High dimensional datasets expose a critical obstacle in machine learning. Feature selection overcomes this obstacle by eliminating duplicated and unimportant features from the dataset to increase the robustness of learning algorithms. This paper introduces a binary version of a hybrid swarm intelligence approach as a wrapper method for feature selection that gathers between the strengths of both the grey wolf and particle swarm optimizers. This approach is named Improved Binary Grey Wolf Optimization (IBGWO). The original version of this hybrid approach was proposed in the literature with a continuous search space as a high-level hybrid form, which runs the optimizers one after the other. Two different types of transfer functions, named S-Shaped and V-Shaped, are applied in this work to turn continuous data into binary. Nine of high-dimensional small-instance medical datasets are employed to assess the proposed approach. The experimental results demonstrate that IBGWO based on S-Shaped (IBGWO-S) outperforms the binary particle swarm and the binary grey wolf optimizers on six out of nine datasets according to the classification accuracy and fitness values. IBGWO-S selects the fewest features on 100% of the datasets. The results show IBGWO based on V-Shaped (IBGWO-V) outperforms the binary particle swarm and binary grey wolf optimizers on five datasets based on the classification accuracy and fitness values. The results indicate that IBGWO-V outperforms IBGWO-S in terms of all studied evaluation metrics. The results also show that IBGWO-S and IBGWO-V outperform eight metaheuristics known in the literature in selecting the relevant features with acceptable classification accuracy.

Enterprise applications are being increasingly deployed on cloud infrastructures. Often, a cloud service provider (SP) enters into a Service Level Agreement (SLA) with a cloud subscriber, which specifies performance requirements for the subscriber's applications. An SP needs systematic Service Level Planning (SLP) tools that can help estimate the resources needed and hence the cost incurred to satisfy their customers' SLAs. Enterprise applications typically experience bursty workloads and the impact of such bursts needs to be considered during SLP exercises. Unfortunately, most existing approaches do not consider workload burstiness. We propose a Resource Allocation Planning (RAP) technique, which allows an SP to identify a time varying allocation plan of resources to applications that satisfies bursts. Extensive simulation results show that the proposed RAP variants can identify resource allocation plans that satisfy SLAs without exhaustively generating all possible plans. Furthermore, the results show that RAP can permit SPs to more accurately determine the capacity required for meeting specified SLAs compared to other competing techniques especially for bursty workloads.

No abstract

To transmit data securely between different parties, a variety of security approaches have been proposed in the literature. Specifically, DNA based cryptography and steganography approaches were used to secure data transmission. In this paper, a substitution-based method for data hiding in DNA sequences is proposed. In the proposed data hiding method, data is encoded using a binary coding rule then the data is hidden into a DNA sequence. The proposed method provides an enhancement on a previously proposed DNA substitution method named Least Significant Base method. The proposed enhancement is based on a simple idea that, to the best of our knowledge, was not applied before. It was noticed that the DNA Amino acids can be organized into groups where each DNA codon in one of the groups can be used to encode two bits of the hidden message rather than only one bit as proposed by the Least Significant Base method. Like the Least Significant Base method, the proposed method is blind, preserves the DNA original biological structure in the fake DNA sequence and provides no expansion in the DNA sequence. The proposed method is evaluated using a public DNA sequences dataset named BALIBASE. The evaluation results showed that the proposed method achieved about 50% increase in the data hiding capacity when compared with the Least Significant Base method. Moreover, the results showed that the proposed method resulted in significant decrease in the cracking probability of the Least Significant Base method.