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A novel low noise trans-impedance amplifier is proposed using low cost 0.18 µm CMOS technology. A resistive-capacitive feedback is used to extend the bandwidth of the amplifier. As the structure is inductor less, it is suitable for low cost integrated optical interconnects. In this paper Improved Particle Swarm Optimization have applied to determine optimal trans-resistance and noise of proposed structure of amplifier. Simulation results showed a -3 dB bandwidth of 5 GHZ with a trans-impedance gain of ≈ 62 dB ohms. The total voltage source power dissipation is less than 5 mW that is much less than that of conventional trans-impedances. The output noise voltage spectral density is 9.5 nV/sqrt(Hz) with a peak of 15nV/sqrt(Hz), while, the input referred noise current spectral density is below 10pA/sqrt(Hz) within the amplifier frequency band.

TIA, CMOS, Noise, Amplifier

[1] Z. Nosal, “Integrated circuits for high speed optoelectronics” International Conference on Microwaves, Radar and Wireless Communications, 2, 445–455, (2004).
[2] J. Pekarik, D. Greenberg, B. Jagannathan, R. Groves, J. Jones, R. Singh, A. Chinthakindi, X. Wang, M. Breitwisch, D. Coolbaugh, P. Cottrell, J. Florkey, G. Freeman, and R. Krishnasamy, “RFCMOS technology from 0.25 µm to 65 nm: The state of the art” In Proceedings of IEEE Custom Integrated Circuits Conference, pp. 217–224, (2004).
[3] Hamid Niyazi, Fakhralsadat Rastegari, Majid Pourahmadi , "Design of A Novel Resistive Capacitive Feedback Trans-impedance Amplifier", International Journal of Computer Sciences and Engineering, Volume-04, Issue-06, Page No (1-7), Jun -2016.
[4] E. Sackinger, “Broadband circuits for optical fiber communication” New York: Wiley-Interscience, (2005).
[5] E. Sackinger, Broadband Circuits for Optical Fiber Communication. New York, NY, USA: Wiley, 2005.
[6]Takemoto,H.Yamashita,T.Yazaki,N.Chujo,Y.Lee,andY.Matsuoka, “A 4× 25-to-28 Gb/s 4.9 mW/Gb/s -9.7 dBm high-sensitivity optical receiver based on 65 nm CMOS for board-to-board interconnects,” in IEEE ISSCC Dig. Tech. Papers, 2013, pp. 118–119.
[7] Dan Li, Gabriele Minoia, Matteo Repossi, Daniele Baldi, and Enrico Temporiti, “A Low-Noise Design Technique for High-Speed CMOS Optical Receivers,” IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 49, NO. 6, JUNE 2014.
[8] J. F. Buckwalter, X. Zheng, G. Li, K. Raj, and A. V. Krishnamoorthy, “A monolithic 25-Gb/s transceiver with photonic ring modulators and Ge detectors in a 130-nm CMOS SOI process,” IEEE J. Solid-State Circuits, vol. 47, no. 6, pp. 1309–1322, Jun. 2012.
[9] C. Toumazou, and S. M. Park, “Wideband low noise CMOS transimpedance amplifier for gigahertz operation”, Electronics Letters, Vol. 32, No.13, pp. 1194-1196, (1996).
[10] Z. Chang, and W. M. C. Sansen, “Low-noise, low distortion CMOS AM wide-band amplifiers matching a capacitive source,” IEEE Journal of Solid-State Circuits, Vol.25, No. 3, pp. 833-840 (1990).
[11] B. Razavi, RF Microelectronics,Prentice Hall, (1998).
[12] B. Razavi, Design of analog CMOS integrated circuits, McGraw Hill, (2001).
[13] J. M. Garcia del Pozo, S. Celma, M. T. Sanz, and J. P Alegre, “CMOS tunable TIA for 1.25 Gbit/s optical Gigabit Ethernet,” Electronics Letters, Vol. 43(23), pp. 1303–1305, (2007).
[14] C. Verhoeven, A. V. Staveren, G. Monna, M. Kouwenhoven, and E. Yildiz, “Structured electronic design Negative feedback amplifiers”, Dordrecht: Kluwer Academic Publishers, (2003).
[15] D. A. Johns, and K. Martin, “Analog Integrated Circuit Design,” John Wiley and Sons, Inc., (1997).
[16] X. Chen, G. Wei, and L. Peh, “Design of low-power short distance opto-electronic transceiver front-ends with scalable supply voltages and frequencies,“ In International Symposium on Low Power Electronics and Design, pp. 277–282, (2008).
[17] F. Aznar, W. Gaberl, and H. Zimmermann, “A highly sensitive 2.5 Gb/s transimpedance amplifier in CMOS technology,” In IEEE International Symposium on Circuits and Systems, pp. 189–192, (2009).
[18] M. J. Lee, J. S. Youn, K. Y. Park, and W. Y. Choi, “A fully-integrated 12.5-Gb/s 850-nm CMOS optical receiver based on a spatially-modulated avalanche photodetector,” Vol. 22, No. 3, pp. 2511–2518, (2014).
[19] J. S. Youn, M. J. Lee, K. Y. Park, and W. Y. Choi, “10-Gb/s 850-nm CMOS OEIC receiver with a silicon avalanche photo-detector,” IEEE Journal of Quantum Electronic, Vol. 48(2), pp. 229–236, (2012).
[20] Samharison D, Rangaraja T. PSO Based Solution for the Optimal Location and Control Parameter Settings of UPFC in a Restructured Power System. Eur J Sci Res 2012; 83: 590- 601.
[21] Kennedy J, Eberhart R. Swarm Intelligence. San Diego, CA, [22] USA: Academic Press, 2001.Hongqing F, Long C, Zuyi S. Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor. Energ Convers Manage 2011; 52: 763-1

Naderbeigi, Mohamadreza Soltani , Iman Chaharmahali, "Optimization of a Resistive Capacitive Feedback Trans-impedance Amplifier Using IPSO Algorithm," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.1-7, 2016.

This study deals with the design of an hostel inmate - informatics system, which addresses the issues to discover the fact likeness to stay. By using Data Mining (DM) techniques, the data stored in a Data Warehouse (DW) can be analyzed for the purpose of uncovering and predicting hidden patterns within the data. So far, different approaches have been proposed to accomplish the conceptual design of Data Warehouse by applying the multidimensional modeling paradigm. This paper presents a novel approach to integrating data mining model into multidimensional models in order to accomplish the conceptual design of Data Warehouse with Association Rules (AR). To this extent, the Association Rules for modeling in the conceptual level. The main advantage of our proposal is that the Association Rules rely on the goals and user requirements of the Data Warehouse, instead of the traditional method of specifying Association Rules by considering only the final database implementation structures such as tables, rows or columns. In this way to show the benefits of our approach, implementation of specified Association Rules would be created on a commercial database management server.

SData Mining ; Data Warehousing; Multidimensional; Association rule

[1] Hamid Mohamadlou,”A method for mining association rules in quantitative and fuzzy data” ,978-1-4244-4136-5/09, IEEE, 2009, pp.453 – 458.
[2] Agrawal, R, and Srikant, R, “Fast algorithms for mining association rules” In: Proceedings of the 20th VLDB Int’l Conf., 1994, pp 487–499.
[3] Agrawal, R, and Srikant, R, “Fast algorithms for mining association rules in large database”, Technical report Fj9839, IBM Almaden Research Center, San jose, CA, jun.1994.
[4] Agrawal, R, and Imielinski, T, Swami, A, “Mining association rules between sets of items in large databases”, In Proc,ACM SIGMOD, May 1993,pp.207-216.
[5] XindongWu and et al, ” Top 10 algorithms in data mining”, Knowl Inf Syst ,Springer-Verlag London Limited , 2008, pp 14:1–37.
[6] Attila Gyenesei, "A Fuzzy approach for mining quantitative association rules", Technical Report: TUCS-TR-336, 2000.
[7] Zhu Ming, datamining, University of Science and Technology, china Press, Hefel, 2002, pp: 115 – 126.
[8] LI Pingxiang, CHEN Jiangping and BIAN Fuling, “A Developed Algorithm of Apriori Based on Association Analysis”, Geo-spatial Information Science ,Vol. 7, Issue 2, 2004, pp 108-112.
[9] HUANG Liusheng, CHEN Huaping, WANG Xun and CHEN Guoliang, “A Fast Algorithm for Mining Association Rules”, J. Comput. Sci. & Technol., Vol. 15 No. 6, 2000, pp 619-624,.
[10] Carlos Ordonez, Norberto Ezquerra and Cesar, A, Santana, “Constraining and summarizing association rules in medical data”, Knowl Inf Syst, 9(3), 2006, pp 259-283.
[11] Berry M.J.A and Linoff, G.S., “Data Mining Techniques for Marketing Sales and Customer Support”, John Wiley & Sons, Inc., 1997.
[12] Brin, S., Motwani, R., Ullman, J., and Tsur, S., “Dynamic itemset counting and implication rules for market basket data”,In Proc. of the ACM-SIGMOD Int’l Conf. on the Management of Data, 1997, PP. 255-264.
[13] Daniel Kunkle, Donghui Zhang, Gene Cooperman, “Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy”, J. Comput. Sci. & Technol., Vol. 23(1), 2008, pp. 77-102.
[14] Calvanesea, D., Dragoneb, L., Nardib, D., Rosatib, R., and Trisolinic, S.M., Enterprise modelling and data warehousing in TELECOM ITALIA. Information Systems 3, 2006.
[15] Comelli, M., Fe´nie` s, P., Gourgand, M., and Tchernev, N., “A generic evaluation model of logistic process for cash flow and activity based costing for a company supply chain”, In: International Conference on Industrial Engineering and Systems Management IESM 2005. Marrakech, Morocco, pp. 113–122, 2005.
[16] Caiyan Dai and Ling Chen, “An Algorithm for Mining Frequent Closed Itemsets with Density
from Data Streams”, IJCSE, pp. 40 – 48, 2016.

N. Balajiraja, "Mining Association Rule of Frequent Itemsets Measures for an Educational Environment," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.8-17, 2016.

The objective of this paper, provide the simulation steps of hybrid filter model that consists image denoising and image enhancement implementation over three different noises such as Salt and Pepper (SPN), Gaussian noise, Speckle (SPKN) with different noise variance in range .02 to .14. Hybrid filter works on spatial filtering techniques such as median filter and high pass filter that is operate on neighbourhood pixels. Median filter technique is used for smoothness and other(High pass) for sharpening of images and extracting the useful information in analysis process for image processing because of the input images are not always in good quality. The same concept is applied to the different images and they are compared with one another. The performance measurement is proposed with the help of Mean Square Errors (MSE), Peak-Signal to Noise Ratio (PSNR) and Signal to Noise (SNR) .So as to choose the appropriate noise for different filtering methods for any image. Result has simulated on MATLAB R2007b.

Different MRI image, , image noise, Filter, Median Filter, High-pass Filter, MSE, PSNR and SNR

[1]. Nichol, J.E. and Vohra, V., Noise over water surfaces In Landsat TM images, International Journal of Remote Sensing, Vol.25, No.11, 2004, PP.2087 - 2093.
[2]. Chi Chang-Yanab, Zhang Ji-Xiana, Liu Zheng-Juna, Study on Methods of Noise Reduction in a Stripped Image, the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol XXXVII. Part B6b, Beijing, 2008.
[3]. Charles Boncelet (2005).”Image Noise Models”. in Alan C. Bovik. Handbook of Image and Video Processing.
[4]. Sedef Kent, Osman Nuri Oçan, and Tolga Ensari (2004)."Speckle Reduction of Synthetic Aperture Radar Images Using Wavelet Filtering". in astrium. EUSAR 2004 Proceedings, 5th European Conference on SyntheticAperture Radar, May 25–27, 2004, Ulm, Germany.
[5]. Nagao M and Matsuyama T. (1997) Computer Graphics and Image Processing, vol. 9, pp. 394-407.
[6]. Lee J (1980) IEEE Transaction on pattern analysis and ma-chine intelligence, pp. 165-168.
[7]. Polesel A (2000) IEEE Transaction on Image Processing, vol.9, pp. 505 510.
[8]. Image Processing Science calculating RMSE and PSNR for color images Retrieved 6 April 2011.
[9]. Ming Zhang and Bahadur Gunturk (2008) ICASSP, IEEE, pp. 929-932.
[10] Muhammad Suzuri Hitam, Wan Nural Jawahir Hj Wan Yussof, Ezmahamrul Afreen Awalludin and Zainuddin Bachok, “Mixture Contrast Limited Adaptive Histogram Equalization for Underwater Image Enhancement”, IEEE, 2013.
[11]K.Sreedhar, B.Panlal, “Enhancement Of Images using Morphological Transformations”, International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 1, Feb 2012.

Vivek Singh Rathore, Vinod Singh Kharsan, "Simulation of Hybrid Filter Model to Enhanced the Quality of Noisy Images," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.18-23, 2016.

The VLAN technology is a technology which is used to logically divide the network into different broadcast domains. So that the packets are delivered within the port of same VLAN group. According to this paper the VLAN is the basis of the Inter-VLAN connection. Inter-VLAN routing technique is a technique which is used to allow different VLANs to communicate. In order to communicate we make use of router interface or multilayer switches. We have implemented this Inter-VLAN routing concepts using Packet Tracer Tool 6.0.1.

VLAN; Subinterface; Inter-VLAN; VLAN ID; Access mode; Trunk mode

[1] K. Okayama , “A Method of Dynamic Interconnection of VLANs for Large Scale VLAN Environment”, IEEE, ISBN: 4-88552-216-1, page.427 - 432
[2] Cisco Press,”CCNA Exploration Course Booklet: LAN Switching and Wireless, Version 4.0” Cisco networking Academy.
[3] Allan Johnson, “LAN Switching and Wireless: CCNA Exploration Labs and Study Guide” Cisco Press,ISBN: 1587132028,2008.
[4] Anthony Sequeira , “Interconnecting Cisco Network Devices, Part 1 (ICND1) Foundation Learning Guide”, Cisco Press, ISBN:978-1-58714-376-2, 2003
[5] Wayne Lewis, “LAN Switching and Wireless, CCNA Exploration Companion Guide”, Pearson Education, ISBN:978-81-317-2196-4, 2009
[6] Cisco Press, “Switched Networks Companion Guide”, Cisco networking Academy, ISBN :978-1-58713-329-9, 2014
[7] Cisco, “Configure InterVLAN Routing on Layer 3 Switches”, 2016, [Online]. Available:http://www.cisco.com /c/en/us/support/docs/lan-switching/inter-vlan-routing/ 41860 -howto-L3-intervlanrouting.pdf
[8] Cisco,” Configuring InterVLAN Routing with Catalyst 3750/3560/3550 Series Switches“, 2014 [Online]. Available: http://www.cisco.com/c/en/us/ support/docs/lan-switching/inter-vlan-routing/41260-189.pdf
[9] Rajiv O. Verma, S.S. Shriramwar “Effective VTP Model for Enterprise VLAN Security” 2013 International Conference on Communication Systems and Network Technologies
[10] A. Mansy, M. B. Tariq, N. Feamster, and M. Ammar, “Measuring vlaninduced dependencies on a campus network,” in Proc. ACM SIGCOMM,IMC, 2009.
[11] Cisco, “Understanding vlan trunk protocol (vtp),” 2007. [Online].Available:http://www.cisco.com/application/pdf /paws /10558/21.pdf
[12] Cisco, “Troubleshooting vlan trunk protocol (vtp),” 2007.[Online].Available:http://www.cisco.com/application/pdf/paws/98155/tshoot-vlan.pdf
[13] Cisco, “Catalyst 2950 Desktop Switch Software Configuration Guide”, 2002, [online] Available : http://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2950/software/release/12-1_11_yj/configuration/guide/scg.pdf
[14] S. D. Krothapalli, S. A. Yeo, Y.-W. E. Sung, and S. G. Rao, “Virtual man:A VLAN management system for enterprise networks,” Demo Session,ACM SIGCOMM, 2009.
[15] Sharada Ramani and R. M. Goudar,”Improved Bandwidth Aggregation using Available Lower Bandwidth Links”, International Journal of Computer Sciences and Engineering, Volume-4, Issue-6, ISSN: 2347-2693, 2016
[16] “Cisco Packet Tracer 6.0.1 Tool” Cisco Networking Academy

S.Somasundaram, M.Chandran, "A Simulation Based Study on Inter-VLAN Routing," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.24-29, 2016.

Paper herewith proposes an optimal predictive class association rule mining techniques for extracting the minimum rule having same predictive power of complete predictive class association rule by using predictive association rule set instead of complete class association rule , proposed methodologies in this paper can avoid the redundant and non-useful computation that would otherwise be required or needed for the mining of predictive class association rules and therefore improving the efficiency and effectiveness of the mining process significantly. Paper herewith presents an efficient and effective algorithm framework for mining the optimal predictive class association rule dataset by using CPAR before they are actually generated. In this paper, techniques have been implemented and obtained experimental results demonstrate that the algorithm generates the optimal class association rule set. Hence paper herewith propose a new data classification approach, Classification based on the Predictive Association Rules, which mainly combines the advantages and knowledge of both traditional rule-based and associative classification. Instead of generating the large number of candidate class association rules as in associative classification techniques, CPAR usually adopts a greedy algorithm for generating rules directly from the training dataset.

Discrimination, Association, CPAR, GC, DDPD, DDPP, IDPD, IDPP, DRP, IRP

[1] Jiuyong Lia, Hong Shenb, Rodney Topor , "Mining the optimal class association rule set" Received 2 April 2001 accepted 22 November 2001.
[2] Xiaoxin Yin Jiawei Han , "CPAR: Classification based on Predictive Association Rules " University of Illinois at Urbana-Champaign {xyin1, hanj}@cs.uiuc.edu.
[3] Asmita Kashid, Vrushali Kulkarni and Ruhi Patankar, “ Discrimination Prevention using Privacy Preserving Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 120 – No.1, June 2015.
[4] Kamal D. Kotapalle and Shyam Gupta, “Discrimination Prevention and Privacy Preservation in Data Mining”, www.ijird.com July, 2014 Vol 3 Issue 7 INTERNATIONAL JOURNAL.
[5] Rakesh Agrawal Tomasz Imielinski Arun Swami , "Mining Association Rules between Sets of Items in Large Databases" , IBM Almaden Research Center 650 Harry Road, San Jose, CA 95120 , 1993 ACM SIGMOD Conference Washington DC, USA, May 1993.
[6] Bing Liu Wynne Hsu Yiming Ma , "Integrating Classification and Association Rule Mining", Department of Information Systems and Computer Science National University of Singapore ,Lower Kent Ridge Road, Singapore 119260.
[7] Sergey Brin Rajeev Motwani Craig Silverstein "Beyond Market Baskets: Generalizing Association Rules to Correlations”, Department of Computer Science Stanford University Stanford,CA 94305.
[8] K. Ali, S. Manganaris, R. Srikant, Partial classification using association rules, in: D. Heckerman, H. Mannila, D. Pregibon, R. Uthurusamy (Eds.), Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), AAAI Press, Menlo Park, CA, 1997, p. 115.
[9] R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, A.I. Verkamo, Fast discovery of association rules, in: U. Fayyad (Ed.), Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, 1996.
[10] M. Houtsma, A. Swami, Set-oriented mining of association rules in relational databases, 11th International Conference Data engineering, 1995.
[11] J.S. Park, M. Chen, P.S. Yu, An effective hash based algorithm for mining association rules, ACM SIGMOD International Conference Management of Data, May, 1995.
[12] J. R. Quinlan , "Improved Use of Continuous Attributes in C4.5”, Basser Department of Computer Science, University of Sydney, Sydney Australia 2006 , Journal of Articial Intelligence Research 4 (1996) 77-90 Submitted 10/95; published 3/96
[13] P. Clark, R. Boswell, Rule induction with CN2: Some recent improvements, in: Y. Kodratoff (Ed.), Machine Learning—EWSL- 91, 1991.
[14]Fabrice Muhlenbachand Ricco Rakotomalala, "Discretization of Continuous Attributes " , Université Jean Monnet – Saint-Etienne, France.
[15]Alexandre Evfimievski,"Randomization in Privacy Preserving Data Mining”, Cornell University,Ithaca, NY 14853, USA Volume 4, Issue 2.
[16] S. Hajian and J. Domingo, "A Methodology for Direct and Indirect Discrimination prevention in data mining." IEEE transaction on knowledge and data engineering, VOL. 25, NO. 7, pp. 1445-1459, JULY 2013.

Krishna Kumar Tripathi, Narendra S. Chaudhari, "Mining Association rules and Differential Privacy Preservation using Randomization," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.30-38, 2016.

This paper is focused on a study of various techniques of brain tumor detection of MRI images using DIP techniques. The study of various techniques is useful for successful diagnosis and treatment planning of brain tumor. Magnetic Resonance Imaging (MRI) method is used for brain imaging and analyzing internal structures in detail. The accuracy of detecting the brain tumor location and size with good quality takes the most important role of detecting the brain tumor. The brain tumor segmentation carried manually from MRI images is very crucial and time consuming task. Therefore, to avoid that, it needs to use computer aided method for detection of brain tumor. The brain MRI images using various image processing methods like preprocessing, segmentation, morphological operation are used; based on different feature combinations as color (intensity), edge, texture and calculated the tumor area as well as measure the quality of input then output images, it gives a satisfactory result. This research work is helpful in the medical field to detect brain tumors and suggest a treatment plan to the patient.

Area, Brain tumor, MRI, Segmentation, Morphological Operation

[1] Vipin Y. Borole, Sunil S. Nimbhore, Seema S. Kawthekar,”Image Processing Techniques for Brain Tumor Detection: A Review”, International Journal of Emerging Trends & Technology in Computer Science, Volume 4, Issue 5(2), page No.(28-32), September – October 2015
[2] J.Mehena, M. C. Adhikary ,”Brain Tumor Segmentation and Extraction of MR Images Based on Improved Watershed Transform”,IOSR Journal of Computer Engineering, Volume 17, Issue 1(2), page No.(01-05), Jan – Feb. 2015
[3] Vinay Parameshwarappa, Nandish S, “A Segmented Morphological Approach to Detect Tumor in Brain Images”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 1, January 2014
[4] Neha Jain, D S Karaulia, “A Comparative Analysis of Filters on Brain MRI Images”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 11, November 2014
[5] R. B. Dubey ,M. Hanmandlu, ShantaramVasikarla, “Evaluation of Three Methods for MRI Brain Tumor Segmentation ”, Eighth International Conference on Information Technology: New Generations (ITNG. 2011.92), 2011
[6] Kanishka Sarkar, ArdhenduMandal, Rakesh Kumar Mandal,”Histogram Peak Normalization Based Threshold to Detect Brain Tumorfrom T1 Weighted MRI”, International Journal of Computer Sciences and Engineering Volume 4(1), Page No.(16-24), Feb 2016
[7] Swathi P S, Deepa Devassy, Vince Paul ,Sankaranarayanan P N,” Brain Tumor Detection and Classification Using Histogram Thresholding and ANN” International Journal of Computer Science and Information Technologies, Volume 6(1) ,page No.( 173-176), 2015
[8] Rajesh C. patil, A.S. Bhalchandra, “Brain tumor extraction from MRI images Using MATLAB”, IJECSCSE, Volume 2, issue1
[9] Mohammed Y. Kamil, “Brain Tumor Area Calculation in CT-scan image using Morphological Operations ”, IOSR Journal of Computer Engineering,Volume 17, Issue 2(V), Page No. 125-128, Mar – Apr. 2015
[10] Amer Al-Badarneh, Hassan Najadat, Ali M. Alraziqi, “A Classifier to Detect Tumor Disease in MRI Brain Images”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (ASONAM-2012).142, 2012
[11] Rohit S. Kabade, M. S. Gaikwad,”Segmentation of brain tumor and its area calculation in brain MR images using K-Means clustering and fuzzy C-Mean algorithm”, IJCSET, volume 4(5),Page No.( 524-531), may 2013
[12] D. Manju1, M. Seetha, K. Venugopala Rao, “Comparison Study of Segmentation Techniques for Brain Tumour Detection”, International Journal of Computer Science and Mobile Computing, IJCSMC, Volume 2, Issue 11, Page No.(261 – 269) ,November 2013
[13] Abdel-Maksoud , Mohammed Elmogy , Rashid Al-Awadi, “Brain tumor segmentation based on a hybrid clustering Technique”, Egyptian Informatics Journal, Page No.(71-81) 2015-16

Vipin Y. Borole, Seema S. Kawathekar, "Study of various DIP Techniques used for Brain Tumor detection and tumor area calculation using MRI images," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.39-43, 2016.

In software documentation, product knowledge and software requirement are very important to improve product quality. Reading of whole documentation of large corpus cannot be possible by developers in maintenance stage. They need to receive software documentation entities i.e. (development, designing and testing etc.) in a short period of time. In software documentation an important documents are able to record. There exists a space between information which developer wants and software documentation. This difference can be experimental whenever developers effort to discover the accurate information in the correct form at the exact time. To solve this problem, an approach for extracting relevant task of the documentation under four phases of software entities (i.e. documentation, development, testing and other etc.) is described. The main idea is task extracted from the software documentation, freeing the developer easily get the required data from software documentation with customize portal using Natural Language Processing (NLP) and then the category of task can be generated easily from existing applications. The machine learning approach that is based on supervised learning technique for training dataset in the form of text files based on text mining. Our approach use WordNet library to identify relevant tasks for calculating frequency of each word which allows developers in a piece of software to discover the word usage and also assigning Part-of Speech (POS) to each word. The result shows that task is extracted by calculating how many sentences, tokens and tasks appearing in a document and also shows task is relevant or not. It also reduced a live space between information which developers want and software documentation. This is used to improve the performance of system by taking feedback of developers. The result is identified through customize portal which helps to developers easily get information in a short period of time. The system is 80% precise to extract task by taking feedback of developers in the form of comment.

Natural language processing, text mining, part-of-speech tagging, text files, machine learning techniques, WordNet library

[1] Christoph Treude, Martin P. Robillard, and Barth_el_emy Dagenais ,”Extracting Development Task To Navigate Software Documentation” in Proc, IEEE Soft,Vol.41 No.6,2015,pp,565-581, June 2015.
[2] S. Gupta, S. Malik, L. Pollock, and K. Vijay-Shanker, “Part-of speech tagging of program identifiers for improved text-based software engineering tools,” in Proc. 21st IEEE Int. Conf. Program Comprehension, pp. 3–12,2013 .
[3] M. Barouni-Ebrahimi and A. A. Ghorbani, “On query completion in web search engines based on query stream mining,” in Proc. IEEE/WIC/ACM Int. Conf. Web Intell., pp. 317–320,2007.
[4] P. Mika, E. Meij, and H. Zaragoza, ”Investigating the semantic gap through query log analysis,” in Proc. 8th Int. Semantic Web Conf., pp. 441–455,2009.
[5] S.L.Abebe and P.Tonella,“Natural language parsing of program element names for concept extraction,” in Proc. 18 th IEEE Int. Conf. Program Comprehension, pp. 156–159,2010.
[6] C. Treude and M.-A. Storey, “Effective communication of software development knowledge through community portals,” in Proc. 8th Joint Meet. Eur. Soft. Eng. Conf. ACM SIGSOFT Symp. Found. Soft. Eng., pp. 91–101,2011.
[7] T. C. Lethbridge, J. Singer, and A. Forward, “How software engineers use documentation: The state of the practice,” IEEE Soft., vol. 20, no. 6, pp. 35–39, Nov./Dec. 2003.
[8] C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky, “The Stanford Core NLP natural language processing toolkit,” in Proc. 52 nd Annu. Meet. Assoc. Computat. Linguistics: Syst. Demonstrations, pp. 55–60,2014.
[9] G. Sridhara, E. Hill, L. Pollock, and K. Vijay-Shanker, “Identifying word relations in software: A comparative study of semantic similarity tools,” in Proc. 16th IEEE Int. Conf. Program Comprehension, pp. 123–132, 2008.
[10] H. Zhong, L. Zhang, T. Xie, and H. Mei, “Inferring resource specifications From natural language API documentation,” in Proc. 24th IEEE/ACM Int. Conf. Automated Soft. Eng., pp. 307–318,2011.
[11] S. Haiduc, G. Bavota, A. Marcus, R. Oliveto, A. De Lucia, and T. Menzies, “Automatic query reformulations for text retrieval in software engineering,” in Proc. 35th Int. Conf. Soft. Eng., pp. 842–851,2013.
[12] J. Yang and L. Tan, “Inferring semantically related words from software context,” in Proc. 9th Working Conf. Min. Softw. Repositories, pp. 161–170,2012.
[13] E. Hill, L. Pollock, and K. Vijay-Shanker, “Automatically capturing source code context of NL-queries for software maintenance and reuse,” in Proc. 31st Int. Conf. Soft. Eng., pp. 232–242,2009.
[14] M. J. Howard, S. Gupta, L. Pollock, and K. Vijay-Shanker, “Automatically mining software-based, semantically-similar words from comment-code mappings,” in Proc. 10th Working Conf. Min. Softw. Repositories, pp. 377–386, 2013.
[15] James H. Martin, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”, by Prentice Hall ,January 2000.

Prachi Rayate, Devendra Singh Thakore, "Extracting Tasks of Text Files using Dictionary Based Approach for Classification and Indexing," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.44-50, 2016.

Nowadays farmers are facing huge difficulties in their cultivation and don’t get expected yields due to several diseases caused by many factors such as unpredictable changes in weather and soil parameters. This problem leads to research in the area of agriculture towards precision agriculture with an intention of improving the crop yield. The lots of research works are underway on precision agriculture. In this regard, this paper present research work of study on how soil moisture affects the growth of tomato plant and its association among other soil parameters such as temperature, pH etc.

Precision Agriculture, Wireless Sensor Network, Soil Temperature, Soil Ph, Classification Tree

[1] P. Patil, V. H., S. Patil and U. Kulkarni. “Wireless Sensor Network for Precision Agriculture”. Int. Conference on Computational Intelligence and Communication Networks (CICN), Page No (763-766), Oct 7-9, 2011.
[2] A. Tagarakis, V. Liakos, L. Perlepes, S. Fountas and T. Gemtos, “Wireless Sensor Network for Precision Agriculture”. 15th Panhellenic Conference on Informatics (PCI),Page No(397-402), Sep 30-Oct 2, 2011.
[3] Wei Dai and Wei Ji, “A MapReduce Implementaion of C4.5 Decision Tree Algorithm”. Int. Journal of Database Theory and Application, Volume-07, Issue-01, Page No(49-60), Feb 2014.
[4] Gates CT, Bonner J. “The Response of The Young Tomato Plant to a Brief Period of Water Shortage. IV. Effects of Water Stress on the Ribonucleic Acid Metabolism of Tomato Leaves”. Int. Journal on Plant Physiology. Volume -34, Issue(1), Page No(49-55), Jan 1959.
[5] Caroline Imani Sibomana, Arnold Mathew Opiyo, Joseph Nyamori Aguyoh. “Influence of soil moisture levels and packaging on postharvest qualities of tomato ( Solanum lycopersicum)”. African Journal of Agricultural Research, Volume -10, Issue-12, Page No(1392-1400),March 2015.
[6] George N Agrios, “Plant Pathology”, USA: Academic Press, Fifth (5th ) Edition, ISBN: 978-0-12-044565-3 Page No(249–263), 2005
[7] Vijitha R, Mahendran S, “Effect of moisture stress at different growth stages of tomato plant (Lycopersicon esculentum Mill.) on yield and quality of fruits”, Journal of Science of the University of Kelaniya Sri Lanka, Volume-5, Issue-0, Page No(1-11), Feb 2010.
[8] S. R. Adams, K. E. Cockshull, C. R. J. Cave. “Effect of Temperature on the Growth and Development of Tomato Fruits”, Annals of Botany, Volume-8, Issue-5, Page No(869-877), Nov 2001.
[9] Bing Liu Wynne Hsu Yiming Ma. Integrating Classification and Association Rule Mining”, Proceedings of the fourth international conference on knowledge discovery and data mining. Page No(80-86), August 27-31, 1998.
[10] Tomato Crop Guide. Growth Stages, http://www.haifagroup.com/knowledge_center/crop_guides/tomato/about_the_crop/growth_stages/ , Retrieved on March, 2016.
[11] Teknomo, kardi, Tutorial on Decision Trees, http://people.revoledu.com/kardi/tutorial/DecisionTree , Retrieved on April, 2016.

Sunil Kumar S, Nagesh H.R, "WSN based Soil Moisture Stress Monitoring and identifying its association on Other Parameters on plants growth using hadoop Framework," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.51-54, 2016.

Since the onset of Cloud computing and its inroads into infrastructure as a service, Virtualization has become peak of importance in the field of abstraction and resource management. However, these additional layers of abstraction provided by virtualization come at a trade-off between performance and cost in a cloud environment where everything is on a pay-per-use basis. Containers which are perceived to be the future of virtualization are developed to address these issues. This study paper scrutinizes the performance of a conventional virtual machine and contrasts them with the containers. We cover the critical assessment of each parameter and its behavior when its subjected to various stress tests. We discuss the implementations and their performance metrics to help us draw conclusions on which one is ideal to use for desired needs. After assessment of the result and discussion of the limitations, we conclude with prospects for future research.

Performance, Virtual Machines(VMs), Containers, Virtualization, Kernel Virtual machines (KVM), Docker, Hypervisor

[1] Xavier, M.G., Neves, M.V. and De Rose, C.A.F., 2014, February. A performance comparison of container-based virtualization systems for mapreduce clusters. In 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (pp. 299-306). IEEE.
[2] Soltesz, S., Pötzl, H., Fiuczynski, M.E., Bavier, A. and Peterson, L., 2007, March. Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. In ACM SIGOPS Operating Systems Review (Vol. 41, No. 3, pp. 275-287). ACM.
[3] Vaughan-Nichols, S.J., 2006. New approach to virtualization is a lightweight.Computer, 39(11), pp.12-14.
[4] Raval, K.S., Suryawanshi, R.S., Naveenkumar, J. and Thakore, D.M., 2011. The Anatomy of a Small-Scale Document Search Engine Tool: Incorporating a new Ranking Algorithm. International Journal of Engineering Science and Technology, 1(3), pp.5802-5808.
[5] Archana, R.C., Naveenkumar, J. and Patil, S.H., 2011. Iris Image Pre-Processing And Minutiae Points Extraction. International Journal of Computer Science and Information Security, 9(6), p.171.
[6] Jayakumar, M.N., Zaeimfar, M.F., Joshi, M.M. and Joshi, S.D., 2014. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET). Journal Impact Factor, 5(1), pp.46-51.
[7] Naveenkumar, J. and Joshi, S.D., 2015. Evaluation of Active Storage System Realized through MobilityRPC.
[8] Jayakumar, D.T. and Naveenkumar, R., 2012. SDjoshi,“. International Journal of Advanced Research in Computer Science and Software Engineering,” Int. J, 2(9), pp.62-70.
[9] Jayakumar, N., Singh, S., Patil, S.H. and Joshi, S.D., Evaluation Parameters of Infrastructure Resources Required for Integrating Parallel Computing Algorithm and Distributed File System.
[10] Jayakumar, N., Bhardwaj, T., Pant, K., Joshi, S.D. and Patil, S.H., A Holistic Approach for Performance Analysis of Embedded Storage Array.
[11] Naveenkumar, J., Makwana, R., Joshi, S.D. and Thakore, D.M., 2015. OFFLOADING COMPRESSION AND DECOMPRESSION LOGIC CLOSER TO VIDEO FILES USING REMOTE PROCEDURE CALL. Journal Impact Factor, 6(3), pp.37-45.
[12] Naveenkumar, J., Makwana, R., Joshi, S.D. and Thakore, D.M., 2015. Performance Impact Analysis of Application Implemented on Active Storage Framework. International Journal, 5(2).
[13] Salunkhe, R., Kadam, A.D., Jayakumar, N. and Thakore, D., In Search of a Scalable File System State-of-the-art File Systems Review and Map view of new Scalable File system.
[14] Salunkhe, R., Kadam, A.D., Jayakumar, N. and Joshi, S., Luster A Scalable Architecture File System: A Research Implementation on Active Storage Array Framework with Luster file System.
[15] Jayakumar, N., Reducts and Discretization Concepts, tools for Predicting Student’s Performance.
[16] Jayakumar, M.N., Zaeimfar, M.F., Joshi, M.M. and Joshi, S.D., 2014. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET). Journal Impact Factor, 5(1), pp.46-51.
[17] Kumar, N., Angral, S. and Sharma, R., 2014. Integrating Intrusion Detection System with Network Monitoring. International Journal of Scientific and Research Publications, 4, pp.1-4.
[18] Namdeo, J. and Jayakumar, N., 2014. Predicting Students Performance Using Data Mining Technique with Rough Set Theory Concepts. International Journal, 2(2).
[19] Naveenkumar, J., Keyword Extraction through Applying Rules of Association and Threshold Values. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN, pp.2278-1021.
[20] Kakamanshadi, G., Naveenkumar, J. and Patil, S.H., 2011. A Method to Find Shortest Reliable Path by Hardware Testing and Software Implementation. International Journal of Engineering Science and Technology (IJEST), ISSN, pp.0975-5462.
[21] Naveenkumar, J. and Raval, K.S., Clouds Explained Using Use-Case Scenarios.
[22] Naveenkumar J, S.D.J., 2015. Evaluation of Active Storage System Realized Through Hadoop. International Journal of Computer Science and Mobile Computing, 4(12), pp.67–73.
[23] Rishikesh Salunkhe, N.J., 2016. Query Bound Application Offloading: Approach Towards Increase Performance of Big Data Computing. Journal of Emerging Technologies and Innovative Research, 3(6), pp.188–191.
[24] Sagar S lad s d joshi, N.J., 2015. Comparison study on Hadoop’s HDFS with Lustre File System. International Journal of Scientific Engineering and Applied Science, 1(8), pp.491–494.
[25] Salunkhe, R. et al., 2015. In Search of a Scalable File System State-of-the-art File Systems Review and Map view of new Scalable File system. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016. pp. 1–8.
[26] Naveenkumar, J. and Raval, K.S., Clouds Explained Using Use-Case Scenarios. INDIACom-2011 Computing for Nation Development, 2011/3.
[27] N. Jayakumar, “Reducts and Discretization Concepts, tools for Predicting Student’s Performance,” Int. J. Eng. Sci. Innov. Technol., vol. 3, no. 2, pp. 7–15, 2014.
[28] BVDUCOE, B.B., 2011. Iris Image Pre-Processing and Minutiae Points Extraction. International Journal of Computer Science & Information Security.
[29] P. D. S. D. J. Naveenkumar J, “Evaluation of Active Storage System Realized through MobilityRPC,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 3, no. 11, pp. 11329–11335, 2015.
[30] Anjali Nigam and Vineet Singh, “Securing Data Transmission in Cloud using Encryption Algorithms”, International Journal of Computer Sciences and Engineering Research Volume 4, Issue 6, June 2016.
[31] Pritika Goel, “An Improved Load Balancing Technique in Weighted Clustering Algorithm”, International Journal of Computer Sciences and Engineering Research Volume 4, Issue 6, June 2016.
[32] Anish Babu S , Hareesh M J , John Paul Martin , Sijo Cherian and Yedhu Sastri.”System Performance evaluation of Paravirtualization, Container virtualization and Full virtualization using Xen, OpenVZ and XenServer” Fourth International Conference on Advances in Computing and Communications.pp 247-250 IEEE 2014

Prashant Ramchandra Desai, "A Survey of Performance Comparison between Virtual Machines and Containers," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.55-59, 2016.

Object oriented platform allow us to create ‘n’ number of objects of a class without imposing any constraints. By taking advantage of this feature, unknown objects of known and unknown classes can also be created in an application by the intruder. This paper presents a security model for object oriented platform to overcome such issues. In this paper, I have raised security issues related to object oriented applications. I have also discussed the needs of security for these applications.

Inheritance; Object; Unknown classes; Unknown Objects

[1 ] E.B. Fernandez, R.C. Summers, and C. Wood, “Database Security and Integrity,” Addison-Wesley, February 1981.
[2 ] T.F. Keefe, “SODA: A Security Model For Object-oriented Management Systems”, Proceedings of 15th International Computer Software and Applications Conference, Tokyo, Japan, September 1991.
[3 ] Jonathan K. Millen and Teresa F. Lunt, “Security for Object-Oriented Database Systems,” IEEE 1992.
[4 ] Elisa Bertino, “Data Hiding and Security in Object-Oriented Databases”, Eighth International Conference on Data Engineering, 338-347, February 1992.
[5 ] Juhnyoung Lee, Sang H.Son, Myung-Joon Lee, “Issues in devolving object-oriented databases systems for real-time applications,” IEEE 1994.
[6 ] Y. Oki, T. Chikaraishi, T. Shimomura and T. Ohta, “A Design Method for Data Integrity in Object-Oriented Database Systems,” IEEE 1995.
[7 ] Roshan K. Thomas and Ravi S. Sandhu, “A Trusted Subject Architecture for Multilevel Secure Object-Oriented Database,” IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 1, February 1996.
[8 ] Elisa Bertino and Ravi Sandhu, “Database Security-Concepts, Approaches, and Challenges”, IEEE Transactions on Dependable and Secure Computing, Vol. 2, No.1, January-March 2005.
[9 ] Ni Xianjun, “A Logic Specification and Implementation Approach for Object Oriented Database Security,” Workshop on Knowledge Discovery and Data Mining, IEEE 2008.
[10 ] Sohail IMRAN and Dr. Irfan Hyder, “Security Issues in Databases”, Second International Conference on Future Information Technology and Management EngineeringIEEE 2009.
[11 ] Leon Pan, “A Unified Network Security and Fine-Grained Database Access Control Model,” Second International Symposium on Electronic Commerce and Security, 2009.
[12 ] Mansaf Alam “Access specifiers Model for Data Security in Object Oriented Databases,”Eighth International Conference on Information Technology: New Generations, IEEE 2011.
[13 ] Hussain Al-Aqrabi; Lu Liu; Richard Hill; Zhijun Ding; Nick Antonopoulos“Buisness Intelligence Security on clouds:Challenges,Solutions and Future Directions”,IEEE 2013
[14 ] Nitish Pathak; Girish Sharma; B. M. Singh “Designing of SPF based secure web application using forward engineering”,IEEE 2015.
[15 ] S. Nagaparameshwara Chary, B.Ram “Analysis of Classification Technique Algorithms in Data mining- A Review“,IJCSE 2016.

Amanpreet Kaur, "Security Model for Object Oriented Applications," International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.60-65, 2016.