http://localhost:8080/xmlui/handle/123456789/303 2026-06-23T06:33:30Z 2026-06-23T06:33:30Z Badera, Meenal Bedse, Pooja Khairnar, Sharvari Kumbhar, Kumbhar Jadhav, R. H. http://localhost:8080/xmlui/handle/123456789/2741 2020-02-14T09:36:48Z 2018-08-16T00:00:00Z

Title: Optimization Approach for Question Routing in Community Question Answering Services Authors: Badera, Meenal; Bedse, Pooja; Khairnar, Sharvari; Kumbhar, Kumbhar; Jadhav, R. H. Abstract: Community Question Answering (CQA) has increasingly become an important service for people asking questions and providing answers online. Recently, with accumulation of users and contents, much concern has arisen over the efficiency and answer quality and also over technical answering but not generalized. To address this problem, question routing has been proposed which aims at routing new technical questions to suitable answerers, who have both high possibility and high ability to answer the questions. The system will formulate question routing as a multi-objective ranking problem, and present a multi-objective learning-to-rank approach for question routing (MLQR), which can optimize the answering possibility and answer quality of routed users. In MLQR, realizing that questions are usually attached with tags, the system will first propose a tagword topic model (TTM) to derive topical representations of questions. It can be captured at both platform level and thread level. System extend a state-of-the-art learning-to-rank algorithm for training a multi-objective ranking model. Real-world datasets are used. The proposed system will allow one-to-one communication through E-mails. New Pop-up-blocks related to recent searches of users. User interactive display. And finally profile generation of user (eg:- How many questions are answered by the same user on different sites

2018-08-16T00:00:00Z Gupta, D. R. Kamlapurkar, S. M. http://localhost:8080/xmlui/handle/123456789/1004 2018-05-30T10:24:53Z 2017-12-01T00:00:00Z

Title: A Review on Outlier Detection Techniques Authors: Gupta, D. R.; Kamlapurkar, S. M. Abstract: The outlier is unexpected behavior of data. Outlier detection is important in various domains like fraud detection, intrusion detection, activity monitoring, etc. Data is generated continuously on large scale in many applications. There is need to detect outlier from static data as well as streaming data. There are basic two types of outlier: global outlier and local outlier. This work aims to study various local and global outlier detection techniques for static and streaming data. The works also focuses on various local and global outlier detection techniques which are efficient in terms of time and memory.

2017-12-01T00:00:00Z Aher, A. S. Kamlapurkar, S. M. http://localhost:8080/xmlui/handle/123456789/1003 2018-05-30T10:19:59Z 2017-12-01T00:00:00Z

Title: A Review on Image Annotation Generation Authors: Aher, A. S.; Kamlapurkar, S. M. Abstract: Image annotation, tagging, semantic descriptors are used to search an image from large image dataset. To search similar image, low level feature matching is used. In text based image search process image tags, annotations or image semantics are used. Annotation can be generated using manual, semi-automatic or automatic process. The manual and semiautomatic annotations are time consuming. In automatic image annotation, tag is automatically assigned to an image. There are various techniques for annotation generation. This work aims to study various image annotations generation, image matching and image searching techniques based on execution strategies and efficiency evaluation.

2017-12-01T00:00:00Z Boraste, P. S. Kanmlapurkar, S. M. http://localhost:8080/xmlui/handle/123456789/1002 2018-05-30T10:16:15Z 2017-12-01T00:00:00Z

Title: Analysis of Graph Clustering Method Authors: Boraste, P. S.; Kanmlapurkar, S. M. Abstract: Network data clustering has vital importance in various domains such as social network analysis, epidemiology, World Wide Web analysis, etc. The clustering technique derives underlying structures present in the graph. Along with the cluster creation, vertices classification is also an important task. To detect hubs and outlier is very important task in graph mining. There are various graph clustering algorithm such as graph partitioning, density based, modularity based, etc. This work aims to study various techniques of graph data clustering

2017-12-01T00:00:00Z