scholarly journals A Vector Space Model for Neural Network Functions: Inspirations From Similarities Between the Theory of Connectivity and the Logarithmic Time Course of Word Production

2020 ◽  
Vol 14 ◽  
Author(s):  
Ortwin Fromm ◽  
Fabian Klostermann ◽  
Felicitas Ehlen
Keyword(s):  
Neural Network ◽  
Vector Space ◽  
Time Course ◽  
Vector Space Model ◽  
Word Production ◽  
Space Model ◽  
Network Functions

Automatic classification of Tamil documents using vector space model and artificial neural network

2009 ◽  
Vol 36 (8) ◽  
pp. 10914-10918 ◽  
Author(s):  
K. Rajan ◽  
V. Ramalingam ◽  
M. Ganesan ◽  
S. Palanivel ◽  
B. Palaniappan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Space ◽  
Vector Space Model ◽  
Automatic Classification ◽  
Space Model ◽  
Artificial Neural

scholarly journals Software Vulnerability Classification Based on Deep Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 3146-3150
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Vector Space ◽  
Deep Neural Network ◽  
Research Work ◽  
Vector Space Model ◽  
Information Leakage ◽  
Vulnerability Detection ◽  
Software Vulnerability ◽  
Space Model

Software vulnerability is most common issues in software engineering, many applications has suffering vulnerability, information leakage, and data hijacking such kind of problems facing since couple of years. Sometimes developers should be making some mistakes during code making which generate vulnerability issues for entire application. In this research work, we carried out an approach to software vulnerability detection using deep learning approach behalf of metadata processing. The system carried software vulnerability detection based on the Deep Neural Network (DNN). a new dynamic vulnerability classification approach has suggested. The model basic build based on TF-IDF as well density based feature selection approach for DNN. basically TF-IDF has used to measured the frequency and weight of specific word of vulnerability description; the Vector Space Model (VSM) is used for feature selection to achieve an finest set of feature term, and; the DNN neural network model is used to built an dynamic weakness classifier to achieve effectiveness into the bug detection. The overall system has categorized into four phases in first phase we detect the code clone to eliminate the data redundancy and execution time complexity, in second we apply Vector Space Model (VSM) recommend the re-factor possibility in entire code while in third section we build DNN module for software vulnerability detection and finally recommend the vulnerability for entire code. The system partial implementation has evaluated in java environment which provide satisfactory results for heterogeneous code modules .

Aplikasi Deteksi Kemiripan Tugas Paper

2017 ◽  
Vol 15 (2) ◽  
pp. 5
Author(s):  
Anthony Anggrawan ◽  
Azhari
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Mean Average Precision ◽  
Average Precision ◽  
Information Searching ◽  
Space Model ◽  
Model Method

Information searching based on users’ query, which is hopefully able to find the documents based on users’ need, is known as Information Retrieval. This research uses Vector Space Model method in determining the similarity percentage of each student’s assignment. This research uses PHP programming and MySQL database. The finding is represented by ranking the similarity of document with query, with mean average precision value of 0,874. It shows how accurate the application with the examination done by the experts, which is gained from the evaluation with 5 queries that is compared to 25 samples of documents. If the number of counted assignments has higher similarity, thus the process of similarity counting needs more time, it depends on the assignment’s number which is submitted.

Aplikasi Rekomendasi Buku Pada Katalog Perpustakaan Universitas Multimedia Nusantara Menggunakan Vector Space Model

2018 ◽  
Vol 9 (2) ◽  
pp. 97-105
Author(s):  
Richard Firdaus Oeyliawan ◽  
Dennis Gunawan
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Vector Model ◽  
Library Management ◽  
Space Model ◽  
Library Management System ◽  
Index Terms ◽  
Library Catalogue ◽  
Language Sample ◽  
F Measure

Library is one of the facilities which provides information, knowledge resource, and acts as an academic helper for readers to get the information. The huge number of books which library has, usually make readers find the books with difficulty. Universitas Multimedia Nusantara uses the Senayan Library Management System (SLiMS) as the library catalogue. SLiMS has many features which help readers, but there is still no recommendation feature to help the readers finding the books which are relevant to the specific book that readers choose. The application has been developed using Vector Space Model to represent the document in vector model. The recommendation in this application is based on the similarity of the books description. Based on the testing phase using one-language sample of the relevant books, the F-Measure value gained is 55% using 0.1 as cosine similarity threshold. The books description and variety of languages affect the F-Measure value gained. Index Terms—Book Recommendation, Porter Stemmer, SLiMS Universitas Multimedia Nusantara, TF-IDF, Vector Space Model

On Generalized Vector Space Model in Information Retrieval

1985 ◽  
Vol 8 (2) ◽  
pp. 253-267
Author(s):  
S.K.M. Wong ◽  
Wojciech Ziarko
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
A Priori ◽  
Vector Space Model ◽  
Smart System ◽  
Space Model ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Index Terms ◽  
Minimal Modification

In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The main difficulty with this approach is that the explicit representation of term vectors is not known a priori. For this reason, the vector space model adopted by Salton for the SMART system treats the terms as a set of orthogonal vectors. In such a model it is often necessary to adopt a separate, corrective procedure to take into account the correlations between terms. In this paper, we propose a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme. We also demonstrate how such correlations can be included with minimal modification in the existing vector based information retrieval systems.

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