An Automated Classification System Based on Regional Accent

2022 ◽  
Author(s):  
Radha Krishna Guntur ◽  
Krishnan Ramakrishnan ◽  
Mittal Vinay Kumar
Keyword(s):  
Classification System ◽  
Automated Classification

An Automated Classification System for the Differentiation of Obstructive Lung Diseases based on the Textural Analysis of HRCT images

2007 ◽  
Vol 57 (1) ◽  
pp. 21
Author(s):  
Seong Hoon Park ◽  
Joon Beom Seo ◽  
Namkug Kim ◽  
June Goo Lee ◽  
Young Kyung Lee ◽  
...  
Keyword(s):  
Classification System ◽  
Lung Diseases ◽  
Textural Analysis ◽  
Automated Classification ◽  
Obstructive Lung Diseases

A new acute leukaemia-automated classification system

2016 ◽  
Vol 6 (3) ◽  
pp. 303-314 ◽  
Author(s):  
Sos Agaian ◽  
Monica Madhukar ◽  
Anthony T. Chronopoulos
Keyword(s):  
Classification System ◽  
Acute Leukaemia ◽  
Automated Classification

The morphologies of distant galaxies. 1: an automated classification system

1994 ◽  
Vol 432 ◽  
pp. 75 ◽  
Author(s):  
Roberto G. Abraham ◽  
Francisco Valdes ◽  
H. K. C. Yee ◽  
Sidney van den Bergh
Keyword(s):  
Classification System ◽  
Automated Classification ◽  
Distant Galaxies

scholarly journals Ensemble Malware Classification System Using Deep Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 721 ◽  
Author(s):  
Barath Narayanan Narayanan ◽  
Venkata Salini Priyamvada Davuluru
Keyword(s):  
Neural Networks ◽  
Classification System ◽  
Short Term Memory ◽  
Ensemble Classification ◽  
Machine Language ◽  
Support Vector ◽  
Automated Classification ◽  
Malware Classification ◽  
Lstm Network ◽  
Computational Resources

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft’s Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using Long Short-Term Memory (LSTM) networks after converting them into sequences. In addition, features are extracted from these architectures (CNNs and LSTM) and are classified using a support vector machine or logistic regression. An accuracy of 97.2% is achieved using LSTM network for distinguishing assembly files, 99.4% using CNN architecture for classifying compiled files and an overall accuracy of 99.8% using the proposed ensemble approach thereby setting a new benchmark. An independent and automated classification system for assembly and/or compiled files provides the luxury to anti-malware industry experts to choose the type of system depending on their available computational resources.

scholarly journals Automated Subject Analysis of Short Phrase by the Method of Comparison with the Bibliographic Records in E-Catalog

2015 ◽  
pp. 41-49
Author(s):  
Vladimir Yu. Kalatchikhin
Keyword(s):  
Classification System ◽  
Subject Classification ◽  
New Method ◽  
Automated Classification ◽  
Negative Consequences ◽  
Subject Analysis ◽  
Successful Method ◽  
Bibliographic Records

There is proposed the new method of automated subject classification in a full-sized natural rubricator, that can be applied to ultra short texts. The method is based on the use of the adopted classification decisions, borrowed from bibliographic records. The solutions found are specified by the information received from the classification system, and generalized by different ways. There are considered four options for obtaining classification decision, which differ by the number of failures and the proportion of incorrect results. There is demonstrated successful method for subject analysis of titles of works by LBC divisions. There are considered the problems of bibliographic catalogue and classification system, reducing the result. There are presented possible negative consequences of using automated classification for the quality of bibliographic catalog.

scholarly journals UMN E-Complaint Application with Automatic Classification Feature

2019 ◽  
Vol 9 (1) ◽  
pp. 296-300
Keyword(s):  
Human Resources ◽  
University Students ◽  
Classification System ◽  
Technology Development ◽  
Automatic Classification ◽  
Automated Classification ◽  
Student Transfer ◽  
Classification Feature

In university, students’ complaints about the services provided are essential things to note because it could lead to higher student transfer if not appropriately handled. In Universitas Multimedia Nusantara (UMN), students can express their complaints through Dewan Keluarga Besar Mahasiswa (DKBM) UMN. By technology development, an online complaint submission system can be applied at UMN. A method that can be used in supporting efficient complaints processing is by using automated classification system since it can save both time and human resources. UMN e-complaints application with automated complaints classification feature was built using the CodeIgniter framework. The evaluation of the e-complaint application was conducted by using the USE Questionnaire. The results show that both DKBM UMN and students agree that the application is useful, easy to use, easy to learn, and satisfying.

Sign in / Sign up

Export Citation Format

Share Document