Medical data classification is used to find the hidden patterns of data by training a large amount of patient data collected from the providers. As the medical data is very sensitive, it must be a safeguard from all the noncollaborative means. Thus, it is important to take steps to preserve the confidential medical data. Accordingly, this paper proposes a classification method termed as crow optimization-based deep belief neural network (CS-DBN) to preserve the privacy of confidential medical data automatically. This classifier works based on three phases, including generation of the privacy-preserved data, construction of ontology, and classification. The Deep convolutional kernel approach is used to provide data confidentiality using the optimal coefficients. The construction of ontology is done with the cardiac heart disease terms used in the medical field for classification. Finally, the classification is performed using the deep belief network (DBN), which is trained using the crow search algorithm (CSA). The performance is analyzed in terms of the metrics, namely, accuracy, fitness, sensitivity, and specificity. The proposed CS-DBN method produces higher fitness, accuracy, sensitivity, and specificity of 0.9007, 0.8842, 1, and 0.8408, respectively.

Semantic web consists of the data in the structure manner and query searching methods can access these structured data to provide effective search result. The query recommendation in the semantic web relevance is needed to be improved based on the user input query. Many existing methods are used to improve the query recommendation efficiency using the optimization technique such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO). These methods involve in the use of many features which are selected from the user query. This in-turn increases the cost of a query in the semantic web. In this research, the query optimization was carried out by using the statistics method. The statistics based optimization method requires fewer features such as triple pattern and node priority etc., for finding the relevant results. The LUBM dataset contains the semantic queries and this dataset is used to measure the efficiency of the proposed Statistical based optimization method. The SPARQL queries are used to plot the query graph and triple scores are extracted from the graph. The cost value of the triple scores is measured and given as input to the proposed statistics method. The execution time of the statistics based optimization method for the query is 35 ms while the existing method has 48 ms.

The semantic web consists of a large number of data that is difficult to retrieve the answer for the user queries. An existing method in the query processing in the semantic web has three main limitations namely, query flexibility, query relevancy or lack of ranking method and high query cost. In this study, Proximity Matrix Completion technique (PMC) is applied to impute the missing data in the dataset that helps to increase the query flexibility and Ranking Ant Colony Optimization (RACO) technique is used to select the relevant features from the dataset and arrange them to increase relevancy. The result shows that the PMC-RACO method has a higher performance compared to the exiting method in semantic web. The mean precision value of the PMC-RACO method in sports data is 87%, while the existing method has the precision value of 83%