Implementation of combined Viola-Jones and NPD Based Face Detection Algorithm
Research Paper | Journal Paper
Vol.6 , Issue.6 , pp.1518-1522, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15181522
Abstract
Now a days video information is growing more and more widespread, their intelligent or automatic examining is becoming exceptionally important. People, i.e. human faces, are one of most common and very specific objects in video, which are tried to trace with help face detection tools. Face detection is a difficult task in image analysis which has each day more and more applications. In this paper we presented comparison of two face detection methods, which are commonly used. The Viola-Jones face detector is first reviewed and different techniques used in this algorithm to extract features are discussed. Secondly color based face detection approach is reviewed. In this paper, we implement Viola-Jons and Normalized pixel difference (NPD) detection methods. These algorithms are explained in brief. These face detection methods that are universally used are elaborated with their capabilities, advantages and disadvantages.
Key-Words / Index Term
Face detection, Viola-Jones face detector, Color Space, NPD, Adaboost, Feature Extraction
References
[1] Paul Viola, Michael J. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision 57(2), 2004.
[2] Sanjay Kr. Singh1, D. S. Chauhan2, Mayank Vatsa3, Richa Singh3 A Robust Skin Color Based Face Detection Algorithm Tamkang Journal of Science and Engineering, Vol. 6, No. 4, pp. 227-234 (2003).
[3] Phung, S. L., Chai, D. K., & Bouzerdoum, A. (2001). Skin colour based face detection. Proceedings of 7th Australian and New Zealand Intelligent Information Systems Conference. (pp. 0). Australia. IEEE.
[4] Ragini Choudhury Verma, Cordelia Schmid, and KrystianMikolajczyk, ―Face Detection and Tracking in a Video by Propagating Detection Probabilities, ieee transactions on pattern analysis and machine intelligence, vol. 25, no. 10, october 2003.
[5] H.A. Rowley, S. Baluja, and T. Kanade, ―Neural Networks Based Face Detection, IEEE Trans. Pattern Analysis an Machine Intelligence, vol. 20, no. 1, pp. 22-38, Jan. 1998.
[6] K.K. Sung and T. Poggio, ―Example-Based Learning for View-Based Human Face Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.
[7] J.-C. Terrillon, M. Shirazi, H. Fukamachi, and S. Akamatsu,―Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for the Automatic Detection of Human Faces in Color Images, Proc. Int‘l Conf. Automatic Face and Gesture Recognition, pp. 54-61, 2000.
[8] D. Decarlo and D. Metaxas, ―Deformable Model Based Face Shape and Motion Estimation, Proc. Int‘l Conf. Face and Gesture Recognition, 1996.
[9] G. Hager and K. Toyama, ―X Vision: A Portable Substrate for Real- Time Vision Applications,‖ Computer Vision and Image Understanding, vol. 69, no. 1, pp. 23-37, 1998.
[10] J. Yang and A. Waibel, ―Tracking Human Faces in Real Time,Technical Report CMU-CS-95-210, School of Computer Science, Carnegie Mellon Univ., Pittsburgh, Pa., 1995.
[11] J. P. Kapur, “Face Detection in Color Images,” University of Washington Department of Electrical Engineering, 1997. [Online]. Available: http://web.archive.org/web/20090723024922/http:/geocities.com/jaykapur/face.html. [Accessed 10 December 2013].
[12] Cahi, D. and Ngan, K. N., “Face Segmentation Using Skin-Color Map in Videophone Applications,” IEEE Transaction on Circuit and Systems for Video Technology, Vol. 9, pp. 551-564. 1999.
[13] Christopher M. Bishop, Pattern Recognition and Machine Learning, first edition, Springer 2006.
[14] Sanjay Kr. Singh, A Robust Skin Color Based Face Detection Algorithm, Tamkang Journal of Science and Engineering, Vol. 6, No. 4, pp. 227-234 (2003).
[15] Crowley, J. L. and Coutaz, J., “Vision for Man Machine Interaction,” Robotics and Autonomous Systems, Vol. 19, pp. 347-358 (1997).
[16] Hsu, Rein-Lien, Mohamed Abdel-Mottaleb, and Anil K. Jain."Face detection in color images." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.5 (2002):696-706.
[17] Hsu, Rein-Lien, Mohamed Abdel-Mottaleb, and Anil K. Jain. "Face detection in color images." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.5 (2002): 696-706.
[18] A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell. 23 (6) (2001) 643–660.
[19] Mayank Chauha and Mukesh Sakle―Study & Analysis of Different Face Detection Techniques.‖ International Journal of Computer Science and Information Technologies, Vol. 5 (2), 2014, 1615-1618.
[20] G. Yang and T. S. Huang, ―Human Face Detection in Complex Background,‖ Pattern Recognition, vol. 27, no. 1, pp. 53-63, 1994.
[21] T.K. Leung, M.C. Burl, and P. Perona, ―Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching, Proc. Fifth IEEE Int’l Conf. Computer Vision, pp. 637-644, 1995.
[22] K.C. Yow and R. Cipolla, ―Feature-Based Human Face Detection, Image and Vision Computing, vol. 15, no. 9, pp 713-735, 1997.
[23] J. Yang and A. Waibel, ―A Real-Time Face Tracker, Proc. Third Workshop Applications of Computer Vision, pp. 142-147, 1996.
[24] S. McKenna, S. Gong, and Y. Raja, ―Modelling Facial Colour and Identity with Gaussian Mixtures, Pattern Recognition, vol. 31, no. 12, pp. 1883-1892, 1998
[25] R. Kjeldsen and J. Kender, ―Finding Skin in Color Images, Proc. Second Int’l Conf. Automatic Face and Gesture Recognition, pp. 312- 317, 1996.
[26] I. Craw, D. Tock, and A. Bennett, ―Finding Face Features,Proc. Second European Conf. Computer Vision, pp. 92-96, 1992.
[27] A. Lanitis, C.J. Taylor, and T.F. Cootes, ―An Automatic Face Identification System Using Flexible Appearance Models, Image and Vision Computing, vol. 13, no. 5, pp. 393-401, 1995.
[28] H. Rowley, S. Baluja, and T. Kanade, Neural Network-Based Face Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, Jan. 1998.
[29] Sharifara, Ali, et al. "A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection." Biometrics and Security Technologies (ISBAST), 2014 International Symposium on. IEEE, 2014.
[30] Zhengming Li; Lijie Xue; Fei Tan, "Face detection in complex background based on skin color features and improved AdaBoost algorithms," Progress in Informatics and Computing (PIC), 2010 IEEE International Conference on ,vol.2, no., pp.723,727, 10-12 Dec. 2010.
[31] Yang, Ming-Hsuan, David J. Kriegman, and Narendra Ahuja. "Detecting faces in images: A survey." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.1 (2002): 34-58.
[32] Shengcai Liao, Anil K. Jain, and Stan Z. Li. “A Fast and Accurate Unconstrained Face Detector: IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. pp.723,727, February 2016
[33] V. Jain and E. Learned-Miller, “FDDB: A benchmark for face detection in unconstrained settings,” Univ. Massachusetts, Amherst, MA, USA, Tech. Rep. UM-CS-2010-009, 2010.
[34] R. Lienhart and J. Maydt, “An extended set of Haar-like features for rapid object detection,” in Proc. IEEE Int. Conf. Image Process., 2002, pp. I-900–I-903.
[35] E. H. Weber, “Tastsinn und gemeingef€uhl,” in Handworterbuch der Physiologie, R. Wagner, Ed. Brunswick, Germany: Vieweg, 1846, pp. 481–588.
[36] M. Jones and P. Viola, “Fast multi-view face detection,” Mitsubishi Electric Res. Lab, Kendall Square, Cambridge, Massachusetts, Tech. Rep. TR-2003-96, 2003.
[37] J. Chen, S. Shan, C. He, G. Zhao, M. Pietik€ainen, X. Chen, and W. Gao, “WLD: A robust local image descriptor,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1705–1720, Sep. 2010.
[38] J. Friedman, T. Hastie, and R. Tibshirani, “Additive logistic regression: A statistical view of boosting,” The Ann. Statist., vol. 28, no. 2, pp. 337–374, Apr. 2000.
Citation
Ramakrishna B B, M Sharmila Kumari, "Implementation of combined Viola-Jones and NPD Based Face Detection Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1518-1522, 2018.
A Comprehensive Survey on Data Mining Techniques in Wireless Sensor Networks
Survey Paper | Journal Paper
Vol.6 , Issue.6 , pp.1523-1527, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15231527
Abstract
Wireless sensor networks (WSN) have emerged as one of the most exciting fields in Computer Science research nowadays. A WSN is a collection of sensors that are incorporated with a physical domain. These sensors are little in size, and equipped for detecting physical wonder and handling them. The most vital reason for conveying the WSNs-built up applications is to make the ongoing determination which has been turned out to be extremely testing due to the absolute asset restricted processing, imparting limits, and the giant amount of speedy changed information created by WSNs. This motivates to investigate a novel and fitting data mining procedure equipped for extricating learning from enormous volume and an assortment of persistently arriving data from WSNs. In this paper diverse existing data mining strategies received for WSNs are inspected with various grouping, assessment approaches. Based on the barriers of the existing process, an adaptive data mining structure of WSNs for future research are proposed.
Key-Words / Index Term
Wireless sensor network,Data mining.Sensor
References
[1] Azhar Mahmood, Ke Shi, Shaheen Khatoon, Mi Xiao
“Data Mining Techniques for Wireless Sensor Networks: A Survey” International Journal of Distributed Sensor Networks July 2013.
[2] Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., Estrin, D.Habitat monitoring with sensor networksCommunications of the ACM200447634402-s2.0-424311408710.1145/990680.990704 Google Scholar, Crossref, ISI
[3]S.Stankovic, O.Rakocevic, N. Kojic, D.Milicev, “A Classification and Comparison of Data Mining Algorithms for Wireless Sensor Networks”, lCIT2012,978-1-4673-0342-2112 IEEE.
[4]V. Maojo and J. Sanandré, “A survey of data mining techniques,” Medical Data Analysis, Lecture Notes in Computer Science, vol. 1933, pp. 17–22, 2000.
[5]J. Cheng, Y. Ke, and W. Ng, “A survey on algorithms for mining frequent itemsets over data streams,” Knowledge and Information Systems, vol. 16, no. 1, pp. 1–27, 2008.
[6]Emad M. Abdelmoghith, “A Data Mining Approach to Energy Efficiency in Wireless Sensor Networks”, 2013 IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks, 978-1-4577-1348-4/13, 2013 IEEE.
[7]Brahim Elbhiri , Sanaa El Fkihi, “A New Spectral Classification for Robust Clustering in Wireless Sensor Networks”, WMNC`2013, 978-1-4673-5616-9/13, 2013 IEEE.
[8] Agrawal, R., Srikant, R.Fast algorithms for mining association rulesProceedings of the 20th International Conference Very Large Data Bases (VLDB `94)1994Citeseer487499 Google Scholar
[9] Agrawal, R., Srikant, R.Mining sequential patternsProceedings of the IEEE 11th International Conference on Data EngineeringMarch 19953142-s2.0-0029212693 Google Scholar [10]Srikant, R., Agrawal, R.Mining sequential patterns: generalizations and performance improvementsProceedings of the Advances in Database Technology (EDBT `96)1996117 Google Scholar [11]Masseglia, F., Cathala, F., Poncelet, P.The PSP approach for mining sequential patternsPrinciples of Data Mining and Knowledge Discovery1998176184 Google Scholar, Crossref [12] Taherkordi, A., Mohammadi, R., Eliassen, F.A communication-efficient distributed clustering algorithm for sensor networksProceedings of the 22nd International Conference on Advanced Information Networking and Applications Workshops/Symposia (AINA `08)March 20086346382-s2.0-4912455110.1109/WAINA.2008.130 Google Scholar, Crossref
[13] Sharma, L. K., Vyas, O. P., Schieder, S.Nearest neighbour classification for trajectory dataInformation and Communication Technologies2010101180185 Google Scholar, Crossref
[14] Halatchev, M., Gruenwald, L.Estimating missing values in related sensor data streamsProceedings of the 11th International Conference on Management of Data (COMAD ’05)2005 Google Scholar
[15] Jiang, N.Discovering association rules in data streams based on closed pattern miningProceedings of the SIGMOD Workshop on Innovative Database Research2007 Google Scholar
[16] Esposito, F., Basile, T. M. A., Di Mauro, N., Ferilli, S.A relational approach to sensor network data miningInformation Retrieval and Mining in Distributed Environments2010163181 Google Scholar, Crossref
[17] Khawaja, F.MavHome: an agent-based smart homeProceedings of the 1st IEEE International Conference on Pervasive Computing and Communications (PerCom `03)March 20035215242-s2.0-33746769349 Google Scholar
[18] Yeo, M. H., Lee, M. S., Lee, S. J., Yoo, J. S.Data correlation-based clustering in sensor networksProceedings of the International Symposium on Computer Science and its Applications (CSA `08)October 20083323372-s2.0-5664909764310.1109/CSA.2008.21 Google Scholar, Crossref
[19] Chikhaoui, B., Wang, S., Pigot, H.A new algorithm based on sequential pattern mining for person identification in ubiquitous environmentsProceedings of the 4th International Workshop on Knowledge Discovery form Sensor Data (ACM SensorKDD `10)2010Washington, DC, USA2028 Google Scholar
[20] Chikhaoui, B., Wang, S., Pigot, H.A A fuzzy predictor model for the occupancy prediction of an intelligent inhabited environmentProceedings of the IEEE International Conference on Fuzzy Systems (FUZZ `08)June 20089399462-s2.0-5524908923410.1109/FUZZY.2008.4630482 Google Scholar, Crossref
Citation
C. Sudha, A. Nagesh, "A Comprehensive Survey on Data Mining Techniques in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1523-1527, 2018.
Improved cancer detection in mammogram images using automated Deep Learning Technique
Research Paper | Journal Paper
Vol.6 , Issue.6 , pp.1528-1539, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15281539
Abstract
Mammography is an exceptionally normal screening apparatus for diagnosing breast growth at beginning time as compare to other screening techniques to reduce female death rate. The techniques and algorithms that were extensively used are convolution neural networks, artificial neural networks, support vector machines, and so on. A comparison pertaining to the supremacy of deep learning techniques over existing machine learning techniques is also stated in terms of data requirements, learning, etc which is the need of the hour as the medical database is ever increasing phenomena demanding faster results. Though these studies are vast, this spectrum of research requires more rigorous investigation in terms of classification with minimal errors. In this paper, a proposed Deep Learning (DL) system is connected on large dataset to assess the prediction on the breast disease mammogram images as compare to state-of-art classification strategy. Despite the fact that this automation is known for its robustness still its execution relies on two key focuses that are: Clustering and Classification. The DL system result shows better qualitative result as compared to Multilayer Perceptron (MLP) method. The best precision of 86% for the given dataset is accomplished through proposed method when compared with different classifiers in terms of accuracy.
Key-Words / Index Term
Breast Cancer, Ultrasound, Mammography, Computer Aided Diagnosis(CAD) , Convolution Neural Network (CNN), Multi-Layer Perceptron(MLP), Machine learning techniques, Accuracy
References
[1]. Jemal, R. Siegel , E. Ward , Y. Hao, J. Xu , T. Murray , M. J. Thun, “Cancer statistics”, CA Cancer Journal Clinics, Vol. 58, Issue. 2, pp.71–96, 2008.
[2]. Secretan, C. Scoccianti, D. Loomis, L. Benbrahim, V. Bouvard, F. Bianchini, K. Straif, “Breast-cancer screening–view point of the IARC working group”, New England Journal Medical, Vol. 372, Issue 24, pp. 2353–2358, 2015.
[3]. M. Giger and A. Pritzker, “Medical imaging and computers in the diagnosis of breast cancer”, International Society for Optics and Photonics, pp. 918-908, 2014.
[4]. G. Carneiro, Y. Zheng, F. Xing and L. Yang, “Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis”, Springer Advances in Computer Vision and Pattern Recognition, pp. 11-32, DOI 10.1007/978-3-319-42999-1_2, 2017.
[5]. www.radiologyinfo.org/en/info.cfm?pg=mammo.
[6]. E. Kozegar, M. Soryani, H. Behnam, M. Salamati and T. Tan , “Breast cancer detection in automated 3D breast ultrasound using iso-contours and cascaded RUSBoosts”, Ultrasonics, Vol. 79,pp. 68–80, 2017.
[7]. Q. Huang, Y. Luo and Q. Zhang, “Breast ultrasound image segmentation: a survey”, International Journal of CARS, DOI 10.1007/s11548-016-1513-1, 2017.
[8]. M. Sahar, H. Nugroho and I. Ardiyanto, “Automated Detection of Breast Cancer Lesions Using Adaptive Thresholding and Morphological Operation”, IEEE International Conference on Information Technology Systems and Innovation, 2016.
[9]. “www.peipa.essex.ac.uk/pix/mias”.
[10]. “www.archive.ics.uci.edu/ml/machine-learning- databases/breast-cancer-wisconsin”.
[11]. J. Shan, S. K. Alam, B. Garra, Y. Zhang, and T. Ahmed, “Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods”, Ultrasound in medicine & biology, Vol. 42, Issue 4, pp. 980-988, 2016.
[12]. N. Abdelwahed, M. Eltoukhy and M. Wahed, “Computer Aided System for Breast Cancer Diagnosis in Ultrasound Images”, 2015.
[13]. S. K. Alam, E. J. Feleppa, M. Rondeau, A. Kalisz, and B. S. Garra, “Computer-aided diagnosis of solid breast lesions using an ultrasonic multi-feature analysis procedure”, Bangladesh Journal of Medical Physics, Vol. 4, Issue 1, pp.1-10, 2013.
[14]. Amin, M. Khalid, A. Shahin and Y. Guo, "A novel breast tumor classification algorithm using neutrosophic score features", Measurement, Vol. 81,pp. 210-220, 2016.
[15]. Breast Cancer Facts & Figures 2015-2016.
[16]. “Breast Imaging Reporting and Data System (BI-RADS)”, Ultrasound, American College of Radiology, 2003.
[17]. H.D. Cheng, J. Shan, W. Ju, Y. Guo and L. Zhang, “Automated breast cancer detection and classification using ultrasound images: A survey”, Pattern Recognition, Vol. 43, Issue 1, pp.299-317, 2010.
[18]. G. Walter, A. Pereira, A. Fernando and C. Infantosi, "Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound", IEEE transactions on medical imaging, Vol. 31, Issue 10, pp. 1889-1899, 2012.
[19]. Liu, H. D. Cheng, J. Huang, J. Tian, X. Tang, and J. Liu, “Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images”, Pattern Recognition, Vol. 43, Issue 1, pp.280-298, 2010.
[20]. Y. Liu, H.D. Cheng, J.H. Huang, Y.T. Zhang, X.L. Tang, J.W. Tian and Y. Wang, “Computer aided diagnosis system for breast cancer based on color Doppler flow imaging” , Journal of medical systems, Vol. 36, Issue 6, pp.3975-3982, 2012.
[21]. W. Moon, C. Lo, N. Cho, M. Chang, S. Huang , H. Chen and F. Chang, “Computer-Aided Diagnosis of breast masses using quantified BI-RADS findings”, Computer methods and programs in biomedicine, Vol. 111, Issue 1, pp.84-92, 2013.
[22]. S. Muthuselvan and K.S. Sundaram, “Prediction of breast cancer using classification rule mining techniques in blood test datasets”, IEEE International Conference on Information Communication and Embedded Systems (ICICES), pp. 1-5, 2016.
[23]. C.M. Sehgal, T.W. Cary, A. Cwanger, B.J. Levenback, and S.S. Venkatesh, “Combined Naive Bayes and logistic regression for quantitative breast sonography”, IEEE International Ultrasonics Symposium, pp. 1686-1689, 2012.
[24]. L. Sellami, O.B. Sassi and A.B. Hamida, “Breast Cancer Ultrasound Images Sequence Exploration Using BI-RADS Features Extraction: Towards an Advanced Clinical Aided Tool for Precise Lesion Characterization”, IEEE transactions on nano bioscience, Vol. 14, Issue 7, pp.740-745, 2015.
[25]. J. Shan, S.K. Alam, B. Garra, Y. Zhang, and T. Ahmed, “Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods”, Ultrasound in medicine & biology, Vol. 42, Issue 4, pp.980-988, 2016.
[26]. X. Shi, H.D. Cheng, L. Hu, W. Ju and J. Tian, “Detection and classification of masses in breast ultrasound images”, Digital Signal Processing, Vol. 20, Issue 3, pp.824-836, 2010.
[27]. E.A. Sickles, C.J. Dorsi, L.W. Bassett, C.M. Appleton, W.A. Berg, and E.S. Burnside, “ACR BI-RADS (Atlas Breast Imaging Reporting and Data System)”, American College of Radiology, 2013.
[28]. S.S. Venkatesh, B.J. Levenback, L.R. Sultan, G. Bouzghar and C.M. Sehgal, “Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis”, Ultrasound in medicine & biology, Vol. 41, Issue 12, pp.3148-3162, 2015.
[29]. P. Wahdan, A. Saad and A. Shoukry, “Automated Breast Tumour Detection in Ultrasound Images Using Support Vector Machine and Ensemble Classification”, Journal of TBA, Vol. 3, 2016.
[30]. F.S. Zakeri, H. Behnam and N. Ahmadinejad, “Classification of benign and malignant breast masses based on shape and texture features in sonography images”, Journal of medical systems, Vol. 36, Issue 3, pp.1621-1627, 2012.
[31]. J.C.M Zelst, T. Tan, B. Platel, M. Jong, A. Steenbakkers, M. Mourits, A. Grivegnee, C. Borelli, N. Karssemeije and R.M. Mann, “Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection”, European Journal of Radiology, Vol. 89,pp. 54–59, 2017.
[32]. Z. Liu, J. Zhang and L. Liu, “Upright orientation of 3D shapes with Convolutional Networks”, Graphical Models, Vol. 0, pp. 1–8, 2016.
[33]. Sreekumari, S. Shriram and V. Vaidya, “Breast Lesion Detection and Characterization with 3D features”, Global Research, Bengaluru, India.
[34]. J. Zhou, Z. Yang, Z. Weiwei, Z. Jingwen, H. Na, D. Yijie and Y. WANG , “Breast Lesions Evaluated by Color-Coded Acoustic Radiation Force Impulse (ARFI) Imaging”, Ultrasound in Medical & Biology, pp. 1–9, 2016.
[35]. K. Xu, G. Kim, Q. Huang and E. Kalogerakis, “Data-Driven Shape Analysis and Processing”, Computer Graphics forum, Vol. 36, 2017.
[36]. VIDYA AND S. MATHEW, “AN ACCURATE METHOD OF BREAST CANCER DETECTION FROM ULTRA SOUND IMAGES USING PROBABILISTIC FUZZY CLUSTERING ALGORITHM”, IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORKS, 2016.
[37]. J. Krzysztof, S. Wolfson, Y. Shen, G. Kim, L. Moy, Cho, “High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks”, IEEE.
[38]. T. SenthilSelvi1 and R. Parimala, “Improving Clustering Accuracy using Feature Extraction Method”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue. 2, pp.15-19, 2018.
[39]. Sherlin and D. Murugan, “A Case Study on Brain Tumor Segmentation Using Content based Imaging”, International Journal of Scientific Research in Network Security and Communication, Vol. 6, Issue. 3, pp. 1-5, 2018.
Citation
P.Kaur, G. Singh, P. Kaur, "Improved cancer detection in mammogram images using automated Deep Learning Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1528-1539, 2018.
Management of Node in VANET by Shifting The Position of Road Side Units
Research Paper | Journal Paper
Vol.6 , Issue.6 , pp.1540-1544, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15401544
Abstract
Vehicular Ad hoc Network (VANETs) is a sub-class network of Mobile Ad hoc Network (MANETs). It has similar behavior as MANETs but different in mobility of nodes and their nodes speed. The mobility of nodes in VANETs organized in fixed pattern and speed of nodes is very high. Basically here VANETs vehicles can communicate to other vehicles directly or via intermediate fixed architectures. Most of time on highway or rural area the density of vehicles varies a lot and if any vehicle wants communicate with other vehicle directly may faces many problems. To overcome these problems the intermediate infrastructure needs to pay a very important role. In this paper we analysis the performance of three different placement strategies of infrastructure based relays and also find cost effective separation of infrastructures intermediate RSUs using NS2 Simulator.
Key-Words / Index Term
Vehicular Ad-hoc Network (VANET), AODV, IEEE 802.11, OBU, RSU
References
[1] Hassan, F, Shah M, Shaikh FK. Information Propagation in Rural Ambience: A VANET Perspective. 1st International Conference on Modern Communication & Computing Technologies. , 26 Feb. 2014
[2] Manvi, S.S., Kakkasageri, M.S., Mahapurush, C.V., Performance Analysis of AODV, DSR, and Swarm Intel-ligence Routing Protocols In Vehicular Ad hoc Network Environment In International conference on future Computer and Communication., pp. 21-25, April. 2009.
[3] Bernsen, J. Manivannan, D., Routing Protocols for Vehicular Ad Hoc Networks That Ensure Quality of Service In the fourth international confernce on Wireless and Mobile Communications., pp.1-6, Aug. 2008.
[4] Wex, P. Breuer, J. Held, A. Leinmuller, T. Delgrossi, L., Trust Issues for Vehicular Ad Hoc Networks IEEE, VTC Spring 2008., pp. 2800-2804, May.2008.
[5] T. Taleb, E. Sakhaee, A. Jamalipour, K. Hashimoto, N. Kato, and Y. Nemoto, A stable routing protocol to support its services in vanet networks IEEE Transactions on Vehicular Technology, vol. 56, no. 6, pp. 33373347, November 2007.
[6] Pierpaolo Salvo, Francesca Cuomo, Andrea Baiocchi and Andrea Bragagnini Road Side Unit coverage extension for data dissemination in VANET s wireless on-demand network Systems and Services(WONS), 2012 9th Annual Conference.
[7] C. Perkins and E. Royer, Ad-hoc on-demand Distance Vector Routing, Proc. 2nd IEEE Wksp. Mobile Comp. Sys. App., Feb. 1999, pp. 90100.
[8] T. Osafune, L. Lin, and M. Lenardi. Multi-hop vehic-ular broadcast (MHVB), ITST, 2006.
[9] M. Mariyasagayam, T. Osafune, and M. Lenardi, Enhanced multi-hop vehicular broadcast (MHVB) for active safety applications, in 7th international Conference on ITS Telecommunications (ITST07), June 2007, pp. 16.
[10] G. Korkmaz, E. Ekici, F. O zguner, Black-burst-based multihop broadcast protocols for vehicular networks, IEEE Transactions on Vehicular Technology, vol. 56, no. 5, pp. 31593167, 2007.
[11] B. Karp, H.T. Kung, GPSR: greedy perimeter state-less routing for wireless networks, Mobile Computing and Networking, 2000, pp. 243254.
[12] V. Namboodiri, L. Gao, Prediction-based routing for vehicular ad hoc networks, IEEE Transactions on Vehicular Technology 56 (4) (2007), p.2.
[13] M.-F. Jhang, W. Liao, On Cooperative and Oppor-tunistic Channel Access for Vehicle to Roadside (V2R) Communications, Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE Nov. 30 2008-Dec. 4 2008, pp.15. 521.
[14] J. Zhang, Q. Zhang, W. Jia, VC-MAC: A Cooperative MAC Protocol in Vehicular Networks, IEEE Transactions on Vehicular Technology, vol.1561 - 1571, 2009.
[15] C.-W. Yi, Y.-T. Chuang, H.-H. Yeh, Y.-C. Tseng, P.-C. Liu, Street cast: An Urban Broadcast Protocol for Vehicular Ad-Hoc Networks, Vehicular Technology Conference, 2010 IEEE 71st,pp 1 5.
[16] S. Wan, J. Tang, and R. S. Wolff, Reliable Routing for Roadside to Vehicle Communications in Rural Areas, The 2008 IEEE International Conference on Communications (IEEE ICC), May 2008.
Citation
Jyoti Pandey,Ravi Verma, "Management of Node in VANET by Shifting The Position of Road Side Units," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1540-1544, 2018.
Analysis Survey and Comparison of Cryptography Algorithms in Wsn
Survey Paper | Journal Paper
Vol.6 , Issue.6 , pp.1545-1550, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15451550
Abstract
Wireless Sensor Networks (WSN) is spreading its root nearly in every field and because of its widespread growth it is vulnerable to many serious attacks. For such reason, security demands increases in WSNs. For achieving high level of security, cryptography plays an important role. Though there exist so many symmetric and asymmetric cryptography algorithms, but they have some drawbacks in terms of key sizes, power consumption, encryption time, etc. in them. So the main objective of this paper is to analyse and compare the asymmetric cryptography algorithm (RSA and ECC) and symmetric cryptography algorithm (AES) with proper implementation. These algorithms are analysed on the basis of total execution time of the algorithm, power & energy consumption and estimated memory requirement for the algorithm.
Key-Words / Index Term
Cryptography, symmetric algorithm, asymmetric algorithms
References
[1] K. A. Shim “A Survey of Public-Key Cryptographic Primitives in Wireless Sensor Networks”, IEEE Communications Surveys & Tutorials, 2012.
[2] K. Shankar, P. Eswaran “Sharing a Secret Image with Encapsulated Shares in Visual Cryptography”, 4th International Conference on Eco-friendly Computing and Communication Systems, ICECCS, Elsevier, Procedia Computer Science 70 ( 2015 ) 462 – 468.
[3] H. Fouchal, P. Hunel, C. Ramassamy, “Towards efficient deployment of wireless sensor networks”, Secure Communication Networks, 2014.
[4] T. Hayajneh, R. Doomun, G. Almashaqbeh, B. J. Mohd, “An energy-efficient and security aware route selection protocol for wireless sensor networks”. Secure Communication Networks, 2013
[5] N. Lasla, A. Derhab, A. Ouadjaout, M. Bagaa, Y. Challal, “SMART: secure multi-paths routing for wireless sensor networks”, In: Ad-hoc, Mobile, and Wireless Networks, Lecture Notes in Computer Science, vol. 8487, pp. 332–345, 2014.
[6] F. Farouk, R. Rizk, F. W. Zaki, “Multi-level stable and energy efficient clustering protocol in heterogeneous wireless sensor network”. IET Wireless Sensor System 4 (4), 159–169, 2014.
[7] N. Yu, L. Zhang, Y. Ren, “A novel D–S based secure localization algorithm for wireless sensor networks” Secure. Communication. Networks, 2013.
[8] A. Mary, E.A., Geetha, E. Kannan, “A novel hybrid key management scheme for establishing secure communication in wireless sensor networks”, Wirel. Pers. Commun, 2015.
[9] R. Rawya, A. Yasmin, “Two-phase hybrid cryptography algorithm for wireless sensor networks”, Elsevier, Journal of Electrical Systems and Information Technology 2 (2015) 296–313.
[10] S. Faye, J. F. Myoupo, “Secure and energy-efficient geocast protocols for wireless sensor networks based on a hierarchical clustered structure”, International Journal of Network Security, 15 (1), 121–130, 2013.
[11] F. Amin, A. H. Jahangir, and H. Rasifard, “Analysis of Public-Key Cryptography for Wireless Sensor Networks Security”, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:2, No:5, 2008.
[12] R. Masram, V. Shahare, J. Abraham, R. Moona “Analysis and Comparison of Symmetric Key Cryptographic Algorithms Based on Various File Features”, International Journal of Network Security & Its Applications (IJNSA), Vol.6, No.4, July 2014, DOI: 10.5121/ijnsa.2014.6404 43.
[13] G. S. Quirino, A. R. L. Ribeiro and E. D. Moreno, “Asymmetric Encryption in Wireless Sensor Networks”, Chapter 10, book: Wireless Sensor Networks - Technology and Protocols.
[14] S. P. Singh, and R. Maini, “Comparison Of Data Encryption Algorithms”, International Journal of Computer Science and Communication Vol. 2, No. 1, January-June 2011, pp. 125-127.
[15] S. U. Rehman*, M. Bilal**, B. Ahmad**, K. M. Yahya**, A. Ullah**, O. U. Rehman*, “Comparison Based Analysis of Different Cryptographic and Encryption Techniques Using Message Authentication Code (MAC) in Wireless Sensor Networks (WSN)”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, January 2012.
[16] A. Faquih, P. Kadam, Z. Saquib, “Cryptographic Techniques for Wireless Sensor Networks: A Survey”, IEEE Bombay Section Symposium (IBSS), 2015.
[17] N. Jirwan, A. Singh, S. Vijay, “Review and Analysis of Cryptography Techniques”, International Journal of Scientific & Engineering Research Volume 4, Issue3, March2013.
[18] R. Bhanot and R. Hans, “A Review and Comparative Analysis of Various Encryption Algorithms”, International Journal of Security and Its Applications, Vol. 9, No. 4 (2015), pp. 289-306.
[19] G. D. Evangelidis, E. Z. Psarakis, "Parametric Image Alignment using Enhanced Correlation Coefficient", IEEE Trans. on PAMI, vol.30, no.10, 2008.
Citation
Pooja, R.K.Chauhan, "Analysis Survey and Comparison of Cryptography Algorithms in Wsn," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1545-1550, 2018.
Promoting Genuine Products Through Textual Review Rating in Collaboration With Social Networking
Research Paper | Journal Paper
Vol.6 , Issue.6 , pp.1551-1555, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15511555
Abstract
In this technology driven world, we get an opportunity to share our views regarding different products by providing our valuable reviews. Through these reviews we a get a chance to extend our help in developing a better society by promoting genuine products into the market there by eliminating many false predictions. Through different review websites we get a chance to implore our ideas on different products. But we get information overloading problem. How to mine valuable information and provide the users with accurate data is a hectic task. Traditional Recommender system uses several factors such as user’s purchase record, product reputation and so on. But the main problem in this system is the rating is generated on whole. There is a chance of considering wrong reviews also. In order to avoid these problems and provide the users with desired information, a new system was developed which is user friendly to the users where the rating is generated from the textual reviews provided by the users individually. When the rating is generated the user can share that whole data to his/her Facebook timeline so that the genuine products can be brought into limelight. By this project an attempt is made to build a better society by promoting genuine products into the market.
Key-Words / Index Term
Recommender System, rating, Facebook, Genuine Products
References
[1]. JAVA Technologies
[2]. Java Script Programming by Yehuda Shiran
[3]. HTML and CSS by John Buckett
[4]. J2EE Professional by Shadab siddiqui
[5]. JAVA server pages by Larne Pekowsley
[6]. Php,mysql by O’ Reilly
[7]. HTML
[8]. HTML Black Book by Holzner
[9]. P5.js
[10]. AFINN-111 sentiment dictionary
[11]. Pressman RS, “Software Engineering”, 7th Edition Published by McGraw Hill Education.
[12]. Richard E Fairly, “Software engineering concepts”, 1st Edition Published by McGraw Hill Education.
[13]. Grady Booch, “The Unified Modelling Language Reference Manual”, 2nd Edition Published by Pearson India.
[14]. Steven Holzner, “The Complete Reference PHP”, 1st Edition Published by McGraw Hill Education.
[15]. Kognet Learning Solutions Inc.,”HTML5 BLACK BOOK”, Published by Dreamtech Press.
[16]. https://www.tutorialspoint.com/r/index.htm
[17]. http://stackoverflow.com/
[18]. https://www.datacamp.com/courses/free-introduction-to-r
Citation
T.Bhargavi, J.Niranjani, "Promoting Genuine Products Through Textual Review Rating in Collaboration With Social Networking," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1551-1555, 2018.
Trust Management in Web Services for Prediction and Selection based on Trust Evaluation Model
Research Paper | Journal Paper
Vol.6 , Issue.6 , pp.1556-1566, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15561566
Abstract
Trust, usually deal as a collaborative term for security and reliability, is also known as base parameter for defining quality of services (web services , cloud services) in cloud based environment in current research trends. In the early service communications, quality of web service was published by service provider which may be unreliable and not credible. For better verification, trust , a more refine parameters is calculated for predicting quality over all possible parameters defined in QWS dataset for evaluating trust of clustered web services. Here, We propose a Trust Management System, in which We implement security and trust policies using WS-Security and WS-Trust. Also, we had included Trust Evaluation Model (TEM) to evaluate the upcoming request /response on cloud based web server and calculate trust values. using evidence and Dempster - Shefer (D-S) rules which will help in updating the QWS dataset in clustered view of trusted web services of similar type of application.
Key-Words / Index Term
(D-S) rules, respective trust values, Cloud Trust, WS-Security; WS- Trust
References
[1] Gaurav Raj, Muhammad Sarfaraz, "Survey on Trust establishment in Cloud computing”, Confluence- The Next Generation Information Technology Summit, Amity University, Noida, 25-26 Sept. 2014, IEEE
[2] Weiliang Zhao, Vijay Varadharajan, "Trust Management for Web Services", IEEE International Conference on Web Services, (pp.818-821),Beijing, (2008).
[3] Ayesha Kanwal. , Rahat Masood. and Muhammad Awais Shibli., “Evaluation and Establishment of Trust in Cloud Federation”, 8th International Conference on Ubiquitous Information Management and Communication, Article No. 12, pp. 1-5, 2014, ISBN: 978-1-4503-2644-5 doi>10.1145/2557977.2558023
[4] Xiaonian Wu, Runlian Zhang, Bing Zeng, Shengyuan Zhou, "A Trust Evaluation Model for Cloud Computing " Elsevier Procedia Computer Science, Information Technology and Quantitative Management, Science Direct, Volume 17, 2013, Pages 1170-1177
[5] E. Chang, T. Dillon, F. K. Hussain"Trust and Reputation for Service-Oriented Environments", Wiley, 2006.
[6] Tyrone Grandison and Morris Sloman, "A Survey of Trust in Internet Application", IEEE Communications Surveys and Tutorials, Fourth Quarter 2000.
[7] R.Joseph Manoj and Dr.A.Chandrasekar, "A Literature Review on Trust Management in Web Services Access Control", "International Journal on Web Service Computing", Vol.4, No.3, September 2013
[8] M. Blaze, J. Feigenbaum, and J. Lacy, "Decentralized Trust Management", IEEE Symposium on Security and Privacy, 1996. Proceedings, ISSN: 1081-6011, Oakland, CA, USA, USA , 1996.
[9] Rich Mogull, J. Arlen, F. Gilbert, A. Lane, D. Mortman, G. Peterson, M. Rothman, “Security Guidance for Critical Areas of Focus in Cloud Computing”, Security Guidance v4.0 © Copyright 2017, Cloud Security Alliance.
[10] National Institute of Standard and Technology, “NIST Cloud Computing Standards Roadmap, NIST Special Publication 500-291,Version 2, (Supersedes Version 1.0, July 2011)" , NIST Cloud Computing Standards Roadmap Working Group , July 2013
[11] Ahmed Taha, Salman Manzoor, Neeraj Suri, "SLA-Based Service Selection for Multi-Cloud Environments", Edge Computing (EDGE) 2017 IEEE International Conference on, pp. 65-72, 2017.
[12] Loubna Mekouar, Youssef Iraqi, "TrustWS: A Trust Management System forWeb Services", Conference: International Symposium on Web Services, At Dubai, UAE, 2010
[13] Jagpreet Sidhu, Sarbjeet Singh, "Design and Comparative Analysis of MCDM-based Multi-dimensional Trust Evaluation Schemes for Determining Trustworthiness of Cloud Service Providers", Journal of Grid Computing, pp. , 2017, ISSN 1570-7873.
[14] Mohammad Mehedi Hassan, Mohammad Abdullah-Al-Wadud, Ahmad Almogren, SK Md. Mizanur Rahman, Abdulhameed Alelaiwi, Atif Alamri, Md. Abdul Hamid, "QoS and trust-aware coalition formation game in data-intensive cloud federations", Concurrency and Computation: Practice and Experience, pp. n/a, 2015, ISSN 15320626.
[15] Talal H, Quan Z. “Trust as a Service: A Framework for Trust Management in Cloud Environments”, ACM 12th international conference on Web information system engineering, pp. 314–321, 2011.
[16] M. D. Priya, A. Lavanya, "Intrusion Detection System Using Raspberry Pi Honeypot in Network Security", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4 Issue 3, pp. 41-45, January-February 2018.
[17] E. Manigandan, C. Kalaiarasi, E. Manigandan, Prof. C. Kalaiarasi, "Cryptography in Cloud Computing : A Basic Approach to confirm Security in Cloud", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4 Issue 3, pp. 58-63, January-February 2018.
Citation
G. Raj, M. Mahajan, D. Singh, "Trust Management in Web Services for Prediction and Selection based on Trust Evaluation Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1556-1566, 2018.
Sum Divisor Cordial Labeling of Ring Sum of a Graph With Star Graph
Survey Paper | Journal Paper
Vol.6 , Issue.6 , pp.1567-1573, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15671573
Abstract
A sum divisor cordial labeling of a graph with vertex set is a bijection from to such that an edge is assigned the label 1 if 2 divides and 0 otherwise, then number of edges labeled with 0 and the number of edges labeled with 1 differ by at most 1. A graph with a sum divisor cordial labeling is called a sum divisor cordial graph. In this paper, we have derived sum divisor cordial labeling of ringsum of some graphs with star graph K1,n.
Key-Words / Index Term
Sum divisor cordial labeling, ringsum of two graphs
References
[1] D. G. Adalja and G. V. Ghodasara, “Some New Sum Divisor Cordial Graphs”, International Journal of Applied Graph Theory, Vol. 2, No.1, pp.19-33, 2018 .
[2] D. M. Burton, “Elementary Number Theory”, Brown Publishers, Second Edition, 1990.
[3] J. A. Gallian, “A Dynamic Survey of Graph Labeling”, The Electronic Journal of Combinatorics, 20, 2017, # DS6.
[4] G. V. Ghodasara and D. G. Adalja, “Divisor Cordial Labeling in Context of Ring Sum of Graphs”, International Journal of Mathematics and Soft Computing, Vol.7, No.1, pp. 23-3, 2017.
[5] J. Gross and J. Yellen, “Graph Theory and Its Applications”, CRC Press, 1999. Indian Acad. Math., 27, 2, pp.373-390, 2005.
[6] A. Lourdusamy and F. Patrick, “Sum Divisor Cordial Labeling For Star And Ladder Related Graphs”, Proyecciones Journal of Mathematics, Vol.35, No.4, pp. 437-455, 2016 .
[7] R. Varatharajan and S. Navanaeethakrishnan and K. Nagarajan, “Divisor Cordial Graphs”, International J. Math. Combin., Vol.4, pp.15-25, 2011.
[8] R. Varatharajan, S. Navanaeethakrishnan and K. Nagarajan, “Special Classes of Divisor Cordial Graphs”, International Mathematical Forum, Vol.7, No. 35, pp. 1737-1749, 2012 .
Citation
D.G. Adalja, G. V. ghodasara, "Sum Divisor Cordial Labeling of Ring Sum of a Graph With Star Graph," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1567-1573, 2018.
Quality Controlled process framework for Procurement and Purchase System
Research Paper | Journal Paper
Vol.6 , Issue.6 , pp.1574-1578, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15741578
Abstract
For public sector units, NGO/ NPO and various governance systems, the purchasing function is often far down the line where still the manual / conventional way of processing is in practice. The financial functions of these organizations can save large amount of time and value of money with appropriate techniques. Efficient Purchase system in the area is central to this article, that is often overlooked for automatisation of a typical UTD system and has the largest potential payback scope in purchasing.
Key-Words / Index Term
NGO , NPO , UTD , MIS , MRP
References
[1]. Gaither, N., & Frazier, G. (2002). Operations management. South-Western/Thomson Learning.
[2]. Bedi, K. (2004). Production and Operations Management: Concepts and Applications. OXFORD university press.
[3]. Kanishka, B. (2006). Quality Management.
[4]. Mathai, M. P., Raikwar, S., Sagore, R., & Prajapat, S. (2015). Challenges Associated With Automation of NPO: Requirement Engineering Phase. In Proceedings of the 21st International Conference on Industrial Engineering and Engineering Management 2014 (pp. 665-669). Atlantis Press, Paris.
[5]. Udpa, S. R. (1992). Quality Circles: Progress Through Participation. Tata McGraw-Hill.
[6]. Acronym Used in the article: NGO ( Non Government Organization) , NPO ( Non profit organisation ), UTD ( University Teaching Departments ), MIS (Management Information System ) MRP (Material Resource Planning)
Citation
Ravindra Yadav, Shaligram Prajapat, Manju Suchdeo, A.K. Sapre, "Quality Controlled process framework for Procurement and Purchase System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1574-1578, 2018.
Comparative Analysis of STATCOM and SVC for Reactive Power Enhancement in A Long Transmission Line
Research Paper | Journal Paper
Vol.6 , Issue.6 , pp.1579-1582, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i6.15791582
Abstract
In recent power system scenario, the main concern is about the maximum power transfer capability from generating station to the distribution station. But between these, the transmission system i.e. transmission of power from generating station to distribution grid is the most vital thing. So in order to have a reliable and quality power transmission FACTS controllers (Flexible AC Transmission System) are introduced in the transmission system. FACTS are an emerging technology which motivates towards power quality improvement and increased control flexibility of power system. Generally FACTS controllers are of series type, shunt type and combined series-series and combined series-shunt type. In this paper a shunt type controllers i.e. STATCOM (Static Synchronous Compensator) and a SVC (Static VAR Compensator) have been considered. Here the variation of voltage and reactive power by the introduction of STATCOM & SCV at middle of long transmission line has been investigated. All these analysis is carried out by the mat lab simulink models of STATCOM & SVC. This comparison output revels that STATCOM performs better than SVC in Volt /VAR control.
Key-Words / Index Term
FACTS, power quality, STATCOM, SVC
References
[1]. Shaswat Chirantam, Ramakanta Jena, Dr.S.C.Swain, Dr.P.C.Panda, “Comparative analysis of STATCOM and TCSC FACTS controller for power profile enhancement in a long transmission line” 2017 2nd International Conference on Communication and Electronics Systems (ICCES).
[2]. M.Mandavian, M.Janghorbani, E.Ganji, I.Eshaghpour, “Voltage Regulation in Transmission Line by Shunt Flexible AC Transmission System Devices” 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).
[3]. R.M. Malkar, V.B.Magdum, “Recent Trends in Real and Reactive Power Flow Control with SVC and STATCOM Controller for Transmission Line” International Journal of Application or Innovation in Engineering & Management (IJAIEM), ISSN 2319 - 4847, Volume 5, Issue 3, March 2016.
[4]. Karisma Hawisa, Rajab Ibsaim, Amer Daeri, "Voltage Instability Remedy using FACTS,TCSC Compensation First A’’,17th International conference on sciences &Techniques of Automatic control and computer engineering- STA’2016, Sousse, Tunisia, December 19-21,2016.
[5]. Singh, Mukesh Kumar, and Nitin Saxena. "Performance Analysis and Comparison of Various FACTS Devices in Power System." July 2013, ISSN No.(Online): 2277 2626 (2013).
[6]. Karthikeyan , M., and P. Ajay-D -Vimalraj "Optimal location of shunt FACTS devices for power flow control." Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on. IEEE, 2011.
[7]. Kumar, Prashant "Application of FACT devices for voltage stability in a power system." Proc. IEEE 9th Int. Conf. Intelligent Systems and Control, Coimbatore, India. 2015.
[8]. Aswin R, Jo Joy "Comparison between STATCOM & TCSC on static voltage stability using MLP Index’’, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering,vol.2,special issue 1,December 2013.
[9]. S Bagchi, R Bhaduri, P N Das, S Banerjee , "Analysis of Power Transfer Capability of a Long Transmission Line using FACTS Devices’’,2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[10]. T. Fetouh, M.S. Zaky, "New approach to design SVC-based stabiliser using genetic algorithm and rough set theory", IET Generation, Transmission and Distribution, Vol. 11, No. 2, pp. 372-382, Jan. 2017.
[11]. Hridya K. R., Mini V., R. Visakhan, Asha Anu Kurian, "Analysis of voltage stability enhancement of a grid and loss reduction using series FACIS controllers," 2015 IEEE Tnternational Conference on Power, instrumentation, Control and Computing (PiCC), 2015, pp. 1-5.
[12]. S.A.Karrare, V. M. Harne,‖ Simulation of D-STATCOM in Power Distribution System for Power Quality Enhancement using MATLAB Simulink Tool‖, International Journal of Electronics, Electrical and Computational System, Volume 6, Issue 4, April 2017.
[13]. H. Myneni, G. S. Kumar and D. Sreenivasarao, "Power quality enhancement by current controlled Voltage Source Inverter based DSTATCOM for load variations," 2015 IEEE IAS Joint Industrial and Commercial Power Systems / Petroleum and Chemical Industry Conference (ICPSPCIC), Hyderabad, 2015, pp. 182-188.
[14]. S. R. Arya, B. Singh, R. Niwas, A. Chandra and K. Al-Haddad, "Power Quality Enhancement Using DSTATCOM in Distributed Power Generation System," in IEEE Transactions on Industry Applications, vol. 52, no. 6, pp. 5203-5212, Nov.-Dec. 2016.
[15]. M. Mangaraj, T. Penthia and A. K. Panda, "Power quality improvement by a 3-phase 4-leg supercapacitor based DSTATCOM," 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), Varanasi, 2016, pp. 91-97.
[16]. S. Singhai, M. N. Ansari and M. Jain, "Application of DSTATCOM for power quality improvement using isolated zig-zag/star transformer under varying consumer load," 2016 International Conference on Electrical Power and Energy Systems (ICEPES), Bhopal, 2016, pp. 270275.
Citation
Nunna Sushma, "Comparative Analysis of STATCOM and SVC for Reactive Power Enhancement in A Long Transmission Line," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1579-1582, 2018.