The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%.

Abstract 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 This paper proposes a novel Extended Particle Swarm Optimization model (EPSO) that potentially enhances the search process of PSO for optimization problem. Evidently, gene expression profiles are significantly important measurement factor in molecular biology that is used in medical diagnosis of cancer types. The challenge to certain classification methodologies for gene expression profiles lies in the thousands of features recorded for each sample. A modified Wrapper feature selection model is applied with the aim of addressing the gene classification challenge by replacing its randomness approach with EPSO and PSO, respectively. EPSO is initializing the random size of the population and dividing them into two groups in order to promote the exploration and reduce the probability of falling in stagnation. Experimentally, EPSO has required less processing time to select the optimal features (average of 62.14 s) than PSO (average of 95.72 s). Furthermore, EPSO accuracy has provided better classification results (start from 54% to 100%) than PSO (start from 52% to 96%).

The interoperability of Web services has resulted in its adoption for recently-emerging cloud platforms. SOAP (Simple Object Access Protocol) is considered as the main platform independent communication tool for the Cloud Web service. Generally, Cloud Web services suffer performance bottlenecks and congestions that are mainly caused by the encoding of XML messages as they are bigger than the real payloads. In this paper, Fractal clustering model is proposed to compute the Fractal clustering similarity of SOAP messages in order to cluster them and enable the aggregation of SOAP messages to significantly reduce the size of the aggregated SOAP messages. Furthermore, as Fractal is a well-known as a time-consuming technique especially for large dataset, two fast Fractal clustering models have been proposed that are aiming to reduce the required clustering time. The proposed fast Fractal models have tremendously outperformed the classical Fractal model in terms of the processing time and have outperformed both K-means and PCA combined with K-means models in terms of both the processing time and SOAP messages size reduction.

SOAP Web services create high network traffic because of its generated large XML messages resulting in poor network performance. Therefore, enhancing the performance of Web services by compressing SOAP messages is considered to be an important issue. Compression ratios achieved by most of the existing techniques and tools are not high enough, and even a tiny improvement could save tremendous amount of network bandwidth in emerging cloud and mobile scenarios. In this paper, we try to achieve this objective by proposing two innovative techniques capable of reducing small as well as very large messages. Instead of encoding the characters of XML message individually, Fixed-length encoding and Huffman encoding as a variable-length technique are developed to deal with XML tags as individual input items. XML tree and binary tree are constructed that support the encoding algorithm by removing the closing tags. A high Compression Ratio has been achieved that is up to 7.8 and around 13.5 for large and very large messages respectively.