2010
TL;DR: A new steganography technique is proposed that helps embed confidential information of patients into specific locations (called special range numbers) of digital ECG host signal that will cause minimal distortion to ECG, and at the same time, any secret information embedded is completely extractable.
Abstract: In Wireless tele-cardiology applications, ECG signal is widely used to monitor cardiac activities of patients Accordingly, in most e-health applications, ECG signals need to be combined with patient confidential information Data hiding and watermarking techniques can play a crucial role in ECG wireless tele-monitoring systems by combining the confidential information with the ECG signal since digital ECG data is huge enough to act as host to carry tiny amount of additional secret data In this paper, a new steganography technique is proposed that helps embed confidential information of patients into specific locations (called special range numbers) of digital ECG host signal that will cause minimal distortion to ECG, and at the same time, any secret information embedded is completely extractable We show that there are 21475 × 109 possible special range numbers making it extremely difficult for intruders to identify locations of secret bits Experiments show that percentage residual difference (PRD) of watermarked ECGs can be as low as 00247% and 00678% for normal and abnormal ECG segments (taken from MIT-BIH Arrhythmia database) respectively

31 citations

Journal ArticleDOI
TL;DR: A fractal self-similarity model is proposed that provides a novel way of computing the similarity of SOAP messages and has shown ''better'' quality clustering, as the aggregated SoAP messages have much smaller size than their counterparts.

17 citations

Proceedings ArticleDOI
04 Jul 2011
TL;DR: The experimental results showed that the proposed Fractal clustering technique can improve the performance of Web services significantly better than other clustering standards such as the K-means and PCA combined with K-Means by enabling the aggregation model to aggregate the most similar messages in one group resulting in better messages size reduction.
Abstract: The Simple Object Access Protocol (SOAP) is an XML based protocol that is widely used over the Internet as it supports interoperability by establishing access among Web servers and clients from the same or different platforms. However, SOAP Web services suffer the bottlenecks and congestions as a result of Web messages being bigger than the real payload in addition to the potentially increasing demand of the requested Web services. Aggregation of SOAP messages is an effective solution that has been developed to significantly reduce network traffic by aggregating SOAP messages at the server side and then multicast them to the Web clients. The major problem of the aggregation techniques is that they require efficient similarity criteria that can compute the similarity of SOAP messages as group-wise and not just pair-wise. In this paper, a new unsupervised auto class Fractal clustering technique is proposed for clustering SOAP messages into a dynamic number of clusters according to their Fractal similarities. The experimental results showed that the proposed Fractal clustering technique can improve the performance of Web services significantly better than other clustering standards such as the K-means and PCA combined with K-means by enabling the aggregation model to aggregate the most similar messages in one group resulting in better messages size reduction. Furthermore, the proposed Fractal clustering technique potentially reduces the required processing time in comparison with other standards.

16 citations

Proceedings ArticleDOI
30 Nov 2010
TL;DR: A person identification method using electrocardiogram (ECG) is presented based on cubic spline interpolation method that achieved better accuracy than the existing method without interpolation when using ECG data with lower sampling rate.
Abstract: In this paper, a person identification method using electrocardiogram (ECG) is presented based on cubic spline interpolation method Three different databases with two different sampling rates containing 36 ECG recordings were used for development and evaluation Each ECG recording is divided into two segments: a segment for enrolment, and a segment for recognition The ECG features are extracted from both the training dataset and the test dataset for model development and identification Two ECG biometric algorithms which are Cross Correlation (CC) and Percent Root-Mean-Square Deviation (PRD) were used for performance evaluation Results of experiments confirmed that the template matching using interpolation method achieved better accuracy (up to 446%) than the existing method without interpolation when using ECG data with lower sampling rate

12 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: This review paper begins at the definition of clustering, takes the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyzes the clustered algorithms from two perspectives, the traditional ones and the modern ones.
Abstract: Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

1,234 citations

01 Aug 2009
TL;DR: PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档;目前包含多参数的心肺。
Abstract: PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档。目前包含多参数的心肺、神经和其他生物医学信号,尤以心电图(ECG)为主。信号来自健康受试者和各种疾病的患者。涉及的疾病包括心脏猝死、充血性心力衰竭、癫痫、步态不稳、睡眠呼吸暂停和衰老等。

287 citations

Journal ArticleDOI
TL;DR: An efficient network model that combines WBAN and Cloud for valid data sharing is proposed and Content Centric Networking (CCN) is integrated with the proposed architecture to improve the ability of the WBAN coordinator.

113 citations

Journal ArticleDOI
TL;DR: A knowledge discovery-based approach that allows the context-aware system to adapt its behaviour in runtime by analysing large amounts of data generated in Ambient assisted living systems and stored in cloud repositories is proposed.
Abstract: Context-aware monitoring is an emerging technology that provides real-time personalised health-care services and a rich area of big data application. In this paper, we propose a knowledge discovery-based approach that allows the context-aware system to adapt its behaviour in runtime by analysing large amounts of data generated in ambient assisted living (AAL) systems and stored in cloud repositories . The proposed BDCaM model facilitates analysis of big data inside a cloud environment. It first mines the trends and patterns in the data of an individual patient with associated probabilities and utilizes that knowledge to learn proper abnormal conditions. The outcomes of this learning method are then applied in context-aware decision-making processes for the patient. A use case is implemented to illustrate the applicability of the framework that discovers the knowledge of classification to identify the true abnormal conditions of patients having variations in blood pressure (BP) and heart rate (HR). The evaluation shows a much better estimate of detecting proper anomalous situations for different types of patients. The accuracy and efficiency obtained for the implemented case study demonstrate the effectiveness of the proposed model.

106 citations

Journal ArticleDOI
TL;DR: This work presents a cloud computing background, a review of several proposals, a discussion of problem formulations, advantages and shortcomings of reviewed works, and provides several open issues, showing the relevancy of the topic in an increasing and demanding market.

92 citations