In today's scenario, publishing the personal content and technical content has become very easy for the publishers, because of the readily available prominent social media called blog. Content developers, teachers, researchers post various articles relevant to their research or newly emerging topics. Blog content in one blog source may resemble the semantics of blog from other source. Readers, who are fascinated in reading the blog content, anticipate retrieving the blogs from different sources for their query. A large number of posts are available in the web. Hence the blog reader's task becomes very complex to search the relevant content for their query. This study introduces a novel idea to collect the blog using unified Semantic Blog Mining Framework (SBMF). SBMF collects blogs from different blog sources using the ontology constructed for education domain. Blogs collected from different sources are collection which contains relevant or irrelevant blogs. The new blog summarizer summarizes the blog content and ranks the blogs according to the similarity of the blog with query given by the user. The proposed blog summarizer check the similarity of each sentence in a blog, sort the order of sentence based on the similarity of the text with query word and reduces the number of sentences. The experimental results shows that the proposed unified SBMF and blog summarizer produces better relevant and summarized number of blogs compared to various search engines. SBMF confirms the hundred percentage relevancy compared to other blog search engines. Blog summarizer yields accurate summarization for the collected blogs.

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A prior-art search on patents ascertains the patentability constraints of the invention through an organized review of prior-art document sources. This search technique poses challenges because of the inherent vocabulary mismatch problem. Manual processing of every retrieved relevant patent in its entirety is a tedious and time-consuming job that demands automated patent summarization for ease of access. This paper employs deep learning models for summarization as they take advantage of the massive dataset present in the patents to improve the summary coherence. This work presents a novel approach of patent summarization named PQPS: prior-art query-based patent summarizer using restricted Boltzmann machine (RBM) and bidirectional long short-term memory (Bi-LSTM) models. The PQPS also addresses the vocabulary mismatch problem through query expansion with knowledge bases such as domain ontology and WordNet. It further enhances the retrieval rate through topic modeling and bibliographic coupling of citations. The experiments analyze various interlinked smart device patent sample sets. The proposed PQPS demonstrates that retrievability increases both in extractive and abstractive summaries.

Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data that considers the amount of changes in gene expression. However, the huge number of genes and the intricacy of biological networks have highly increased the challenges of comprehending and interpreting the resulting group of data, increasing processing time. The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering. The construction of the closest pair data structure is an each level is an important time factor, which determines the processing time of clustering. The proposed model reduces the processing time and improves analysis of gene expression data.