Volume-6 , Issue-9 , Sep - 2018 Go Back

Electronic Commerce has revolutioned the entire way business is conducted and is being used in almost every sphere of activity. The true essence of electronic commerce stems from electronic payments- the facility to make and receive payments online. While customers crave for a variety of payment options, merchants manage to live up to the expectations, thanks to third-party payment service providers (PSPs), what the industry calls Payment Facilitators. A payment facilitator is an entity that acts as a seamless, integrated digital payment acceptance platform which receives customer payments on behalf of mer-chants. This relieves merchants from the burden of handling payments, along with a host of technical infrastructure, risk man-agement and regulatory obligations. The current work provides a behind-the-curtain scenario of retail online payments and explains how a payment facilitator is the simplest way for online merchants to accept payments from their customers. Payment facilitators have yielded a new business model, which renders tremendous benefits to online businesses- both in terms of technology and procedures. All such benefits are visited.

Payment facilitator, payment gateway, electronic payment system, online merchant, issuer, acquirer, transaction, m-wallet, MID, sub-merchant.

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H.M. Qazi, "Payment Facilitators and Their Role in Online E-Commerce Transactions," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.188-191, 2018.

Mobile Ad hoc Network is a wireless network that is formed for a temporary purpose. Unlike wired network, there is no centralized control over the network. So, all the devices act as both node and router and have the property of moving that leads to the dynamic change in the in the network structure. At any instance of time, there is a change in network size and speed. The moving speed of a node affects the frequency of topological changes in the networks, which in return influences the ability of routing the data packets and maintaining steady routes. These two properties have a noticeable impact on the performance of MANET. The study of effect of one of the most important parameters i.e. network size on the performance of MANET is studied while implementing LANMAR routing protocol.

Ad hoc Network, LANMAR, Fisheye, EXata

[1] Mehran Abolhasan., Tadeusz Wysocki, Eryk Dutkiewicz., “A review of routing protocols for mobile ad hoc networks”, Faculty of Engineering and Information Sciences, 2004.
[2] Neeraj Verma., Sarita Soni.,” A Review of Different Routing Protocols in MANET”, International Journal of Advanced Research in Computer Science, Volume 8, No. 3, 2017.
[3] P. Koushik., P. Vetrivelan., R. Ratheesh., “Energy Efficient Landmark Selection for Group Mobility Model in MANET”, Indian Journal of Science and Technology, Volume 8, 2015.
[4] G. Pei., M. Gerla., X. Hong., “LANMAR: Landmark Routing for Large Scale Wireless Ad Hoc Networks with Group Mobility”, Proceedings of IEEE/ACM MobiHOC 2000, Boston, MA, 2000.
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[6] Mario Gerla., “Landmark Routing Protocol (LANMAR) for Large Scale Ad Hoc Networks”, Internet Draft, draft-ietf-manet-lanmar-05.txt, work in progress, 2003.
[7] Yeng-Zhong Lee., Jason Chen., Xiaoyun Hong., Kaixin Xu, Teresa Breyer., Mario Gerla., “Experimental Evaluation of LANMAR, a Scalable Ad-Hoc Routing Protocol”, “MINUTEMAN” project, 2003-08.
[8] Xiaoyan Hong., Kaixin Xu., Mario Gerla., “Scalable Routing Protocols for Mobile Ad Hoc Networks”, IEEE Network Magazine, pp. 11-21, 2002.
[9] G. Pei., M. Gerla., T.-W. Chen., “Fisheye State Routing: A Routing Scheme for Ad Hoc Wireless Networks”, Proceedings of ICC 2000, New Orleans, LA, Jun. 2000.
[10] M. Gerla., “Fisheye state routing protocol (FSR) for ad hoc networks”, Internet Draft, draft-ietf-manet-fsr-03.txt, work in progress, 2002.
[11] G.M.Tamilselvan, “Performance Analysis of Coexistent Heterogeneous Networks for various Routing Protocols using Qualnet Simulation”, International Journal of Computer Theory and Engineering, Vol. 2, No. 2 April, 2010.
[12] Bharti Vermani., Naveen Sharma., Bhupender Yadav., “Performance Comparison of Wireless Mobile Ad-Hoc Networks on the basis of Various Simulation Parameters”, International Journal of Latest Trends in Engineering and Technology (IJLTET), Volume 1, Issue 2, 2012.
[13] Chingrace Guite, Kamaljeet Kaur Mangat, "A Study on Energy Efficient VM Allocation in Green Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.37-40, 2018.
[14] Anurag Singh, Rajnesh Singh, Sunil Gupta, "Evaluating the Performance of TCP over Routing Protocols in MANETs Using NS2", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.4, pp.1-4, 2018.

Anveshini Dumala, S. Pallam Setty, "A Comparative Study of Various Mobility Speeds of Nodes on the Performance of LANMAR in Mobile Ad hoc Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.192-198, 2018.

Data, information and meaning are three prime characteristics of any communication scenario. Information is generated by data, and the meaning is extracted from information. Search of a mathematical model to measure meaning of communication has become a discipline of study known as semantic information theory. In his recent paper Zadeh claims that information is equivalent to a restriction and it can be represented as probabilistic and possibilistic restrictions. These restrictions can be modified to represent different aspects of communication (content + meaning) in a hybrid system. In present paper we discuss some vital results from our research on possibilistic modelling of semantic information in a hybrid system. We also present a scheme for information analysis system, with various phases, and define measures of information and meaning based on mode of data set and closeness value of possibility and probability distributions. We shall show that this scheme provides a feasible method to capture both information and meaning in hybrid system.

Hybrid systems, Possibility Distribution, Restriction, Semantic Information

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Jayesh Karanjgaonkar, Purushottam Jha, "A Semantic Information Analysis Method for Man -Machine Hybrid System Based on Possibilistic Restrictions," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.199-203, 2018.

Affects like lower cost, boom throughput, availability however it additionally Information and telecommunication generation (ICT) has through deep into the person lives and is impacting person life fashion in one-of-a-kind components. The quick increase in ICT has commenced upgrade in computing gadget and computing expertise. Presently cloud computing is one of the extreme promoted transformation. It has various effective have positive safety problems that have to be treated delicately. There are various procedures that may be used to conquer this foremost hassle. Here this paper will research biometric authentication for data safety in cloud computing, it’s numerous techniques and the way they`re useful in decreasing the security warning. It gives an expansive and organized evaluation of biometric authentication for boosting cloud protection.

Cloud Computing, Safety Issues; Information Access; Licensed User; Biometric Authentication; Cloud Resource Supplier (CRP), Validation

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Available:http://csrc.nist.gov/publications/ fips/fips197/fips-197.pdf.
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[4] Manoj Diwakar and Manish Maharshi,
“An Extraction and Recognition of Tongue-Print Images for Biometrics AuthenticationSystem”,International
Journal of Computer Applications, ISSN:
0975–8887, Vol. 61, No. 3, January 2013
[5] Yashpal Kadam, “Security Issues in Cloud Computing A Transparent View”, International Journal of Computer Science Emerging Technology, Vol-2 No 5 October 2011 , 316-322.

Nisha Sharma, Er.Amit kishor, "Research Paper on Data Security Through Speech Recognition In Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.204-206, 2018.

A computer program such as software application that stops functioning properly is called software crash. Software crash is tedious problem in software development environment. Upon user permission, the crash report which contains the stack traces is sent to the developer or vendor. Software development team receives hundreds of crash reports from many deployment sites. There are many duplicate crash reports are generated, because many users submit the crash reports for the same problem. For analysing each crash reports, it may take more time. This motivates, to present the solution to analyse the crash reports and cluster the duplicate crash reports based on call stack similarities and store them into unique bucket, so that development resources can be optimized. In this paper, clustering the duplicate crash report of open source is proposed based on the similar information in the call stack. Hierarchical clustering technique is used to cluster the duplicate crash reports into unique bucket. Mozilla and Firefox open source crash reports are used for experiment and performance evaluation is done using purity determined the purity of clusters up to 80%. This method helps to increase the efficiency and reduce the number of developers along with an improved time to fix the bug.

Crash reports, clustering technique

[1] Asha Ramaraddi Belahunashi, Pushpalatha M N,” A Survey on analysing the crash reports of software applications”, International Research Journal of Engineering and Technology , Volume 4, Issue 6, pp.1014-1017, June 2017.
[2] Divya R S, Pushpalatha M N, “Software CrashLocator: Locating the Faulty Functions by Analyzing the Crash Stack Information in Crash Reports”, International Journal of Advanced Engineering, Management and Science (IJAEMS), Vol-2, Issue-5, pp.269-273, May- 2016
[3] Rongxin Wu, Hongyu Zhang, Shing-Chi Cheung, and Sunghun Kim, “CrashLocator: Locating Crashing Faults Based on Crash Stacks”, ISSTA 2014 Proceedings of the 2014 International Symposium on Software Testing and Analysis, Pages 2014-214, 2014


[4] Yingnong Dang, Rongxin Wu, Hongyu Zhang, Dongmei Zhang, and Peter Nobel, “Rebucket: a method for clustering duplicate crash reports based on call stack similarity”. In Proceedings of the 34th International Conference on Software Engineering, pages 1084– 1093. IEEE Press, 2012.
[5] P. Runeson, M. Alexandersson, and O. Nyholm, “Detection of Duplicate Defect Reports Using Natural Language Processing”, in Proc. ICSE 2007, Minneapolis,USA, pp. 499-510, May 2007.
[6] X. Wang, L. Zhang, T. Xie, J. Anvik, and J. Sun, "An approach to detecting duplicate bug reports using natural language and execution information", in Proc. ICSE’08, Leipzig, Germany, pp. 461-470, 2008
[7] D. Kim, X. Wang, S. Kim, A. Zeller, S. Cheung, and S. Park, “Which crashes should i fix first? Predicting top crashes at an early stage to prioritize debugging efforts”, IEEE Transactions on Software Engineering, pp. 430-447, 2011

Pushpalatha M N, Mrunalini M, "Clustering The Duplicate Open Crash Reports Based on Call Stack Traces of Crash Reports," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.207-210, 2018.

The day-by-day growing data can compromise the performance of the prediction system, because its obvious that the growing data will require more storage and the system will also consume more time for its processing. In prediction system, testing is part where time is consumed. If the entire data is given to the test model, it will run for the entire input size, and becomes time consuming. For this effective reduction strategy for processing time of testing must be introduced. To reduce this processing time introducing parallelism concept can help. The framework used here is based on fork join pool. In this the input size is divided into parts which are small enough to be processed and then the divided parts are given for testing. Thus reducing the time consumed in testing, and making it better than the other system.

Fork Join Pool, Open NLP, Sentiment Analysis, Data Parallelism

[1] X. Li, Q. Peng, Z. Sun, L. Chai, and Y. Wang, “Predicting social emotions from readers perspective”, IEEE Transactions on Affective Computing, no. 1, pp. 1-1, 2017.
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[3] S. Khatri and A. Srivastava, “Using sentimental analysis in prediction of stock market Investment , ” proceeding of 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 566-569,2016.
[4] Y. Shen, W. R. Yazhi Gao, and Z. Xiong, “Convolutional neural network based sentiment analysis using adaboost combination,” Proceeding of 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1333-1338, 2016.
[5] R.Hong, M. Chuan He, Yong Ge, and X. Wu, “User vitality ranking and prediction in social networking services: a dynamic network perspective,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 6, pp. 1343-1356, June 2017.
[6] I. Smith, “A parallel artificial neural network implementation,” Proceedings of The National Conference On Undergraduate Research (NCUR, pp. 1-4, April 2006.
[7] HS.Kisan, HA.Kisan, and AP.Suresh, “Collective intelligence and sentimental analysis of twitter data by using standfordnlp libraries with software as a service saas,” Proceeding of 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-4, 2016.
[8] R.Krchnavy, M.Krchnavy, and M. Simko, “Sentiment analysis of social network posts in slovak language,” 2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 20-25, July 2017.
[9] D. Cenni, G. P. Paolo Nesi, and I. Zaza, “Twitter vigilance: a multi-user platform for cross-domain twitter data analytics, nlp and sentiment analysis,” , Proceeding of 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation ( Smart World / SCALCOM / UIC / ATC / CBD Com / IOP / SCI), pp. 1- 8, Aug 2017.
[10] F. Nausheen and S. H. Begum, “Sentiment analysis to predict election results using python,” Proceeding of 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 1259-1262, Jan 2018.

K.B. Borole, S.D. Rajput, "Data Parallelism : A New Approach in Prediction Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.211-214, 2018.

The objective of this study is to diagnose the patients with hypertension using Artificial Neural Network. Learning rate and momentum coefficient are the parameters used to construct network. The values of parameters are selected randomly and then the values are increased or decreases in every step iteratively. The topologies like Number of hidden layers, number of hidden nodes and the type of activation functions are also used to construct network. In order to improve the accuracy of the network, the result obtained by Back Propagation algorithms has been fused using Proportional Conflict Redistribution (PCR) rule. The output of the Back Propagation networks is considered as the primary diagnosis results and fused this result with Proportional Conflict Redistribution (PCR) rule to get the final results. Fusion method proposed in this paper is to enhance the target performance and reduce the uncertainty level. The experimental result shows that the fusion method produced higher accuracy and lower level of uncertainty.

Hypertension, Fusion, Back Propagation, uncertainty, Diagnosis

[1] John Trinder, Mahmoud Salah, “Combining Statistical and Neural classifiers using Dempster-Shafer Theory of Evidence for Improved Building Detection,” ARSPC, Alice Springs, pp. 13-17, 2010.
[2] Zhang Tao and Qi Yong-Qi, “Uncertainty Analysis of Integrated Navigation Model for Underwater Vehicle,” Research Journal of Applied Sciences, Engineering and Technology, Vol.6, pp. 1614-1620, 2013.
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[18] Koushal Kumar and Abhishek, “Artificial Neural Networks for Diagnosis of Kidney Stones Disease”, International Journal of Information Technology and Computer Science, Vol.7, pp. 20-25, 2012.
[19] B. Sumathi, “Neural Networks Evidence Combination for the Diagnosis of Hypertension”, National Conference on Emerging Trends in Information and Computer Technology NCETICT- at Kingston Engineering College, 2013.
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[21] Deepali Kamath, Anupama Ajith, Kavita Pujari, Praveena Kumari MK , “A Survey on Data Mining Techniques Applied on Cardiovascular Diseases and Cancer, Diagnosis and Prognosis”, International Journal of Computer Science and Engineerin, Vol.6, Issue.8, pp. 544-550, 2018.
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[23] Praveen Tripathi, R. Belwal, A.K.Bhatt, “Assessment of Apple Quality based on Scaled Conjugate Gradient Technique, using Artificial Neural Network Model”, International Journal of Computer Science and Engineerin, Vol.6, Issue.7, pp. 103-108, 2018.
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B. Sumathi , "Enhance the Performance of Back Propagation Algorithm Using Proportional Conflict Redistribution Rule for the Diagnosis of Hypertension," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.215-219, 2018.

Identification of plants species has become one of the challenges for image processing and machine learning. The need to find an efficient solution to such a problem is essential as medicinal plants and new plants’ existence need to be studied. Most of the researches in identifying this plants species are based on color, shape and textures. This paper is based on these features with Data-Augmentation. Data-augmentation is an important technique in increasing the number of training dataset which further helps in increasing the prediction of classification. This paper uses machine learning algorithms in classifying the flower classes based on FLOWERS17 dataset. Data-augmentation is applied to the training dataset to enhance the prediction. It has been observed that Random Forest classifies flowers with an accuracy of 64% before data-augmentation and 94% after data-augmentation. This paper also shows that after increasing the number of classes from 17 to 21, the performance of Random Forest is consistent to 94%.

Data Augmentation, Flower Recoginition, Image Processing, Machine Learning

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Th. S. Kumar, "Enhancement of Image Classification through Data Augmentation using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.220-224, 2018.

This paper`s approach based on discrete wavelet transform and Fuzzy c-means clustering for the detection and classification of the complex power quality disturbances. The complex power quality disturbances have been generated in MATLAB by various combinations of the mathematical models single stage power quality disturbances such as voltage sag, voltage swell, momentary interruption, oscillatory transient, impulsive transient, harmonics, notch and spike. The investigated complex power quality disturbances are (voltage sag + harmonics), (voltage swell + harmonics), (momentary interruption + harmonics), (oscillatory transient + voltage sag), (oscillatory transient + harmonics), (impulsive transient + voltage sag), (Impulsive Transient + harmonics) and (oscillatory Transient, Voltage Sag and Harmonics). The DWT based plots up to fourth level of decomposition of the voltage signal with complex PQ disturbance are used for the recognition of the complex PQ disturbances. The DWT based features have been given as input to the Fuzzy c-means clustering for classification purpose of the complex PQ disturbances. It is observed that the proposed algorithm is effective in the detection and classification of the complex power quality disturbances. The proposed approach has been implemented using the MATLAB codes.

Power Quality, Complex power quality disturbance; Discrete wavelet transform; Fuzzy C-means clustering

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Umesh Kumar Sharma, Tanuj Manglani, "Recognition of Complex Power Quality Disturbances Using Discrete Wavelet Transform and Fuzzy C-means Clustering," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.225-236, 2018.

Routing offers a challenge in MANET because mobility of nodes will motivate frequent link breaks and therefore frequent modifications in topology due to mobility, leading to frequent route exchange. Accordingly, QoS provisioning for software application turns into a challenge. When a link break takes place, the route has to be repaired locally or a new route needs to be found. All through change in route discovery after link break, packets may be dropped. This results in unnecessary wastage of the scarce node resources along with battery energy. In this paper, a novel mechanism has been proposed to predict the duration of latest route availability. This approach pursuits to enhance the Quality of Service (QoS) by predicting a link failure earlier than its incidence and routing the packets via an alternate path, while nodes are moving dynamically in Mobile Ad-hoc network. Availability of route is determined by using availability of links among the devices which are making the route. To estimate path`s future availability, the prediction is mandatory for those links. Availability of a link among nodes depends on the mobility of nodes, energy intake by the nodes, channel fading and shadowing etc. However, node mobility is major contributing issue for link failures. The proposed mechanism for predicting the link is used to estimate the active link availability to the neighbors. Primarily based on this data, when link failure is anticipated among two nodes, proactively an alternative path is building up earlier to link breaks. This reduces the packet drops (data) and hence the recovery time. With the proposed approach the nodes life time will be improved and performance of the network will be improved.

PCLP, PDR, SDMANET, QoS

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N. Satheesh Kumar ,N. Sathyanarayana, "Optimizing Power Control and Link Availability Prediction in Software Defined Mobile Ad-hoc Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.237-345, 2018.