Volume-6 , Issue-7 , Jul - 2018 Go Back

The present study is an approach to evaluate the groundwater condition near Tiruchendur, Tamil Nadu, Tuticorin District, India using Vertical Electrical Sounding method. Vertical Electrical Sounding is one of the most suitable geophysical method for groundwater prospecting and it provides the vertical variation of subsurface layer in terms of thickness and resistivity values. A total of 12 VES were carried out using the Schlumberger configuration in the area. DDR3 resistivity meter (IGIS Pvt. Ltd) was used for the data acquisition. The obtained field data was analyzed and interpreted with the help of IPI2WIN software which gives an automatic interpretation of the apparent resistivity. The results of quantitative interpretation of geo electrical data show that the study area comprises of three to five electrical layers. The layer resistivity obtained is ranging from 1.3Ωm to 1512Ωm respectively.Among the total 12 vertical electrical soundings, three VES are found to have good groundwater potentiality as well as quality and six VES are found to have good groundwater potentiality with poor quality.

VES, Aquifer quality, Tiruchendur, Schlumberger, IPI2WIN

[1] B.M. Vasantrao, P.J. Bhaskarrao, B.A. Mukund, G.R. Baburao, P. S. Narayan, Comparative study of Wenner and Schlumberger electrical resistivity method for groundwater investigation: a case study from Dhule district (M.S.), India, Applied Water Science, Volume 7, Issue 8, pp 4321–4340, 2017
[2] Vorosmarty CJ, Green P, Salisbury J, Lammers RB (2000), Global water resources: vulnerability from climate change and population growth.Science289:284-288.
[3] S. Hadj-Said, A. Zeddouri L. Djabri& B Hamdi-Aissa, Determination of the aquifers geometry in arid zones by using geoelectrical method, Arab J Geosci 6:1081–1089, 2013.
[4] K. S. Okiongbo& P. Mebine, Estimation of aquifer hydraulic parameters from geoelectrical method—a case study of Yenagoa and environs, Southern Nigeria, Arab J Geosci 2014.
[5] R. Kumar & G. S. Yadav, Geohydrological investigation using Schlumberger sounding in part of hard rock and alluvial area of Ahraura region, Mirzapur district, Uttar Pradesh, India, Arab J Geosci, 2014.
[6] Y. Srinivas, D. H. Oliver, A. S. Raj & N. Chandrasekar, Geophysical and geochemical approach to identify the groundwater quality in AgastheeswaramTaluk of Kanyakumari District, Tamil Nadu, India. Arab J Geosci 8:10647–10663,2015.
[7] D. Karunanidhi, G. Vennila, M. Suresh & P. Karthikeyan, Geoelectrical Schlumberger investigation for characterizing the hydrogeological conditions using GIS in OmalurTaluk, Salem District, Tamil Nadu, India, Arab J Geosci, 2013.
[8] Lazarus G. Ndatuwong& G. S. Yadav, Identifications of fractured zones in part of hard rock area of Sonebhadra District, U.P., India using integrated surface geophysical method for groundwater exploration, Arab J Geosci 7:1781–1789, 2014.
[9] M Madi, M Meddi, D Boutoutaou& A Pulido-Bosch, Assessment of aquifer vulnerability using a geophysical approach in hyper-arid zones. A case study (In Salah region, Algeria), Arab J Geosci 9: 460, 2016
[10] K.S.AnilKumar, C.P.Priju, N.B.NarasimhaPrasad Study on Saline Water Intrusion into the Shallow Coastal Aquifers of Periyar River Basin, Kerala Using Hydrochemical and Electrical Resistivity Methods ICWRCOE Volume 4, Pages 32-40, 2015.
[11] Al Barwani A, Helmi T. Seawater intrusion in a coastal aquifer: a case study for the area between Seeb and Suwaiq in the Sultanate of Oman. Agric Mar Sci;11:55–69. 2006.
[12] Tipaza, A.A. Bouderbala, B. Remini and A. S. HamoudiGeoelectrical investigation of saline water intrusion into freshwater aquifers: A case study of Nador coastal aquifer, 2016.
[13] Zohdy AAR, Eaton GP, Mabey DR Applications of surface geophysics to groundwater investigations. Techn Water ResourInvestig US GeolSurv 2:116,1974.
[14] O.A.E. Abdalla, M. Ali, K. Al-Higgi, H. Al-Zidi, El-Hussain, S. Al-Hinai.Rate of seawater intrusion estimated by geophysical methods in an arid area. Hydrogeology J. 18:1437–1445.doi: 10.1007/s10040-010-0606-0. 2010.
[15] G. GUPTA, S. MAITI and V. ERRAM, Analysis of Electrical Resistivity Data in Resolving the Saline and Fresh Water Aquifers in West Coast Maharashtra, Journal Geological Society Of India Vol.84,pp 555-568,2014.

S.Arunbose, Y.Srinivas, S.Rajkumar, Nithya C.Nair, Junais C.P, "Evaluation of Groundwater Condition Using Geo-electrical Soundings in Parts of Tiruchendur Taluk, Tamilnadu, India," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.137-144, 2018.

In these research methods for Test case prioritization to perform schedule test case has been discussed. It has been discussed for running in an order that try to raise effectiveness. It has been discussed that goals is Average Percentage Fault Detection (APFD) . This paper, we describe several methods in case of prioritizing of test cases. Such work has been dependent on thought of prioritization of test case methods. It takes the objective to expose maximum fault potential. The mutant based testing is used on the primary basis to get the effectiveness of test cases. It is used to expose the known faults. It is based on the version information. It also considers the historical aspects of effectiveness. The effectiveness is tested against known faults like heuristics. The approach is a probabilistic estimate. It is made for test cases and mutants testing. The problem lies in finding the best sequence of the application. It is of test cases. The aim is to detect average percentage of APFD. This is over lifetime in case of test suite. It should be maximized. The outputs are suggesting that several methods could significantly improve. They are improved due to hybridization of algorithm execution time complexity of algorithm has been increased.

Test Case Prioritization, BAT, Levy Flight, APFD ,Metaheuristc Algorithms

B. Korel and J. Laski, “Algorithmic Software fault localization”, International Conference on System Sciences, pp. 246-252, 1991.
[2] Dennis Jeffrey and Neelam Gupta, “Test Case Prioritization Using Relevant Slices”, Proceedings of the 30th Annual International Computer Software and Applications Conference, Vol. 1, pp. 411-420, 2006.
[3] J. Karlsson and K. Rayan, “A Cost Value Approach for Prioritizing Requirements”, IEEE Software, Vol. 14, No. 5, 1997.
[4] Alexy G. Malishevsy, Gregg Rothermel and Sebastian Elbaum, “Modeling the Cost- Benifts Tradeoffs for Regression Testing Techniques”, Proceeding of the International Conference on Software Maintenance (ICSM), 2002.
[5] Malishevsky A.G., J.R. Ruthruff, G. Rothermel and S. Elbaum, “Cost-cognizant test case prioritization technical Report”, TR-UNL-CSE-2006-0004, 2006.
[6] H. Do and G. Rothermel, “On the use of Mutation faults in Empirical Assessments of Test case prioritization Techniques”, IEEE Transaction on Software Engineering., Vol. 32, No. 9, 2006.
[7] Zheng Li, Mark Harman and Robert M. Hierons, “Search Algorithms for Regression Test Case Prioritization”, IEEE Transaction on Software Engineering, Vol. 33, No. 4, pp. 225-237, 2007.
[8] Lionel C. Briand, Victor R. Basili, Christopher J. Hetmanski, “Developing Interpretable Models with Optimized Set Reduction for Identifying High-Risk Software Components”, IEEE Transactions on Software Engineering, Vol. 19, No. 11, pp. 1028-1044, 1993.
[9] Garlan, C., R. Allen and J. Ockerbloom, “Architectural Mismatch or Why It’s Hard to Build Systems Out of Existing Parts”, Proceeding of 17th International Conference on Software Engineering, pp. 179–185, 1995.
[10] L. Mei, W. K. Chan, T. H. Tse, and R. G. Merkel, ”Tag-Based Techniques for Black-Box Test Case Prioritization for Service Testing,” in 2009 Ninth International Conference on Quality Software, 2009, pp. 2130.
[11] Elbaum S.,G. Rothermel, S. Kanduri and A.G. Malishevsky, “Selecting a cost-effective test case prioritization Techniques”, Software Quality Journal, Vol. 12, pp. 185-210, 2004.
[12] Malishevsky A.G., J.R. Ruthruff, G. Rothermel and S. Elbaum, “Cost-cognizant test case prioritization technical Report”, TR-UNL-CSE-2006-0004, 2006.
[13] H. Do and G. Rothermel, “On the use of Mutation faults in Empirical Assessments of Test case prioritization Techniques”, IEEE Transaction on Software Engineering., Vol. 32, No. 9, 2006.
[14] Zheng Li, Mark Harman and Robert M. Hierons, “Search Algorithms for Regression Test Case Prioritization”, IEEE Transaction on Software Engineering, Vol. 33, No. 4, pp. 225-237, 2007.
[15] A. Panichella, R. Oliveto, M. Di Penta, and A. De Lucia, ”Improving Multi-Objective Test Case Selection by Injecting Diversity in Genetic Algorithms,” IEEE Trans. Softw. Eng., vol. 41, no. 4, pp. 358383, Apr. 2015.
[16] Jeffery A. Solheim and John H. Rowland, “An Empirical Study of Testing and Integration Strategies using Artificial Software Systems”, IEEE Transactions on Software Engineering, Vol. 19, No. 10, pp. 941-949 , 1993.
[17] Sahil Gupta, Himanshi Raperia, Eshan Kapur, Harshpreet Singh and Aseem Kumar, “A Novel Approach for Test Case Prioritization” International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.3, pp. 53-60, 2012.
[18] Shifa-e-Zehra Haidry and Tim Miller, “Using Dependency Structures for Prioritization of Functional Test Suites”, IEEE Transactions on Software Engineering, Vol. 39, No. 2, 2013.
[19] Praveen Ranjan Srivastava, “Test Case Prioritization”, Journal of Theoretical and Applied Information Technology, pp. 178-181, 2008.
[20] Rothermel G, Elbaum, S, Kinneer, A and Do H, 2006. Software- artifact infrastructure repository. URL http:// sir.unl. edu/portal.
[21] Suri, Bharti, and Shweta Singhal. "Implementing ant colony optimization for test case selection and prioritization." International journal on computer science and engineering 3.5 (2011): 1924-1932.
[22] Kavitha, R., and N. Sureshkumar. "Test case prioritization for regression testing based on severity of fault." International Journal on Computer Science and Engineering 2.5 (2010): 1462-1466.

S. Chaudhary, R. Singh, "Test Case Prioritization Using Modified Bat Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.145-149, 2018.

With recent advancement in deep learning areas, computer vision research has changed from hard coded features to end-to-end trained deep neural network. Super-resolution is one of such areas which is influenced by deep learning advancement. Super-resolution is the technique for reconstructing high-resolution images from a given set of images. It is very important to acquire better quality images in satellite images, medical images and surveillance monitors where analysis of low quality images is extremely difficult. In this paper a novel approach to solve the problem of super-resolution image is presented. Proposed method trained the network using feedforward convolutional neural network and combined with perceptual loss function which measure the semantic differences between images and helps in reducing the computational complexity of overall super-resolution images. The proposed method also uses the adversarial network which helps in achieving the finer details in images.

Super-Resolution, Convolutional Neural network, Sub-Pixel Convolutional Layer, Perceptual Loss

[1] C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks” IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2):295–307, 2016.
[2] Yang, C.Y, Huang,J.B., Yang, M.H,”Exploiting self-similarities for single frame super-resolution”, In: IEEE Asian Conference on Computer Vision, pp.497–510 (2010).
[3] D. Glasner, S. Bagon and M. Irani,”Super-Resolution from a Single Image”,Proc. Int. Conf. Computer Vision, Kyoto, Japan,2009.
[4] Shreyas Fadnavis Int, Image Interpolation Techniques in Digital Image Processing:”An Overview, Journal of Engineering Research and Applications” ISSN: 2248-9622, Vol. 4, Issue 10( Part -1), pp.70-73, October 2014.
[5] Amisha J Shah, Suryakant B.Gupta and Rujul Makwana, “Single Image Super-Resolution via Non Sub-sample Contourlet Transform based Learning and a Gabor Prior”, International Journal of Computer Applications (0975–8887)Volume 64–No.18, February 2013.
[6] M. Elad. “Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing”, Springer Publishing Company, Incorporated, 1st edition, 2010.
[7] Yang C.Y, Ma C, Yang M.H,“Single-image super resolution: a benchmark”, Springer, Computer Vision (ECCV) pages 372-386,2014.
[8] S. Schulter, C. Leister, and H. Bischof,“Fast and accurate image upscaling with super-resolution forest” IEEE, Conference on computer vision and pattern recognition, pages 3791-3799,2015.
[9] N.S.Lele,”Image Classification Using Convolutional Neural Network”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.22-26 , 2018.
[10] W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network”, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1874–1883, 2016.
[11] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution”, European Conference on Computer Vision. Springer International Publishing, 2016.
[12] He, K., Zhang, X., Ren, S., Sun, J. “Deep residual learning for image recognition” arXiv preprint arXiv: 1512.03385 (2015).
[13] C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunning-ham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, arXiv.org, Sept. 2016.
[14] Lin, T.Y, Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L: “Microsoft coco: Common objects in context. In: Computer Vision– ECCV 2014”, Springer (2014) 740–755.

Nisha Singh, Myna A.N, "Image Super-Resolution Using Deep Learning Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.150-155, 2018.

In the Prediction of total stock index, many are faced with some parameters as they are uncertain in future and they can undergo changes, and this uncertainty has a few risks, and for a true analysis, the calculations should be performed under risk conditions. The empirical tests suggest that the stochastic differential equation of GBM model can be used to predict the direction of stock price movement. In terms of predicting the stock price values, the empirical findings suggest that the GBM model performs well in stock market.

Prediction, Stock index, Geometric Brownian Motion (GBM), Stochastic differential equation, Stock Market

[1] Elias M. Stein, Jeremy C. Stein “Stock Price Distributions with Stochastic Volatility: An Analytic Approach”, The Review of Financial Studies, Vol.4, Issue.4, pp.727-752, 1991.
[2] Sottinen T. “Fractional Brownian motion, random walks and binary market models”, Finance and Stochastics, Vol.5, Issue.3, pp.343-355, 2001.
[3] Merton RC. “Optimum consumption and portfolio rules in a continuous-time model”, Elsevier Publisher, pp. 621-661, 1975.
[4] Marathe RR, Ryan SM. “On the validity of the geometric Brownian motion assumption”, The Engineering Economist, Vol.50, Issue.2, pp.159-192, 2005.
[5] Hu Y, Øksendal B. “Optimal time to invest when the price processes are geometric Brownian motions”, Finance and Stochastics, Vol.2, Issue.3, pp.295-310, 1998.
[6] Rascanu A. “Differential equations driven by fractional Brownian motion”, Collectanea Mathematica. Vol.53, Issue.1, pp.55-81, 2002.
[7] Kramers HA. “Brownian motion in a field of force and the diffusion model of chemical reactions”, Physica, Vol.7, Issue.4, pp.284-304, 1940.
[8] stali FA, Picchetti P. “Geometric Brownian motion and structural breaks in oil prices: a quantitative analysis”, Energy Economics, Vol.24, Issue.4, pp.506-522, 2006.
[9] Coutin L, Qian Z. “Stochastic analysis, rough path analysis and fractional Brownian motions”, Probability theory and related fields, Vol.122, Issue.1, pp.108-140, 2002.
[10] He Z. “Optimal executive compensation when firm size follows geometric brownian motion”. The Review of Financial Studies, Vol.22, Issue.2, pp.859-892, 2008.

G. Srinaganya, "Simulation of Stochastic Geometric Brownian Motion of Stock Market – Using R Programming," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.156-160, 2018.

the Internet of Things has the potential has to transform the globe we live in; more-competent industries, automatic cars, and revolution of cities all these application are design using internet of things. However, the application of technology like Internet of things in agriculture has the greatest impact. There are seventy percent of people depends on the farming in India and one third people depend on other work. Facing the problem in agriculture often cause hindrance in the development of the country. Solution of this problem can be solve by using recent technologies. In this system, it is proposed to develop a Smart Farming System possess the benefits of cutting edge technologies such as internet of things and Wireless Sensor Network to help farmers for improving their farm. Using sensors like temperature, humidity, moisture, pH level sensor, wind speed sensor one can get the information about the parameters of the farm and then received data is transferred via raspberry pi to the server. Then the data received on the server, can be accessed by user to take necessary action. Also, an android application is installed in user’s mobile phone provides which an additional advantage of accessing data from any location.

Agriculture, Internet of Things, Wireless Sensor Network, Wireless-Fidelity, Environment

[1] Jin-Shyan Lee, Yu-Wei Su, and Chung-Chou Shen “A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi”, The 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON) Nov. 5-8, 2007.
[2] Jeonghwan Hwang, Changsun Shin and Hyun Yoe “ Study on an Agricultural environment Monitoring Server System using Wireless Sensor Networks” ISSN 1424-8220 www.mdpi.com/journal/sensors, 2010.
[3] Hui Changl, Nan Zhou1, Xiaoguang Zhaol,” A New Agriculture Monitoring System Based on WSNs” Journal of Anhui Agricultural Sciences, vol. 38, pp. 4370-4372, 2010.
[4] Nattapol Kaewmard, Saiyan Saiyod” Sensor Data Collection and Irrigation Control on vegetable Crop Using Smart Phone and Wireless Sensor Networks for Smart Farm” 2014 IEEE Conference on Wireless Sensors (ICWiSE), October, 26-28 2014.
[5] Marcello A. Gómez Maureira, Daan Oldenhof, “ThingSpeak – an API and Web Servicefor the Internet of Things”, https://thingspeak.com/channels/2567. 2014-06-08.
[6] M.K.Gayatri, J.Jayasakthi, Dr.G.S.Anandha Mala, “ Providing Smart Agricultural Solutions to Farmers for better yielding using IoT”, IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development, vol 45, 2015.
[7] Soundhar Ganesh,Venkatash,Vidhyasagar,Maragatharaj, “Raspberry Pi Based Interactive Home Automation System through Internet of Things”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 3 Issue III, March 2015.
[8] S. Shanthi, Abinaya. R, Akshaya. V, Gowri. S “ Agriculture Crop Monitoring using GSM in WSN” International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 3, March 2016.
[9] Abdullah Na, William Isaac, “Developing a Human-Centric Agricultural Model in the IoT Environment”, International Conference on Internet of Things and Applications (IOTA) Maharashtra Institute of Technology, Pune, India 22 Jan - 24 Jan, 2016.
[10] Arindam Giri, Subrata Dutta, Sarmistha Neogy, “Enabling Agricultural Automation to Optimize Utilization of Water, Fertilizer and Insecticides by implementing Internet of Things (IoT)”, International conference on information technology,2016.
[11] The Internet of Things, http://www.gartner.com/it-glossary/internet-of-things, on February 20, 2016.
[12] IOT Devices, http://blog.atollic.com/one-trillion-iot-evices-expected-by-2025-what-development-tools-to-use-for-development-of-internet-connected-iot-products, on February 18, 2016.
[13] Research papers on IOT, http://www.engpaper.net/free-research-papers-iot-internet-of-thing.htm, on February 20, 2016.
[14] The official RaspberryPi website, www.raspberrypi.org, on January 31, 2016.
[15] James Brown and Utz Roedig, Carlo Alberto Boano and Kay Romer, Nicolas Tsiftes, “Demo Abstract: How Temperature Affects IoT Communication”, February 19, 2016.

Neha Dayaram Amrutkar, S. S. Morad, "Agile Agricultural System Using Wireless Fidelity," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.161-167, 2018.

The objective of the next generation communication system is to construct a “global information village”, which encompasses a range of components at different scales extending from global to pico cellular size. The Circular Filter Bank Multicarrier Communication (C-FBMC) is an innovative transmission method that works by linking the standard FBMC with circular convolution. This arrangement is in the form of a block and acquires orthogonality amongst subcarriers. This research paper employs a Walsh-Hadamard precoding system to the C-FBMC system in order to manipulate frequency levels within a channel that is multipath in nature. The hypothetical estimation for the subsequent BER arrangement is derived with the help of this method in the paper. The performance and working of the arrangement is compared with the pre-coded Generalised Frequency Division Multiplexing (GFDM) arrangement. Results of the paper highlight that the results drawn are quite similar or matching to the outcomes of simulation and the amount or frequency of WHTC-FBMC is quite higher in comparison to the WHT-GFDM.

C-FBMC, Orthogonality, Subcarriers, Walsh and Hadamard Precoder, Bit Error Rate (BER), GFDM

[1] N. Michailow, M. Matthe, I. S. Gaspar, A. N. Caldevilla, L. L. Mendes, A. Festag, and G. Fettweis, “Generalized frequency division multiplexing for 5th generation cellular networks (invited paper),” IEEE Transactions on Communications, vol. 62, pp. 3045–3061, Sept. 2014.
[2] F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta, and P. Popovski, “Five disruptive technology directions for 5G,” IEEE Communications Magazine, vol. 52, pp. 74–80, February 2014.
[3] J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang, “What will 5G be?,” IEEE Journal on Selected Areas in Communications, vol. 32, pp. 1065–1082, June 2014.
[4] R. Datta, N. Michailow, M. Lentmaier, and G. Fettweis, “GFDM interference cancellation for flexible cognitive radio phy design,” in Proc. IEEE Vehicular Technology Conference, 2012.
[5] B. Farhang-Boroujeny, “OFDM versus filter bank multicarrier,” IEEE Signal Processing Magazine, vol. 28, pp. 92–112, 2011.
[6] X. GAO, W. Wang, X. G. Xia, E. K. S. Au, and X. You, “Cyclic prefixed OQAM-OFDM and its application to single-carrier FDMA,” IEEE Transactions on Communications, vol. 59, pp. 1467–1480, May 2011.
[7] H. Lin and P. Siohan, “Multi-carrier modulation analysis and WCPCOQAM proposal,” EURASIP Journal on Advances in Signal Processing, vol. 2014:79, 19 pages, 2014.
[8] Y.-P. Lin, S.-M. Phong and P. P. Vaidyanathan, Filter Bank Transceivers for OFDM and DMT Systems. Cambridge University Press, 2010.
[9] A. Rezazadeh Reyhani, A. Farhang, and B. Farhang-Boroujeny, “Circularly pulse-shaped waveforms for 5G: Options and comparisons,” in IEEE Global Communications Conference (GLOBECOM), pp. 1–7, Dec. 2015.
[10] Quang Duong and Ha H. Nguyen, “Walsh-Hadamard Precoded Circular Filter bank Multicarrier Communications”, 2017 International Conference on Recent Advances in Signal Processing, Telecommunications & Computing.
[11] B. Farhang-Boroujeny and C. H. (George) Yuen, “Cosine modulated and offset QAM filter bank multicarrier techniques: A continuous time prospect”, EURASIP Journal on Advances in Signal Processing, vol. 2010, pp. 1–17, 2010.
[12] A. B. ¨Uc¸u¨ncu¨ and A. ¨O. Yilmaz, “Pulse shaping methods for OQAM/OFDM and WCP-COQAM”, 2015. Fundamentals of Wireless Communication. Cambridge University Press, 2005.

K. Pramidapadma, Chandra Mohan Reddy Sivappagari, "Improvement of Bit Error Rate Using Novel Precoded Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.168-172, 2018.

In past few years prevalence of internet usage is increasing. Different online shopping sites have many options of purchasing the product while shopping. Users share experiences in the form of reviews. The number of reviews shared by people are increasing. So it is difficult to find the right information about the product. Traditional recommender systems (RS) makes use of different factors, such as users purchase records, geographical location etc. We propose sentiment based recommender system. Based on the sentiment word in the CPRST system, the review has been rated by finding sentiment score. Also textual reviews are categorized into different feature of product using text classification technique. Experimental results of CPRST system show that user preference can be characterized by the sentiment from text review and it can improve the performance of recommendation system. Using LDA method we classified text review and this resulted in good results in nickel.

Item reputation, Text Reviews, Rating prediction, Recommender system, Sentiment influence, User sentiment, Sentiment analysis, Text classification

[1] F. Li, N. Liu, H. Jin, K. Zhao, Q. Yang, X. Zhu, “Incorporating reviewer and product information for review rating prediction” , in Proceedings of the Twenty-Second international joint conference on Artificial Intelligence, 2011, pp. 1820-1825.
[2] G. Ganu, N. Elhadad, A Marian, “Beyond the stars: Improving rating predictions using Review text content”, in 12th International Workshop on the Web and Databases (WebDB 2009). pp. 1-6.
[3] Y. Ren, J. Shen, J. Wang, J. Han, and S. Lee, “Mutual Verifiable Provable Data Auditing in Public Cloud Storage”, Journal of Internet Technology, vol. 16, no. 2, 2015, pp. 317-323.
[4] Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, S. Ma, “Explicit factor models for explainable recommendation based on phrase-level sentiment analysis”, in proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014.
[5] X. Lei, and X. Qian, “Rating prediction via exploring service reputation”, 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), Oct 19-21, 2015, Xiamen, China. pp.1-6.
[6] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms”, in Proc.10th International Conference on World Wide Web, 2001, pp. 285-295.
[7] K.H. L. Tso-Sutter, L. B. Marinho, L. Schmidt-Thieme, “Tag-aware recommender systems by fusion of collaborative filtering algorithms”, in Proceedings of the 2008 ACM symposium on Applied computing, 2008, pp. 1995-1999.
[8] R. Salakhutdinov, and A. Mnih, “Probabilistic matrix factorization” , in NIPS, 2008.
[9] X. Qian, H. Feng, G. Zhao, and T. Mei, “Personalized recommendation combining user interest and social circle”, IEEE Trans. Knowledge and data engineering. 2014, pp. 1763-1777.
[10] L. Qu, G. Ifrim, G. Weikum, “The bag-of-opinions method for review rating prediction from sparse text patterns”, in Proc. 23rd International Conference on Computational Linguistics, 2010, pp. 913–921.
[11] K. Zhang, Y. Cheng, W. Liao, A. Choudhary, “Mining millions of reviews: a technique to rank products based on importance of reviews”, in Proceedings of the 13th International Conference on Electronic Commerce, Aug. 2011, pp. 1-8.
[12] B. Pang, Bo, L. Lee, and S. Vaithyanathan, “Thumbs up? sentiment classification using machine learning techniques”, in Proc. EMNLP, 2002, pp. 79-86.
[13] D. Tang, Q. Bing, T. Liu, “Learning semantic representations of users and products for document level sentiment classification”, in Proc. 53th Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, July 26-31, 2015, pp. 1014–1023.
[14] T. Nakagawa, K. Inui, and S. Kurohashi, “Dependency tree-based sentiment classification using CRFs with Hidden Variables”, NAACL, 2010, pp.786-794.
[15] Xiaojiang Lei, Xueming Qian, Member, IEEE, and Guoshuai Zhao, “Rating Prediction based on Social Sentiment from Textual Reviews”, IEEE TRANSACTIONS ON MULTIMEDIA, MANUSCRIPT ID: MM-006446.
[16] D.M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation”, Journal of machine learning research3. 2003, pp. 993-1022.
[17] Baizhang Ma, Dongsong Zhang, Zhijun Yan, Taeha Kim “AN LDA AND SYNONYM LEXICON BASED APPROACH TO PRODUCT FEATURE EXTRACTION FROM ONLINE CONSUMER PRODUCT REVIEWS” Journal of Electronic Commerce Research, VOL 14, NO 4, 2013
[18] W. Xiong, Y. Jin, Z. Liu, “Chinese sentiment analysis using appraiser-degree-negation combinations and PSO”, Journal of Computers, 2014, 9(6).
[19] H. Nie, Z. Rong, “Review helpfulness prediction research based on review sentiment feature sets”, New Technology of Library and Information Service, 2015, 31(7): 113-121.
[20] https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html #lexicon
[21] http://jmcauley.ucsd.edu/data/amazon
[22] https://blog.algorithmia.com/lda-algorithm-classify-text-documents/
[23] http://times.cs.uiuc.edu/~wang296/ Data/LARA/Amazon/AmazonReviews.zip

M. M. Sutar, T. I. Bagban, "Applying Sentiment Analysis to Predict Rating and Classification of Text Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.173-178, 2018.

SUPERVISORY Control and Data Acquisition (SCADA) networks control the distributed assets of many industrial systems. Power generation, water distribution and factory automation are just a few examples that illustrate the critical nature of these networks. SCADA devices are built for reliability, but often lack built-in security features to guard them from cyber-attacks. Consequently, these devices depend on firewalls for protection. Hence, firewalls are integral to SCADA networks control the distributed assets of many industrial systems. Power generation, water distribution and factory automation are just a few examples that illustrate the critical nature of these networks. SCADA devices are built for reliability, but often lack built-in security features to guard them from cyber-attacks. Consequently, these devices depend on firewalls for protection. Hence, firewalls are integral to the safe and reliable operation of SCADA networks. Firewall configuration is an important activity for any modern day business. It is particularly a critical task for the SCADA networks that control power stations, water distribution, factory automation, etc. Lack of automation tools to assist with this critical task has resulted in un-optimized, error prone configurations that expose these networks to cyber-attacks. Automation can make designing firewall configurations more reliable and their deployment increasingly cost-effective. In order to increase the security in firewall we are providing extra automation that would help to detect the packet level conflicts such DoS.

SCADA network security, Zone-Conduit model,firewall autoconfiguration, security policy, SCADA best practices, IP Fragmentation, Port Fragmentation

[1] Dinesha Ranathunga, Mathew Roughan: Case Studies of SCADA Firewall Configurations and the Implications for Best Practices. IEEE Transactions on Network and service manaagement, Dec 2016
[2] ANSI/ISA-62443-1-1. Security for industrial
automation and control systems part 1-1:
Terminology, concepts, and models, 2007.
[3] Y. Bartal, A. Mayer, K. Nissim, and A. Wool.
Firmato: A novel firewall management toolkit. ACM Transactions on Computer Systems (TOCS),
22(4):381–420, 2004.
[4] S. Bellovin and R. Bush. Configuration management
and security. IEEE Journal on Selected Areas in
Communications, 27(3):268–274, 2009.
[5] J.D. Guttman: Filtering postures: local enforcement for global policies. In IEEE Symposium on Security and Privacy, pages 120-129, 1997
[6] Ondrej Rysavy, Jaroslav Rab, Microslav Sveda: Improving security in SCADA systems through firewall policy analysis Federated Conference on Computer Science and Information Systems March 2013
[7] Khaled Salah, Khalid Elbadawi, Raouf Boutaba: Performance Modelling and Analysis of Network Firewalls, IEEE Transactions on Network and Service Management ( Volume: 9, Issue: 1, March 2012 )
[8] Avishal Wool: Trends in Firewall Configuration Errors: Measuring the Holes in Swiss Cheese, IEEE Internet Computing 14(4):58 - 65 • September 2010
[9] S. Bellovin and R. Bush. Configuration management and security. IEEE Journal on Selected Areas in Communications, 27(3):268–274, 2009.
[10] E. Byres. Using ANSI/ISA-99 standards to improve control system security. White paper, Tofino Security, May 2012.
[11] E. Byres, J. Karsch, and J. Carter. NISCC good practice guide on firewall deployment for SCADA and process control networks. National Infrastructure Security Co-Ordination Centre, 2005.
[12] M. Casado, T. Garfinkel, A. Akella, M. J. Freedman, D. Boneh, N. McKeown, and S. Shenker. SANE: A protection architecture for enterprise networks. In Usenix Security, 2006.
[13] W. R. Cheswick, S. M. Bellovin, and A. D. Rubin. Firewalls and Internet security: repelling the wily hacker. Addison-Wesley Longman Publishing Co., Inc., 2003.
[14] Cisco Systems. Cisco ASA 5500 Series Configuration Guide using the CLI. Cisco Systems, Inc., 170 West Tasman Drive, San Jose, CA 95134-1706, USA, 2010.
[15] Cisco Systems. Cisco ASA 5585-X adaptive security appliance architecture. White paper, Cisco Systems, May 2014.
[16] R. Jamieson, L. Land, S. Smith, G. Stephens, and D. Winchester. Critical infrastructure information security: Impacts of identity and related crimes. In PACIS, page 78, 2009.
[17] K. Stouffer, J. Falco, and K. Scarfone. Guide to Industrial Control Systems (ICS) security. NIST Special Publication, 800(82):16–16, 2008.
[18] T. Tuglular, F. Cetin, O. Yarimtepe, and G. Gercek. Firewall configuration management using XACML policies. In 13th International Telecommunications Network Strategy and Planning Symposium, Sep, 2008.

Sai Pradeep Kumar. M, Haritha. D, "Secure SCADA Firewall Autmation and Implication for Best Practices," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.179-190, 2018.

Online applications like Facebook, Google Apps, WhatsApp, Amazon, Flipkart etc. are huge companies that have a huge customer base and many of customers use their apps concurrently. It put much load over their servers and relatively more load on the application. This overload degrades the performance of the application. The more request-response to an application, the more it applies load on the server. However, a server infrastructure consists various limits to serve a total number of requests per second. Therefore, the server infrastructure and architecture of the application must be developed in such a manner that can be deployed on multiple servers. When the number of users increased, the application can serve to all users by just deploying the same application to more servers. Such kind to development architecture requires more knowledge and more experienced developers. Also, the cost of such deployment need lots of money to purchase/subscribe various third-party packages. In this paper, I am presenting a design architecture and deployment method for Active-Active application clustering that will help to develop applications, which can scale-up at any time without making any changes to application code. The application can handle any number of requests and can serve more users than expected. This architecture uses open source tools and technologies so that it is a low cost solution and provides high performance.

Cloud computing, virtualization, application clustering, distributed application, database clustering, load balancer, HAProxy, Keepalived, Memcache, Postgres database, hypervisor

[1] R. Aluvalu, M. A. Vardhaman and J. Kantaria, "Performance evaluation of clustering algorithms for dynamic VM allocation in cloud computing", In the Proceedings of the International Conference On Smart Technologies For Smart Nation (SmartTechCon), Bangalore, India, pp. 1560, 2017.
[2] A. Awasthi and R. Gupta, "Multiple hypervisor based Open Stack cloud and VM migration", In the Proceedings of the Cloud System and Big Data Engineering (Confluence), Noida, India, pp. 130, 2016.
[3] A. Babar and B. Ramsey, "Building Secure and Scalable Private Cloud Infrastructure with Open Stack", In the Proceedings of the Enterprise Distributed Object Computing Workshop (EDOCW), Adelaide, SA, Australia, pp. 166, 2015.
[4] K. A. Bakar, M. H. Shaharill and M. Ahmed, "Performance evaluation of a clustered memcache", In the Proceedings of the International Conference on Information and Communication Technology for the Moslem World, Jakarta, Indonesia, pp. 54, 2010.
[5] J. M. Clarence, S. Aravindh and A. B. Shreeharsha, "Comparative Study of the New Generation, Agile, Scalable, High PerformanceNOSQL Databases", International Journal of Computer Applications, Vol. 48, Issue. 20, pp. 888-975, 2012.
[6] J. E. C. Cruz and I. C. A. R. Goyzueta, "Design of a high availability system with HAProxy and domain name service for web services", In the Proceedings of the IEEE XXIV International Conference, Cusco, Peru, pp. 1, 2017.
[7] M. Deris, M. Rabiei, A. Noraziah And H.M. Suzuri, "High service reliability for cluster server systems", In the Proceedings of the IEEE International Conference on Cluster Computing, Hong Kong, China, pp. 280, 2003.
[8] A. Dixit, A. K. Yadav and S. Kumar, "An Efficient Architecture and Algorithm for Server Provisioning in Cloud Computing using Clustering Approach", In the Proceedings of the International Conference on System Modeling & Advancement in Research Trends, Moradabad, India, pp. 260, 2016.
[9] M. E. Elsaid and C. Meinel, "Multiple Virtual Machines Live Migration Performance Modelling -- VMware vMotion Based Study", IEEE International Conference on Cloud Engineering (IC2E), Berlin, Germany, pp. 212, 2016.
[10] S. M. Hemam and K. W. Hidouci, "Replicated Database Transactions Processing in Peer-To-Peer Environments", Journal of Networking Technology, Vol. 2, Issue. 1, pp. 63-72, 2011.
[11] G. Kecskemeti, P. Kacsuk, G. Terstyanszky, T. Kiss and T. Delaitre, "Automatic Service Deployment Using Virtualisation", In the Proceedings of the Parallel, Distributed and Network-Based Processing, Toulouse, France, pp. 628, 2008.
[12] R. Khan, M. Haroon and M. S. Husain, "Different technique of load balancing in distributed system", In the Proceedings of the Global Conference on Communication Technologies (GCCT), Thuckalay, India, pp. 371, 2015.
[13] C. H. Lien, Y. W. Bai, M. B. Lin and P. A. Chen, "The saving of energy in web server clusters by utilizing dynamic server management", In the Proceedings of the 12th IEEE International Conference on Networks, Singapore, pp. 253, 2004.
[14] J. Liu, L. Xu, B. Gu and J. Zhang, "A Scalable High Performance Internet Cluster Server", In the Proceedings of the The Fourth International Conference/Exhibition, Beijing, China, pp. 941, 2000.
[15] C. Mancaş, "Performance improvement through virtualization", In the Proceedings of the RoEduNet International Conference - Networking in Education and Research (RoEduNetNER), Craiova, Romania, pp. 253, 2015.
[16] M. C. Mazilu, "Database Replication", Database Systems Journal, Vol. 1, Issue. 2, pp. 33-38, 2010.
[17] A. Tchana, L. Broto and D. Hagimont, "Approaches to cloud computing fault tolerance", In the Proceedings of the International Conference on Computer, Information and Telecommunication Systems (CITS), Amman, Jordan, pp. 1, 2012.
[18] S. A. Moiz, P. Sailaja, G. Venkataswamy and S. N. Pal, "Database Replication: A Survey of Open Source and Commercial Tools", International Journal of Computer Applications, Vol. 13, Issue. 6, pp. 0975 – 8887, 2011.
[19] T. Moyo and G. Bhogal, "Investigating Security Issues in Cloud Computing", In the Proceedings of the Eighth International Conference on Complex, Intelligent and Software Intensive Systems, UK, pp. 141, 2014.
[20] E. Pacitti, M. T. Özsu and C. Coulon, "Preventive Multi-master Replication in a Cluster of Autonomous Databases", Euro-Par 2003 Parallel Processing, Vol. 2790, Issue. 1, pp. 318-327, 2003.
[21] E. Pacitti, C. Coulon and P. Valduriez, "Preventive Replication in a Database Cluster", Distributed and Parallel Databases, Vol. 18, Issue. 3, pp. 223–251, 2005.
[22] A. B. Prasetijo, E. D. Widianto and E. T. Hidayatullah, "Performance Comparisons of Web Server Load Balancing Algorithms on HAProxy and Heartbeat", In the Proceedings of the Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, Indonesia, pp. 393, 2016.
[23] T. Agrawal and N. Sharma, "Efficient Load Balancing Using Restful Web Services in Cloud Computing: A Review", International Journal of Scientific Research in Computer Sciences and Engineering, Vol. 6, Issue. 3, pp. 67–70, 2018.
[24] N. Sharma and S. Maurya, "A review on:VM management in Cloud & Datacenter", International Journal Of Advanced Studies In Computer Science And Engineering, Vol. 6 Issue. 9, pp. 12-18, 2017.
[25] R. Shingade, A. Patil, S. Suryawanshi and M. Venkatesan, "Efficient Resource Management in Cloud Computing", International Journal of Engineering and Technology, Vol. 7, Issue. 6, pp. 2045-2053, 2016.
[26] P. Devi, "Attacks on Cloud Data: A Big Security Issue", International Journal of Scientific Research in Network Security and Communication, Vol. 6, Issue. 2, pp. 15–18, 2018.
[27] H. Tang, R. She, C. He, and Y. Dou, "Construction and Application of Linux Virtual Server Cluster for Scientific Computing", In the Proceedings of the International Conference on Network and Parallel Computing, Shanghai, China, pp. 287, 2008.
[28] A. D. Tesfamicael, V. Liu and W. Caelli, "Design and Implementation of Unified Communications as a Service Based on the Open Stack Cloud Environment", In the Proceedings of the IEEE International Conference on Computational Intelligence & Communication Technology, Ghaziabad, India, pp. 117, 2015.
[29] S. Varrette, M. Guzek, V. Plugaru, X. Besseron and P. Bouvry, "HPC Performance and Energy-Efficiency of Xen, KVM and VMware Hypervisors", In the Proceedings of the 25th International Symposium on Computer Architecture and High Performance Computing, Porto de Galinhas, Brazil, pp. 89, 2013.
[30] M. Xia and G. Qin, "The research and implementation of a highly concurrent and highly available system for acquiring personal virtual assets", In the Proceedings of the International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, pp. 1, 2017.
[31] C. Yan, J. Shen and Q. Peng, "Parallel Web Prefetching On Cluster Server", In the Proceedings of the Electrical and Computer Engineering Canadian Conference, Saskatoon, Sask., Canada, pp. 2284, 2005.
[32] H. Yuusuf and S. Vidalis, "On the Road to Virtualized Environment", In the Proceedings of the Third International Conference on Emerging Intelligent Data and Web Technologies, Bucharest, Romania, pp. 270, 2012.

D. Dashora, "An Efficient Approach to Design a Low Cost and High Performance Active-Active Clustering for Applications along with Database," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.191-198, 2018.

Cloud is the technology which works on distributed and shared environment, with sharing memory, resources, services, virtual infrastructure and platform. This all services can be accessed through internet. However, with several advantages cloud also provides with the disadvantage of security and privacy. Public network and publically access makes cloud insecure from intruders. Sensitive data of cloud is at the big risk because of security threats like attack, man-in-middle, eavesdropping etc. Cloud stores files in a file system with reliable storage of file on the basis of local file system. This storage of file is stored in different computers and thus called as servers which can be accessible to other computers, these are clients. Existing work only deals with achieving confidentiality with not concentrating on integrity and privacy of data at the time of storage. Proposed work achieves user’s trust and improves trust on cloud service provider. Architecture for security service is implemented for secure and safe storage of data using web services and technologies.

ECC, RC6, Storage, Replication, Cloud environment

[1]. Babitha.M.P and K.R. Ramesh Babu,” Secure Cloud Storage Using AES Encryption” published in International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT),International Institute of Information Technology (I²IT), Pune 2016.
[2]. Prof. Vishwanath S. Mahalle, “Implementing RSA encryption algorithm to enhance the data security of cloud in cloud computing”, International journal of pure & applied research in engineering and technology, 2013, volume (8):220-227, ISSN-2319- 507X IJPRET.
[3]. (U.S.) Nicholas. Carr, fresh Yan Yu, "IT is no longer important: the Internet great change of the high ground - cloud computing," The Big Switch:Rewining the World, from Edison to Google, CITIC Publishing House, October 2008
[4]. Jin-Mook Kim and Jeong-KyungMoon,” Secure Cloud Storage Using AES Encryption” published in International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT),International Institute of Information Technology (I²IT), Pune 2016.

Arpit Agrawal, Sakshi Joshi, "Secure Storage and Replication using Hybrid Cryptographic Algorithm for Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.199-203, 2018.