User Query Optimisation: A Creative Computing Approach

2016 ◽  
pp. 68-78
Author(s):  
Xuan Wang ◽  
Hongji Yang
Keyword(s):  
User Query ◽  
Creative Computing ◽  
Query Optimisation ◽  
Computing Approach

SOFT COMPUTING APPROACH TO DETECT FAULT IN INDUCTION MOTOR

2018 ◽  
Vol 12 (2) ◽  
pp. 6
Author(s):  
SEKHAR PUHAN PRATAP ◽  
BEHERA SUDARSAN ◽  
◽  
Keyword(s):  
Induction Motor ◽  
Soft Computing ◽  
Computing Approach

Rancang Bangun Aplikasi UMN Library Catalog Menggunakan Metode Rocchio Relevance Feedback

2018 ◽  
Vol 9 (1) ◽  
pp. 9-17
Author(s):  
Marcel Bonar Kristanda ◽  
Seng Hansun ◽  
Albert Albert
Keyword(s):  
Information System ◽  
User Experience ◽  
Relevance Feedback ◽  
Android Application ◽  
Library Catalog ◽  
Relevant Result ◽  
User Query ◽  
Index Terms ◽  
Feedback Method ◽  
F Measure

Library catalog is a documentation or list of all library collections. Unfortunately, there is a problem identified in the process of searching a book inside library catalog in Universitas Multimedia Nusantara’s library information system regarding the relevant result based on user query input. This research aims to design and build a library catalog application on Android platform in order to increase the relvancy of searching result in a database using calculated Rocchio Relevance Feedback method along with user experience measurement. User experience analysis result presented a good respond with 91.18% score based by all factor and relevance value present 71.43% precision, 100% recall, and 83.33% F-Measure. Differences of relevant results between the Senayan Library Information system (SLiMS) and the new Android application ranged at 36.11%. Therefore, this Android application proved to give relevant result based on relevance rank. Index Terms—Rocchio, Relevance, Feedback, Pencarian, Buku, Aplikasi, Android, Perpustakaan.

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

Designing a Chat-Bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

2020 ◽  
Vol 01 ◽  
Author(s):  
Radha Guha
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Text Summarization ◽  
The Internet ◽  
Specific Domain ◽  
User Query ◽  
College Information ◽  
Chat Bot

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

Sign in / Sign up

Export Citation Format

Share Document