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Recommender System provides various choices of the user preferences for suggesting the product/service to purchase. Collaborative filtering is one of the techniques in Recommender system used to find reviews and ratings of the users for similar products or users. To improve the performance of the recommendation, methods have been sometimes combined in hybrid recommenders. In this paper, the researcher have proposed an item based recommendation using Hybrid method called Item Recommendation Using Hybrid Method (IRHM), based on collaborative filtering approach that recommends the user for choosing the best item. The aim of the paper is to find the maximum value of precision and recall in hybrid method.

Item, Movie, Recommendation System, Hybrid, Collaborative Filtering, IRHM

[1] Saurabh Kumar Tiwari, Shailendra Kumar Shrivastava, “An Approach for Recommender System by Combining Collaborative Filtering with User Demographics and Items Genres”, International Journal of Computer Applications, Vol.128, No.13, pp. 16-24, 2015.
[2] Manoj Kumar, D.K. Yadav, Ankur Singh, Vijay Kr. Gupta, “A Movie Recommender System: MOVREC”, International Journal of Computer Applications, Vol.124, No.3, pp.7-11, 2015.
[3] Prerna Agarwal, Richa Verma, Angshul Majumdar , “Indian Regional Movie Dataset for Recommender Systems”, https://arxiv.org/pdf/1801.02203.pdf, arXiv:1801.02203v1 [cs.IR], pp.1-7, 2018.
[4] Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl, “Analysis of recommendation algorithms for e-commerce”, In the Proceedings of the 2nd ACM Conference on Electronic Commerce, Minnesota, USA, pp.158-167, 2000.
[5] R. Suguna, D. Sharmila, “An Efficient Web Recommendation System using Collaborative Filtering and Pattern Discovery Algorithms”, International Journal of Computer Applications, Vol. 70, No.3, pp.37-44, 2013.
[6] Mohamed Koutheair Khribi, Mohamed Jemnil, Olfa Nasraoui, “Automatic Recommendations for E-Learning Personalization based on Web Usage Mining Techniques and Information Retrieval”, Educational Technology and Society, 12(4), pp.30-42, 2009.
[7] Hamidreza Koohi, Kourosh Kiani, “User based Collaborative Filtering using fuzzy C-means”, Science Direct, Measurement 91, pp.134–139, 2016.
[8] Ziming Zeng, “An Intelligent E-Commerce Recommender System Based on Web Mining”, International Journal Business and Management, Vol.4, No.7, pp.10-14, 2009.
[9] Shivani Diwan, Komal Dani, Sahil Desai, ”Dynamic Recommendation System for E-Commerce Users”, International Research Journal of Engineering and Technology, Vol.03, Issue. 05, pp.141-144, 2016.
[10] Amer Al-Badarenah, Jamal Alsakran, “An Automated Recommender System for Course Selection”, International Journal of Advanced Computer Science and Applications, Vol.7, No.3, pp. 166- 173, 2016.
[11] Mohammad Daoud, S.K. Naqvi, Asad Ahmad, “Opinion Observer:Recommendation System on E-Commerce Website”, International Journal of Computer Applications, Vol.105, No.14, pp.37-42, 2014.
[12] Robin Burke, “Hybrid Recommender Systems: Survey and Experiments”, User Modeling and User - Adapted Interaction”, Vol.12, Issue.4, pp. 331-370, 2002.
[13] Senthil Kumar Thangavel, Neetha Susan Thampi, Johnpaul C I, “Performance Analysis of Various Recommendation Algorithms Using Apache Hadoop and Mahout”, International Journal of Scientific & Engineering Research, Vol.4, Issue 12, pp.279-287, 2013.
[14] Suresh K. Gorakala, Michele Usuelli, “Building a Recommendation System with R”, Packt Publishing Ltd, UK, pp. 9-10, 2015.
[15] Michael Hahsler, “Recommenderlab: A Framework for Developing and Testing Recommendation Algorithms”, Southern Methodist University, pp.1-40, 2011.
[16] Mojtaba Salehi, “An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product”, International Journal of Business Information Systems, Vol.14, No.4, pp.480-496, 2013.

M. Munafur Hussaina, R. Parimala, "Item Recommendation Using Hybrid Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.266-270, 2018.

The world is moving towards a digital generation and more number of users are being added to the wireless technologies. The spectrum availability is being reduced with the increase in number of users. The problem of spectrum scarcity can be addressed by using cognitive radio, a comparatively newer technology. Spectrum sensing is the core part of cognitive radio which helps us in identifying the unused frequencies or part of the spectrum that can be allocated to new users without any disturbance to the ongoing transmission. Energy detection is such a spectrum sensing technique which is simple to implement, and we do not require to have the prior knowledge of the primary user signal. In this paper, we will implement and analyze energy detection with various values of SNR. We will investigate the optimal threshold in cooperative sensing also.

Cognitive Radio; Spectrum Sensing; Energy Detection; SNR; Spectrum Access

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Rohit, V. Sindhu, "Evaluation of Energy Detection at various SNR values and optimal Cooperative Spectrum Sensing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.271-278, 2018.

The process of optimizing mobile apps at a high level in search results of the ASO App Store is that your app is more in search results of the app store, which is more visible to potential customers, translating into more traffic to your app`s page in the increasingly visible App Store. ASO`s aim is to drive more traffic to your app`s page in the app store, so that the locator can perform certain actions: downloading your app. Also, an important aspect of your target customer needs to do in the ASO process. Search apps as well as your potential customers. When you learn more about which keyword is being used, you know the language of your potential customers - an important part of any marketing plan - and you can take your keyword choices. Google Play Store is an Android Market site specifically for the Google App Store. It is automatically pulled from the Google Play website and displays the top chart and new releases of the game. It provides detailed information and reviews of applications with related apps. App store optimization (ASO) app store is a process of improving the visibility of the mobile app (such as iPhone, iPad, Android, Blackberry or Windows Phone Application).

ASO,SEO,iOS,Google play, etc

1. Craig Weinberg “The complete guide to learn App store Optimization “, VP of Mobile Strategy, 3Q Digital,and Gary Yentin, CEO and Founder, AppPromo
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4. Roy Deddy Hasiholan Tobing1, Liza Venita Debora Pardede,: Customizable Commerce Mobile Application 2016 3rd International Conference On Computer And Information Sciences (ICCOINS)
5. Ryan Weber, Co-Founder / Chief Product Officer, NativeX : App Store Optimization (ASO) White Paper NativeX Holdings2016
6. Jianye Liu, Jianka Yu, ‘Research On Development Of Android Applications’ in Fourth International Conference on Intelligent System -2011
7. Suhas Holla, Mahima M. Katti, ‘Android Based Mobile Application Development And its Security’ in International Journal of Computer Trend And Technology – 2012
8. Haipeng Cai; Barbara G. Ryder ‘
DroidFax: A Toolkit for Systematic Characterization of AndroidApplications’ 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)Year: 2017

Y. C. Kulkarni, Harshad Kale, "Design and implementation of app store optimization tool for an app market," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.279-284, 2018.

As online purchase has growing nowadays, recommendation becomes important field for today. Due to the regard of privacy, user’s unwillingness to expose their private data has become considerable obstacle for the growth of customized recommendation system. So the motive is to safeguard the user’s private data. In this work, it is proposed to formulate the dummy preferences set to protect user’s sensitive subjects. Firstly, a client based structure for user security assurance is introduced, which does not need any modification to existing algorithms, as well as no trade off to the proposal exactness. Then a privacy protection model formulated by the prime requirements such as similarity in the feature distribution and the degree of exposure is put forth. Feature distribution measures the success of dummy preference profile to envelop actual user profile and the degree of exposure measures the favorable result of dummy preferences to envelop sensitive subject. Finally the implementation algorithm is introduced to meet the actual privacy goal. Proposed system also aims to provide the sentiment analysis of the reviews for the products in order to help the people to identify the good products among the huge number of products available.

Personalized Recommendation, Individual Privacy, sensitive subjects, Feature Distribution, Dummy Preferences

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[3] Hwee Hwa PANG, Xiaokui XIAO, Jialie SHEN, “Obfuscating the Topical Intention in Enterprise Text Search”, ICDE 2012: IEEE 28th International Conference on Data Engineering, Arlington Virginia, 1-5 April 2012: Proceedings. 1168-1179.
[4] Feng Zhang, Victor E. Lee, and Ruoming Jin, “k-CoRating: Filling Up Data to Obtain Privacy and Utility”, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence 2014.
[5] Yilin Shen and Hongxia Jin, “Privacy-Preserving Personalized Recommendation: An Instance-based Approach via Differential Privacy”, 2014 IEEE International Conference on Data Mining.
[6] Zhifeng Luo, Shuhong Chen , Yutian Li, “A Distributed Anonymization Scheme for Privacy-preserving Recommendation Systems”, Supported by University Innovation Research and Training Program of Guangdong Province(1056111033) 2013 IEEE.
[7] Zongda Wu, Guiling Li, et al, ”Covering the sensitive subjects to protect personal privacy in personalized recommendation”, IEEE transaction on serviced computing 2016.
[8] Guandong Xu, ZongdaWu, Guiling Li et al. “Improving contextual advertising matching by using wikipedia thesaurus knowledge”, Knowledge and Information Systems, 2015, 43 (3): 599–631.
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[11] Liang Hu, Guohang Song, Zhenzhen Xie, and Kuo Zhao, “Personalized Recommendation Algorithm Based on
Preference Features”, Tsinghua science and Technology, Vol. 19, No. 3, 11llpp293-299, June 2014
[12] Yande M, Wakchaure M, Student ME. “Cross-Site Cold-Start Product Recommendation for Social Media and E-Commerce Websites.” International Journal of Engineering Science. 2017 Jul;13751.
[13] Khalid O, Khan M U S, Khan S U et al. ”OmniSuggest: A ubiquitous cloud-based context-aware recommendation system for mobile social networks”. IEEE Transactions on Services Computing, 2014, 7 (3):401414.
[14] Varpe P. “A Preserving Personal Privacy in Personalized Recommendation by protecting the Sensitive Subjects. ”ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT)-UGC LISTED. 2018 Apr 15; 4(I).
[15] Shitole MA, Wakchaure MA. “Patient-Centric and Privacy Preserving Clinical Decision Support System Using Naive Bayesian Classification.” 2016, pp. 999-1003
[16] Guandong Xu, ZongdaWu, Guiling Li et al. “Improving contextual advertising matching by using wikipedia thesaurus knowledge”.Knowledge and Information Systems, 2015, 43 (3): 599–631
[17] Wakchaure MM., Survey on Discrimination Prevention in Data-Mining.
[18] Mankar A, Patil H, Arage C, Gaikwad M. “A Survey on Sentiment Computing for the Opinions Based on the Twitter.” International Journal of Scientific Research in computer Science and, Engineering and information Technology, ISSN : 2456-3307, Volume 3 Issue 1, pp.361-364 , 2018
[19] N.Rajganesh, S.Seetha Devi, J. Keerthana, R.Poovizhi, “A Personalized Job Recommender System Using Hybrid Collaborative Filtering Algorithm”, International Journal of Scientific Research in computer Science and, Engineering and information Technology, ISSN : 2456-3307, Volume 3 Issue 3, pp.192-196 , 2018

P.B.Varpe, M.A.Wakchaure, "Protecting the Users Information in Personalized Recommendation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.285-290, 2018.

Prediction and forecasting has been significant area of study in computer science since last decades. Out of various approaches, soft computing data driven models are very effective for forecasting. Soft Computing Models are usefully applicable when the relationship between the parameters are very complex to understand. India a disaster prone country which requires such major soft computing based data driven models to handle disasters like flood, drought, landslide etc. Flood has a major impact in many parts of India out of which Cauvery, Godavari and Ganges river basins are the mostly affected regions. The paper attempts to forecast floods by modeling river flow in the area of Cauvery river basin of India which has a complicated topography. In this study, the potential of two data driven techniques namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Gaussian Process Regression (GPR) were used for forecasting floods by predicting river flow in Cauvery river sub-basin of southern India. The techniques were applied on various models constructed from combinations of various antecedent river flow values from two gauging stations and the results were compared for the best fit models of each technique. To get more accurate assessment of results of the models, three standard statistical quantitative performance assessment parameters, the Mean Squared Error (MSE), the coefficient of correlation (R) and the Nash-Sutcliffe coefficient (NS) were used to analyze the performances of the models developed. A complete comparison of the overall performance indices demonstrated that the ANFIS models performed better than GPR models in flood prediction.

Adaptive Neuro Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Mean Squared Error (MSE), the coefficient of correlation (R), Nash-Sutcliffe coefficient (NS)

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P. Misra, S. Shukla, "Implementation of Neuro-Fuzzy and Statistical Technique for Flood Forecasting in Cauvery Basin, India," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.291-298, 2018.

Software testing plays an indispensable part in the software development process. A huge number of test cases are required to be tested to improve the quality of the software which is a tedious and time-consuming process. In this paper we aim to minimize the number of test cases by eliminating redundant test cases and thereby assisting us in reducing the time consumed in testing huge number of test cases. We have used the popular data mining k-means algorithm along with an elbow method to reduce the number of test cases required to be tested. Experimental result presents better clustering accuracy and significant elimination of redundant test cases by using the proposed approach.

Testing; data mining; test case reduction; test case minimization; test suite reduction; test suite minimization; cluster

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[14] A. K. Upadhyay, A. K. Misra, “Prioritizing Test Suites Using Clustering Approach in Software Testing”, International Journal of Soft Computing and Engineering (IJSCE), Vol. 2, Issue-4, pp. 222-226, 2012.
[15] Marie Fernandes , “Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, 2017.
[16] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, “A Review: Design and Development of Novel Techniques for Clustering and Classification of Data”, International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.19-22, 2018.
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A. Pandey, A. K. Malviya, "Enhancing test case reduction by k-means algorithm and elbow method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.299-303, 2018.

The data which involves sound, image and video was kept in numeral system. Audiovisual data is digital form propositions several benefits and different abilities for normal user. Likely the most common used potential of digital media is the untroubled copy without degradation of the medium. Watermark have numerical environment, combination documents and it will copied, altered, distorted, and dispersed precise simply. For that, it is necessary to improve the structure for patent safety, defense against duplication, and validation of documents. Hence, digital watermarking is best solution for providing more security to client/server. In open communication channel during the transaction process. Now, in some existing methods only provide security against some normal attacks like Gaussian attack, jpeg attack, salt and pepper attack, etc. But it is not against RST invariant attacks. RST invariant Watermarking approach using DWT-SVD into block manner with Pseudo Zernike moment, Surf feature and Affine transform give batter recovery on watermark image after geometric attacks.

Data hiding; Block DWT-SVD; PZM; SURF; Affine transform, Geometric attacks

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S.D. Degadwala, A.D. Mahajan, D.J. Vyas, "Novel Image Watermarking On Geometric Attacks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.304-309, 2018.

Testing is one of the most critical and time consuming phase in development of a software and when it comes to regression testing in which compatibility is checked of the previous code with the updated one. It definitely increases the size of test case and budget so, to decrease the number of test cases in regression testing this paper presents a QBGA (Queen Bee Genetic Algorithm) technique for test case reduction and also increases the coveragence that would makes a software more efficient. When it is in contrast with the existing GA algorithm, the number of test cases is found to be reduced and covered area is enhanced and results are found to be better.

Regression Testing, Test Case Reduction, Coveragence

[1] Khan, S. U. R., Lee, S. P., Javaid, N., & Abdul, W. (2018). “A Systematic Review on Test Suite Reduction”: Approaches, Experiment’s Quality Evaluation, and Guidelines. IEEE Access, 6, 11816-11841.
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[3] Velmurugan, P., & Mahapatra, R. P. (2016, July). “Effective testcase retaining using branch coverage technique.” In Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on(pp. 504-508). IEEE.
[4] Lawanna, A. (2017, October). “Extended regression test model for test suite reduction.” In Consumer Electronics (GCCE), 2017 IEEE 6th Global Conference on (pp. 1-5). IEEE.
[5] Jyoti, K. S. (2014). A Comparative Study of Five Regression Testing Techniques: A Survey. International Journal of Scientific & Technology Research, 3(8).
[6] Yamu, A., Cingiz, M. ., Biricik, G., & Kalpsz, O. (2017, June). “Solving test suite reduction problem using greedy and genetic algorithms.” In Electronics, Computers and Artificial Intelligence (ECAI), 2017 9th International Conference on (pp. 1-5). IEEE.
[7] Sharma, N., & Purohit, G. N. (2014, December). “Test case prioritization techniques “an empirical study”. In High Performance Computing and Applications (ICHPCA), 2014 International Conference on (pp. 1-6). IEEE.
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[11] Hui, Z. (2016, June). “Fault Localization Method Generated by Regression Test Cases on the Basis of Genetic Immune Algorithm.”In Computer Software and Applications Conference (COMPSAC), 2016 IEEE 40th Annual (Vol. 2, pp. 46-51). IEEE.
[12] You, L., & Lu, Y. (2012, May).”A genetic algorithm for the time-aware regression testing reduction problem.”
In Natural Computation (ICNC), 2012 Eighth International Conference on(pp. 596-599). IEEE.
[13] Hui, Z. (2016, June). “Fault Localization Method Generated by Regression Test Cases on the Basis of Genetic Immune Algorithm.”In Computer Software and Applications Conference (COMPSAC), 2016 IEEE 40th Annual (Vol. 2, pp. 46-51). IEEE.
[14] Mohapatra, S. K., & Prasad, S. (2014, March). “Minimizing test cases to reduce the cost of regression testing.” In Computing for Sustainable Global Development (INDIACom), 2014 International Conference on (pp. 505-509). IEEE.
[15] Indumathi, C. P., & Madhumathi, S. (2017, May).”Cost aware test suite reduction algorithm for regression testing.”In Trends in Electronics and Informatics (ICEI), 2017 International Conference on (pp. 869-874). IEEE.
[16] Hui, Z. (2016, June). “Fault Localization Method Generated by Regression Test Cases on the Basis of Genetic Immune Algorithm.”In Computer Software and Applications Conference (COMPSAC), 2016 IEEE 40th Annual (Vol. 2, pp. 46-51). IEEE.
[17] Rosero, R. H., Gómez, O. S., & Rodríguez, G. (2017). “Regression Testing of Database Applications Under an Incremental Software Development Setting.”IEEE Access, 5, 18419-18428
[18] Liu, P. (2014, June). “An efficient reduction approach to test suite. In Software Engineering,”Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2014 15th IEEE/ACIS International Conference on (pp. 1-5). IEEE.
[19] Alagöz, I., Herpel, T., & German, R. (2017, March).”A Selection Method for Black Box Regression Testing with a Statistically Defined Quality Level.”In Software Testing, Verification and Validation (ICST), 2017 IEEE International Conference on (pp. 114-125). IEEE.
[20] Muzammal, M. (2016, December).”Test-Suite Prioritisation by Application Navigation Tree Mining.”In Frontiers of Information Technology (FIT), 2016 International Conference on (pp. 205-210). IEEE.
[21] Haider, A. A., Nadeem, A., & Akram, S. (2016, December).’Safe regression test suite optimization: A review.”In Open Source Systems & Technologies (ICOSST), 2016 International Conference on (pp. 7-12). IEEE.
[22] Liu, P. (2014, June).”An efficient reduction approach to test suite. In Software Engineering,”Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2014 15th IEEE/ACIS International Conference on (pp. 1-5). IEEE.
[23] Campos, J., & Abreu, R. (2013, April).”Encoding test requirements as constraints for test suite minimization.” In Information Technology: New Generations (ITNG), 2013 Tenth International Conference on (pp. 317-322). IEEE.
[24] Salehie, M., Li, S., Tahvildari, L., Dara, R., Li, S., & Moore, M. (2011, March).”Prioritizing requirements-based regression test cases: A goal-driven practice.”In Software Maintenance and Reengineering (CSMR), 2011 15th European Conference on(pp. 329-332). IEEE.
[25] Gonzalez-Sanchez, A., Abreu, R., Gross, H. G., & van Gemund, A. J. (2011, November).”Prioritizing tests for fault localization through ambiguity group reduction.”In Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering (pp. 83-92). IEEE Computer Society.RE, 2016 IEEE 27th International Symposium on (pp. 47-58) IEEE.
[26] Bhuyar, P. R., & Gawande, A. D. (2015). Analysis using Non- Functional Static Testing Framework.
[27] Sahoo, R. K., Mohapatra, D. P., & Patra, M. R. (2016). A Firefly Algorithm Based Approach for Automated Generation and Optimization of Test Cases. International Journal of Computer Sciences and Engineering, 4(8), 54-58.

Arzu, S. Singh, "An Algorithm for Test Case Reduction in Regression Testing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.310-315, 2018.

A Greater demands for fast and accurate user identification and authentication in electronic transaction increases day by day. Continuous development of technology, Security involvements are also increasing. ATM helps in a transaction of money any time & anywhere, faces the threat of attack, fraud, theft, etc., thus to deal with high security which provides safety to consumers, Authentication plays an important role. A new system approach for enhancing security and privacy in biometric applications like face detection, IRIS scan, fingerprint, voice, signature, etc. In this biometric system card-less operation done by biometric technology for operating ATMs. Proposed model provide high security in authentication which protects from illegal transactions. By this user required to authenticate him/her self with biometric identification and personal identification number. This proposed system is designed for illiterate, semi-literate and literate people. System decreases complexity with authentication as “you as Security” with high security. It reduces the problem of an excess number of plastic cards & saves environmental pollution. It saves time, cost, effort compared with a card-based system.

Automatic Teller Machine, Biometric, Fingerprint, IRIS, Personal Identification Number, Smartcards

[1]. Sonakshi Bhagat et al, “ATM Security using Iris Recognition Technology and RFID”, International Journal of Engineering Science and Computing, Vol.7, No.5, pp.11486-11488, May, 2017
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[3]. P. Nagarajan, Dr. Ramesh S M, “IRIS Recognition Based Authentication System In ATM”, International journal for trends in engineering & technology, Vol-5, No.1, pp.126-131, May, 2015

[4]. Mr. Mahesh A. Patil et al, “ATM Transaction Using Biometric Fingerprint Technology”, International Journal of Electronics, Communication & Soft Computing Science and Engineering, Vol-2, No.6, pp.22-27
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[6]. Prof. Selina Oko, Jane Oruh, “Enhanced ATM security system using biometrics”, International Journal of Computer Science Issues, Vol-9, No.5, pp.352-357, September, 2012
[7]. Namit Gupta, Anu Sharma, “Review of biometric technologies used for ATM security”, International Journal of Engineering and Innovative Technology, Vol-3, No.2, pp.460-465, August, 2013
[8]. R. Maheshwri et al, “Security ATM With Help Of Biometric Security”, International Journal of Electronics and Communication Engineering, pp.47-51, March, 2017
[9]. Moses Okechukwu Onyesolu, Ignatius majestry ezeani, “ATM Security Using Fingerprint Biometric Identifer: An Investigative Study”, International Journal of Advanced Computer Science and Applications, Vol-3, No.4, pp.68-72, 2012
[10]. N. Geetanjali, K.Thamaraiselvi, “Enhancing the Security of Biometrics in ATM”, International Journal of Scientific & Engineering Research, Vol-4, No.4, pp.1192-1198, April, 2013
[11]. Parvinder S. Sandhu et al, “Biometric Methods and Implementation of Algorithms”, International Journal of Computer and Information Engineering, Vol-3, No.4, pp.1033-1038, 2009
[12]. Ayushi Mathur, Mr. Rupal Gupta, “Analysis of Algorithms used in Biometric using Fingerprint Authentication for 3D Authentication System”, International Conference on Advanced Computing, pp.398-403, 2016
[13]. Abinandhan Chandrasekaran, Dr.Bhavani Thuraisingham, “Fingerprint Matching Algorithm Based on Tree Comparison using Ratios of Relational Distances”, International Conference on Availability, Reliability and Security, April, 2007
[14]. I. G. Damousis, S. Argyropoulos, “Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study”, Applied Computational Intelligence and Soft Computing, 2011
[15]. Roaya Salhalden A. Abdalrahman et. al., “A cascaded voice biometric system”, Procedia Computer Science, pp.1223–1228, 2018
[16]. Mithun Dutta et. al., “ATM Transaction Security Using Fingerprint Recognition”, American Journal of Engineering Research, Vol-6, No.8, pp.41-45, 2017
[17]. Nisha Bhanushali et. al., “Fingerprint based ATM system”, Journal for Research, Vol-2, No.12, pp.31-34, February, 2017
[18]. Sneha Ramrakhyani et. al, “Fingerprint Based ATM System: Survey”, International Journal of Innovative Research in Science, Engineering and Technology, Vol-6, No.11, pp.21515-21520, November, 2017
[19]. Paschal A. Ochang and Paulinus O. Ofem, “An Enhanced Automated Teller Machine Security Prototype using Fingerprint Biometric Authentication”, International Journal of Advanced Networking and Applications, Vol-8, No.4, pp-3110-3117, 2017
[20]. Reshma Begum et. al., “Security of ATM System Using Biometric and OTP”, International Journal of Innovative Research in Computer and Communication Engineering, Vol-5, No.4, pp.72-77, June, 2017

[21]. Aneesh Chandran et. al., “ATM for Visually Challenged People”, International Research Journal of Engineering and Technology, Vol-4, No.3, pp.370-375, March, 2017
[22]. Ankit Singla and Neetu Jain, “Fraud Reduction in ATM Machines using Voice Recognition- A Review”, International Journal of Innovative Research in Science, Engineering and Technology, Vol-6, No.5, pp.7525-7530, May, 2017
[23]. Fahreddin Sadikoglu and Selin Uzelaltinbulat, “Biometric Retina Identification Based on Neural Network”, Procedia Computer Science, Vol-102, pp.26-33, 2016
[24]. Reshmi K.C. et. al., “A Novel Approach to Brain Biometric User Recognition”, Procedia Technology, pp.240-247, 2016
[25]. Nana Kwame Gyamfi et. al., “Enhancing the Security Features of Automated Teller Machines (ATMs): A Ghanaian Perspective”, International Journal of Applied Science and Technology, Vol-6, No.1, pp.102-111, February, 2016
[26]. Ceren Tarkmen and Alper Deayerli, “Transformation of Consumption Perceptions: A Survey on Innovative Trends in Banking”, Procedia - Social and Behavioral Sciences, Vol-195, pp.376-382, July, 2015
[27]. Firas S. Assaad and Gursel Serpen, “Transformation based Score Fusion Algorithm for Multi-modal Biometric User Authentication through Ensemble Classification”, Procedia Computer Science, Vol-61, pp.410-415, 2015
[28]. Mohsin Karovaliya et. al., “Enhanced security for ATM machine with OTP and Facial recognition features”, Procedia Computer Science, pp.390-396, 2015
[29]. Michael Mireku Kwakye et. al., “Adoption of Biometric Fingerprint Identification as an Accessible, Secured form of ATM Transaction Authentication”, International Journal of Advanced Computer Science and Applications, Vol-6, No.10, pp.263-265, 2015
[30]. Rathishala Rajendran et. al., “ATM Security using Fingerprint Authentication and OTP”, International Journal of Current Engineering and Technology, Vol-5, No.2, pp.1157-1159, April, 2015
[31]. Zameer Afshan, Pawan Kumar Chaurasia, “A Review on Crypto-Currency”, International Journal Of Computer Science and Mobile Application, Vol-6, pp.95-100, 2018
[32]. V.K. Jain , N. Tripathi, “Speech Features Analysis and Biometric Person Identification in Multilingual Environment”, International Journal of Scientific Research in Network Security and Communication, Vol-6 , No.1 , pp.7-11, Feb, 2018
[33]. Namrata Ghuse et. al., “An Improved Approch For Fraud Detection In Health Insurance Using Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Vol-5, No.5, pp.28-32, June, 2017
[34]. Savitha G. et. al., “Selective Small Reconstruction Error Based LDRC Multimodal Biometric Authentication”, International Journal of Scientific Research in Computer Science and Engineering, Vol-6, No.1, pp.1-10, Feb, 2018
[35]. Oluwabunmi Ayankemi ONI, “A Framework for Verifying the Authenticity of Banknote on the Automated Teller Machine (ATM) Using Possibilistic C-Means Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol-6, No.2, pp.57-63, April, 2018

Priya Sharma, Pawan Kumar Chaurasia, "Proposed Algorithm for Secured Transaction using 3-Tier Architecture," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.316-321, 2018.

To represent the image on a screen, several bits is used. Image compression a technique which is used to reduce the number of bits used for representation. Image compression helps in reducing the size of the image which results in less storage space and less cost of the transmission. The image is as compressed as the quality of the image is retained. In this paper, the image is compressed first and then the secret message is hidden in it using Tri-way Pixel Value Difference method. A neural network algorithm called Back Propagation Neural Network Algorithm is used for image compression. The benefit of using back propagation algorithm for using image compression is the vast increase in performance of the system as well as less convergence time for neural network training which not only maintain the quality of the image but also reduce the overall size of the image. This neural network method for image compression has shown a very promising result in image compression. After compression of the image, the secret message is embedded using Tri-way pixel Value Difference method which not only provides imperceptible stego image but also enlarges the capacity of the hidden secret information.

Artificial Neural Network, Steganography, Image Compression, Back Propagation Algorithm, Pixel-Value Differencing, Data Hiding

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[13] Ko-Chin Changa, Chien-Ping Changa, Ping S. Huangb, and Te-Ming Tua, “A Novel Image Steganographic Method Using Tri-way Pixel-Value Differencing,” Journal Of Multimedia, Vol. 3, NO. 2, 2008.
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Jyoti Pandey, Kamaldeep Joshi, Mohit Jangra, Tanu Garg, Sangeeta, Parth kaushik, "High Capacity PVD Steganography Using Back Propagation Artificial Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.322-330, 2018.