Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.
Uthayan. K.R. (2019): A novel microarray gene selection and classification using intelligent dynamic grey wolf optimization. - Genetika, Vol 51, No.3, Effective diagnosis of cancer in the medical field is very important to specific treatment. Exact prediction of different cancer types will provide a better treatment and minimization of toxicity in patients. Microarray high dimensionality of gene expression dataand large number of genes against small sample size, noise and repetition in datasets are the main issues which lead to poor classification accuracy. The selection of informative genes and to reduce dimensionality, Gene Selection technique is used in Microarray. In this paper, a novel meta-heurists algorithm based on Grey Wolf Optimization (GWO) and Artificial Intelligence (AI) is combined to design a model for cancer classification. This proposed work consists of two stages. First, a filter method such as Laplacian and Fisher score, are applied to extract the significant subset of features for faster classification and then Intelligent Dynamic Grey Wolf Optimization (IDGWO) is employed to identify the relevant genes. GWO is a swarm-based algorithm selected for gene expression data classification problem, because it makes classification easy about training and testing cancer data. The significant differences between filter methods of datasets are found by using several analyses. The proposed method was applied on five benchmark datasets by considering top 100 ranked genes selected by fisher score in Lymphoma and SRBCT that had a 100% performance using the IDGWO classifier.
Twitter is a micro-blogging site that facilitates users to exchange short messages. Twitter is predominantly used in fields like business, healthcare, education and nation security. Twitter is being used by a large number of users for updating real time information and sentiment expression. The objective of this paper is to automate the classification of tweets into particular category using various machine learning algorithms like naïve bayes, SVM, and linear regression model. The proposed ensemble model aims to improve performance metrics of these algorithms. A comparative study of the algorithms used for tweet classification is done and results are discussed in the paper.
Global Cancer Incidence, Mortality and Prevalence (GLOBOCAN) status report for the year of 2020, suggests the occurrence of 10.0 million cancer deaths and 19.3 million new cancer cases. Clearly, cancer incidence and mortality are rapidly growing worldwide. Also, the leading causes of cancer deaths are found to be lung cancer and breast cancer. Cancer cells are having the probability of spreading to other parts of the body too. Most chronic cancers are not curable, but some can be controlled for a few months or years. Also, there is a possibility of high rate of relapse of the disease. These remissions can be partial or complete. But, if detected early, certain cancers can be treated by surgery, chemotherapy, and radiation therapy. This research work focuses on detecting cancer in its early stage so that right measures can be taken to combat the disease. In this attempt to create a beneficial working model, the combination of Artificial Neural Network (ANN), Convolution Neural Network, Graph based Neural Network with Genetic Algorithm (GA) have proven to be successful. As a proof of concept, we present a combination of feature selection techniques that can effectively reduce the feature set and optimize the classification techniques. The proposed method, when applied on a benchmark dataset, gave a higher accuracy by selecting most relevant 7 features out of 10 with an accuracy of 95.7%. Using Convolution Neural Network, the accuracy improved to 98.3% with optimal hyperparameter tuning.
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