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A biometric system is an automatic recognition of an individual based on physiological or behavioural characteristics. In the present study, a new method for feature extraction was proposed. The different samples of same user differ in the case of feature vectors. So detection of feature points is a vital role in the recognition system. Face, Palmprint and Finger knuckle print are the biometric traits used for this system. The features are obtained by SUSIFTGEN algorithm which gives unique feature sets. To classify the train dataset images, Support vector machine (SVM) is used. The unimodal system achieved good results but suffers from non-universality and spoofing problem. To minimize the problems occurred by unimodal, multimodal biometric system was introduced which combines the matching scores of different biometric systems. The similarity measure is used to find the matching scores of the images. The matching scores of the three biometric traits are fused at matching score level. The experimental results showed that the proposed system achieved excellent performance for the multimodal system than the unimodal

Feature extraction; SUSIFTGEN; SVM; matching scores; Similarity Measure

[1] G. Kaur, G. Singh and V. Kumar “A review on biometric recognition,” International Journal of Bio-Science and Bio-Technology, Vol. 6, pp.69-76, 2014.
[2] K. P. Tripathi, “A comparative study of biometric technologies with reference to human interface,” International Journal of Computer Applications, Vol. 14, pp.10-15, 2011.
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[4] S.D. Lin, B.F. Liu and J.H. Lin, “Combining speeded-Up robust features with principal component analysis in face recognition System,” International Journal of Innovative Computing, Information and Control, Vol. 8, pp.8545-8556, 2012.
[5] A. Kong, D. Zhang and G. Lu, “A study of identical twins palmprint for personal verification,” Pattern recognition, Vol. 39, pp.2149-2156, 2006.
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[7] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, Vol. 60, pp. 91–110, 2004.
[8] H. Bay, T. Tuytelaars and L. Van Gool, “SURF: Speeded up Robust Features,” in Proc. European Conference on Computer Vision, pp.407-417, 2006.
[9] A.A. Majida, N.I. Ahmad and M.H. Zubadi, “Pattern recognition using genetic algorithm,” International Journal of Computer and Electrical Engineering, Vol. 2, pp.583-588, 2010.
[10] M. Nageshkumar, P. Mahesh and M. Swamy, “An efficient secure multimodal biometric fusion using palmprint and face image,” International Journal of Computer Science, Vol. 1, pp.49-53, 2009.
[11] N. Aditya and G. Phalguni, “Comparing human faces using edge weighted dissimilarity measure”, in Proc. International Conference on Control Automation Robotics & Vision, pp.1831-1836, 2010.
[12] M. Zamalloa, L.J. Rodríguez-Fuentes , M. Peñagarikano, G. Bordel & J.P. Uribe, “Comparing Genetic Algorithms to Principal Component Analysis and Linear Discriminant Analysis in Reducing Feature Dimensionality for Speaker Recognition”, In: GECCO’08,Atlanta,USA, pp. 1153-1154, 2008.
[13] A. Meraoumia, S. Chitroub and A. Bouridane, “Fusion of finger-knuckle-print and palm print for an efficient multi-biometric system of person recognition,” in Proc. IEEE International Conference on Communications, Kyoto, Japan, pp.1-5, 2011.
[14] P. Esther and R. Shanmugalakshmi, “A multimodal biometric system based on palmprint and finger knuckle print recognition methods,” The International Arab Journal of Information Technology, Vol. 12, pp.118-128, 2015.
[15] S. S. Taherim and B. P. Archana, “Multiple feature extraction techniques in image stitching,” International Journal of Computer Applications, Vol. 123, pp.29-33, 2015.
[16] S. Pratibha, B. Sunny and S. Pritpal, “Face Recognition System Using Genetic Algorithm” Procedia Computer Science, Vol. 85, pp.410 – 417, 2016.
[17] L. Lenc and P. Kral, “A Combined SIFT/SURF Descriptor for Automatic Face Recognition”, 6th International Conference on Machine Vision (ICMV 2013), London, UK, pp. 90672C-90672C-6, 2013.
[18] A. Vinay, H. Dixit, S. Vinay, K.N. Shekhar, M. Balasubramanya & Natarajan, S, “Two Novel Detector-Descriptor Based Approaches for Face Recognition using SIFT and SURF”, International Conference on Eco-friendly Computing and Communication Systems, Vol. 70, pp. 185 – 197, 2015.
[19] A. Ashraf, A.E.D. Kamal, A.K. Eman & A.E. Ebeid, “Score Level Fusion for Fingerprint, Iris and Face Biometrics” International Journal of Computer Applications, Vol. 111, pp.47 -55, 2015.
[20] D. Rana, D. Sunita, Bhawna, M. Sujata, N. Prasanna and T.T. Sahu, “Comparative Analysis of Face Recognition using DCT, DWT and PCA for Rotated faces” International Journal of Scientific Research Engineering & Technology, Vol. 3, pp. 852-857, 2014.
[21] Ch. R. Babu and D. S. Rao, “Comparison of Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Stationary Wavelet Transform (SWT) based Satellite Image Fusion Techniques” International Journal of Current Research and Review, Vol. 9, pp.49-53, 2017.
[22] T. Connie, A.T.B. Jin, M.G.K. Ong and D.N.C. Ling, “An automated palmprint recognition system” Image and Vision Computing, Vol. 23, pp.501–515, 2005.
[23] D. Chaitali and K. H. Wanjale, “Survey On Image Classification Methods In Image Processing”, International Journal of Computer Science Trends and Technology, Vol. 4, pp.245-248, 2016.
[24] P. M. Rupali and S. K. Nanda, “A Survey on Analysis of ANN and KNN Classifier for Image Classification with Discrete Wavelet Transform” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, pp.20193-20197, 2016.
[25] B. Anand and P. K. Shah, “Face Recognition using SURF Features and SVM Classifier” International Journal of Electronics Engineering Research, Vol. 8, pp.1-8, 2016.
[26] Z. Wang, W. Erfu, W. Shuang and D. Qun “Multimodal biometric system using face-iris fusion feature”, Journal of computers, Vol. 1, pp.931- 938, 2011.

J. Anne Wincy, Y. Jacob Vetha Raj, "A New Feature Extraction Method for Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1386-1393, 2018.

Bank sector gives the proper economic structure and support in a country. Frauds in the banking sector have become a major issue in the banking arena. Therefore, it has become a necessity in implementing fraud pattern detection mechanisms to unmask the fraudsters. Ideal mechanism of recognizing such fraud patterns can be implemented using a graphical structure. Graph database provides such a graphical structure with node-relationship analysis. Typical pattern fraudulent methods like bust-out fraud (BOF) and credit-card fraud (CRF) can be recognized via such a graphical structure analysis. The motive behind such a proposal is to detect fraudulent patterns and implement transaction analysis in a bust-out fraud and credit-card fraud. We are trying to observe possible fraud rings in the bust-out fraud and in the credit-card fraud we are trying to identify the origin of the scam. This proposal provides the ideal solution for the investigation of large amounts of heterogeneous data that is required to recognize the fraudulent patterns in the bust-out and credit-card fraud.

Graph database, Bust-out fraud, Credit card fraud

[1]. Harsha R. Vyawahare, Dr. P. P. Karde, An Overview on Graph Database Model, Vol. 3, Issue 8, August 2015, IJIRCCE.
[2]. Shefali Patil, Gaurav Vaswani, Anuradha Bhatia, Graph Databases- An Overview , IJCSIT, Vol. 5(1), 2014,657-660
[3]. Arnaud Castelltort, Anne Laurent, Rogue behavior detection in NoSQL graph databases. Journal of Innovation in Digital Ecosystems, Elsevier 2016, 3 (2), pp.70-82.
[4]. In: Saeed K., Homenda W ,Pokorný J. (2015) Graph Databases: Their Power and Limitations, (eds) Computer Information Systems and Industrial Management. CISIM 2015. Lecture Notes in Computer Science, vol. 9339. Springer, Cham
[5]. Graph Databases – Book by Emil Eifrem, Ian Robinson, and Jim Webber,-O’REILLY
[6]. Harsha R Vyavahare, Dr.P.P.Karde, SHORT SURVEY ON GRAPHICAL DATABASE, ISSN: 0976-5166 Vol. 6 No.4 Aug-Sep 2015-IJCSE.
[7]. R.Satraboyina, G.K Chakravarthi, Discovery of ranking fraud detection system for mobile apps-, vol.4, Issue 4, p.p.7-10, Aug-2016-IJSRCSE.

Sonali Sen, Trishita Mukherjee, Sunanda Pal, Sumana Ghosh, "Fraud Pattern Recognition In Banking Sector Using Graph Database," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1394-1398, 2018.

In a multiprogramming environment, the Scheduling technique decides which process will be selected and assigned to the CPU next so that the efficiency of the CPU can be increased. One of the well-known techniques of scheduling is round-robin technique. A number of modifications have been made in the basic round robin scheduling algorithm but still work is going on to make it the ideal one. The performance of this technique mainly depends upon the selected value of time quantum i.e. a fair share of time for which a process can get the CPU and if the process still not completed, it will join the ready queue for completion of the remaining task. For achieving this aim, this paper proposed a new mode based round-robin scheduling algorithm that offers the reduction of average turnaround time as compared to average turnaround time calculated by existing modulus based technique, a best reported similar technique available in the literature. Experimental evaluation is done using C language.

CPU Scheduling, Scheduling Algorithm, Round-Robin Scheduling, Turnaround-Time, Time-Quantum

[1] S. Arif, S. Rehman, F. Riaz, “Design of A Modulus Based Round Robin Scheduling Algorithm”, 9th Malaysian Software Engineering Conference, pp. 230-235, Dec. 2015.
[2] M. Ghazizadeh and M. Naghibzadeh et al., “Fuzzy Round Robin CPU Scheduling (FRRCS) algorithm”, International Conference on Systems, Computing Sciences and Software Engineering (SCSS), Part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering, pp: 348-353, 2007.
[3] R. Matarneh, “Self-Adjustment Time Quantum in Round Robin Algorithm Depending on Burst Time of the Now Running Processes”, American Journal of Applied Sciences, pp. 1831-1837, 2009.
[4] A. Singh et. al., “An Optimized Round Robin Scheduling Algorithm for CPU Scheduling”, International Journal on Computer Science and Engineering, Vol. 02, No. 07, pp. 2383-2385, 2010.
[5] R. Mohanty, H. Behera, K. Patwari et al. "Design and Performance Evaluation of a New Proposed Shortest Remaining Burst Round Robin (SRBRR) Scheduling Algori thm"
[6] A. Noon, A. Kalakech, S. Kadry, "A New Round Robin Based Scheduling Algorithm for Operating Systems: Dynamic Quantum Using the Mean Average ", IJCSl lnternational Journal of Computer Science, Issues, Vol. 8, Issue 3, No. I,2011.
[7] B. Alam, M. Doja, R. Biswas,“Finding Time Quantum of Round Robin CPU Scheduling Algorithm Using Fuzzy Logic”, International Conference on Computer and Electrical Engineering, pp. 795-798, 2008.
[8] N. Kumar, A. Kumar, “A Task set Based Adaptive Round Robin (TARR) scheduling algorithm for improving performance”, 1st International conference on futuristic trend in computational analysis and knowledge management, pp. 347-352, 2015.

S.Jain, H. Rohil, "Mode Based Round Robin Scheduling Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1399-1403, 2018.

In MANET frequent development of topology and their changes are also affecting the performance of network now a day. Therefore a new technique of routing is required to minimize the resource consumption and maximization of performance factors. In this presented work the energy preservation is the key aim. Therefore a weight based clustering approach for effecting routing for data communication in ad hoc environment is proposed. This paper propose a weight based clustering algorithm for cluster-head election i.e. “CHEM-WCA” in ad-hoc network environment, which takes into consideration the number of nodes a cluster-head can handle ideally (without any severe degradation in the performance), transmission power, mobility, and battery power of the nodes. The implementation of the proposed CHEM-WCA is implemented using network simulation environment and the AODV routing protocol is used to incorporate the proposed algorithm. The investigational results are measured in terms of end to end delay, throughput, packet delivery ratio, and energy consumption and routing overhead. The results show the proposed CHEM-WCA advance the flexibility of network node and performance of network when node propagating the high overhead.

MANET, Clustering, Cluster-head, Network Simulator, RREQ, RREP, AODV, WCA, Routing Protocols

[1] “NCTE Advice Sheet – Wireless Networks”, available online at: http://www.eoiniosagain.ie/iosagain/sites/default/files/ncte_wireless_networks.pdf
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[3] Mäki, Silja, "Security Fundamentals in Ad Hoc Networking", Proceedings of the Helsinki University of Technology, Seminar on Internetworking-Ad Hoc Networks. 2000.
[4] Fan Wu, “Economic Incentive Mechanisms for Wireless Ad Hoc Networks Principal Investigator”, Natural Science Foundation of China (NSFC), 2012.
[5] Ms. Deepika, “Cluster Based Routing Protocol in MANETs”, International Journal of in Multidisciplinary and Academic Research (SSIJMAR), Vol. 5, No. 2, April 2016.
[6] Sayani Chandra and Ipsita Saha, “A Brief Overview of Clustering Schemes Applied on MobileAd-hoc Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 2, February 2015
[7] The Network Simulator, NS-2 [Online] http://www.isi.edu/nsnam/ns/.

Sudhir Kumar Patidar, Sunil Kushwaha, "CHEM-WCA: Cluster Head Election Method using Weight based Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1404-1411, 2018.

This paper is a survey on how the Machine Learning & Information Mining techniques have been employed to automate the cyber detection system and discusses necessary background knowledge on Digital Security. After identifying various issues on digital intrusion detection and security, also various Machine Language and Information Mining approaches that have been employed to resolve this. This paper reveals insight into complexities, quirks and capability of utilizing Machine Learning algorithms for Digital Security. The machine learning and information mining algorithms and procedures discussed below are applied in digital security intrusion detection systems in real time scenarios.

Intrusion Detection System,Anomaly Detection, Misuse Detection, Data Mining, Machine Learning

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[5]. F. Amiri, M. Mahdi, R. Yousefi, C. Lucas, A. Shakery, and N. Yazdani, “Mutual information-based feature selection for IDSs,” J. Netw. Comput. Appl., vol. 34, no. 4, pp. 1184–1199, 2011.
[6]. C. Wagner, F. Jérôme, and E. Thomas, “Machine learning approach for IP-flow record anomaly detection,” in Networking 2011.New York, NY, USA: Springer, 2011, pp. 28–39.
[7]. D. Brauckhoff, A. Wagner, and M. May, “Flame: A low-level anomaly modeling engine,” in Proc. Conf. Cyber Secur. Exp. Test, 2008

Muralidhara S, "Study of Information Mining (DM) and Machine Learning (ML) Strategies on Digital Security," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1412-1417, 2018.

Sometimes from the whole image we need only some part of Image is required for Diagnosis purpose by the doctors. Fig1.shows combined images original baby picture with the picture of it’s color ultrasound medical image. My work focus on grayscale uncompressed ultrasound medical image of Fetus. Aim of ROI compression for Fetus images is compressing the important region; region of interest (ROI) required for diagnosis here Position of baby is important region is compressed with supreme quality as compared to rather unimportant area in order to get better compression performance. As a part of ROI compression technique CROI [4] with JPEG & WAVELET compression algorithm have been implemented on Image fetus.bmp using MATLAB. A detailed analysis on the basis of parameters like CR, MSE, PSNR and COC has been used to evaluate these algorithms. With the use of CROI approach with JPEG algorithm, we get PSNR of 38.22 for image and CROI with WAVELET gives PSNR of 39.82 db with extremely good quality of image area.

DWT, ROI, Region of Interest, US, Ultra sound, WAVELET, DCT, JPEG,bior

[1] M.A. Ansari * Member IEEE and R.S. Anand. “DWT Based Context Modeling of Medical Image Compression” XXXII ATIONAL SYSTEMS CONFERENCE, NSC 2008, December 17-19, 2008.
[2] Rahul kher, Chintan Modi, R S Anand. “Ultrasound Medical Image Compression Using Contextual Approach”
[3] Xu Yan et al.: "The Coding Technique of Image with MultipleROIs sing Standard Maxshift Method “The 30 Annual con! Of the IEEE,Industrial Electronic society Busan , Korea, pp 2077-2080, 2004.
[4] M.A. Ansari• Member IEEE and R.S. Anand “Context Based Medical Image Compression with Application to Ultrasound Images” Department of Electrical Engineering, Indian Institute of Technology.Roorkee-247667.INDIA..978-1-4244-2746-8/08/$25.00 © 2008 IEEE.
[5] M.A. Ansari R.S.Anand “Performance Analysis of Medical Image Compression Techniques with respect to the quality of compression” Department of Electrical Engineering Research Scholar IIT Roorkee, India. Indian Institute of Technology Roorkee. inIET-UK International Conference on Information and Communication Technology in ectricalciences (ICTES 2007),Dr. M.G.R. University, Chennai, Tamil Nadu, India. Dec. 20-22, 2007. pp. 743-750 .
[6] SejalThakkar Yogesh Dangar “Performance Analysis of Crime Images Using CROI with JPEG & WAVELET” SejalThakkar1 GCET, VallabhVidyanagar, Anand, Gujarat, India1, Yogesh Dangar2 GCET, VallabhVidyanagar, Anand, Gujarat, India2 International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April 2013 ISSN 2229-5518 93.
[7] www.sweetbabyomine4d.com/testimonials.html.
[8] T.N. Baraskar1* , V.R. Mankar2 ”A Survey and Analytical Approach on Image Compression for DICOM Images” Department of Electronics Engineering, SGBA University, Amravati, India 2 Department of Electronics Engineering, Government Polytechnic, Amravati, India International Journal of Computer Sciences and Engineering Open Access Survey Paper Volume-6, Issue-1 E-ISSN: 2347-2693. Jan 2018.

Sejal Thakkar, "“Survey of CROI based Compression on Grayscale Medical Image of Fetus”," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1418-1424, 2018.

Diabetes is considered as one of the deadliest diseases in the world, therefore medical professionals need a reliable prediction and decision making methodology. The main aim of this paper is to implement data mining in diabetes diagnosis to discover new patterns and to interpret the data patterns to provide meaningful and useful information for medical practitioners. The analytical technique for this project consists of five stages data collection, preprocessing, feature extraction, implementation and then interpretation and evaluation. In this research, the analytical technique implores the use of k-means clustering algorithm and association rules (A-priori algorithm) was used to analyse diabetes dataset collected from two hospitals in Ondo State, Nigeria. The analytical technique proposed was implemented in PyCharm Community Edition. Three clusters were generated using K-means clustering algorithm and A-priori algorithm was used to generate patient’s profile for each cluster. Performance evaluation on the technique was carried out using accuracy and showed result of 85% which indicates that the technique is efficient.

Data Mining, Diabetes, K-Means Clustering, Association Rule Mining

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[3] N. Gbuse, P. Pawar and A. Potgantwar. “An Improved Approach for Fraud Detection in Health Insurance Using Data Mining Techniques” International Journal of Scientific Research in Network Security and Communication (IJSRNSC), Volume 5, Issue 5, pp. 27-32. 2017.
[4] L. Guo. “Applying Data Mining Techniques in Property Casualty Insurance”, Casualty Actuarial Society Forum, Casualty Actuarial Society, pp. 1-25, 2003.
[5] A. Aljumah, M. G. Ahamad and M. K. Siddiqui “Application of data mining: Diabetes health care in young and old patients”, Journal of King Saud University – Computer and Information Sciences, Volume 2, Issue 5, pp. 127–136, 2013.
[6] S. Nagarajan and R. M. Chandrasekaran. “Design and Implementation of Expert Clinical System for Diagnosing Diabetes using Data Mining Techniques”, Indian Journal of Science and Technology (IJST), Volume 8, Issue 8, pp. 771–776, 2015.
[7] P. Padmaja, V. , Nilofer I. S., Praveen D., A Bikkina., R. Venkata, M. V. Shaik and Raju R. “Characteristic evaluation of diabetes data using clustering techniques’, International Journal of Computer Science and Network Security (IJCSNS), Volume 8, Issue 11, pp. 244-251, 2008.
[8] Pramanand P. and Sankaranarayanan S. “Diabetic prognosis through Data Mining Methods and Techniques”, International Conference on Intelligent Computing Applications, Volume 2, Issue 2, pp. 162-166, 2014.
[9] T. Pala and I. Yucedag. “A Data Mining Approach for Diagnosis of Diabetes Using Association Rules and Clustering”, International Artificial Intelligence and Data Processing Symposium. Volume 2, Issue 2, pp. 187-199, 2016.
[10] P. S. Kumar and V. Umatejaswi “Diagnosing Diabetes using Data Mining Techniques”, International Journal of Scientific and Research Publications, Volume 7, Issue 6, pp. 705-709, 2017.
[11] A. Iyer, S. Jeyalatha and R Sumbaly. “Diagnosis of Diabetes using Classification Mining Techniques”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Volume 5, Issue 1, pp. 1-14, 2015.
[12] B. Vani and J. Priyadharshni. “Discovering the Diagnosis of Diabetes Mellitus by using Association Rule Mining”, International Journal of Research Instinct, Volume 3, Issue 2, pp. 87-95, 2016.
[13] A, Al-Rofiyee, M. Al-Nowiser, N. Al-Mufadi and M. A. ALHagery. “Using Prediction Methods in Data Mining for Diabetes Diagnosis”, Third Symposium on Data Mining Applications. Volume 3, pp: 5-6, 2014.
[14] S. G. U Tugba. “Defining Characteristics of Diabetic Patients by Using Data Mining Tools”, Journal of Hospital & Medical Management. Volume 2, Issue 2, pp. 1-8, 2016.
[15] P. P. Sondwale. “Overview of Predictive and Descriptive Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSE), Volume 5, Issue 4, pp. 262-265, 2015.
[16] M. Ramya and A. J Pinakas. “Different Type of Feature Selection for Text Classification”, International Journal of Computer Trends and Technology (IJCTT), Volume 10, Issue 2, pp. 102-107, 2014.

O.Turoti, O. O. Obe, "Discovering Hidden Patterns in Diabetes Data Using K-Means Clustering Algorithm and Association Rules," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1425-1232, 2018.

Validation and Verification in Composite web service development process is basic need to provide trust in between developers who are handling this development process using cloud service through different geographical locations. In this research work, V&V process simulated through Web Service Simulator using Asp. Net Web Services controlled by Web application. In continuity, complete demonstration of customers and web service interaction is simulated. This research work answers the questions as How data has been verified and validated so that it does not create any threats for the developers system, How intruder have not access to data without complete authentication. The research is also demonstrates the role based limitation in web service development. Web Services Simulator (WSS) used the concept of SOAP and it monitored and controlled security threats through a web application that are imposed via attackers at several points.

Web Services, Composition of Web Services, SOAP , Validation and Verification, WSDL, UDDI, Web security Attacks, Threats Classification

[1] M. D. Priya, A. Lavanya, "Intrusion Detection System Using Raspberry Pi Honeypot in Network Security", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4 Issue 3, pp. 41-45, January-February 2018.
[2] E. Manigandan, C. Kalaiarasi, E. Manigandan, Prof. C. Kalaiarasi, "Cryptography in Cloud Computing : A Basic Approach to confirm Security in Cloud", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4 Issue 3, pp. 58-63, January-February 2018.
[3] W. T. Tsai, Y. Chen, R. Paul N. Liao, and H. Huang, “Cooperative and Group Testing in Verification of Dynamic Composite Web Services”, in Workshop on Quality Assurance and Testing of Web-Based Applications, in conjunction with COMPSAC, September 2004, pp.170-173.
[4] Vicente Aguilera Diaz, Robert Auger, Amit Klein, Dennis Groves, Cesar, “MX Injection : Capturing and Exploiting Hidden Mail Servers”, OWASP Spain chapter meeting of June 16th, 2006.
[5] TasosLaskos, “Arachni Framework v1.4 &WebUI v0.5.10”, Web Application Security Scanner, Feb 09 2016,01:04AM,http://www.securityfocus.com/archive/107/537488/30/0/threaded
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[9] Web Application Security Consortium.http://www.webappsec.org
[10] Roberto Chinnici, Hugo Haas, Amelia A. Lewis, Jean-Jacques Moreau, David Orchard,SanjivaWeerawarana, “Web Services Description Language (WSDL) Version 2.0 Part 2”: AdjunctsW3C Recommendation 26 June 2007.Copyright © 2007 W3C® (MIT, ERCIM, Keio)
[11] Kung-Kiu Lau and Cuong Tran, “Composite Web Services”, Chapter: Emerging Web Services Technology, Volume II, Part of the series:Whitestein Series in Software Agent Technologies and Autonomic Computing, pp 77-95
[12] DimkaKarastoyanova and Alejandro Buchmann, “Development Life Cycle of Web Service-based Business Processes. Enabling Dynamic Invocation of Web Services at Run Time”, ICSOC’05: The 3rd International Conference On Service Oriented Computing, 2004.
[13] Karthikeyan Bhargavan, Ricardo Corin,CedricFournet,Andrew D. Gordon, “Secure Sessions for Web Services”,ACM Workshop on Secure Web Services, October 29, 2004, Fairfax VA,USA.Copyright 2004 ACM 1-58113-973-X/04/0010
[14] Muhammad Ahtisham Aslam, Jun Shen,Michael Herrmann, “An Integration Life Cycle for Semantic Web Services Composition”,11th International Conference on Computer Supported Cooperative Work in Design, 2007, E-ISBN :1-4244-0963-2, Print ISBN:1-4244-09624 DOI:10.1109/CSCWD.2007.4281485, Page(s):490 – 495.
[15] Zhaohao Sun, Dong Dong, Jun Han, “A Demand Driven Web Service Lifecycle”, New Trends in Information and Service Science, 2009. NISS `09, DOI: 10.1109/NISS.2009.135
[16] Oracle® Application Server Web Services Developer`sGuide, 10g (10.1.3.5.0), Part Number E13982-01
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G. Raj, M. Mahajan, D. Singh, "V&V Analysis of Composite Web Service using WS Simulator for Trust Management in WS Lifespan," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1433-1440, 2018.

A continuous parameter monitoring of machine is required in certain applications where failure of machine leads to loss of quality control, productivity and safety against catastrophic failure. Machine faults are often linked to the bearing faults. Parameter monitoring of machine involves continuous assessment on the performance of rotating components i.e. bearings, gears and motors and predicting the faults before it cause any adversity. Rotating machines are commonly used in the industry for different applications such as railways, pumps, conveyors, blowers, elevator, mining industry, etc. Parameter monitoring of rotating machines has been an important task for technicians, engineers, and researchers mainly in industrial application. This paper presents an enlarged survey on the expansion and the recent approaches in the parameter monitoring of rotating machines. In current scenario, parameter monitoring proved their ability for fault detection of incipient faults in electrical machines and equipment. Several techniques such as vibration monitoring, acoustic emission monitoring, invasive monitoring, oil monitoring are available for determining the health of rotating machines but all these monitoring methods needs expensive transducers and sensors.

Condition monitoring, fault detection, wireless monitoring, motor currrent signature analysis

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Shefali Jamwal, Shimi Sudha Letha, "Paramerter Monitoring of Induction motor : A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1441-1446, 2018.

Contention resolution mechanism for QoS provisioning is considered as a significant issue in optical burst switching network. Further, optical burst switching network necessitates a mechanism for contention resolution and service differentiation for enabling support in internet traffic. In this paper, a Novel Contention Prevention Scheme-based on Delayed Reservation for QoS Enforcement (NCPS-DRE-QoS) in optical burst switching network. This NCPS-DRE-QoS approach allocates the wavelength based on the available wavelength information obtained along the forward path by means of PROBE packet in the backward reservation. Further, the information gathered by PROBE packet are outdated due to the link propagation or processing delay, since the possibility remains of request is blocked by the utility of PROBE based inspection. The performance of NCPS-DRE-QoS are exhaustively studied through ns-2 simulations with the aid of evaluation parameters such as Jitter, Packet delivery ratio, Burst loss ratio and average goodput. From the simulation results obtained, it is transparent that NCPS-DRE-QoS successfully reduces the burst loss probability when compared to the other delayed reservations based contention resolution oriented QoS provisioning mechanisms available in the literature.

Delayed Reservation, Contention Resolution, Just-in-Time Provisioning, Forward blocking , PROBE packet, Wavelength prediction table, Wavelength topology ring

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P.Boobalan, "Novel Contention Prevention Scheme-based on Delayed Reservation for QoS Enforcement in Optical Burst Switched Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1447-1453, 2018.