Feature extraction and duplicate detection for text mining: A survey

Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Proce- ssing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algo- rithms are needed to extract useful features from huge amount of data. The survey covers different text summarization, classi- fication, clustering methods to discover useful features and also discovering query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query thereby reducing time taken by the user

Immuno-affinity Purification of Insect Cell Expressed Rabies Virus Glycoprotein using a Conformational Specific Monoclonal Antibody

.Rabies is a disease of nervous system and causes progressive encephalitis with fatal outcome. The conformation-dependent epitopes on the glycoprotein (G) of rabies virus (RV) is responsible for the induction of virus neutralizing antibodies which is ultimately required to get complete protection from viral challenge. Therefore, a suitable chromatography technique is necessary to purify the tag free recombinant rabies virus glycoprotein (rRVG) without altering its immunogenic epitopes. The present study was undertaken to purify the rRVG using a conformational specific anti-rabies virus glycoprotein (RVG) mAb, M5B4, which binds to the natively folded G. The mAb had shown a significant kinetic interaction with RVG. The mAb immobilized onto the NHS-activated Sepharose 4 fast flow™ was used for the purification of rRVG by immuno-affinity chromatography (IAC). The bound rRVG was eluted in IAC using 0.1M glycine with pH 2.5 and the identity of the purified protein was confirmed by MALDI-TOF. The IAC purified rRVG induced neutralizing antibody response and 83% of the immunized mice were protected against intra-cerebral rabies virus challenge. The results indicate that the mAb based IAC method can be an effective purification technique for tag free rRVG with significant level of purity, without compromising the protein’s immunogenic potential

Drdlc: Discovering Relevant Documents using Latent Dirichlet Allocation and Cosine Similarity

In recent years, the availability of digital documents over web is increased drastically and there is a need for effective methods to retrieve and organize the digital documents. Since data is dispersed globally and is unorganized, it is a challenging task to develop an effective methods that can generate high quality features in these documents. It is necessary to reduce the gap between users search intention and the retrieved results known as semantic gap. In this paper, Discovering Relevant Documents using Latent Dirichlet Allocation and Cosine Similarity (DRDLC) is proposed. Word similarity is computed using CS Cosine Similarity present in search results documents. LDA is applied on extracted patterns and documents. Hashing is used to extract high relevant documents efficiently. Further, term synonyms are identified using word net and the documents are re-ranked. Experiments using the model Relevance

Real Time Emotion Support System in Text Mining [RTESTM]

Mining opinions from online reviews is an essential step in obtaining the overall sentiment of a product. Deep learning procedure is applied over various fields. User ratings are huge for recommender structures since they consolidate various kinds of energetic information that may influence the exactness of the suggestion. In this work, a deep learning model is utilized to process the user remarks and to create a potential user rating for user comments is proposed. To start with, the system uses sentiments to create a feature vector as the input nodes. Further, the framework tools reduce the noise in the dataset to recover the classification of information mining. To finish, Deep Belief Network (DBN) and sentiment analysis reaches data learning for the approvals

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Not AvailableField studies were taken up to evaluate the efficacy of various intercrops in wheat for the management of Odontotermes obesus Rambur during. The termite population was recorded at fifteen days interval and at 15 DAS (Days after Sowing) the termite population ranged from 35.33 – 66.67 and 31.33-61.67 in intercropped plots during first season and second season, respectively. On 135 DAS, the population density of termites were the least in safflower, mustard, gram and pea intercropped plots which recorded 41.33, 41.67, 42.00 and 43.00 numbers, respectively and were also on par with each other. The trend continued to be the same during second season and mustard intercropped plots recorded 28.33 numbers at 105 DAS, which was on par with the plots intercropped with safflower (28.33), gram (29.33) and pea (30.67). The order of intercrops influencing the reduction in termite population for both the season was safflower > mustard > pea > gram > fenugreek > coriander > ajowain > linseed.Not Availabl

Real-Time Multi-View Face Recognition using Alignment-RMFRA

Face Recognition plays an important role in most of the disciplines in the recent world. One of the most common discipline is security and fraud detection. Facial alignment is a method of arranging the facial landmarks points on the face and those landmarks gives fine points for face recognition. Face detection and identification is important in the fraud detection. Therefore, detection of profile and semi-profile faces plays a vital role in security purpose. The face alignment by utilizing Hourglass model gives better accuracy for face recognition by using Haar-Cascade Classifier can be obtained by facial aligned dataset. The performance is measured by the accuracy rate and precision. It gives better results when compared to face recognition by using Support Vector Machine [SVM] and Principal Component Analysis [PCA]. The model gives alignment of facial points with 68 landmark points and the aligned data is sent as an input for the face rec

Automatic Classification of Community Question Answer (CQA) for Non Factoid Queries

Owing to the steep increase in the Internet population, the content over the web is increasing exponentially so as Community Question Answer (CQA) have acquired very huge amount of questions and answers. In this article, a machine learning algorithms are utilized for Question Classification (QC) and Answer Classification (AC). We identify the category of the question posted and further map with the corresponding question. Similarly for the answers posted by the multiple user will be processed for category mapping. Here the result shows the effective classifier that can be chosen to perform the mapping task for both Question classifier as well as answer classifier. Here the results shows that, for Question Classification (QA), Linear Support Vector Classification (LSVC) is found to be best classifier and Multinomial Logistic Regression (MLR) is most suitable for Answer Classification (AC). Using the probability of

Predicting Social Emotions based on Textual Relevance for News Documents

Due to the rapid rise in internet population, the content over web is increasing and a large number of documents assigned by reader's emotions have been generated through new portals. Earlier works have focused only author's perspective, our work focuses on reader's emotions generated by news articles. Social emotions of news articles from reader's perspective are predicted with the help of user ratings. More specifically, we form Communities based on the ratings that are present in the news articles. Further, a Textual Relevance is computed based on the word frequency for a particular document. Experiments are conducted on the news articles and as a result, it is observed that the proposed method results in predicting reader's emotions are much better when compared with the existing method Opinion Network Community (ONC) [1]