An approach for document retrieval using cluster-based inverted indexing

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

Document Retrieval and Cluster Based Indexing using Rider Spider Monkey Optimization Algorithm

Document retrieval process is more significant in the field of research community for retrieving the highly-relevant documents that fit for the user query. Even though various document retrieval methods are introduced, retrieving the exact document based on the indexing is a quite challenging task in the document retrieval framework. Thus, an effective document retrieval algorithm named Rider Spider Monkey Optimization Algorithm (RSOA) is proposed in this research. Initially, the documents are pre-processed by the stop word elimination and the stemming process, and the features are extracted to find the key words of the documents by applying the Term FrequencyInverse Document Frequency (TF-IDF). The selected keywords are passed into the cluster-based indexing phase, where the cluster centroids are identified by using the proposed Rider Spider Monkey Optimization Algorithm. Moreover the query matching is carried out at two levels, at first, the query is forwarded and is matched to the entire cluster centroid to find the appropriate centroid. At the second level; the user query is matched based on the records present inside the matched centroid. Moreover, the query matching is progressed using the distance measure by the Bhattacharya distance to retrieve the documents. The performance is analyzed using the metrics, namely precision, F-measure, and recall and accuracy with the values of 90.141%, 91.876%, 91.178%, and 91.202%, respectively using 20 news group dataset .

Dr. Query

Drugs can help us to treat disease, but sometimes medication can cause severe side effects. With a little knowledge, one can have drugs that are intended to prevent or avoid adverse outcome. Recognizing potential drugs enhances the quality of the healthcare system and reduces the risk associated with drug intake. Several factors like drug-drug interactions and side effects should be known to us before we intake drugs. So, the authors' motive is to develop a predictive mobile-based healthcare tool that would help drug consumers to find drugs which suit them best. As an outcome, the tool will provide the names of the top 10 medicines that will be best for specified indications and do not cause specified side effects and do not or least interact with mentioned drugs. Proposed mobile-based drug query tool will provide exact query matching drugs as well as close matches by leveraging machine learning in the tool.

Research on anthropometry information obtaining system in garment MTM on the network mode

Purpose – The research is made in view of the anthropometry information obtaining problem in garment MTM on the network mode. The purpose of this paper is to obtain anthropometry information in a convenient and detailed way in garment MTM on the network mode. Design/methodology/approach – First of all, 24 main measurement sizes of 427 young females are collected to constitute the measurement database. The database is used as background data support of the system. The images are captured to simplify the way of inputting the anthropometry information to the system. Through the 2D feature sizes extracted from body image and the basic dimensions provided by customer input to the system, so that to gain the body sample which is closest to the customer body type through query matching in the database. The detailed anthropometry information of the closest sample is used to describe the customer. The human body measurement database and the technology of body image acquisition are used to extract the feature sizes to achieve obtaining the anthropometry information in a convenient and detailed way. Findings – Through query matching to the customer in a test, the body sample closest to the customer is gained, and the matching error rate is 0.0132. In the end, some customer samples are input to test the system, in order to verify the effectiveness of system functions. The matching error rates of five body types are gained all less than 0.006. The error is small, and the matching result is ideal. Research limitations/implications – The size of database established in the paper can be increased constantly in the future to obtain the more accurately matching result. Practical implications – The research of anthropometry information obtaining system in garment MTM on the network mode is the basis to achieve gaining the anthropometry information in a convenient and detailed way. Social implications – Applying the established system of human body measurement information acquisition in this paper, it can achieve to obtain the detailed measurement information of customer through a convenient way, combining the method of human body parameter model establishment in the existing research, it can achieve the complete network tailored mode with detailed measurement information acquisition and 3D virtual fitting functions. And it can provide the most convenient experience and the most ideal garment MTM effect to the customer. This mode can be forecast to be an ideal form of garment MTM on the network in the future. Originality/value – The anthropometry information obtaining system is the important part of garment MTM system on the network mode. It should be applied to the network mode and can obtain the detailed measurements for garment MTM. In this paper, the human body measurement database and the technology of body image acquisition are used in order to extract the feature size to obtain the anthropometry information in a convenient and detailed way.