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ECG signal is widely used in most cardiology e‐health systems. Patients may be monitored continuously for at least 12 h a day. Therefore, the ECG signal size transmitted to a hospital server during continuous monitoring is significant. Furthermore, transmission of the large size ECG signal is a power consuming process. ECG compression is one of the proposed solutions to overcome this problem. In this paper, a new fractal‐based ECG lossy compression technique is proposed. It is clear that fractal can use ECG signal self similarity characteristics efficiently to achieve high compression ratios. The proposed technique is based on developing the fractal model in conjunction with Iterated Function System. Fractal is well known as a time consuming technique, and therefore, new mathematical development is proposed to potentially reduce fractal computations. Experiments have proven the significant performance of fast fractal in comparison with the traditional version. Furthermore, the resultant compression ratios are close to the traditional fractal results and higher than other existing techniques. [ABSTRACT FROM AUTHOR]

This research presents a survey for analyzing and classifying the EEG signal based on feature selection approaches. Moreover, The increasing complexity of high-dimensional medical datasets necessitates efficient feature selection methods for early disease detection and safeguarding public health. Intelligent feature selection represents an advanced stage in machine learning and innovative computer applications, as it reduces the number of features required for accurate classification. Generally, The main goal of feature selection is to improve the predictive model's performance and reduce the computational cost of modeling. This paper contains a survey of considerable research several on feature selection. The main measures to analyze this paper are Accuracy, precision, Recall, and F1-score assessment. In order to evaluate performance used stander dataset are EEG Bonn University. The results have proven that they have achieved the highest accuracy rate of around 99% compared with different techniques. [ABSTRACT FROM AUTHOR]

Distinguishing between network traffic activity, intrusion, and normal bahavour is very difficult and very time-consuming. An analyst has to review all the large and wide data to find the order of intrusion in the network connection. Therefore, a method that can detect network intrusion and reflect the current network traffic is required. In this paper, a new EDFC model (Evolving Dynamic Fuzzy clustering) algorithm is generated to improve and enhance the detection mechanism. The proposed model contains two parts: the cluster part and evolving part. This paper's main objective is to design and implement a novel and data density-based clustering scheme that provides high system performance and persistent grouping of data with high similarity and performance on big data for efficient machine learning. Compared to previous techniques, the suggested model's performance with several standard datasets such as the UNSW-NB15 dataset, KDD99 dataset, and NSLKDD dataset indicates a higher silhouette coefficient. In the EDFC model two metrics have been used to verify the quality of clusters, and these are the silhouette coefficient and the number of clusters. The EDFC model has achieved a high silhouette coefficient, and a low number of clusters compare to other models. Our focus is to enhance the detection mechanism for the DRDoS_DNS attacks. Therefore, The EDFC model has been implemented on the standard CICDDoS2019 dataset which contains DRDoS_DNS attacks and achieved a silhouette coefficient of 0.76 and a number of cluster 13. [ABSTRACT FROM AUTHOR]

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 2.1475 × 10(9) 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 0.0247% and 0.0678% for normal and abnormal ECG segments (taken from MIT-BIH Arrhythmia database) respectively.

Most organizations exchange, collect, store and process data over the Internet. Many hospital networks deploy Web services to send and receive patient information. SOAP (Simple Object Access Protocol) is the most usable communication protocol for Web services. XML is the standard encoding language of SOAP messages. However, the major drawback of XML messages is the high network traffic caused by large overheads. In this paper, two XML-aware compressors are suggested to compress patient messages stemming from any data transactions between Web clients and servers. The proposed compression techniques are based on the XML structure concepts and use both fixed-length and Huffman encoding methods for translating the XML message tree. Experiments show that they outperform all the conventional compression methods and can save tremendous amount of network bandwidth.

Many organizations such as hospitals have adopted Cloud Web services in applying their network services to avoid investing heavily computing infrastructure. SOAP (Simple Object Access Protocol) is the basic communication protocol of Cloud Web services that is XML based protocol. Generally,Web services often suffer congestions and bottlenecks as a result of the high network traffic that is caused by the large XML overhead size. At the same time, the massive load on Cloud Web services in terms of the large demand of client requests has resulted in the same problem. In this paper, two XML-aware aggregation techniques that are based on exploiting the compression concepts are proposed in order to aggregate the medical Web messages and achieve higher message size reduction.