Abstract Medical data clustering is an important part of medical decision systems as it refines highly sensitive information from the huge medical datasets. Medical data clustering includes processes, like determine random clusters, set data into specified clusters and handle data clusters dynamically. Hence, handling of medical data streams and clustering remains a challenging issue. This paper proposes a technique, namely Rider-based sunflower optimization (RSFO) for medical data clustering. Initially, the significant features are selected using the Tversky index with holoentropy that is established from the input data. The holo-entropy is utilized to analyze the relationship between the attributes and features. Here, the clustering is done by a Black Hole Entropic Fuzzy Clustering (BHEFC) algorithm, where the optimal cluster centroids are selected by the proposed RSFO algorithm. The proposed RSFO is designed by incorporating the Rider optimization algorithm (ROA) and sunflower optimization (SFO). The effectiveness of the proposed BHEFC+RSFO algorithm is analyzed by the Dermatology Data Set, and the proposed method has the maximal accuracy of 94.480%, Jaccard coefficient of 94.224% and Rand coefficient of 91.307%, respectively.

Summary Cloud computing is a growing environment. Many of the users are interested to outsource their data in cloud; however, load balancing in cloud is still at risk. Resource allocation plays a major role in load balancing. In this scheduling problem, independent task in cloud computing can allocate resource by the summary of modified canopy fuzzy c‐means algorithm (MCFCMA). To allocate task to their corresponding resource, particle swarm‐based optimization algorithm (PSO) is used. In proposed scheme, first independent task selected based on load feed‐back, cluster the requested task using MCFCMA and schedule task to each virtual machine. VM selects parallel execution in virtual machine manager. Calculate feature value using PSO algorithm. Allocate resource to the task. Since our proposed system selects resource based on parallel execution, it reduces load balancing in Cloud Quantum Computation (CQC). The proposed system overcomes issues in load balancing and load scheduling; this can be proved by its precision and privacy calculation.

Summary Cognitive network is a modern networking technology that utilizes both the radio spectrum and the wireless station. The resources can be effectively utilized based on availability of knowledge gathered from the past experiences. By using cognitive radio technology, the new network is constructed to every node. Wireless devices' necessity is being increased nowadays, which leads to the demand for spectrum. The problems are high fluctuations of the different spectrum bands and the disconnections of the network. The cognitive radio network is proposed to be an optimal solution for both the spectrum scarcity and the spectrum inefficiency problems. Using the cognitive radio network, the wireless channel is shared with the primary users. The cognitive radio network provides high bandwidth for the mobile users by using specialized heterogeneous wireless architecture. These challenges are overcome by CR networks that define the concept of spectrum sensing. While using the spectrum, the performance of the network should not degrade. Hence, the framework planned will able to do this situation.

Summary In the present trend, availability of data increases more and more in all the fields in more complex way. It is very difficult for handling in an effective way with best scalability for proper decision making of knowledge extraction. K‐Means algorithm is the most familiar extensive old‐style partitioned and faster clustering algorithm than other clustering methods. But it is very subtle for initial centroids and it can be simply surrounded. So, in need of effective optimum centroid with faster clustering, this paper proposed Rainfall Flow Optimization (RFFO) based on K‐Means algorithm. RFFO is a new flood optimization technique like other optimization methods such as PSO, ACO, BCO, and so forth. RFFO is based on the nature of rainfall flow with various environments and its behavior of flow from shallow to depth. Scientific calculations are also used to find flow of data from one location to another location by its present location of waterfall, its velocity, and its most neighboring flow depth. This water storage mainly depends on nearest storage location, maximum depth, size of the storage area, and condition of the storage location. RFFO has some of the unique features than other optimization methods like speed of flow, total quantity of flow, location of flow, and so forth, that determines the optimal centroid. The performance of this proposed RFFO technique is measured with Accuracy, Jaccard Coefficient, Random Coefficient, and also compared with other existing methods using medical data set to cluster risk factor of heart disease with 300 data set.