Volume-6 , Issue-1 , Jan - 2018 Go Back

In this paper, the differential equation of motion of the classical Helmholtz-Duffing oscillator, Van der Pol, Duffing oscillator and Duffing-Van der Pol oscillator equations have been solved analytically with the help of a new integral transform named Aboodh transform and homotopy perturbation method. By recasting the governing equations as nonlinear eigenvalue problems, we have obtained the excellent approximate analytical solution of the displacement and the relation between amplitude and angular frequency. We have also compared our results with exact numerical results graphically for few cases. Here, we have also demonstrated the sophistication and simplicity of this technique.

Aboodh Transform, Homotopy Perturbation Method, Helmholtz-Duffing Oscillator, Van der Pol, Duffing Oscillator, Duffing-Van der Pol Oscillator, Approximate Analytical Solution

[1] A.H. Nayfeh, D.T. Mook, “Nonlinear Oscillations”, John Willey and Sons., New York, 1979.
[2] N.N. Bogoliubov, Y.A. Mitropolsky, “Asymptotic Methods in the Theory of Nonlinear Oscillations” Hindustan Publishing Company, Delhi, Chap. I, 1961.
[3] V.P. Agrwal, H. Denman, “Weighted linearization technique for period approximation in large amplitude Nonlinear Oscillations”, J. Sound Vib., Vol.57, pp.463-473, 1985.
[4] S.H. Chen, Y.K. Cheung, S.L. Lau, “On perturbation procedure for limit cycle analysis”, Int. J. Nonlinera Mech., Vol.26, pp.125-133, 1991.
[5] Y.K. Cheung, S.H. Chen, S.L. Lau, “A modified Lindstedt-Pioncare method for certain strong nonlinear oscillations”, Int. J. Non-Linear Mech., Vol.26, pp.367-378, 1991.
[6] G. Adomain, “A review of the decomposition method in applied mathematics”, J. Math. Anal. and Appl., Vol.135, pp.501-544, 1998.
[7] G.L. Lau, “New research direction in singular perturbation theory, artificial parameter approach and inverse-perturbation technique”, In the proceedings of the 1997 National Conference on 7th Modern Mathematics and Mechanics, pp.47-53.
[8] A.F. Nikiforov, V.B. Uvarov, “Special functions of mathematical physics”, Birkhauser, Basel, 1988.
[9] P.K. Bera, T. Sil, “Exact solutions of Feinberg-Horodecki equation for Time dependent anharmonic oscillator”, Pramana-J. Phys., Vol.80, pp.31-39, 2013.
[10] J.H. He, “Homotopy perturbation technique”, Comp. Methods in Appl. Mech. and Engg., Vol.178, pp.257-262, 1999.
[11] J.H. He, “A coupling method of a homotopy technique and a perturbation technique for nonlinear problems”, Int. J. Non-Linear Mech., Vol.3, pp.37-43, 2000.
[12] J. Biazar, M. Eslami, “A new homotopy perturbation method for solving systems of partial differential equations”, Comp. and Math. with Appli., Vol.62, pp.225–234, 2011.
[13] A. Yildirim, “Homotopy perturbation method to obtain exact special solutions with solitary pattern for Boussinesq-like B(m,n) equations with fully nonlinear dispersion”, J. Math. Phys., Vol.50, pp.5–10, 2009.
[14] M. Gover, A.K. Tomer, “Comparison of Optimal Homotopy Asymptotic Method with Homotopy Perturbation Method of Twelfth Order Boundary Value Problems”, International Journal of Computer Sciences and Engineering, Vol.3, pp.2739-2747, 2011.
[15] P.K. Bera, T. Sil, “Homotopy perturbation method in quantum mechanical problems”, Applied Math. and Comp., Vol.219, pp. 3272–3278, 2012.
[16] K.S. Aboodh, “The New integral Transform Aboodh Transform”, Global Journal of Pure and Applied Mathematics, Vol.9, pp.35-43, 2013.
[17] K. Abdelilah, S. Hassan, M. Mohand, M. Abdelrahim, A.S.S. Muneer, “An application of the new integral transform in Cryptography”, Pure and Applied Mathematics Journal, Vol.5, pp.151-154, 2016.
[18] P.K. Bera, S.K. Das, P. Bera, “A Study of Nonlinear Vibration of Euler-Bernoulli Beams Using Coupling Between The Aboodh Transform And The Homotopy Perturbation Method”, International Journal of Computer Sciences and Engineering, Vol.5, pp.84-93, 2017.
[19] B. Bulbul, M. Sezer, “Numerical Solution of Duffing Equation by Using an Improved Taylor Matrix Method”, Article ID691614.
[20] M. Najafi, M. Moghimi, H. Massah, H. Khoramishad, M. Daemi, “On the Application of Adomian Decomposition Method and Oscillation Equations”, In the 9th International Conference on Applied Mathematics, Istanbul, Turkey, 2006.
[21] Gh. Asadi Cordshooli , A.R. Vahidi, “Silution of Duffing –Van der Pol Equation using Decomposition Method”, Adv. Studies Theor. Phys., Vol.5, pp.121-129, 2011.
[22] R.E. Mickens, “Iteration method solutions for conservative and limit-cycle x^(1/3) force oscillators”, Journal of Sound and Vibration, Vol.292, pp.964-968, 2006.
[23] I.S. Gradshteyn, I.S. Ryzhik, “Table of Integrals, Series and Products”, Academic Press, New York, 1980.
[24] H.Li Zhang, “Periodic solutions for some strongly nonlinear oscillations by He’s energy balance method”, Computer & Mathematics with Applications, Vol.58, pp.2480-2485, 2009.
[25] C.W. Lim, S.K. Lai, “Accrate higher order analytical approximate solutions to nonconservative nonlinear oscillators and applications to van der Pol damped oscillators”, International Journal of Mechanical Sciences, Vol.48, pp.483-892, 2006.
[26] J.J. Stoker, “Nonlinear Vibration in Mechanical and Electrical Systems”, Wiley, New York, 1992.

P.K. Bera, S.K. Das, P. Bera, "Applications of the Aboodh Transform and the Homotopy Perturbation Method to the Nonlinear Oscillators," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.1-10, 2018.

An important factor for any wireless communication system is estimation of its channel and channel parameters. In order to achieve good performance a communication receiver needs to know the impact of channel on received signal. This is called channel estimation. The motive of a channel estimation process is to minimize Mean Squared Error (MSE) between desired signal and received signal. Different channel estimation algorithm had been designed so as to achieve high performance. Using channel estimation algorithm impulse response of a channel and its behaviour can be approximated. By employing channel estimation techniques, coherent demodulation technique can be implemented at the receiver. In communication system for channel estimation a known signal sequence called pilot signals are inserted at specific location within the information signal. These symbol sequences allow receiver to extract channel attenuations and phase rotation estimates for each received symbol. By identifying channel parameters error in the received signal can be reduced. In this paper comparison of FPA optimization and without optimization with Rayleigh channel in 16, 32 and 64 bit modulation is performed.

optimization, FPA modulation, channel

[3] M. Irfan, N. Ahmad, H. A. Shah, A. U. Rabi, “PAPR Optimization of SC-FDMA Signal Using M-ARY Signalling”, IEEE 3rd International Conference on Computer, control & Communication (IC4), pp.1-4, 2013.
[4] Md. M. Rana, J. Kim, and W. K. Cho, “Performance Analysis of Sub-carrier Mapping in LTE Uplink Systems” IEEE 9th International Conference on Optical Internet, pp 1-3, 2010.
[5] Ishu, N. Kumar, “PAPR Reduction in Wavelet Based SC-FDMA Using PTS Scheme for LTE Uplink Transmission” IEEE International Conference on Advances in Computing, Communications and Informatics, pp. 1810 – 1814, 2014.
[6] R. Krishnamoorthy, N. S. Pradeep , “Forward Error Correction Code for MIMO-OFDM System in AWGN and Rayleigh Fading Channel”, International Journal of Computer Applications , Vol. 69, No.3, pp 8-13, 2013.
[7] M. F. Pervej, M. Z. I. Sarkar, T. K. Roy, M.M. Hasan, M. M. Rahman and S. K. Bain “Analysis of PAPR Reduction of DFT-SCFDMA System Using Different Sub-Carrier Mapping Schemes” IEEE 17th International conference on computer and Information Technology,pp.435-439, 2014.
[8] H. G. Yeh and H. M. Abdul “Hadamard SCFDMA- A Modified Uplink Transmission Scheme with Low PAPR and SER”, IEEE 9th Annual International Systems Conference, pp. 711 – 715, April 2015.
[9] G. Mathur, T. Ramya, Jagadish. M, “Performance Analysis of Single-Carrier FDMA in 2x1 MIMO Systems”, IEEE International Conference on Communication and Signal Processing, pp.1050-1053, 2014.
[10] Sanjana T and Suma M N, “Comparison of Channel Estimation and Equalization Techniques for OFDM Systems”, Circuits and Systems: An International Journal (CSIJ), Vol. 1, No. 1, pp 1-10, January 2014
[11] D. Vijyan, P. Singh, “Adaptive channel estimation for SC-FDMA system”, International Conference on Recent Advances in Sciences and Engineering (ICRASE) 2013, Vol. 3, pp 131-133, May 2013.
[12] H. Myungkim, D. Kim, T. K. Kim, G. H. Im, “Frequency Domain Channel Estimation for MIMO SC-FDMA Systems with CDM pilots”, IEE Journal of Communications and Networks, Volume 16, No. 4, pp 447-457, 2014.

Malook Singh, Jaget Singh, Sarvjit Singh, "Channel Estimation in SC-FDMA using Flower PollinationAlgorithm (FPA)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.11-17, 2018.

It is a difficult and complex task for a radiologist or clinical practitioner to segment, detect, and extract infected tumor area and classify the type of tumor from magnetic resonance (MR) images. This paper presents a PSO (Particle Swarm Optimization) based LLRBFNN (Local Linear Radial Basis Function Neural Network) model to classify and detect brain tumors into malignant (cancerous) and benign (noncancerous). In this paper we have used wavelet transform to improve the performance of MR image segmentation process and feature extraction. For the validation of the proposed PSO based LLRBFNN model, the machine learning approach support vector machine (SVM) and LMS (Least Mean Square) based LLRBFNN classifier also investigated. The research work follows the steps such as feature extraction out of which relevant features are considered for the research work. In the second step the features are fed as input to the proposed PSO based LLRBFNN Model for the classification task. In the third step the machine learning approach SVM and LMS based LLRBFNN has been applied for classification task and the results are compared. It is found that the proposed model takes less computational time than the SVM and LMS based LLRBFNN machine learning approach. In contrast to classification results the proposed model gives better classification results. Based on accuracy it is also noticed that the proposed model shows better performance in accuracy and quality analysis on MRI brain images.

SVM, Wavelet Transform, DWT, PSO, LLRBFNN, Brain tumour, Feature extraction

[1] Pauline John,Brain Tumor, “Classification Using Wavelet and Texture Based Neural Network”, International Journal of Scientific & Engineering Research Vol. 3, Issue 10, October-2012 1 ISSN 2229-5518.
[2] Carlos Arizmendi, Alfredo Vellido, Enrique Romero, “Binary Classification of Binary Tumors using a Discrete Wavelet Transform and Energy Criteria”, Second Latin American Symposium on Circuits and Systems (LASCAS), IEEE, pp.1-4, 2011
[3] Adrian Ion-Margineanu, Sofie Van Cauter, Diana M Sima, Frederik Maes, Stefaan W. Van Gool, Stefan Sunaert, Uwe Himmelreich and Sabine Van Huffel, “ Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients”, BioMed Research International, Article ID 842923, in press
[4] Kailash D.Kharat1, Pradyumna P.Kulkarni, M.B.Nagori , “Brain Tumor Classification Using Neural Network Based Methods”, International Journal of Computer Science and Informatics ISSN (PRINT): 2231 –5292, Vol-1, Issue-4, 2012
[5] Mohd Fauzi Bin Othman, Noramalina Bt Abdullah, “MRI Brain Classification using Support Vector Machine,” IEEE, 2011
[6] Mohd Fauzi Othman and Mohd Ariffanan Mohd Basri, “Probabilistic Neural Network for Brain Tumor Classification,” Second International Conference on Intelligent Systems, Modelling and Simulation. IEEE, ISMS.2011.32, 2011.
[7] S. Damodharan and D. Raghavan, “Combining tissue segmentation and neural network for brain tumor detection,” International Arab Journal of Information Technology, vol. 12, no. 1, pp. 42–52, 2015.
[8] M. Alfonse and A.-B. M. Salem, “An automatic classification of brain tumors through MRI using support vector machine,” Egyptian Computer Science Journal, Vol. 40, pp. 11–21, 2016.
[9] Q. Ain, M. A. Jaffar, and T.-S. Choi, “Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor,” Applied Soft Computing Journal, vol. 21, pp. 330– 340, 2014.
[10] E. Abdel-Maksoud, M. Elmogy, and R. Al-Awadi, “Brain tumor segmentation based on a hybrid clustering technique,” Egyptian Informatics Journal, vol. 16, no. 1, pp. 71–81, 2014.
[11] E. A. Zanaty, “Determination of gray matter (GM) and white matter (WM) volume in brain magnetic resonance images (MRI),” International Journal of Computer Applications, vol. 45, pp. 16–22, 2012
[12] T. Torheim, E. Malinen, K. Kvaal et al., “Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines,” IEEE Transactions on Medical Imaging, vol. 33, no. 8, pp. 1648–1656, 2014.
[13] J. Yao, J. Chen, and C. Chow, “Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform,” IEEE Journal on Selected Topics in Signal Processing, vol. 3, no. 1, pp. 94–100, 2009.
[14] P. Kumar and B. Vijayakumar, “Brain tumour Mr image segmentation and classification using by PCA and RBF kernel based support vector machine,” Middle-East Journal of Scientific Research, vol. 23, no. 9, pp. 2106–2116, 2015.
[15] W. Cui, Y. Wang, Y. Fan, Y. Feng, and T. Lei, “Localized FCM clustering with spatial information for medical image segmentation and bias field estimation,” International Journal of Biomedical Imaging, vol. 2013, Article ID 930301, 8 pages, 2013.
[16] G. Wang, J. Xu, Q. Dong, and Z. Pan, “Active contour model coupling with higher order diffusion for medical image segmentation,” International Journal of Biomedical Imaging, vol. 2014, Article ID 237648, 8 pages, 2014.
[17] A. Chaddad, “Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models,” International Journal of Biomedical Imaging, vol. 2015, Article ID 868031, 11 pages, 2015.
[18] S. N. Deepa and B. Arunadevi, “Extreme learning machine for classification of brain tumor in 3D MR images,” Informatologia, vol. 46, no. 2, pp. 111–121, 2013.
[19] B. Willmore, R. J. Prenger, M. C. Wu, and J. L. Gallant, “The Berkeley wavelet transform: a biologically inspired orthogonal wavelet transform,” Neural Computation, vol. 20, no. 6, pp. 1537– 1564, 2008.
[20] P. Remya Ravindran and K. P. Soman, “Berkeley wavelet transform based image watermarking,” in Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom ’09), pp. 357–359, IEEE, Kerala, India, October 2009.
[21] C.H. Lin and C.H. Wang, “Adaptive Wavelet Networks for Power-Quality Detection and Discrimination in a Power System”, IEEE Transactions on Power Delivery, Vol.21, No.3, July 2006.
[22] P. K. Nayak, S. Mishra, P. K. Dash, Ranjeeta Bisoi, “Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances”, Neural Computing & Application,Springer,2016. DOI 10.1007/s00521-015-2010-0
[23] T. McConaghy, H. Lung, E. Bosse, V. Vardan, “Classification Of Audio Radar Signals Using Radial Basis Function Neural Network”, IEEE Transactions on Inst .And Measurement, Vol. 52, No.6, pp.1771-17779, Dec. 2003
[24] Z. L. Gaing, “Wavelet-Based Neural Network For Power Disturbance Recognition And Classification”, IEEE Trans. Power Deilivery, Vol.19, No.4, pp.1560–1568, Oct 2004.
[25] N. Najkar, F. Razzazi, H. Sameti. “A Novel Approach To HMM-based Speech Recognition Systems Using Particle Swarm Optimization”, Elsevier Science, Mathematical And Computer Modeling, Vol.52, No.11, pp.2157-2165, Dec.2010

T.Gopi Krishna, K.V.N. Sunitha, S. Mishra, "Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.18-23, 2018.

Efficient methods to generate data cubes for On-Line Analytical Processing or OLAP are required for query processing and data analysis. OLAP involves multidimensional analysis of data and as well as selectively extracting and viewing data from different perspectives or points of view. In OLAP, a complex query can lead to many scans of the base relational database, leading to poor performance. This research paper provides an algorithm for the data cube generation suitable for OLAP systems in a fast way. The OLAP cube structure, based on aggregation operations and capable of fast retrieval of data, is extensively explored. The inverted index data structure, which is a mapping from content to index of the said content in any indexed data storage system, is used as an efficient tool for shell fragment computation. A study of efficiency and trade-offs involved in terms of processing complexity and storage space when compared to full cube computation are also provided here.

OLAP, data cube, cube shell, shell fragmentation, inverted index data structure, multidimensional analysis

[1] X. Li, J. Han, and H. Gonzalez, “High-dimensional OLAP: A minimal cubing approach”, In Proceedings of 30th International Conference on VLDB, pp. 528 – 539, 2004.
[2] Chaudhari, S., U. Dayal, “An overview of Data Warehousing and OLAP Technology”, ACM SIGMOD, pp. 65 – 74, 1997.
[3] S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan and S. Sarawagi. On the computation of multidimensional aggregates. In Proceedings of 22nd International Conference on VLDB, pp. 506 – 521, 1996.
[4] J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow and H. Pirahesh. “Data cube: A relational aggregation operator generalizing group-by, cross-tab and subtotals”, Data Mining and Knowledge Discovery, pp. 29 – 54, 1997.
[5] V. Harinarayan, A. Rajaraman, and J. D. Ullman, “Implementing data cubes efficiently”, ACM SIGMOD, pp. 205 – 216, 1996.
[6] K. Beyer and R. Ramakrishnan, “Bottom-up computation of sparse and iceberg cubes”, ACM SIGMOD, pp. 359 – 370, 1999.
[7] Y. Zhao, P. M. Deshpande, and J. F. Naughton, “An array-based algorithm for simultaneous multidimensional aggregates”, ACM SIGMOD, pp. 159 – 170, 1997.
[8] J. Han, J. Pei, G. Dong, and K. Wang, “Efficient computation of iceberg cubes with complex measures”, ACM SIGMOD, pp. 1 – 12, 2001.
[9] D. Xin, J. Han, X. Li, Z> Shao and B. W. Wah, “Computing iceberg cubes by top-down and bottom-up integration, The StarCubing Approach”. IEEE Transactions on Knowledge and Data Engineering, Vol. 19, Issue – 1, pp. 111 – 126, 2007.
[10] W. Wang, H. Lu, J. Feng, and J. X. Yu, “Condensed cube: An effective approach to reducing data cube size”, In Proceedings of 18th International Conference on Data Engineering, 2002.
[11] Y. Sismanis, N. Roussopoulos, A. Deligianannakis, and Y. Kotidis, “Dwarf: Shrinking the petacube”. ACM SIGMOD, pp. 464 – 475, 2002.
[12] L. V. S. Lakshmanan, J. Pei, and J. Han, “Quotient cube: How to summarize the semantics of a data cube”, In Proceedings of 28th International Conference on VLDB, pp. 778 – 789, 2002.
[13] D. Barbara and M. Sullivan, “Quasi-cubes: Exploiting approximation in multidimensional databases. CM SIGMOD, pp. 12 – 17, 1997.

D. Datta, A. Koley, A. Sarkar, S. Chatterjee, "Precomputing Shell Fragments for OLAP using Inverted Index Data Structure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.24-30, 2018.

The searching of coefficient and blocks in video compression is important phase. For the searching of blocks and coefficient used zig-zag and some other random searching technique for symmetry of blocks. In this paper used particle swarm optimization for the searching of block coefficient in domain and range of fractal transform function. The particle swarm optimization enhances the searching capacity of encoder for the process of compression. The particle swarm optimization decides two dual functions one for the mapping of symmetry and other is mapping of video encoded block. For the process of fractal transform encoding used H-V partition technique. H-V partition technique mapped the data in terms of range and domain for the processing of video compression. The H-V partition process creates multiple rectangle blocks the processing of video. The process of video compression methods simulated in MATLAB software and used some standard parameters for the evaluation of compression results.

Video Compression, Fractal Transform, H-V partitioning, MATLAB, MSE

[1] Rakhi Ashok Aswani and Prof.S.D.Kamble “Fractal Video Compression using Block Matching Motion Estimation - A Study”, IOSR, 2014, Pp 82-90.
[2] Ehsan Lotfi “A Novel Hybrid System Based on Fractal Coding for Soccer Retrieval from Video Database”, Majlesi Journal of Electrical Engineering, 2012, Pp 40-47.
[3] Nevart A. Minas and Faten H. Mohammed Sediq “Compression of an AVI Video File Using Fractal System”, IJCSI, 2013, Pp 182-189.
[4] R. E. Chaudhari and S. B. Dhok “Review of Fractal Transform based Image and Video Compression”, International Journal of Computer Applications, 2012, Pp 23-31.
[5] Vitor de Lima, William Robson Schwartz and HelioPedrini “3D Searchless Fractal Video Encoding at Low Bit Rates”, J Math Imaging Vis, 2013, Pp 239–250.
[6] Shiping Zhu, Liyun Li, Juqiang Chen and KamelBelloulata “An Efficient Fractal Video Sequences Codec with Multiviews”, Hindawi Publishing Corporation, 2013, Pp 1-9.
[7] Ravindra E. Chaudhari and Sanjay B. Dhok “Fractal Video Coding Using Fast Normalized Covariance Based Similarity Measure”, Hindawi Publishing Corporation, 2016, Pp 1-12.
[8] R. E. Chaudhari and S. B. Dhok “Fast Quadtree Based Normalized Cross Correlation Method for Fractal Video Compression using FFT”, JEET, 2016, Pp 709-718.
[9] MambayeNdiaye, Lisa Terranova, Romain Mallet, Guillaume Mabilleau and Daniel Chappard “Three-dimensional arrangement of b-tricalcium phosphate granules evaluated by microcomputed tomography and fractal analysis”, Acta Biomaterialia, 2015, Pp 404–411.
[10] Shailesh D. Kamble, Nileshsingh V. Thakur and Preeti R. Bajaj “A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding”, IJIMAI, 2016, Pp 91-104.
[11] Shiping Zhu, Liyun Li, Juqiang Chen and KamelBelloulata “An automatic region-based video sequence codec based on fractal compression”, Int. J. Electron. Commun. 2014, Pp 1-12.
[12] KamelBelloulata, Amina Belalia and Shiping Zhu “Object-based stereo video compression using fractals and shape-adaptive DCT”, Int. J. Electron. Commun., 2014, Pp 687–697.
[13] G.Sandhiya, M.Rajkumar and S.G.Vishnu Prasad “Hardware Implementation of 2D - DWT for Video Compression using Bit Parallel Architecture”, IJSETR, 2015, Pp 1211-1215.
[14] Dr. Fadhil Salman Abed and Iraq-Diyala-Jalawla “A Proposed Encoding and Hiding Text in an Image by using Fractal Image Compression”, IJCSE, 2013, Pp 1-13.
[15] Ryan Rey M. Daga and John Paul T. Yusiong “Image Compression Using Harmony Search Algorithm”, IJCSI, 2012, Pp 16-23.
[16] Tanudeep Kaur and Anupam Garg “Review of various Fractal Detection Techniques in X-Ray Images”, IJEDR, 2016, Pp 553-559.
[17] Chun-Ho Wu, W.H. Ip, C. Y. Chan and Kai Leung Yung “A flexible H.264/AVC compressed video watermarking scheme using particle swarm optimization based dither modulation”, International Journal of Electronics and Communications, 2011, Pp 29-36.
[18] Jean-Francois Connolly, Eric Granger and Robert Sabourin “Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition”, Pattern Recognition, 2012, Pp 2460-2477.
[19] J.F. Connolly, E. Granger and R. Sabourin “Incremental adaptation of fuzzy artmap neural networks for video-based face classification”, IEEE, 2009, Pp 1-8.
[20] D.O. Gorodnichy “Video-based framework for face recognition in video”, Conference on Computer and Robot Vision, 2005, Pp 325–344.
[21] Ran Ren, Madan mohan Manokar, Yaogang Shi and Baoyu Zheng “A Fast Block Matching Algorithm for Video Motion Estimation Based on Particle Swarm Optimization and Motion Prejudgment”, IEEE, 2006, Pp 1-5.
[22] Yuanyuan Sun, Rudan Xu, Lina Chen and Xiaopeng Hu “Image compression and encryption scheme using fractal dictionary and Julia set”, IET Image Processing, 2014, Pp 173-183.
[23] 22] D.Sophin Seeli and Dr.M.K.Jeyakumar “A Study on Fractal Image Compression using Soft Computing Techniques ”, IJCSI, 2012, Pp 1-11.

Shraddha Pandit, Piyush Kumar Shukla, Akhilesh Tiwari, "Enhance the Performance of Video Compression Based on Fractal H-V Partition Technique with Particle Swarm Optimization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.31-35, 2018.

Wireless Sensor Network (WSN) has tremendous growth due to low cost sensors and well planned techniques. Remote sensor systems are considered as expansive systems made of countless hubs with energy to detect the earth and discuss it with administrator. Event detection in remote systems is a method for handling inspected information specifically on the sensor hubs, accordingly, decreasing the requirement for multi-bounce correspondence with the b ase station of the system. This research has studied the existing work of event detection as well as health monitoring using WSN. We have studied various algorithms for route discovery like trust model and GA (Genetic Algorithm). Also studied about the impacts of health monitoring is done by ANN (Artificial Neural System). The assessment depends on QoS parameters, to be specific, Throughput, Energy Consumption, PDR (Packet Delivery Ratio).

WSN, WSN applications, Multiple Event detection, Multiple Event detection in Health Monitoring, Compressed Sensing, Genetic Algorithm, ANN

[1] Akyildiz, I.F., Weilian Su; Sankarasubramaniam, Y.; Cayirci, E., “A survey on sensor networks,” Communications Magazine, IEEE, vol.40, no.8, pp.102-114, Aug 2002.
[2] Vieira, M.A.M., Coelho, C.N., da Silva, D.C., Jr., da Mata, J.M., “Survey on wireless sensor network devices,” Emerging Technologies and Factory Automation, vol.1, pp.537-544 vol.1, 16-19 Sept. 2003
[3] Mark, J.W., “Evolution of wireless communications and networking,” in Quality of Service in Heterogeneous Wired/Wireless Networks, 2005. Second International Conference, pp. - 24-24 Aug. 2005.
[4] Introduction to Wireless Sensor Networks. http://www.worldscibooks.com/ compsci/etextbook/6288/6288-chap1pdf on October 8. 2008
[5] Othman, Mohd Fauzi, and Khairunnisa Shazali, “Wireless sensor network applications: A study in environment monitoring system,” Procedia Engineering, vol.41, 2012, pp.1204-1210.
[6] Mitchell C. Kerman,Wei Jiang,Alan F. Blumberg, Samuel E.Buttrey, “Event Detection Challenges ,Methods and Applications in Natural and Artificial Systems”ICCRTS, June 2009.
[7] Saoji, Apurva, and Poonam Lambhate, “Survey paper on event detection techniques in wireless sensor network for disaster recovery,” International Journal of Engineering Research and Technology, vol. 2. No. 12, ESRSA Publications, 2013.
[8] H. Zheng, F. Yang, X. Tian, X. Gan, X. Wang and S. Xiao, “Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 1, pp. 35-44, Jan. 2015.
[9] Marta, Mirela, Yinying Yang, and Mihaela Cardei, “Energy-efficient composite event detection in wireless sensor networks,” International Conference on Wireless Algorithms, Systems, and Applications,Springer Berlin Heidelberg, pp-94-103,2009.
[10] Malazi, HadiTabatabaee, Kamran Zamanifar, and Stefan Dulman, “Fed: Fuzzy event detection model for wireless sensor networks,” International Journal of Wireless & Mobile Networks, vol. 3, no. 6, 2011
[11] R. Madan, N. B. Mehta, A. F. Molisch and J. Zhang, “Energy-Efficient Decentralized Cooperative Routing in Wireless Networks,” IEEE Transactions on Automatic Control, vol. 54, no. 3, pp. 512-527, March 2009.
[12] Deepapriya, “Wireless Sensor Networks in Health Care Applications,” 4th international academic and research conference Manchester, vol.7,pp.21-31,2014.
[13] Rana Ejaz Ahmed, and Abdul Khaliq, “A Low-Overhead Checkpointing Protocol for Mobile Networks”, Electrical and Computer Engineering, IEEE CCECE 2003, pp. 1779-1782
[14] Xuqi Zhu ; Cong Ma ; Lin Zhang “Multiple event detection in wireless sensor networks using compressed sensing” IEEE 8 may 2011
[15] Tellez, Mauricio, Samy El-Tawab, and M. Hossain Heydari, “IoT security attacks using reverse engineering methods on WSN applications,” Internet of Things (WF-IoT), pp. 182-187. IEEE, 2016.
[16] Bader and M. S. Alouini, “Mobile Ad Hoc Networks in Bandwidth-Demanding Mission-Critical Applications: Practical Implementation Insights,” IEEE Access, vol. 5, no. , pp. 891-910, 2017.
[17] P. Suri, R. K. Bedi and S. K. Gupta, "Review paper on various clustering protocols used in Wireless Sensor Network (WSN)," 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), Visakhapatnam, 2015, pp. 1-4.
[18] http://ibeyonde.com/wordpress/multiple-multi-modal-sensor-event-detection-framework-2/
[19] M. Marin Perianu and P. Havinga, “D-FLER: a distributed fuzzy logic engine for rule-based wireless sensor networks”, In Proceedings of the 4th International Conference on Ubiquitous Computing Systems, LNCS, pp. 86-101, [retrieved: August, 2007
[20] . G. Wittenburg, N. Dziengel, S. Adler, Z. Kasmi, M. Ziegert and J. Schiller, “Cooperative event detection in wireless sensor networks,” IEEE Communications Magazine, vol. 50, no. 12, pp. 124-131, December 2012.
[21] C. T. Vu, R. A. Beyah, and Y. Li, “Composite event detection in wireless sensor network”, In Proceedings of the IEEE International Performance, Computing, and Communications Conference, pp. 264-271, [retrieved: April, 2007].
[22] Z. Han, J. Wu, J. Zhang, L. Liu and K. Tian, “A General Self-Organized Tree-Based Energy-Balance Routing Protocol for Wireless Sensor Network,” IEEE Transactions on Nuclear Science, vol. 61, no. 2, pp. 732-740, April 2014
[23] Deepapriya, “Wireless Sensor Networks in Health Care Applications,”2014, 4th international academic and research conference Manchester, UK,vol.7,pp.21-31
[24] H. Zheng, F. Yang, X. Tian, X. Gan, X. Wang and S. Xiao, “Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach,” in IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 1, pp. 35-44, Jan. 2015.
[25] G. Wittenburg, N. Dziengel, S. Adler, Z. Kasmi, M. Ziegert and J. Schiller, “Cooperative event detection in wireless sensor networks,” IEEE Communications Magazine, vol. 50, no. 12, pp. 124-131, December 2012.
[26] Jia Meng, Husheng Li and Zhu Han, “Sparse event detection in wireless sensor networks using compressive sensing,” 43rd Annual Conference on Information Sciences and Systems, Baltimore, MD, pp. 181-185,2009.
[27] Ngoc, Tam Vu, “Medical Applications of Wireless Networks,” Washington University, St. Louis, Student Reports on Recent Advances in Wireless and Mobile Networking, 2008.
[28] Banerjee, Torsha, Bin Xie, and Dharma P. Agrawal, “Fault tolerant multiple event detection in a wireless sensor network,” Journal of Parallel and Distributed Computing, vol.68, no. 9, pp.1222-1234, 2008.
[29]Rozenshtein,Polina,ArisAnagnostopoulos,Aristides Gionis, and Nikolaj , “Event detection in activity networks,” Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1176-1185, 2014.
[30] Hodge, V.J., O`Keefe, S., Weeks, M. and Moulds, A., “Wireless sensor networks for condition monitoring in the railway industry: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 3, pp.1088-1106, 2015.
[31] Pin Nie and Zhihua Jin, “Requirements, challenges and opportunities of wireless sensor networks in structural health monitoring,” 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), Beijing, pp. 1052-1057,2010.
[32] Pin Nie and Zhihua Jin, “Requirements, challenges and opportunities of wireless sensor networks in structural health monitoring,” 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), Beijing, pp. 1052-1057,2010.

Pooja Rani, Archna Gupta, Yashwant Singh, "Multiple Event Detection In Wireless Sensor Networks Using Compressed Sensing: Health Monitoring Perspective," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.36-41, 2018.

Software engineering is a collaborative effort. It involves processes, people and technology. As a massive action, it needs sound evaluation techniques to ensure its efficacy and appropriateness. No software engineering firm look anything lower than the most efficient model. A proper build up will then decide the prospects including the successful completion of the project. This study intends to develop similarity measures between intuitionistic fuzzy soft sets (IFSSs). The proposed model is applied to five software life cycle models (SLCMs) so as to select the most appropriate one.

Similarity measure, Software life cycle, Fuzzy decision making, Intuitionistic fuzzy soft sets

[1] B. Boehm, "A Spiral Model of Software Development and Enhancement", ACM SIGSOFT Software Engineering Notes, ACM, Vol. 11, No. 4, pp.14-24, August 1986.
[2] B. Efe, “An integrated fuzzy multi-criteria group decision-making approach for ERP system selection”, Applied Soft Computing, Vol. 38, pp.106 - 117, 2016.
[3] D. A. Wood, “Supplier selection for development of petroleum industry facilities, applying multi-criteria decision-making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting”, Journal of Natural Gas Science and Engineering, Vol. 28, pp. 594 - 612, 2016.
[4] D. Molodtsov, “Soft Set Theory-First Results”, Computers and Mathematics with Applications,Vol. 37, pp. 19-31, 1999.
[5] G. Buyukozkan, C. Kahraman, D. Ruan, “A fuzzy multi-criteria decision approach for software development strategy selection”, International Journal of General Systems, Vol. 33, No. 2-3, pp. 259-280, 2015.
[6] G. Buyukozkan and D. Ruan, “Evaluation of software development projects using a fuzzy multi-criteria decision approach”, Mathematics and Computers in Simulation, Vol. 77, pp. 464 - 475, 2008.
[7] G. Singh and A. Kaur, “An Improved Fuzzy Logic System for Handoff Controller Design”, International Journal of Computer Sciences and Engineering, Vol. 3, No. 7, pp. 1-5, 2015.
[8] H. Wu and X. Su, “Group Generalized Interval-Valued Intuitionistic Fuzzy Soft Sets and Their Applications in Decision Making”, Iranian Journal of Fuzzy Systems, Vol. 14, No. 1, pp. 1-21, 2017.
[9] K. Atanassov, “Intuitionistic Fuzzy sets. Fuzzy Sets and Systems”, Vol. 20, pp. 87 - 96, 1985.
[10] K. Forsberg and H. Mooz, "The Relationship of System Engineering to the Project Cycle", in Proceedings of the First Annual Symposium of National Council on System Engineering, pp. 57–65, October 1991.
[11] L. A. Zadeh, “Fuzzy Sets”, Information and Control, Vol. 8, pp. 338 - 353, 1965.
[12] L. Abdullah and N. Zulkifli, “Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management”, Expert Syst. Appl. Vol.42, No. 9, pp. 4397–4409, 2015.
[13] M. Hicdurmaz, “A Fuzzy Multi-Criteria Decision Making Approach to Software Life Cycle Model Selection”, 38th Euromicro Conference on Software Engineering and Advanced Applications, pp. 384-391, 2012.
[14] M. Saini and K. Kaur, "A Review of Open Source Software Development Life Cycle Models", International Journal of Software Engineering and Its Applications, Vol.8, No.3, pp.417-434, 2014.
[15] P. K. Maji, R. Biswas, A. R. Roy, “Fuzzy Soft Sets”, The Journal of Fuzzy Mathematics, Vol.9, No. 3, pp.589-602,2001.
[16] P. K. Maji, R. Biswas, A. R. Roy, “Intuitionistic Fuzzy Soft Sets”, The Journal of Fuzzy Mathematics, Vol. 9, No. 3, pp. 677-692, 2001.
[17] R. B. K. Dewar, G. A. Fisher Jr., E. Schonberg, R. Froelich, S. Bryant, C. F. Goss and M. Burke, "The NYU Ada Translator and Interpreter", ACM SIGPLAN Notices - Proceedings of the ACM-SIGPLAN Symposium on the Ada Programming Language, Vol. 15, No. 11, 194–201, November 1980.
[18] R. Kaur, Abhishek, S. Singh, “Inference of Gene Regulatory Network using Fuzzy Logic – A Review”, International Journal of Computer Sciences and Engineering, Vol. 4, No. 1, pp. 22–29, 2016.
[19] R. Kaura, S. Arora, P. C. Jhac and S. Madand, “Fuzzy Multi-criteria Approach for Component Selection of Fault-Tolerant Software System under Consensus Recovery Block Scheme”, Procedia Computer Science, Vol.45, pp. 842 – 851, 2015.
[20] S. J. Kalayathankal, J. T. Abraham, “A Fuzzy Decision-Making Approach to SLCM Selection”, International Journal of Civil Engineering and Technology, Vol. 8, No. 6, pp. 178-185, 2017.
[21] S. J. Kalayathankal, G.S. Singh, P. B. Vinodkumar, “Ordered Intuitionistic Fuzzy Soft Sets”, Journal of fuzzy mathematics, Vol.18, No. 4, pp. 991 - 998, 2010.
[22] S. J. Kalayathankal, J. T. Abraham, J. V. Kureethara, “A Modified Fuzzy Similarity Measure Decision Making Approach to SLCM Selection”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.32-39, 2017.
[23] S. J. Kalayathankal, J.T. Abraham, “A Fuzzy Soft Software Lifecycle Model”, International Journal of Civil Engineering and Technology, Vol. 8, No. 8, pp. 755-761, 2017.
[24] S. S. Ghuman, “SDLC Models- A Survey”, International Journal of Computer Science and Mobile Computing, Vol. 2, No. 1, pp. 33 – 38, January 2013
[25] T. L. Mien, “Design of Fuzzy Self-Tuning LQR Controller for Bus Active Suspension”, International Journal of Mechanical Engineering and Technology, Vol. 7, No. 6, pp. 493–501, 2016.
[26] T. Pressman, Software Engineering: A Practitioner`s Approach. Boston: McGraw Hill, pp. 41–42, 2010.
[27] W. Pedrycz, “System Modelling with Fuzzy Models: Fundamental Developments and Perspectives”, Iranian Journal of Fuzzy Systems, Vol. 13, No. 7, pp. 1-14,2016.
[28] W. Royce, "Managing the Development of Large Software Systems", Proceedings of IEEE WESCON, 26 (August): pp. 1–9, 1970.
[29] X. Wang, J. Wang and X. Chen, “Fuzzy Multi-Criteria Decision Making Method Based on Fuzzy Structured Element with Incomplete Weight Information”, Iranian Journal of Fuzzy Systems, Vol. 13, No. 2, pp. 1-17, 2016.

S. J. Kalayathankal, J. T. Abraham, J. V. Kureethara, "An Intuitionistic Fuzzy Soft Software Life Cycle Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.42-48, 2018.

Mobile ad hoc networks has been facing problems, when nodes are transmission in multi-hop nodes that time a contention and congestion due to node mobility, the network has unpredictable characteristics; its topology changes and signals strength fluctuates because of the broadcast nature of radio transmission. The broadcast operation is very important in MANETs and major challenges to reducing redundant, rebroadcast and broadcast latency problems. In this paper we comparative study of the DFCN and PEGSP algorithms for efficiently bandwidth utilize in multi-hop MANETs. Our validated simulation result shows that PEGSP algorithm is high reliability and high efficiency, channel`s bandwidth is efficiently utilized in wide area networks context of low speed of mobile nodes. The DFCN protocol operates well in high density and low density networks, it well in high speed mobile nodes. Nodes are high reachability within transmission range of the network, this protocol is advantages for energy conservation in multi-hop mobile ad hoc network.

Broadcast latency, Bandwidth, Reachability, Reliability and Efficiency

[1] Alireza KeshavarzHaddad, Vinay Ribeiro and Rudolf Riedi, “Broadcast Capacity in Multi-hop Wireless Networks”, Department of Electrical and Computer Engineering and Department of Statistics , Rice University, 6100 Main Street, Houston, TX 77005, USA, MOBICOM in 2006.
[2] M.Murugesan and A. Krishnan, “Reliable and Efficient Algorithm for Broadcasting in Asymmetric Mobile Ad hoc Networks”, K.S.R.College of Engineering Tiruchengode, Namakkal-637 215, International Journal of Computer Applications , Volume 4 – No.5, July 2010.
[3] Eroll L. Lloyd, “Broadcast Scheduling for TDMA in wireless multi hop networks”, Department of Computer Science and Information Sciences, University of Delaware,Hand book of wireless Networks and Mobile Computing Pages 347-368, 2002.
[4] Brian J. Wolf, Joseph L. Hammond, Daniel L.Noneaker and Harlan B. Russell, “Distributed Formation of Broadcast Transmission Schedules for Mobile Ad Hoc Networks”, Dept. of Electrical and Computer Engineering, Clemson University, Clemson SC, IEEE Volume-6, 2007.
[5] Luc Hogie, Marcin Seredynski and Frederic Guinand, “A Bandwidth-Efficient Broadcasting Protocol for Mobile Multi-hop Ad hoc Networks”, Pascal Bouvry University, duLuxembourg 6, rue R.Coudenhove Kalergi L-1359 Luxembourg, Polish Academy of Sciences Ordona 2102-237, Warsaw, Poland, University du Havre 25, rue Philippe Lebon 76600 Le Havre, France. Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL’06) Computer Society IEEE 2006.
[6] H.A Ali, M.S Saleh, M.A. Sadik, “An Efficient Reliable Broadcast Algorithm in Ad hoc Networks Based on Self-Pruning”, IJICIS, Vol.5, No. 1, July 2005.
[7] Ching-Chuan Chiang and Mario Gerla, “ Routing and Multicast in Multi hop Mobile Wireless Networks”, University of California, Los Angeles. In Proc.ICUPC’97.
[8] Oscar Somarriab jarquin, “Traffic Sensitive MAC Algorithms in Multi-hop Radio Networks”, Dept. of Digital System and Telecommunication,FEC National University of engineering (UNI) PO Box- 5595, Managua, Nicaragua. IEEE, in 2001.
[9] Marco Ajmone Marsan, Andrea Bianco, Emilio Leonardi, Fabio Neri and Antonio Nucci, “Multi hop Packet Scheduling in WDM/TDM Networks with Non negligible Transceiver Tuning Times”, Fellow IEEE, Member IEEE, Member IEEE,Member IEEE and Student Member IEEE, IEEE Transaction on Communications, VOL. 48, NO. 4, April 2000.
[10] L. Hogie, F. Guinand, and P. Bouvry, “A heuristic for efficient broadcasting in the Metropolitan ad hoc networks”, University du Havre, University du Luxembourg. University du Havre, 25, rue Philippe Lebon 76600 Le Havre, France. University du Luxembourg Campus Kirchberg 6, rue R.Coudenhove-Kalergi L-1359 Luxembourg. In KES, pages 727–733, 2004.
[11] Wei Lou and Jie Wu, “A Reliable Broadcast Algorithm with Selected Acknowledgements in Mobile Ad Hoc Networks”, Department of Computer Science and Engineering, Florida Atlantic University, Conference on Global Telecommunications Conference, 2003. GLOBECOM `03. IEEE, Volume: 6, January 2004.
[12] Pei-Kai Hung, Jang-Ping Sheu, and Chih Shun Hsu, “Scheduling of Broadcasts in Multi hop Wireless Networks”, Dept. of Computer Sci. and Info. Eng., National Central University, Chung-Li, 320 Taiwan, R.O.C., EW 2002 proceedings 2002.
[13] Shree Murthy and J. J. Garcia-Luna-Aceves,“An efficient routing protocol for wireless networks”, Computer Engineering, University of California, Santa Cruz, CA 95064, Baltzer Journals Mobile Networks and Applications, 1(2):183–197, 1996.
[14] Jie Wu and Fei Dai, “Broadcasting in Ad Hoc Networks Based on Self-Pruning”, Department of Computer Science and Engineering , Florida Atlantic University, Boca Raton, FL 33431, 2003 IEEE.
[15] GloMoSim: “Global Mobile Information Systems Simulation Library”, http://pcl.cs.ucla.edu/ Projects/Glomosim.
[16] Mario Gerla Lokesh Bajaj, Mineo Takai, Rajat Ahuja, Rajive Bagrodia, “GloMoSim: A Scalable Network Simulation Environment”, Technical Report 990027, University ofCalifornia, 13, 1999.

C.K. Samal, R. K. Choudhury, "Bandwidth Efficient Broadcast Protocols in MANETs: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.49-54, 2018.

Ongoing research on Unmanned Aerial Vehicles (UAVs) is aimed at determin¬ing the utility of UAVs for agricultural remote sensing applications. Aerial pho¬tography from unmanned aerial vehicles bridges the gap between ground-based observations and remotely sensed imagery from aerial and satellite platforms. In the present study, Crop area measurements are carried out by analysis of aerial imagery acquired through Low Altitude Remote Sensing (LARS) carried out using a Quadcopter UAV. The area per pixel or the Ground Separation Distance (GSD) is computed using the altitude measurements from a barometer. Image processing clus¬tering techniques are applied to classify non crop and crop area in the image extent. Further the physical crop area and non crop area is determined using GSD. In this study K-Means and Mean shift clustering techniques are used to classify crop and non crop area. Performance of determining crop area is compared for K-means and Mean shift techniques. The results indicate crop area classification using Meanshift outperforms classification using K-means.

Unmanned Aerial Vehicle, Low Altitude Remote Sensing, Crop Area Mapping, Image clustering

REFERENCES
[1] Colomina .I, Molina .P, “Unmanned aerial systems for photogrammetry and remote sensing”, A review ISPRS Journal of Photogrammetry and Remote Sensing 92, 7997 (2014)
[2] Swain Kishore Chandra, Jayasuriya, H.P.W. , “ Low Altitude Remote Sensing Applications for Diversified Farming Conditions in Developing Countries: An Overview”, AsiaPacific Journal of Rural Development. Vol. XVIII, No. 2, De¬cember 2008
[3] Herwitz S.R, Johnson L.F, Dunagan S.E, Higgins R.G, Sullivan D.V, Zheng J, Lob¬itz B.M , Leung .J, Gallmeyer B.A ,Aoyagi M, Slye R.E, Brass J.A, 2004, “Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support”, Computers and Electronics in Agriculture 44 4961, 2004
[4] Juan I. Corcoles, Jose F. Ortega, David Hernandez , Miguel A. Moreno, 2013, “Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle”, Biosystems Engineering 115(2013) 31-42, 2013
[5] Jose A. J. Berni, Pablo J. Zarco-Tejada, Surez Lola , and Fereres Elias, “Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle”, IEEE transactions on Geoscience and Remote Sensing , vol. 47, NO. 3, march 2009
[6] Suzuki Taro, Amano Yoshiharu, Jun-ichi Takiguchi, Hashizume Takumi, Suzuki Shinji and Yamaba Atsushi, “Development of Low-cost and Flexible Vegetation Monitoring System Using small Unmanned Aerial Vehicle”, ICROS-SICE International joint Conference, Fukuoka International Congress Center, Japan, 2009.
[7] Dugo Gonzalez .V , Zarco-Tejada .P, Nicols .E , Nortes P.A, Alarcn J.J, Intrigli¬olo D.S and Fereres .,”Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard”, International Journal on Advances in Precision Agriculture, 2013
[8] Swain, Kishore C., and Qamar Uz Zaman. "Rice crop monitoring with unmanned helicopter remote sensing images." Remote Sensing of Biomass-Principles and Applications. InTech, 2012.
[9] Carlson Tobu N and Riziley A.David, “ On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index”, REMOTE SENS. ENVIRON. 62241-252 1997.
[10] Selim S.Z. and Ismail M.A. , 1984 ,”K-means type algorithms: a generalized con-vergence theorem and characterization of local optimality”, IEEE Transactions on Pattern Analysis and Machine Intelligence 6 8187, 1984 8187
[11] Comaniciu, D. and Meer, P. “Mean shift: A robust approach toward feature space analysis”. IEEE Transactions on pattern analysis and machine intelligence, 24(5), pp.603-619, 2002
[12] Fawcett T ,”Roc graphs: notes and practical considerations for researchers”. Technical Report HPL-2003-4, HP Labs, 2006.

Ramesh K.N, Meenavathi M.B , "Agriculture Crop Area mapping in images acquired using Low Altitude Remote Sensing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.55-62, 2018.

Big data is a wonderful supply of information and knowledge from the systems to other end users. However handling such quantity of knowledge needs automation, and this leads to a trend of data processing and machine learning techniques. Within the ICT sector, as in several different sectors of analysis and trade, platforms and tools are being served and developed to assist professionals to treat their knowledge and learn from it automatically. Most of these platforms return from huge firms like Google or Microsoft, or from incubators at the Apache Foundation. This review explains Machine learning Algorithms in Big data Analytics, and machine learning challenges us to take decisions where there is no known “right path” for the specific problem based on previous lessons and enumerates some of the foremost used tools for analyzing and modeling big-data.

Machine Learning Algorithms, Big data Analytics, Apache Foundation

[1]. M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, pp. 255-260, 2015.
[2]. Hey, T., Tansley, S., Tolle, K., editors (2009). The Fourth Paradigm: Data- Intensive Scientific Discovery. Microsoft Research. S.Hasan et a.l
[3]. Sun, Y. et al., 2014. Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things. International Journal of Distributed Sensor Networks, 14, p.11.
[4]. Fan, J., Han, F. & Liu, H., 2014. Challenges of Big Data analysis. National Science Review , 1 (2 ), pp.293– 314.
[5]. Parmar, V. & Gupta, I., 2015. Big data analytics vs Data Mining analytics. IJITE, 3(3), pp.258–263.
[6]. K. Sayood, Introduction to Data Compression, Morgan Kaufinarm Publishers, San Francisco, CA, 2000.
[7]. M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, pp. 255-260, 2015.
[8]. N. Japkowicz and M. Shah, Evaluating Learning Algorithms: A Classification Perspective: Cambridge University Press, 2011.
[9]. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed.: Prentice Hall, 2010.
[10]. Y.Bengio ,A.Courville, and P. Vincent, "Representation learning: A review and new perspectives," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 35, 2013.
[11]. T. Yu, "Incorporating Prior Domain Knowledge into Inductive Machine Learning,"Computing Sciences, University of Technology Sydney, Sydney, Augtralia, 2007.
[12]. Q. Chen, J. Zobel, and K. Verspoor, "Evaluation of a Machine Learning Duplicate Detection Method for Bioinformatics Databases," in Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics, 2015, pp. 4- 12.
[13]. T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, et al., "Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 7, p. 10, 09/01/2013 2013.
[14]. J. J. Pfeiffer III, J. Neville, and P. N. Bennett, "Overcoming Relational Learning Biases to Accurately Predict Preferences in Large Scale Networks," in Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 853-863.
[15]. L. Cao, M. Wei, D. Yang, and E. A. Rundensteiner, "Online Outlier Exploration Over Large Datasets," in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 89-98.
[16]. G. Cavallaro, M. Riedel, M. Richerzhagen, J. A. Benediktsson, and A. Plaza, "On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, pp. 4634-4646, 2015.
[17]. 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)-2017
[18].J. Zhu, J. Chen, and W. Hu. (2014, 2014/11/24). Big Learning with Bayesian Methods. Available: http://arxiv.org/pdf/1411.6370
[19]. Triguero, D. Peralta, J. Bacardit, S. García, and F. Herrera, "MRPR: A MapReduce solution for prototype reduction in big data classification," Neurocomputing, vol. 150, Part A, pp. 331-345, 2/20/ 2015.
[20]. M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar,
N.Seliya, R. Wald, and E. Muharemagic, "Deep learning applications and challenges in big data analytics," Journal
of Big Data, vol. 2, pp. 1-21, 2015.
[21]. Lin Li, S. Bagheri, H. Goote, A. Hasan, and G. Hazard, "Risk adjustment of patient expenditures: A big data analytics approach," in 2013 IEEE International Conference on Big Data, 2013.
[22]. G. Zhang, S.-X. Ou, Y.-H. Huang, and C.-R. Wang, "Semi-supervised learning methodsfor large scale healthcare data analysis," International Journal of Computers inHealthcare, vol. 2, pp. 98-110, 06/01/2015 2015.
[23]. J. Suzuki, H. Isozaki, and M. Nagata, "Learning condensed feature representations fromlarge unsupervised data sets for supervised learning," in Proceedings of the 49th AnnualMeeting of the Association for Computational Linguistics: Human LanguageTechnologies: short papers - Volume 2, 2011, pp. 636-641.
[24]. Improving deep neural network design with new text data representations Joseph D. Prusa* andTaghi M. Khoshgoftaar J Big Data (2017) 4:7DOI 10.1186/s40537-017-0065-8
[25]. A survey of transfer learning Karl Weiss* , Taghi M. Khoshgoftaar and DingDing Wang Weiss et al. J Big Data (2016) 3:9 DOI 10.1186/s40537-016-0043-6
[26]. S. Owen, R. Anil, T. Dunning, and E. Friedman, Mahout in Action: Manning Publications Co., 2011.
[27]. White, T. (2009). Hadoop: The Definitive Guide (1st edition). O`Reilly Media, Inc. Software available from https://hadoop.apache.org
[28]. Zaharia, M.; Chowdhury, M.; Franklin, M.J.; Shenker, S. & Stoica, I. (2010). Spark: cluster computing with working sets. In Proceedings of the 2nd USENIX conference on Hot topics in cloud computing. USENIX Association, Berkeley, CA, USA. Software available from https://spark.apache.org

K. Sree Divya, P. Bhargavi, S. Jyothi, "Machine Learning Algorithms in Big data Analytics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.63-70, 2018.