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Routing is the process of finding optimal path between source and destination. Because of the fact that packet may be necessary to hop or several hops before a packet reach the target, a routing protocol is needed. Routing protocols allow routers to dynamically advertise and discover routes, decide which routes are available and which are the most efficient routes to a target. In this paper we review different existing protocols and their applicability in current scenario.

MANET, Routing, Performance, Qualitative, Quantitative Characteristics

[1]. Laiali Almazaydeh, Eman Abdelfattah, Manal Al- Bzoor, and Amer Al- Rahayfeh “Performance Evaluation Of Routing Protocols In Wireless Sensor Networks” International Journal of Computer Science and Information Technology, Volume 2, Number 2, April 2010
[2]. Jamal N. Al- Karaki, Ahmed E. kamal,”Routing Techniques In Wireless Sensor Networks:A Survey”,IEEE Wireless Communications December 2004.
[3]. Smt. Rajashree.V. Biradar & Prof V. C. Patil, “Classification and Comparison of routing Techniques in Wireless Ad-hoc Networks”, Proceedings of international Symposium on Ad-hoc Ubiquitous Computing (ISHUC’06), pages 7-11, 2006.
[4]. Yi-Chun Hu, Adrian Perrig, “A Survey of Secure Wireless Ad Hoc Routing”, IEEE Security and Privacy, 2(3): pages 28-39, May/June 2004.
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[9]. M. R. Pearlman, Z. J. Haas, P. Sholander and S. S. Tabrizi, “On the Impact of Alternate Path Routing for Load Balancing in Mobile Ad Hoc Networks”, Proceedings of the ACM MobiHoc, pages 3-10, 2000.
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Suman Saroj, Raj Gaurang Tiwari and Pankaj Kumar, "Review of Different Criteria for Designing Routing Protocols," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.56-59, 2015.

World is facing drought severity in every year. Drought is hard to predict and it is a very complex natural disaster to understand. It has high impact on human habitat and the agricultural sector. Satellite remote sensing is a revolutionary technology which is helpful for monitor natural disaster on earth from the space. This research study investigates drought severity level in Vaijapur tehsil on the basis satellite drought indicator. This study is focused on Vaijapur tehsil which is coming under scanty rainfall region. The water scarcity in throughout the year has been an unsolved problem for decades. In this research paper we have used Landsat 8 TIRS (Thermal Infrared) Sensor data, to extract the Temperature Condition Index (TCI) of the year 2013-2014. We have collected rainfall and temperature data from Indian Meteorological Department (IMD) web portal.

Drought Indices; Landsat 8; TCI; ATCOR

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Sandeep V. Gaikwad, Kale K.V., Rajesh K. Dhumal and Amol D. Vibhute, "Analysis of TCI Index Using Landsat8 TIRS Sensor Data of Vaijapur Region," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.60-64, 2015.

Deploying application over the web is increasing day by day. Such deployed application useful for client to store as well as retrieve database to/from particular server. Over the web data stored in distributed manner so flexibility, reliability, scalability and security are important aspects need to be considered while constructed data management system. After analyzing Distributed data aggregation service(DDAS) system which maintain a catalog which is relying on Blobseer it found that it provide a good performance in aspects such as data storage as a Blob (Binary large objects) and fast retrieval of data by data aggregation process. For highly complex analysis and instinctive mining of scientific data, Blobseer act as a repository backend for easy retrieval of data. By using this Kerberos method client will able to done a secure authentication as using this method only authorized clients are able to access distributed database. Kerberos consist of 4 steps i.e. Authentication Key exchange, Ticket granting service Key exchange, Client/Server service exchange and Build secure communication. After that aggregation of data carried out and aggregated data catalog is generated. From that catalog user is able to search a required data and this data in zip file form saved at client side. For zipping purpose Adaptive Huffman method is used (also referred to as Dynamic Huffman method) which is based on Huffman coding. It permits compression as well as decompression of aggregated data.

Adaptive Huffman Method; Blobseer; Distributed Database; Kerberos; Data Aggregation

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Suraj Gulhane and Sonali Bodkhe, "Implementation of Kerberos method on DDAS system and search data speedily from extracted Zip data," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.65-71, 2015.

chronic kidney disease refers to the condition of kidneys caused by conditions, diabetes, glomerulonephritis or high blood pressure. These problems may happen gently for a long period of time, often without any symptoms. It may eventually lead to kidney failure requiring dialysis or a kidney transplant to preserve survival time. So the primary detection and treatment can prevent or delay of these complications. The aim of this work is to reduce the diagnosis time and to improve the diagnosis accuracy through classification algorithms. The proposed work deals with classification of different stages in chronic kidney diseases using machine learning algorithms. The experimental results performed on different algorithms like Naive Bayes, Decision Tree, K-Nearest Neighbour and Support Vector Machine. The experimental result shows that the K-Nearest Neighbour algorithm gives better result than the other classification algorithms and produces 98% accuracy.

Chronic Kidney Disease (CKD), Machine Learning (ML), End-Stage Renal Disease (ESRD), Cardiovascular disease, data mining, machine learning,

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N. Radha and S. Ramya , "Performance Analysis of Machine Learning Algorithms for Predicting Chronic Kidney Disease," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.72-76, 2015.

Sensor nodes in Wireless Sensor Networks having restricted power, weak computing capacity and reduced storage capacity. Therefore an proficient energy saving method is required to extend the duration of a network. LEACH is one of the popular algorithm, but it's some negatives such as for example each node may possibly not be established often in clusters, even though nodes have different power, CH is selected unreasonably and many other. These limitations are over come in I-LEACH algorithm by which sensor node with higher remaining power, more neighbors and lesser range from Base station is selected as a Cluster Head. I-LEACH algorithm more could be revised and thereby we could reduce the energy consumption of the network. Thus it's be great for prolonging the network lifetime. This research paper has dedicated to increasing the network lifetime by using the reactive I-LEACH protocol. The comparison among LEACH, I-LEACH and proposed method has also been performed based on power usage and network life time. Even in case of node scalability analysis the proposed technique shows rather effective results.

Leach, I-Leach, Cluster Head, Energy Efficiency, Reactivity

[1] Beiranvand, Z., Patooghy, A. and Fazeli M., “I-LEACH: An Efficient Routing Algorithm to Improve Performance & to Reduce Energy Consumption in Wireless Sensor Networks”, IEEE 5th International Conference on Information and Knowledge Technology, May 2013, pp. 13-18.
[2] Elbhiri, B., Fkihi, S. E., Saadane, A., Lasaad N., Jorio, A., Driss, Aboutajdine, E.R. and Morocco “A New Spectral Classification for Robust Clustering in Wireless Sensor Networks”, IEEE Conference on Wireless and Mobile Networking (WMNC), April 2013, pp. 1-10.
[3] Renold A.P., Poongothai R.and Parthasarathy R., “Performance Analysis of LEACH with Gray Hole Attack in Wireless Sensor Networks”, IEEE 2012 International Conference on Computer Communication an Informatics (ICCCI-2012) on IEEE, Jan 2012.
[4] Sen A., Gupta M.D., and De D., “Energy Efficient Layered Cluster Based Hierarchical Routing Protocol with Dual Sink”, IEEE 5th International Conference on Computer and Devices for Communication (CODEC), IEEE, May 2012.
[5] Kim D. S., Cha H. S. and Yoo S., “Improve Far-Zone LEACH Protocol for energy Conserving” MKE(Ministry of Knowledge Economy) support this research, under the Convergence- ITRC(Convergence Information Technology Research Center) support program supervised by NIPA(National IT Industry Promotion Agency) on IEEE 2012.
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[13] HOANG V-T, JULIEN N. And BERRUET P. “Cluster-Head Selection Algorithm to Enhance Energy-Efficiency and Reliability of Wireless Sensor Networks”, European Wireless 2014, pp. 933 – 938
[14] Ruperee A., Nema S. And Pawar S. “ Achieving Energy Efficiency and Increasing Network Life in Wireless Sensor Network”, IEEE International Advance Computing Conference (IACC), 2014 IEEE, pp. 171 – 175
[15] Yadav J., Dr. Dubey S.K. “ Analytical Study of Cluster Head Selection Schemes in Wireless Sensor Networks” International Conference on Signal Propagation and Computer Technology (ICSPCT), 2014 IEEE, pp. 81 – 85
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Jagwant Singh and Jaswinder Singh, "Performance Evaluation of Sensor Node Scalability on Reactive Modified I-Leach Protocol," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.77-84, 2015.

The enhancement of digital devices and the popularity of social networking sites like Facebook, twitter, Instagram etc. The large numbers of peoples are shearing their images and videos by different social networking sites. The users are very much interested in uploading the images or videos on the internet in which most of the photos and videos contain faces. Thus with the rapidly growing photos and videos on the internet the large scale content base face image retrieval is a facilitating technology for many prominent applications. In this project, our aim is to detect a human face image which is present in the video frame and retrieving the similar human face images from the large scale database. By using human attributes in a systematic and scalable framework. The attribute-enhanced sparse coding is used to improve the performance of face retrieval in the offline stage. With this method the performance improvement to greater extent. Experimenting on public photo and video datasets, the result shows that the implementation of above method by using video.

Face image, human attributes, content-based image retrieval, Face image retrieval, Face occurrences in videos

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[18] B. Siddiquie, R. S. Feris, and L. S. Davis, “Image ranking and retrieval based on multi-attribute queries,” IEEE Conference on Computer Vision and Pattern Recognition, 2011.
[19] W. Scheirer, N. Kumar, K. Ricanek, T. E. Boult, and P. N. Belhumeur, “Fusing with context: a bayesian approach to combining descriptive attributes,” International Joint Conference on Biometrics, 2011.
[20] W. Scheirer and N. Kumar and P. Belhumeur and T. Boult, “Multi-Attribute Spaces: Calibration for Attribute Fusion and Similarity Search,” IEEE Conference on Computer Vision and Pattern Recognition, 2012.
[21] W. J. Scheirer, A. Rocha, R.Michaels, and T. E. Boult. Meta-Recognition: The Theory and Practice of Recognition Score Analysis. IEEE TPAMI, 33(8):1689–1695, August 2011.
[22] W. J. Scheirer, A. Rocha, R. Micheals, and T. E. Boult. Robust Fusion: Extreme Value Theory for Recognition Score Normalization. In ECCV, September 2010
[23] Z. Wu, Q. Ke, J. Sun, and H.-Y. Shum, “Scalable face image retrieval with identity-based quantization and multi-reference re-ranking,” IEEE Conference on Computer Vision and Pattern Recognition, 2010.
[24] H. Jegou, M. Douze, and C. Schmid, “Hamming embedding and weak geometric consistency for large scale image search,” European Conference on Computer Vision, 2008.
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Devendra Sakharkar and Sonali Bodkhe, "Face Matching for Similar Faces Evaluation from Videos Using Low Level Facial Geometries," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.85-89, 2015.

Six Sigma at various esteemed organizations merely means a calculation of excellence that strives for near perfection. Six Sigma is a restricted, data-driven move toward methodology for eliminating defects in any process – from developed to transactional and from manufactured goods to service. The statistical representation of Six Sigma describes quantitatively of how a procedure is performed. To attain Six Sigma, a process must not produce more than 3.4 defects per million opportunities. A Six Sigma defect is distinct, no matter at which peripheral of customer stipulation it occurs. A Six Sigma prospect is entire extent of probability for a defect. Six Sigma calculator can be used to design process sigma. Following paper works on different approaches of Six Sigma such as Lean Six Sigma, how it affects the overall customer experience, work and effort when put together in a process. It studies of how Lean and Six Sigma supplement each other and their use at different levels of organization. It focuses on how use of six sigma approach affects reliability; different reliability metrics on which reliability has been measured upon in past and changes which six sigma has induced in. Its overall effect on the working of an organization process and number of failures faced when it is implemented thoroughly.

Six sigma, Lean Six Sigma, Reliability metrics

[1] Forrest W. Breyfogle Implementing Six Sigma:Smarter Solutions Using Statistical Methods :
[2] 6σ OJT Six Sigma Application – GEPS Playbook,GE Power Systems
[3] Darshak A.Desai,“ Improving productivity and profitability through Six Sigma: experience of a small-scale jobbing Industry”, Int. J. Productivity and Quality Management, Vol. 3, No. 3, (2008)
[4] Coleman, S. (2008). Six Sigma: an opportunity for statistics and for statisticians. Significance,Vol. 5, Issue 2, pp. 94-96
[5] Harry, M. J. (1998, May), “Six-Sigma: A breakthrough strategy for profitability”, Quality Progress, Vol. 31, No. 5, pp. 60-64.
[6] Mrinal Singh Rawat1, Arpita Mittal(2012), “Survey on Impact of Software Metrics on Software Quality,(IJACSA) International Journal of Advanced Computer Science and Applications”,Vol. 3, No. 1
[7] Aasia Quyoum, Mehraj – Ud - Din Dar 9November 2010).” Improving Software Reliability using Software Engineering Approach- A Review , International Journal of Computer Applications (0975 – 8887)”,Volume 10– No.5,
[8] DMADV ,http://www.governica.com/DMADV,8/08/15
[9] DMADV,http://whatis.techtarget.com/definition/DMADV,17/3/15
[10] Reliability,http://www.ausairpower.net/PDF-A/Reliability-PHA.pdf,15/08/15
[11] James V. Earle(2009), “DEVELOPMENT OF A STRATEGIC PLANNING PROCESS MODEL”
[12] International Journal of Quality & Reliability Management, Volume 26, Issue 7 2012
[13] SixSigmaMethodology,http://blog.royaleinternational.com/2013/10/the-six-sigma-methodology.html,18/07/15
[14] Reliability and six sigma, http://reliabilityweb.com/index.php/articles/bridging_the_gap_between_reliability_six_sigma/,19/08/15

[15] Duane Model ,http://reliawiki.org/index.php/Duane_Model,1/8/15
[16] Quality and Six Sigma,http://www.simplilearn.com/quality-assurance-six-sigma-rar91-article,2/08/15
[17] Metrices and Six Sigma,http://www.intechopen.com/books/six-sigma-projects-and-personal-experiences/demystifying-six-sigma-metrics-in-software,20,08/15
[18] Reliablity and six Sigma Relation,http://reliabilityweb.com/index.php/articles/bridging_the_gap_between_reliability_six_sigma/,21/08/15
[19] Zscore,http://stattrek.com/statistics/dictionary.aspx?definition=z%20score,18/08/15
[20] B.Anni Princy, “Software Reliability of Proficient Enactment”, Vol. 5 No.3 Jun-Jul 2014

Kumar Shashvat, Arshpreet Kaur and Raman Chadha , "Affect of Six Sigma Methdology on Reliability and Quality of Product," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.90-96, 2015.

The article gives an overview of some of the most popular machine learning methods (Naïve Bayesian classifier, naïve Bayesian k-cross validation, naïve Bayesian info gain, Bayesian classification and Bayesian net with correlation based feature subset selection) and of their applicability to the problem of spam-filtering. Brief descriptions of the algorithms are presented, which are meant to be understandable by a reader not familiar with them before. Classification and clustering techniques in data mining are useful for a wide variety of real time applications dealing with large amount of data. Some of the application areas of data mining are text classification, medical diagnosis, intrusion detection systems etc. The Naive Bayesian Classifier technique is based on the Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayesian can often outperform more sophisticated classification methods. The approach is called “naïve” because it assumes the independence between the various attribute values. Naïve Bayesian classification can be viewed as both a descriptive and a predictive type of algorithm. The probabilities are descriptive are used to predict the class membership for a untrained data.

Bayesian Classifier, Feature Subset Selection, Naïve Bayesian Classifier, Correlation Based FSS, Info Gain, K-cross validation, Spam, Non-Spam

[1] Rushdi Shams and Robert Mercer,” Classifying Spam Emails using Text and Readability Features,” IEEE 13th International Conference on Data Mining (ICDM), 2013, pp. 657-666.
[2] Chotirat “ANN” Ratana Mahatana and Dimitrios Gunppulos,” Feature Selection For the Naïve Bayesian Classifier Using Decision Trees,” Applied Artificial Intelligence, Volume-17, 2003, pp. 475-487.
[3] Mehdi Naseriparsa, Amir-Masoud Bidgoli, Touraj Varaee,”A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms,” International Journal of Computer Applications (0975-8887), Volume 69, No-17, May 2013.
[4] Aakriti Aggarwal and Ankur Gupta, “Detection of DDoS Attack Using UCLA Dataset on Different Classifiers, International Journal of Computer Science and Engineering, Volume-03, Issue-08, August 2015, pp. 33-37.
[5] Ioannis Kanaris, Konstantinos Kanaris, Ioannis Houvardas, And Efstathios Stamatatos, “Words Vs. Character N-Grams For Anti-Spam Filtering,” International Journal on Artificial Intelligence Tools, 2006, pp.1-20.
[6] Mehdi Naseriparsa, Amir-Masoud Bidgoli and Touraj Varaee,” A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms” International Journal of Computer Applications (0975 – 8887),Volume 69, Issue- 17,May 2013
[7] Sanjeev Dhawan and Meena Devi, “Spam Detection in Social Networks Using Correlation Based Feature Subset Selection,” International Journal of Computer Applications Technology and Research, Volume 4, Issue-8, August 2015, pp. 629-632.
[8] Dipali Bhosale and Roshani Ade,” Feature Selection based Classification using Naive Bayesian, J48 and Support Vector Machine,” International Journal of Computer Applications (0975 – 8887) Volume 99– No.16, August 2014.
[9] Anjana Kumari,” Study on Naive Bayesian Classifier and its relation to Information Gain,” International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 2, Issue- 3, March 2014, pp.601 – 603.

Sanjeev Dhawan, Kulvinder Singh and Meena Devi, "A Comparative Study of Spam Detection in Social Networks Using Bayesian Classifier and Correlation Based Feature Subset Selection," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.97-100, 2015.

Speaker verification is feasible method of controlling access to computer and communication network. It is an automatic process that uses human voice characteristics obtained from a recorded speech signal, as the biometric measurements to verify claimed identity of speaker. It can be classified into two categories, text–dependent and text-independent system. This paper introduces the fundamental concepts of speaker verification for security system. It focuses on techniques and their unique features.

Speaker identification, Gamma tone frequency cepstral coefficient, Mel frequency cepstral coefficient

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[5] U.Shrawankar, and V.M. Thakare, “Techniques for Feature Extraction In Speech Recognition System: A Comparative Study.”
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[13] G.S.V.S.Sivaram, Thomas, and H.Hermansky, “Mixture of Auto-Associative Neural Networks for Speaker Verification,”INTERSPEECH, Baltimore, USA,2011, pp. 2381-2384.
[14] S.Gfroerer, “Auditory instrumental forensic speaker recognition” Proceedings of Eurospeech,Geneva, 2003,pp. 705–708.
[15] H.R.Bolt,andF.S.Cooper, “Identification of a Speaker by Speech Spectrograms,” American Association for the Advancement in Science, Science, Vol. 166, 1969.pp. 338–344.
[16] D.Charlet, D.Jouvet, and O.Collin, “An Alternative Normalization Scheme in HMM-based Text-dependent Speaker Verification,” Speech Communication, Vol. 31,2000, pp. 113-20.
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[18] M.Ben, M.Betser, F.Bimbot, and G.Gravier, "Speaker diarization using bottom-up clustering based on a parameter-derived distance between adapted GMMs." 2004,Proc. ICSLP, France.
[19] A.A.M. AbushariahT.S,Gunawan, O.O. Khalifa, and M.A.M. Abushariah, “Voice based automatic person identification system using vector quantization”. In Computer and Communication Engineering (ICCCE), International Conference, Kuala Lumpur, 2012,pp. 549–554.
[20] S.Agrawal, A.Shruti,and A.C.Krishna , “Prosodic Features Based Text Dependent Speaker Recognition Using Machine Learning Algorithms,” International Journal of Engineering Science and Technology, Vol. 2,No.10,2010, pp. 5150-5157.

Sukhandeep Kaur and Anwalvir Singh Dhindsa , "Speaker Recognition System Techniques and Applications," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.101-104, 2015.

The paper presents modelling and implementation of an automated system which can be used to generate attendance monitoring system. ARM NXP CORTEX-M3 LPC1768 version is employed in which RFID module is interfaced along the controller to track the number of absent and present students. The detailed information about the student can be seen on the central computer system as a single application created on Visual Basic which thus can be printed.

ARM-Cortex based controller, Visual Basic, RFID module

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[2] K.G.M.S.K. Jayawardana, T.N. Kadurugamuwa, R.G. Rage and S. Radhakrishnan, ―Timesheet: An Attendance Tracking System”, Proceedings of the Peradeniya University Research Sessions, Sri Lanka, vol.13, Part II, 18th Dec. 2008.
[3] D. Maltoni, D. Maio, A. K. Jain, S. Prabhaker, “Handbook of Fingerprint Recognition”, Springer, New York, 2003.
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[6] S. Chitresh, K. Amit ,An efficient Automatic Attendance Using Fingerprint Verification Technique ,International Journal on Computer Science and Engineering (IJCSE), 2 (2) (2010), pp. 264–269

[7] Cary D. Snyder,“ARM FAMILY EXPANDS AT EPF” JUNE 3, 2002, http://www.arm.com.

Amit Kumar Yadav, "ARM-Cortex Based Control System to Generate Attendance Monitoring System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.105-108, 2015.