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Knowledge mining is an emerging field where various patterns, rules, etc. can be generated which helps us in the analysis of the result. Medical information systems in modern hospitals and medical institutions become larger and larger, it causes great difficulties in extracting useful information for decision support. Especially, when traditional manual data analysis has become inefficient and methods for efficient computer-based analysis are indispensable. Decision support system is a suitable tool for medical application which helps in the extraction of useful data. A new hybrid model, weighted-quantum particle swarm optimization (WQPSO) for data clustering in sequence with smooth support vector machine (SSVM) is proposed for classification. The parameters considered to evaluate the clustering methods are intercluster distance, intra-cluster distance, and validation index. Experiments are performed in MATLAB and the performance analysis, comparisons are made with real-world datasets that are retrieved from UC Irvine Machine Learning Repository. The proposed approach overcomes the drawbacks of existing algorithms in terms of accuracy, performance and complexity. To validate the proposed algorithm, experiments are conducted on two data sets, i.e., liver disorders dataset and Wisconsin Breast Cancer Diagnosis (WBCD) dataset. The accuracy of proposed WQPSO-SSVM classification methodology is 83.76% for liver disorder, 98.42% for WBCD, 95.21% of mammography mass data. Among the considered algorithms such as WPSO-SVM, fuzzy, K-means, and fuzzy C-means methods, WQPSO-SSVM is found to yield a better convergence and provide an improved optimal solution.

In Cloud Computing (CC), load balancing tasks remain an essential problem of spreading resources from a data center to ensure that each Virtual Machine (VM) has a balanced load to achieve maximum utilization of its capabilities. In the CC world, load balancing is a Non-Polynomial (NP) problem solved with metaheuristic algorithms. A new Quasi Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) was developed to achieve the optimal resource scheduling in a CC setting. The proposed QODA-LB algorithm uses three variables to compute an objective function: run time, running cost, and load. The QODA-LB algorithm assigns tasks to VM based on its potential and the derivative objective function. Also, the QODA-LB algorithm uses the principle of Quasi-Oppositional Based Learning (QOBL) to increase the standard Dragonfly Algorithm's (DA) convergence rate. A comprehensive series of experiments were conducted, and the findings were analyzed in a variety of ways to ensure the efficient performance increased by the QODA-LB algorithm. The simulation's results demonstrated optimum load balancing efficiency and outperformed the leading approaches.

Clustering is considered as one of the most prominent solutions to preserve the energy in the wireless sensor networks. However, for optimal clustering, an energy efficient cluster head selection is quite important. Improper selection of cluster heads (CHs) consumes high energy compared to other sensor nodes due to the transmission of data packets between the cluster members and the sink node. Thereby, it reduces the network lifetime and performance of the network. In order to overcome the issues, we propose a novel cluster head selection approach using grey wolf optimization algorithm (GWO) namely GWO-CH which considers the residual energy, intra-cluster and sink distance. In addition to that, we formulated an objective function and weight parameters for an efficient cluster head selection and cluster formation. The proposed algorithm is tested in different wireless sensor network scenarios by varying the number of sensor nodes and cluster heads. The observed results convey that the proposed algorithm outperforms in terms of achieving better network performance compare to other algorithms.

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