Special Issue No. – 3, September, 2019

Abstract:

This paper proposes elevated performances of control technique in UPQC, Which increases the toughness against parametric perturbation of supply voltage and load and increases the tracking performances of compensating reference signal. In this paper three phase four wires Unified Power Quality Conditioner (UPQC) with four leg shunt Active Power Filter (APF) is used to compensate load voltage and supply current against distortions. A Synchronous Reference Frame theory (SRF) is used for generation of reference signal for both shunt and series converters. To improve the performances of UPQC, a fuzzy logic controller, a principal component of soft computing is used to regulate the capacitor voltage. To achieve symbolic mitigation with excellent accuracy and very quick response fuzzy adaptive hysteresis controller is designed for PWM signal generation for both series and shunt converters of UPQC. To validate the proposed controllers, different power quality issues like distorted utility voltage, voltage sag/swell, current harmonics, neutral current compensation, transient load and unbalanced load conditions are considered. From the simulation results it is proved that the proposed controllers give better compensation and fast response than conventional controllers.

Keywords:

Voltage Sag/Swell,Harmonics,Power Quality,Fuzzy Controller,

Refference:

I. A. Ghosh, A.K. Jindal, A. Joshi, “A unified power quality conditioner
for voltage regulation of critical load bus”, in Proc. IEEE Power Eng.
Society General Meeting, vol.1, pp.471-476, June 2004
II. B.Mazari and F.Mekri, Fuzzy Hysteresis Control and Parameter
Optimization of a Shunt Active Power Filter, Journal of Information
Science and Engineering, 21, 1139-1156, 2005
III. D. D.Sabin, Sundaram, “A Quality enhances reliability, IEEE
Spectrum”,33(2):34- 41, 1996
IV. H.Akagi, “New trends in active filters for power conditioning”, IEEE
Trans. Ind. Applicat,32(6),1312- 1322,1996
V. K. Murat, O.Engin, An adaptive hysteresis band current controller for
shunt active power filter, Electric Power Systems Research 73, 113–
119,2005
VI. K. P. Rajesh,M Kamalakanta, “High-performance unified power quality
conditioner using non-linear sliding mode and new switching dynamics
control strategy”, IET Power Electron, Vol. 10 Iss. 8, pp. 863-874, 2017.
VII. K. Vinod, “Enhancing Electric Power Quality Using UPQC: A
Comprehensive Overview”, IEEE Transactions on Power Electronics,
vol. 27, no. 5, may 2012.
VIII. P.Rathika, D.Devaraj,“A Novel Fuzzy Adaptive Hysteresis Controller
Based Three Phase Four Wire-Four Leg Shunt Active Filter for
Harmonic and Reactive Power Compensation”,Energy and Power
Engineering, 2011; 422-435, doi:10.4236/epe..34053 Published Online
September 2011.
IX. P.Nageswara, V. Chandra, Dr V.C.V Reddy, “Harmonic Reduction in
Hybrid Filters for Power Quality Improvement in Distribution systems”,
Journal of Theoretical and Applied Information Technology,Vol. 35
No.1, 15th January 2012
X. P.Yash, A.Swarup, S.Bhim,“A Comparative Analysis of Three-Phase
Four- Wire UPQC Topologies”, 978-1-4244-7781-4/10, 2010

XI. R.Sriranjani, M.Geetha, S.Jayalalitha, “Harmonics and Reactive Power
Compensation Using Shunt Hybrid Filter”, Research Journal of Applied
Sciences, Engineering and Technology 5(1): 123-128,2013
XII. S K Khadem, M.Basu and M. Conlon, “Power Quality in Grid
Connected Renewable Energy Systems: Role of Custom Power Devices”,
International Conference on Renewable Energies and Power
Quality(ICREPQ-10) , Spain, 23rd to 25th March 2010.
XIII. S K Khadem, M.Basu and M. Conlon, “Power Quality in Grid
Connected Renewable Energy Systems: Role of Custom Power Devices”,
International Conference on Renewable Energies and Power
Quality(ICREPQ-10) , Spain, 23rd to 25th March 2010.
XIV. S.Sasitharan, K. Mishra, “Constant switching frequency band controller
for dynamic voltage restorer”, IET Power Electron, Vol. 3, Iss. 5, pp.
657–667doi: 10.1049/iet-pel.2008.0267,2010
XV. Tan Zhili, Li Xun, Chen Jian, Kang Yong and DuanShanxu, “A direct
control strategy for UPQC in three-phase four-wire system”, in Proc.
IEEE Conf. on Power Electron.and Motion Controlvol.2,pp.1-5,2006
XVI. V.Sudheer,K. Venkata Reddy, Performance analysis of unified power
quality conditioner under different power quality issues using dq based
control,Journal of Engg. Research,Vol.5 No. (3) pp. 91-109, September
20

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Abstract:

This paper proposes to investigate the performance of UPS inverter under linear and non-linear loading conditions. It has been observed that the inverter’s output voltage distorts particularly under non-linear loading conditions. Conventional way of improving the quality of inverter output is through multiple feedback schemes. These conventional schemes also been developed in MatlabSimulink in order to estimate their performance both under linear and nonlinear loading conditions. Though they perform better under linear loading conditions, there seems to be a droop in their performance under non-linear loading conditions. Hence, the proposed neural network controller for the inverter has been designed and tested for the performance enhancement of the UPS inverter both under linear and non-linear conditions. Load variations and reference voltage variation methodologies have been followed for testing the proposed topology under closed loop for improving the performance of UPS inverter.

Keywords:

UPS Inverter,Neural Network Controller for the inverter,THD,

Refference:

I. O.Soares, H.Gonçalves, A.Martins, A.Carvalho, “Neural Networks Based
Power Flow Control of the Doubly Fed Induction Generator”, IEEE 2009.
II. S.Buso, S.Fasolo, P.Mattavelli, “Uninterruptible power supply multi-loop
control employing digital predictive voltage and current regulators”, Proc.
IEEE APEC’01, pp: 907–913, 2001.
III. S.Mohagheghi, R.G.Harley, T.G.Habetler, D.Divan, “Condition Monitoring
of Power Electronic Circuits Using Artificial Neural Networks”, IEEE
Transactions on power electronics, Vol.: 24, Issue: 10, October 2009.
IV. W.Liu, L.Liu, D.A.Cartes, X.Wang, “Neural Network Based Controller
Design for Three-Phase PWM AC/DC Voltage Source Converters”,
International Joint Conference on Neural Networks, 2008.
V. X.Sun, H.L.Martin, “Analog Implementation Of a Neural Network Controller
for UPS Inverter Applications”, IEEE Trans. Power Electron., Vol.: 17, pp:
305-313, May 2002.
VI. X.Wang, B.Xu ,L.Ding, “Simulation Study on A Single Neuron PID Control
System of DC/DC Converters”, Workshop on Power Electronics and
Intelligent Transportation System, 2008.
VII. Y.C.Fang, B.W.Wu, “Prediction of the Thermal Imaging Minimum
Resolvable (Circle) Temperature Difference with Neural Network
Application”, IEEE Transactions on pattern analyis and machine
intelligence, Vol.: 30, Issue: 12, Dec. 2008.
VIII. Y.Dong, Y.Wang, Z.Lin, T.Watanabe, “High Performance and Low Latency
Mapping for Neural Network into Network on Chip Architecture”, IEEE
Transactions on power electronics, Vol.: 24, Issue: 10, October 2009.

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Abstract:

Scaling down of CMOS in Nano meter range has many difficulties such as high leakage current, smaller gate control, high power consumption, high density, a wide range of interconnect net. Carbon Nanotube Field Effect Transistor (CNTFET) and Graphene Nanoribbon Field Effect Transistor (GNRFET) are the promising and effective technologies for advanced circuit design and implementation to overcome the difficulties faced in CMOS technology. In this work, analyzed the different device physical structure such as MOSFET, GNRFET, and CNTFET by varying different device parameters like chirality, oxide thickness, channel length, and temperature. Effect of a threshold voltage and device performance has been observed by varying all these device parameters. The simulation shows that advanced GNRFET and CNTFET can work effectively for nano dimensions due to the little variation of a threshold voltage. These devices may also consume less power due to the less leakage current and operating with higher speed due to the ballistic transport of electrons compared to the MOSFET device. All the simulation has done with HSPICE at 32nm technology node.

Keywords:

MOSFET,CNTFET,GNRFET,Temperature,Oxide Thickness,Chirality,Channel Length,

Refference:

I. Baughman R H, Zhakidov A A, DeHeer W A. Carbon nanotubes-the route
toward applications. Science, pp. 297: 787, 2002
II. C.Venkataiah, K. Satya Prasad, T. Jaya Chandra Prasad “Effect of Interconnect
parasitic variations on circuit performance parameters” IEEE International
conference on communication and electronics systems(ICCES), Coimbatore,
India, October, 2016.
III. C.Venkataiah, K. Satyaprasad, T. Jayachandra Prasad, “Crosstalk induced
performance analysis of single walled carbon nanotube interconnects using
stable finite difference time domain model”, Journal of nanoelectronics and
optoelectronics. Vol. 12, pp. 1-10, 2017.
IV. C.Venkataiah, K. Satyaprasad, T. Jayachandra Prasad, “FDTD algorithm to
achieve absolute stability in performance analysis of SWCNT interconnects”,
Journal of computational electronics, pp. 1-11, 2018.
V. C.Venkataiah, K. Satyaprasad, T. Jayachandra Prasad, “Insertion of optimal
number of repeaters in pipelined nano interconnects for transient delay
minimization”, Circuit systems and signal processing, 2019.
VI. C.Venkataiah, K. Satyaprasad, T. Jayachandra Prasad, “Signal integrity
analysis for coupled SWCNT interconnects using stable recursive algorithm”,
Microelectronics Journal, Vol.: 74, pp. 13-23, 2018.
VII. Chern J G J, Chang P, Motta R F, et al. A new method to deter-mine MOSFET
channel length. IEEE Electron Device Lett, EDL-1: 170, 1980.
VIII. Deng J, Wong H S P. A compact SPICE model for carbon nanotube field-effect
transistors including non-idealities and its application—Part I: model of the
intrinsic channel region. IEEE Trans Electron Devices, Vol.: 54, Issue:12, pp.
3186, 2007.
IX. H. Jiang, J. Wei, X. Dai, M. Ke, I. Deviny; P. Mawbym, SiC Trench MOSFET
With Shielded Fin-Shaped Gate to Reduce Oxide Field and Switching Loss,
IEEE Electron Device Letters, IEEE, Vol. 37, Issue 10, pp. 1324-1327, 2016.
X. Jain S. Measurement of threshold voltage and channel length of submicron
MOSFET’s. ProcInst Elect Eng, Vol.: 135, Issue: 1, pp. 162, 1988.

XI. Moore G. Progress in digital electronics. IEDM Tech Digest, 1975: 11 Terada
K, Muta H. A new method to determine effective MOS-FET channel length. Jpn
J ApplPhys, Vol.: 18 Issue:953, 1979.
XII. Nasser Masoumi, Ying-Yu, Asymmetric Gate Schottky-Barrier
GrapheneNanoribbon FETs for Low-Power Design Morteza Gholipour, IEEE
Transactions on Electron Devices, Vol.: 61, Issue: 12, December 2014.
XIII. Raychowdhury A, Mukhopadhyay S, Roy K. A circuitcompatible model of
ballistic carbon nanotube field-effect transistors. IEEE Trans Comput Aided
Design Integr Circuits Syst, Vol.:23 Issue:10, pp. 1411, 2004.
XIV. S. Choudhary. V. Singh, Understanding the Effect of n-type and p-type Doping
in the Channel of Graphene Nanoribbon Transistor, Bulletin of Materials
Science, Vol.: 39, Issue 5, pp. 1303-1309, 2016.
XV. S. Farhana, A.H.M. ZahirulAlam; S. Khan, S.M.A. Motakabber, CNTFET
SPICE Model: Design of a Carbon Nanotube Field Effect Transistor,
International Conference on Computer and Communication Engineering, IEEE,
pp. 262-264, 2014.
XVI. Sanjeet Kumar Sinha. and Saurabh Chaudhury Comparative study of leakage
power in CNTFET over MOSFET device Journal of Semiconductors, Vol.: 35,
Issue: 11, November 2014.
XVII. V. R. Kumar, M. K. Majumder, A. Alam, N. R. Kukkam, and B. K. Kaushik,
“Stability and delay analysis of multi-layered GNR and multi-walled CNT
Interconnects”, Journal of Computational Electronics , Vol.: 14, Issue: 2, pp.
611-618, 2015.

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Abstract:

Detection of abnormalities in the ECG signal to achieve an automatic diagnosis of several heart related diseases has become an increased research aspect. This paper focused to develop an automatic detection system to detect abnormalities in ECG. These abnormalities results in different cardiac arrhythmias. Towards the detection of different cardiac arrhythmias, this paper analyzed the ECG signal through Dual Tree Complex Wavelet Transform (DTCWT) as a feature extraction technique and further proposed a new selective band coding technique to extract only the informative features from the sub bands obtained from DTCWT. The novelty of this proposed system is to remove the redundant information, thereby achieving a fast and accurate detection results. Multi-Class Support Vector Machine (MC-SVM) is used for classification purpose. Extensive simulations are carried out for the MITBIH database and the performance is measured through the performance metrics such as Accuracy, Precision, Recall, False Positive Rate, F-Measure and overall computational time. The proposed method is also compared with conventional approaches to alleviate the performance enhancement in the detection of Cardiac Arrhythmias (CAs) with less time span.

Keywords:

Accuracy,Cardiac Arrhythmia,Detection Rate,DTCWT,ECG,MCSVM,SA,

Refference:

I. A.Ebrahimzadeh, B.Shakiba, A.Khazaee, “Detection of electrocardiogram
signals using an efficient method” Applied Soft Computing, Vol.: 22, pp.
108–117 (2014).
II. A.E.Fatin, “Arrhythmia recognition and classification using combined linear
and nonlinear features of ECG signals” Computer Methods and Programs in
Biomedecine, Vol.:127, pp. 52–63 (2016).
III. A.E.Zadeh, A.Khazaee, V.Ranaee, “Classification of the electrocardiogram
signals using supervised classifiers and efficient features” Computer Methods
and Programs in Bio medicine, Vol.:99, pp. 179–194 (2010).
IV. A.Mert, N.Kilic, A.Akan, “Evaluation of bagging ensemble method with
time-domain feature extraction for diagnosing of arrhythmia beats” Neural
Computing and Applications, Vol.:24, pp. 317–326 (2014).
V. C.C.Lin, & C.M.Yang, “Heartbeat classification using normalized RR
intervals and morphological features” Mathematical Problems in
Engineering, Vol.:1, pp. 1–11 (2014). 2014
VI. C.K.Chua, “Cardiac health diagnosis using higher order spectra and support
vector machine” Open Medical Informatics Journal, Vol.:3, pp. 1–8 (2010).
VII. C.P.Shen, “Detection of cardiac arrhythmia in electrocardiograms using
adaptive feature extraction and modified support vector machines” Expert
Systems with Applications, Vol.:39, pp. 7845–7852 (2012).
VIII. C.W.Hsu, C.C.Chang, C.J. Lin “A Practical Guide to Support Vector
Classification” Department of Computer Science National Taiwan
University, Taipei 106, Taiwan, 2016.
IX. E.D.Ubeyli, “ECG beats classification using multiclass support vector
machines with error correcting output codes” Digital Signal Processing,
Vol.:17, pp. 675–684 (2007).
X. F.Alonso-Atienza, “Detection of life-threatening arrhythmias using feature
selection and support vector machines” IEEE Trans. Biomed. Eng., Vol.:61,
pp. 832–840 (2014).

XI. F.K.Aya, I.O.Mohamed, A.Y.Inas, “A novel technique for cardiac arrhythmia
classification using spectral correlation and support vector machines” Expert
Systems with Applications, Vol.:42, pp. 8361–8368 (2015).
XII. G.Ramesh, D.Satyanarayana, M.Sailaja,“ECG Signal Enhancement through
Subband Adaptive Soft Thresholding and EMD for Efficient Cardiac
Arrhythmia Analysis” International Journal of Intelligent Engineering and
Systems, Vol.: 11, Issue:5, 2018.
XIII. [3] H.Khamis, R.Weiss, Y.Xie, C.W.Chen, N.Lovell, S.Redmond, “QRS
detection algorithm for tele-health electrocardiogram recordings” IEEE
Transactions on Biomedical Engineering, Vol.:63, pp. 1377–1388, 2016.
XIV. H.Q.Li, “Arrhythmia classification based on multi-domain feature extraction
for an ECG recognition system” Sensors, Vol.:16, pp. 1–16(2016).
XV. H.Q Li, “Heartbeat classification using different classifiers with non-linear
feature extraction” Transactions of the Institute of Measurement and Control,
Vol.: 38, pp. 1033–1040 (2016).
XVI. J.J.Zhu, L.S.He, Z.Q.Gao, “Feature extraction from a novel ECG model for
arrhythmia diagnosis” Biomedical Materials and Engineering, Vol.:24, pp.
2883–2891 (2014).
XVII. K.Aik, W.Seng, G.Nanyang, S.M.Kuo, “Subband Adaptive Filtering Theory
and Implementation” Wiley and Sons publishers, 2009.
XVIII. L.C.Lin, Y.C.Yeh, T.Y.Chu, “Feature selection algorithm for ECG signals
and its application on heart beat case determining” International Journal of
Fuzzy Systems, Vol.:16, pp. 483–496 (2014).
XIX. Li, P. F. et al. “High-performance personalized heartbeat classification model
for long-term ECG signal” IEEE Trans. Biomed. Eng., Vol.:64, pp. 78–86
(2017).
XX. M.Elgendi, “Fast QRS detection with an optimized knowledge based method:
evaluation on 11 standard ECG databases” PLoS One, Vol.: 8, article e73557,
2013.
XXI. M.K.Song, S.E.Kim, Y.S.Choi, W.J.Song “A Selective Normalized Subband
Adaptive Filter Exploiting an efficient subset of Sub bands” Proc. of
European Signal Processing Conference (EUSIPCO), 2011.
XXII. M.R.Homaeinezhad, “ECG arrhythmia recognition via a neuro-SVM-KNN
hybrid classifier with virtual QRS image-based geometrical features” Expert
Systems with Applications, Vol.: 39, 2047–2058 (2012).
XXIII. M.S.E.Abadi, M.S.Shafiee, “A New Variable Step-Size Normalized Subband
Adaptive Filter Algorithm Employing Dynamic Selection of Subband Filters”
Proc. of International Conf. On Electrical Engineering (ICEE), 2013.
XXIV. M.Thomas, M.K.Das, S.Ari, “Automatic ECG arrhythmia classification using
dual tree complex wavelet based features” International Journal of
Electronics and Communications, Vol.:69, pp. 715–721 (2015).

XXV. N.Acir, “A support vector machine classifier algorithm based on a
perturbation method and its Application to ECG beat recognition systems”
Expert Systems with Applications, Vol.:31, pp. 150–158 (2006).
XXVI. N.Acir, “Classification of ECG beats by using a fast least square support
vector machines with a Dynamic programming feature selection algorithm”
Neural Computing and Applications, Vol.:14, pp. 299–309 (2005).
XXVII. N.Kingsbury, “Complex wavelets for shift invariant analysis and filtering of
signal,” Applied and Computational Harmonic Analysis, Vol.:10, pp. 234-
253, 2010.
XXVIII. O.Sayadi, M.B.Shamsollahi, G.D.Clifford, “Robust detection of premature
ventricular contractions using a wave-based Bayesian framework” IEEE
Trans. Biomed. Eng., Vol.:57, pp. 353–362 (2010).
XXIX. P.Sharma, K.C.Ray, “Efficient methodology for electrocardiogram beat
classification” IET Signal Processing, Vol.:10, pp. 825–832 (2016).
XXX. R.Benali, F. B. Reguig, Z. H. Slimane, “Automatic classification of
heartbeats using wavelet neural network” J. Med. Syst. , Vol.: 36, pp. 883–
892 (2012).
XXXI. R.Ceylan, Y.Ozbay, “Comparison of FCM, PCA and WT techniques for
classification ECG arrhythmias using artificial neural network”. Expert
Systems with Applications, Vol.:33, pp. 286–295 (2007).
XXXII. R.Chen, D.Y.Sun, D.T.Qin, F.B.Hu “A novel engine identification model
based on support vector machine and analysis of precision-influencing
factors” Journal of Central South University (Science and Technology), Vol.:
41, Issue: 4, pp.1391–1397, 2010.
XXXIII. R.J.Martis, “Characterization of ECG beats from cardiac arrhythmia using
discrete cosine transform in PCA framework” Knowledge-based Systems,
Vol.:45, pp. 76–82 (2013).
XXXIV. R.J.Martis, “Computer aided diagnosis of atrial arrhythmia using
dimensionality reduction methods on transform domain representation”.
Biomedical Signal Processing and Control, Vol.:13, pp. 295–305 (2014).
XXXV. R.Mark, G.Moody MIT-BIH Arrhythmia Database. Available:
http://ecg.mit.edu/dbinfo.html, (1997, May)
XXXVI. S.Osowski, T.H.Linh, “ECG beat recognition using fuzzy hybrid neural
network” IEEE transactions on Biomedical Engineering, Vol.: 48, Issue: 11,
pp. 1265–1271, 2001.
XXXVII. U.R.Acharya, P.S.Bhat, S.S.Iyengar, A.Rao, S.Dua, “Classification of heart
rate data using artificial neural network and fuzzy equivalence relation,”
Pattern Recognition, Vol.: 36, Issue:1, pp. 61–68, 2003.
XXXVIII. V.Kalpana, S.T.Hamde, L.M.Waghmare, “ECG feature extraction using
principal component analysis for studying the effect of diabetes” J. Med.
Eng. Technol., Vol.:37, pp. 116–126 (2013).

XXXIX. Y.Can, B.V.K.V.Kumar, M.T.Coimbra, “Heartbeat classification using
morphological and dynamic features of ECG signals” IEEE Trans. Biomed.
Eng, Vol.:59, pp. 2930–2941 (2012).
XL. Y.C.Yeh, W.J.Wang, “QRS complexes detection for ECG signal: the
difference operation method” Computer Methods and Programs in
Biomedicine, Vol.: 91, Issue: 3, pp. 245–254, 2008.
XLI. Y.Kutlu, D.Kuntalp, “A multi-stage automatic arrhythmia recognition and
classification system” Computers in Biology and Medicine. Vol.:41, pp. 37–
45(2011).
XLII. Y.Kutlu, D.Kuntalp, “Feature extraction for ECG heartbeats using higher
order statistics of WPD coefficients” Computer Methods and Programs in
Biomedicine, Vol.:105, pp. 257–267 (2012).

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Abstract:

This paper “an ensemble of Support Vector Machines (SVM)” for networkbased intrusion detection. Bootstrapping is applied to derive various training sets from the given training set. Then a SVM is derived for each training set. The decisions of all SVMs is taken and majority voting is considered to classify the given query pattern as a normal or an anomalous one. We have shown the results of applying an ensemble of Support Vector Machines to the two standard data sets,viz.,1999KDDCupandCreditcarddatasets.

Keywords:

Bootstrapping,classification,svm,ensemble techniques,intrusion detection,

Refference:

I. A.K.Jain, B.Yu, “An automatic text location in images and video
frames”, Pattern Recognition, Vol.: 31, Issue: 12, pp. 2055–2976, 1998.
II. A.W.H.Debar, M.Dacier, “Towards a taxonomy of intrusion- detection
systems”, Computer Networks, Vol.: 31, pp. 805–822, 1999.

III. B.H.Drucker, D.C.Gibbon, “Support vector machines:relevance feedback
and information retrieval”, Information Processing Management, Vol.:
38, Issue: 3, pp. 305–323, 2002.
IV. C.Burges, “A tutorial on support vector machines for pattern
recognition”, Data Mining and Knowledge Discovery, Vol.: 2, pp. 1–47,
1998.
V. C.Chang, C.Lin, “Libsvm: a library for support vector machines”,
Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm, 2001.
VI. D.Mckay, C.Fyfe, “Probability prediction using support vector
machines”, Proceedings of Int Conferecne on Knowledge-Based
Intelligent Engineering Systems and Allied Technologies, Vol.: 1, pp.
189–192, 2000.
VII. E.Hjelams, B.K.Low, “Face detection : A survey. Computer Vision and
Image Understading”, Vol.:83, pp. 236–274, 2001.
VIII. F.Seraldi, J.Bigun, “Retinal vision applied to facial features detection and
face authentication”, Pattern Recognition Letters, Vol.: 23, pp. 463– 475,
2002.
IX. F.Y.S.R.E, “Experiments with a new boosting algorithm”, Proceedings
13th International Conference on Machine Learning. San Francisco:
Morgan Kaufmann, pp. 148–146, 1996.
X. G.K.Kumar, P.Viswanath, A.A.Rao, “Ensemble of Soft Decision Trees
for Robust Classification”, Sadhana- Indian Academy of Sciences, Vol.:
41, Issue: 3, pp. 273-282, 2016.
XI. G.K.Kumar, P.Viswanath, A.A.Rao, “Ensemble of Soft Decision Trees
Using Multiple Approximate Fuzzy-Rough Set Based Reducts”,
International Journal of Information Processing, Vol: 9, Issue: 2, 36-46,
2015.
XII. G.K.Kumar, P.Viswanath, A.A.Rao, “Intrusion Detection Using an
Ensemble of Decision Trees”, In the proceedings of fifth Indian
International Conference on Artificial Intelligence (IICAI), pp 382-392,
2011.
XIII. H.Byun, S.-W.Lee, “Application of support vector machines for pattern
recognition: A survey”, Lecturer Notes in Computer Science, Vol.: 2388,
pp. 571–591, 2002.

XIV. I.B.E.Boser, V.Vapnik, “A training algorithm for optimal margin
classifiers”, Proceedings of the Fifth Annual Workshop on
Computational Learning Theory. ACM Press, pp. 144–152,1992.
XV. I.R.P.Lippman, D.J.Fried, M.A.Zissman, “Evaluating intrusion detection
systems: The 1998 darpa off-line intrusion detection evaluation”,
Proceedings of DARPA Information Survivability Conf. and
Exosition(DISCEX’00), pp. 12–26, 2000.
XVI. J.D.Gao, L.Xin, “Svm-based detection of moving vehicles for automatic
traffic monitoring”, IEEE Intelligent Transportation System, pp. 745–
749, 2001.
XVII. J.Han, M.Kamber, “Data Mining: Concepts and Techniques”, Academic
Press, 2001.
XVIII. J.K.Jonsson, Y.P.Matas, “Support vector machines for face
authentication”, Image and Vision Computing, Vol.: 20, pp. 369–375,
2002.
XIX. J.R.Quinlan, “C4.5 Programs for Machine Learning”, Morgan
Kaufmann,los Atlos,1993.
XX. J.R.Quinlan, “Simplifying decision trees”, International Journal of Man
Machine Studies, Vol.: 27, no. 3, pp. 221–234,1987.
XXI. L.L.MIT, DARPA Intrusion Detection Data Sets,
http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/data/in
dex.html
XXII. M.C.Ma, J.Drish, “A support vector machines-based rejection technique
for speech recognition”, Proceedings of IEEE Int Conferecne on
Acoustics, Speech and Signal Processing, Vol.: 1, pp. 381–384, 2001.
XXIII. M.Pontil, A.Verri, “Support vector machines for 3-d object recognition”,
IEEE Transportations on Pattern Analysis and Machine Intelligence, pp.
637–646, 1998.
XXIV. P.Cunningham, “Ensemble techniques”, Technical Report UCDCSI2007-
5, 2007.
XXV. P.Q.Tian, T.S.Huang, “Update relevant image weights for content based
image retrieval using support vector machines”, Proceedings of IEEE Int
Conferecne on Multimedia and Expo, Vol.: 2, pp. 1199– 1202, 2000.

XXVI. R.E.Osuna, F.Girosi, “Training support machines: An application to face
detection”, Proceedings of IEEE Conferece on Computer Vision and
Pattern Recongition, pp. 130–136, 1997.
XXVII. R.O.Duda, P.E.Hart, D.G.Stork, “Pattern Classification”, 2nd ed. John
Wiley & Sons: A Wiley-interscience Publication, 2002.
XXVIII. S.G.Guo, K.L.Chan, “Support vector machines for face recognition”,
Journal of Image and Vision Computing, Vol.: 19, pp. 631–638, 2001.
XXIX. S.W.Lee, “A data mining framework for building intrusion detection
models”, Proceedings of the IEEE Symposium on Security and Privacy,
1999.
XXX. T.Joachims, “Text categorization with support vector machines: learning
with many relevant features”, Proceedings of 10th European Conferece
on Machine Learning, 1999.
XXXI. V.N.Vapnik, “An overview of statistical learning theory”, IEEE
Transactions on Neural Networks, Vol.: 10, Issue: 5, 1999.
XXXII. V Vapnik, “Statistical Learning Theory”, New York Wiley, 1998.
XXXIII. V.Vapnik, “The Nature of Statistical Learning Theory”, New York:
Springer-Verlag, 1995.
XXXIV. V.Wan, W.M.Campbell, “Support vector machines for speaker
verification and identification”, Proceedings of IEEE Workshop on
Neural Networks for Signal Processing, Vol.: 2, 2000.
XXXV. W.L.M.Tavallaee, Ebrahim Bagheri and A. A. Ghorbani, “A deatiled
analysis of the kdd cup 99 data set”, IEEE Symposium on Computer
Intelligence in Security and Defense Applications(CISDA), 2009.
XXXVI. Y.B.Zhao, S.W.Xia, “Support vector machines and its application in
handwritten numeircal recognition”, Proceedings of 15th International
Conferece on Pattern Recongition, Vol.: 2, pp. 720–723, 2000.

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Abstract:

Cluster analysis refers to the process of combining the group of objects based on similarity features; Traditional methods such as k-means, graphbased clustering etc. are used for clustering of given data objects. Other clustering models, namely, visual access tendency (VAT), cosine based VAT (cVAT), Spectral VAT (SpecVAT), cosine based spectral VAT (cSpecVAT) are more effective because they shows the clustering results with visual evidence for big datasets. These methods compute an initial difference matrix for a set of objects and re-order the same based on ordering of dissimilarity values between objects. Image of re-ordered dissimilarity matrix shows the dark color shaded square blocks along the diagonal, in which each square shaped block represented as a cluster. Synthetic and other benchmarked datasets are taken in the experimental study for proving the efficiency of visual model based clustering approaches.

Keywords:

VAT,cVAT,SpecVAT,cSpecVAT,

Refference:

I. C.Lacey, S.Cole, “Merger rates in hierarchical models of galaxy formation.
II: Comparison with N-body simulations”, Mon. Not. Roy. Astron. Soc.,
Vol.: 271, Issue: 3, pp. 676–692, Feb. 1994.
II. D.Zhang, K.Ramamohanarao, S.Versteeg, R.Zhang, “RoleVAT: Visual
assessment of practical need for role based access control”, in Proc. Conf.
Comput. Security Appl., Honolulu, HI, USA, pp. 13–22, Dec. 2009.
III. E.B.S.D.D.Agrawal, “Challenges and opportunities with big Data-A
community white paper developed by leading researchers across The United
States”, The computing Research Association, CRA white Paper, Feb. 2012.
IV. J.C.Bezdek, R.J.Hathaway, “VAT: A tool for visual assessment of (cluster)
tendency”, in Proc. Int. Joint Conf. Neural Netw. (IJCNN), Honolulu, HI,
USA, pp. 2225–2230, May 2002.
V. J.Dean, S.Ghemawat, “Mapreduce: Simplified data processing on large
clusters”, Commun. ACM, Vol.: 51, Issue: 1, pp. 107-113, 2008.
VI. J.Gantz, D.Reinsel, “The digital universe in 2020: Big data, bigger Digital
shadows, and biggest growth in the Far East”, in Proc. IDC iView, IDC Anal.
Future, 2012.
VII. J.H.Howard, “Scale and performance in a distributed _file system”, ACM
Trans. Comput. Syst., Vol.: 6, Issue: 1, pp. 51-81, 1988.
VIII. J.Wilbik, M.Keller, J.C.Bezdek, “Linguistic prototypes for data from
eldercare residents”, IEEE Trans. Fuzzy Syst., Vol.: 22, Issue: 1, pp. 110–
123, Mar. 2013.
IX. L.O.Hall, S.Eschrich, J.Ke, D.B.Goldgof, “Fast accurate fuzzy clustering
through data reduction”, IEEE Trans. Fuzzy Syst., Vol.: 11, Issue: 2, pp. 262–
270, Apr. 2003.
X. M.Moshtaghi, “Clustering ellipses for anomaly detection”, Pattern Recognit.,
Vol.: 44, Issue: 1, pp. 55–69, Jan. 2011.
XI. M.Schmidt, D.Feldman, C.Sohler, “Turning big data into tiny data: Constantsize
coresets for k-means, PCA and projective clustering”, in Proc. 24th
Annu. ACM Symp. Discrete Algorithms, New Orleans, LA, USA, pp. 1434–
1453, 2013.
XII. P.H.A.Sneath, R.R.Sokal, “Numerical Taxonomy—the Principles and
Practice of Numerical Classification”, San Francisco, CA, USA: W. H.
Freeman, 1973.
XIII. P.Rathore, D.Kumar, J.C.Bezdek, “A Rapid Hybrid Clustering Algorithm for
Large Volumes of High Dimensional Data”, IEEE Transactions On
Knowledge and Data Engineering, 2018.

XIV. R.Cattell, “Scalable SQL and NoSQL data stores”, SIGMOD Rec., Vol.: 39,
Issue: 4, pp. 12-27, 2011.
XV. T.White, “Hadoop: The Definitive Guide”, Sebastopol, CA, USA: O’Reilly
Media, 2012.
XVI. V.R.Borkar, M.J.Carey, C.Li, “Big data platforms: What’s next?” XRDS”,
Crossroads, ACM Mag.Students, Vol.:19, Issue:1, pp.44-49, 2012.

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