December 2024 - Top Read Articles in Soft Computing

DECEMBER 2024: TOP
READ ARTICLE IN SOFT
COMPUTING
International Journal on Soft Computing (IJSC)
ISSN: 2229 - 6735 [Online]; 2229 - 7103 [Print]
https://airccse.org/journal/ijsc/ijsc.html

CLASSIFICATION OF VEHICLES BASED ON AUDIO SIGNALS USING QUADRATIC DISCRIMINANT
ANALYSIS AND HIGH ENERGY FEATURE VECTORS
A. D. Mayvana
, S. A. Beheshtib
, M. H. Masoomc a
a
Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
b
Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
c
Department of Mechanical Engineering, BabolNoshirvani University of Technology, Babol, Iran.
ABSTRACT
The focusof this paper is on classification of different vehicles using sound emanated from the vehicles.
In this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor,
and truck categories based on features such as short time energy, average zero cross rate, and pitch
frequency of periodic segments of signals. Simulation results show that just by considering high energy
feature vectors, better classification accuracy can be achieved due to the correspondence of low energy
regions with noises of the background. To separate these elements, short time energy and average zero
cross rate are used simultaneously.In our method,we have used a few features which are easy to be
calculated in time domain and enable practical implementation of efficient classifier. Although, the
computation complexity is low, the classification accuracy is comparable with other classification
methodsbased on long feature vectors reported in literature for this problem.
KEYWORD
Classification accuracy; Periodic segments; Quadratic Discriminant Analysis; Separation criterion; Short
time analysis.
ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/6115ijsc05.pdf
http://airccse.org/journal/ijsc/current2015.html

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DETERMINATION OF OVER-LEARNING AND OVER-FITTING PROBLEM IN BACK
PROPAGATION NEURAL NETWORK
Gaurang Panchal1
, Amit Ganatra2
, Parth Shah3
, Devyani Panchal4
Department of Computer Engineering, Charotar Institute of Technology (Faculty of
Technology and Engineering), Charotar University of Science and Technology, Changa,
Anand-388 421, INDIA
ABSTRACT
A drawback of the error-back propagation algorithm for a multilayer feed forward neural network is
over learning or over fitting. We have discussed this problem, and obtained necessary and sufficient
Experiment and conditions for over-learning problem to arise. Using those conditions and the concept
of a reproducing, this paper proposes methods for choosing training set which is used to prevent over-
learning. For a classifier, besides classification capability, its size is another fundamental aspect.
In pursuit of high performance, many classifiers do not take into consideration their sizes and
contain numerous both essential and insignificant rules. This, however, may bring adverse situation to
classifier, for its efficiency will been put down greatly by redundant rules. Hence, it is necessary to
eliminate those unwanted rules. We have discussed various experiments with and without over
learning or over fitting problem.
KEYWORDS
Neural Network, learning, Hidden Neurons, Hidden Layers

REFERENCES
[1] Carlos Gershenson , “Artificial Neural Networks for Beginners”
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[3] “Artificial Neural Networks” ocw.mit.edu
[4] Guoqiang Peter Zhang , “Neural Networks for Classification: A Survey”, IEEE
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[5] V.P. Plagianakos, G.D. Magoulas, M.N. Vrahatis, “Learning rate adaptation in stochastic
gradient descent”, ,Department of Mathematics, University of Patras,
[6] Wen Jin-Wei Zhao, Jia-Li Luo Si-Wei and Han Zhen “ The Improvements of BP Neural Network
Learning Algorithm”, Department of Computer Science & Technology,Northem Jiaotong
University ,BeiJing, 100044, P.R.China,
[7] Wenjian Wang, Weizhen Lu, Andrew Y T Leung, Siu-Ming Lo, Zongben Xu, “Optimal feed-
forward neural networks based on the combination of constructing and pruning by genetic algorithms”,
IEEE TRANSACTIONS ON NEURAL NETWORKS 2002
[8] “A Detailed Comparison of Backpropagation Neural Network and Maximum- Likelihood
Classifiers for Urban Land Use Classification”,IEEE TRANSACTIONS ON GEOSCIENCE AND
REMOTE SENSING, VOL. 33, NO. 4, JULY 199.5
[9] Z. J. Liu C. Y. Wang Z. Niu A. X. Liu ”Evolving Multi-spectral Neural Network Classifier Using
a Genetic Algorithm”. Laboratory of Remote Sensing Information Sciences, the Institute
of Remote Sensing Applications,
[10]Fiszelew, A., Britos, P., Ochoa, A., Merlino, H., Fernández, E., García-Martínez “Finding
Optimal Neural Network Architecture Using Genetic Algorithms”, R.Software & Knowledge
Engineering Center. Buenos Aires Institute of Technology.Intelligent Systems Laboratory. School
of Engineering. University of Buenos Aires.
[11]M.P.Craven, “A FASTER LEARNING NEURAL NETWORK CLASSIFIER
USING SELECTIVE BACKPROPAGATION” Proceedings of the Fourth IEEE International
Conference on Electronics, Circuits and Systems
[12] Wenjian Wang, Weizhen Lu, Andrew Y T Leung, Siu-Ming Lo, Zongben Xu, “Optimal feed-
forward neural networks based on the combination of constructing and pruning by genetic algorithms”,
IEEE TRANSACTIONS ON NEURAL NETWORKS 2002
[13] Teresa B. Ludermir, Akio Yamazaki, and Cleber Zanchettin, “An Optimization Methodology for
Neural Network Weights and Architectures” IEEE TRANSACTIONS ON NEURAL NETWORKS,
VOL. 17, NO. 6, NOVEMBER 2006

[14]S. Rajasekaran, G.A Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic, and Genetic Algorithms
Synthesis and Applications” International Journal on Soft Computing (IJSC), Vol.2,No.2, May2011
51
[15]Mrutyunjaya Panda and Manas Ranjan Patra, “NETWORK INTRUSION DETECTION USING
NAÏVE BAYES” IJCSNS International Journal of Computer Science and Network
Security, VOL.7 No.12, December 2007
[16]S. SELVAKANI1 and R.S.RAJESH2, “Escalate Intrusion Detection using GA – NN”, Int. J.
Open Problems Compt. Math., Vol. 2, No. 2, June 2009
[17] Nathalie Villa*(1,2) and Fabrice Rossi(3), Recent advances in the use of SVM for functional data
classification, First International Workshop on Functional and Operatorial Statistics. Toulouse, June
KDD Cup’99 Data set , http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

APPLICATION OF FUZZY LOGIC IN TRANSPORT PLANNING
Amrita Sarkar1
, G.Sahoo2
and U.C.Sahoo3
1
Research Scholar, Department of Information Technology, B.I.T Mesra, Ranchi
2
Professor and Head,Department of Information Technology, B.I.T, Mesra, Ranchi
3
Assistant Professor, Department of Civil Engineerng, I.I.T, Bhabaneswar
ABSTRACT
Fuzzy logic is shown to be a very promising mathematical approach for modelling traffic and
transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic
premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems
developed to solve various traffic and transportation planning problems. Emphasis is put on the
importance of fuzzy logic systems as universal approximators in solving traffic and transportation
problems. This paper presents an analysis of the results achieved using fuzzy logic to model complex
traffic and transportation processes.
KEYWORDS
Fuzzy Logic, Transportation Planning, Mathematical modeling

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AUTHORS
Amrita Sarkar
Amrita Sarkar is a graduate Engineer in Information Technology with a post
graduation in Remote Sensing. She is presently a PhD Research Fellow at the
Department of Information Technology, Mes ra, India. She has got few research
publications in her area of specialization. Her areas of interests include Soft
Computing, Artificial Intelligence, Data Mining, DBMS and Image Processing.
Dr. G. Sahoo
Dr. G. Sahoo received his MSc in Mathematics from Utkal University in the year
1980 and PhD in the area of Computational Mathematics from Indian Institute of
Technology, Kharagpur in the year 1987. He has been associated with Birla Institute
of Technology, Mesra, Ranchi, India since 1988, and currently, he is working as a
Professor and Head in the Department of Information Technology. His r esearch
interest includes theoretical computer science, parallel and distributed computing,
evolutionary computing, information security, image processing and pattern
recognition.
Dr. U. C. Sahoo
Dr. U. C. Sahoo is working as an Assistant Professor in the Department of Civil
Engineering, Indian Institute of Technology, Bhubaneswar and is an expert in the
field of Transportation Engineering. He has more than eight years of teaching and
research experience. Presently he is engaged in re search in the area of transportation
planning, road safety and pavement engineering and published many papers in these
areas.

APPLICATION OF GENETIC ALGORITHM OPTIMIZED NEURAL NETWORK
CONNECTION WEIGHTS FOR MEDICAL DIAGNOSIS OF PIMA INDIANS DIABETES
Asha Gowda Karegowda1
, A.S. Manjunath2
, M.A. Jayaram3
1,3
Dept. of Master of Computer Applications ,Siddaganga Institute of Technology, Tumkur,
India
2
Dept. of Computer Science, Siddaganga Institute of Technology, Tumkur India
ABSTRACT
Neural Networks are one of many data mining analytical tools that can be utilized to make predictions
for medical data. Model selection for a neural network entails various factors such as selection of the
optimal number of hidden nodes, selection of the relevant input variables and selection of optimal
connection weights. This paper presents the application of hybrid model that integrates Genetic
Algorithm and Back Propatation network(BPN) where GA is used to initialize and optmize the
connection weights of BPN. Significant feactures identified by using two methods :Decision tree and
GA-CFS method are used as input to the hybrid model to diagonise diabetes mellitus. The results prove
that, GA-optimized BPN approach has outperformed the BPN approach without GA optimization. In
addition the hybrid GA-BPN with relevant inputs lead to further improvised categorization accuracy
compared to results produced by GA-BPN alone with some redundant inputs.
KEYWORDS
Back Propagation Network, Genetic algorithm, connection weight optimisation.
ORIGINAL SOURCE URL :http://airccse.org/journal/ijsc/papers/2211ijsc02.pdf

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MULTISPECTRAL IMAGE ANALYSIS USING RANDOM FOREST
Barrett Lowe and Arun Kulkarni
Department of Computer Science, The University of Texas at Tyler
ABSTRACT
Classical methods for classification of pixels in multispectral images include supervised classifiers such
as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector
machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a
method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin
2001 for classification and clustering. Random Forest grows many decision trees for classification. To
classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a
classification. The forest chooses the classification having the most votes. Random Forest provides a
robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in
analyzing multispectral satellite images. To evaluate the performance of Random Forest, we classified
multispectral images using various classifiers such as the maximum likelihood classifier, neural network,
support vector machine (SVM), and Random Forest and compare their results.
KEYWORDS
Classification, Decision Trees, Random Forest, Multispectral Images

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AUTHORS
Barrett Lowe received his bachelor’s degree in drama from the University of North
Carolina at Greensboro. He is currently a graduate student in the computer science
department at the University of Texas at Tyler. His research interests include data
mining, pattern recognition, machine learning, and decision trees. He is a student
member of IEEE and aspires to pursue a Ph. D. in computer science.
Dr. Arun Kulkarni, Professor of Computer Science, has been with The University of
Texas at Tyler since 1986. His areas of interest include soft computing, data mining,
artificial intelligence, computer vision. He has more than seventy refereed papers to his
credit, and he has authored two books. His awards include the Office of Naval Research
(ONR) 2008 Senior Summer Faculty Fellowship award, 2005-2006 President’s Scholarly
Achievement Award, 2001-2002 Chancellor's Council Outstanding Teaching award, and
the 1984 Fulbright Fellowship award. He has been listed in who's who in America. He
has successfully completed eight research grants during the past twenty years. Dr. Kulkarni obtained his
Ph.D. from the Indian Institute of Technology, Bombay, and was a post-doctoral fellow at Virginia Tech.

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