Classification of Research Articles Hierarchically: A New Technique

Author(s):  
Rajendra Kumar Roul ◽  
Jajati Keshari Sahoo
2008 ◽  
Vol 17 (05) ◽  
pp. 957-971
Author(s):  
ATAOLLAH EBRAHIMZADEH ◽  
ABOLFAZL RANJBAR ◽  
MEHRDAD ARDEBLILPOUR

Classification of the communication signals has seen under increasing demands. In this paper, we present a new technique that identifies a variety of digital communication signal types. This technique utilizes a radial basis function neural network (RBFN) as the classifier. Swarm intelligence, as an evolutionary algorithm, is used to construct RBFN. A combination of the higher-order moments and the higher-order cumulants up to eight are selected as the features of the considered digital signal types. In conjunction with RBFN, we have used k-fold cross-validation to improve the generalization potentiality. Simulation results show that the proposed technique has high performance for classification of different communication signals even at very low signal-to-noise ratios.


2009 ◽  
Vol 285 (1-2) ◽  
pp. 124-130 ◽  
Author(s):  
Craig Hardgrove ◽  
Jeffrey Moersch ◽  
Stephen Whisner

Author(s):  
Anilesh Dey ◽  
Sayan Mukherjee ◽  
Sanjay Kumar Palit ◽  
D. K. Bhattacharya ◽  
D. N. Tibarewala

2019 ◽  
Vol 7 (2) ◽  
pp. 54-59
Author(s):  
R. Raja Aswathi ◽  
◽  
K. Pazhani Kumar ◽  
B. Ramakrishnan

The algorithm C4.5 is an efficient decision tree based classification, which is derived from the ID3 approach. C4.5 is also a rule based classification algorithm. The main importance of the C4.5 algorithm is that it can deal with categorical data, over fitting of data and handling of missing values. The performance of C4.5 is superior to ID3 even with equal number of attributes. The EC4.5 (Exponential C4.5) is an extension of C4.5 algorithm which uses exponential of split value to predict the gain of attributes and handled the set back reported in C4.5. However the EC4.5 has some misclassification of data and to avoid this problem a new technique is introduced. This paper proposes a proficient technique TMC4.5 (Taylor-Madhava C4.5) to reduce the uncertainty in classification of data by integrating an exponential split value in EC4.5 and sin splitting value derived from the Madhava series. By using this technique an optimized gain value is obtained that reduces uncertainty. From the obtained result the TMC4.5 has far better results than the C4.5 and EC4.5 algorithms.


Author(s):  
Tanko Titus Auta ◽  
Wiwin Martiningsih ◽  
Suparji Suparji

EDITORIAL Apart from being a journal that can publish scientific articles from all disciplines of science and technology, Aloha International Journal of Multidisciplinary Advancement (AIJMU) also opens opportunities for authors to publish scientific papers in various forms, both research and non-research articles; what is important is scientific and beneficial to the scientific community. One segment of the scientific community that is in dire need of important information from journals is diploma and undergraduate students. They really need basic level sciences, which can help them to complete their studies. Therefore, AIJMU invited lecturers and researchers to publish articles in tutorial form. The tutorial material is expected to be a new technique or procedure which is a scientific finding that needs to be introduced to the scientific community immediately, but it is also possible to publish other types of tutorials that are considered useful for the scientific community. As an article that is a practical guide, of course, it is likely that the size of the article is relatively long, so especially for tutorial articles, the number of words allowed is not limited, but it is hoped that the author uses a concise and simple presentation style. It is hoped that the tutorial articles published in this journal can really help practitioners, academics and students, who in turn can contribute to the development of science and technology in all disciplines of science and technology.


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