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In cloud computing environment, resource allocation problem identifies that the cloud provider expects profit and the users expects best resources by considering budget and time constraints. In this paper, game theory mechanism has been used and an auction-based method is proposed which determines the auction winner and holding a repetitive game with incomplete information in a non-cooperative environment. In the proposed method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based on the suggested utility function. In the proposed technique, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the concluding bid also satisfies the auctioneer’s utility function. The proposed model is simulated in the Cloudsim and the results are compared with previous work.

Cloud Computing, Resource Allocation, Game Theory, Auction, Nash Equilibrium

[1]Parsa S., Shokri A., and Nourossana S., “A novel market-based grid resource allocation algorithm”, International Conference on Network Digital Technologies, Tehran, Iran,2009.
[2]R.Wolski, J.Brevik, J.S.Plank and T. Bryan, “Grid resource allocation and control using computational economics”, Berman F., Fox G., and Hey A., editors, Grid Computing: Making The Global Infrastructure a Reality, John Wiley & Sons, 2003.
[3]Izakian H., Abraham Ajith and Ladani T.B., “An Auction Method for Resource Allocation in Computational Grids”, Future Generation Computer Systems, 26(2): 228-235, 201.
[4]Das Anubhav and Grosu Daniel, “Combinatorial Auction-based Protocols for Resource Allocation in Grids”, 19th IEEE International Symposium on Parallel and Distributed processing (IPDPS’05) – Workshop 13, Vol. 14, 2005.
[5]Wang X., Teo Y.M. and Gozali J.P., “A Compensation-based Scheduling Scheme for Grid Computing”, In Proceedings of the Seventh International Conference on High Performance Computing and Grids in Asia pacific Region, 2004.
[6]N.Nisan, Algorithmic Game Theory, Cambridge University Press, 2007. View at MathSciNet.
[7] M. J. Osborne, An Introduction to Game Theory, vol. 3, Oxford University Press, New York, NY, USA, 2004.
[8]Y.Shoham,“Computer science and game theory,” Communications of the ACM, vol. 51, no. 8, pp. 75–79, 2008. View at Publisher • View at Google Scholar • View at Scopus.
[9] L. Guijarro, V. Pla, J. R. Vidal, and J. Martinez-Bauset, “Entry, competition, and regulation in cognitive radio scenarios: a simple game theory model,” Mathematical Problems in Engineering, vol. 2012, Article ID 620972, 13 pages, 2012. View at Publisher • View at Google Scholar • View at Zentralblatt MATH.
[10]Wei-Yu Lin, Guan-Yu Lin, Hung-Yu Wei, “Dynamic Auction Mechanism for Cloud Resource Allocation”, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

Gagandeep Kaur, "Resource Provision Scheduling in Cloud using Game Theory," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.49-53, 2019.

Now a day’s online resources are increasing very rapidly like amazon and flipchart, eBay etc. The main role of recommendation systems is to provide recommendations based upon the ratings given by the users.it suffers from the sparsity to reduce that we are going to introduce a reliable solution that motives to perform better results using a demographic approach. Each prediction consorts with a reliability measure. Reliability is a measure of how liable a prediction is. So each recommendation for a user is associated with a pair of values those are Prediction and reliability. Quality of reliability is also discussed. Experimental results show that our proposed reliable solution using demographic approach has increased the overall recommendation and reduced the sparsity.

Recommender systems, Collaborative filtering, prediction, reliability, location

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[11] K. Ali and W. van Stam, “TiVo: Making show recommendations using a distributed collaborative filtering architecture,” in ACM KDD ’04, pp. 394–401, ACM, 2004.
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[13] X. Amatriain, J. M. Pujol, N. Tintarev, and N. Oliver, “Rate it again:Increasing recommendation accuracy by user re-rating,” in ACM RecSys ’09,pp. 173–180, ACM, 2009.
[14] C. De Rosa, J. Cantrell, A. Havens, J. Hawk, L. Jenkins, B. Gauder, R. Limes, D. Cellentani, and OCLC,Sharing, privacy and trust in our networked world: A report to the OCLC membership. OCLC, 2007.
[15] X. Zhou, Y. Xu, Y. Li, A. Josang, and C. Cox, “The state-of-the-art in personalized recommender systems.

Kaira Nithin Goud, "A Reliable Solution for Sparsity Problem in Collaborative Filtering Using Demographic Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.54-59, 2019.

Blockchain, since its inception in 2009, has fuelled up innovation over a wide spectrum of applications that have beget from the technology and have harvested it manifolds in arenas such as smart contracts, digital currency, decentralised record keeping, securities in terms of crowdfunding and escrow. Bitcoin, the first brainchild of the Blockchain has been fine-tuned furthermore by the impending inflow of the many consensus algorithms that are being introduced by the many researchers, with the intent of making spectacular the impact of the technology, keeping in mind the network parameters, efficiency of the blockchain and optimization in the deployment of a decentralised application over the internet. This paper aims at introducing a novel qualitative framework of consensus algorithm called the Proof of Longevity (PoL), that would improvise the current blockchain in a way so as to rule out adversaries in terms of Node Identity management, computation energy and cost saving and strategize the double spending and selfish mining problems in the real world and to catalyse the blockchain network to its zenith.

Blockchain, Bitcoin Mining, Consensus Algorithms, Smart Contracts, Distributed cryptocurrency

[1]. Abigail Christina Fernandez and Dr. R. Thamarai Selvi, “A Comprehensive Overview of the Constructive Minutiae of the Bitcoin - Blockchain”, IOSR Journal of Engineering (IOSRJEN), vol. 09, no. 08, 2019, pp. 69-77
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[4]. Bentov, I., Gabizon, A., & Mizrahi, A. Cryptocurrencies without proof of work. In International Conference on Financial Cryptography and Data Security, 2016, February, pp. 142-157, Springer, Berlin, Heidelberg.
[5]. Daniel, F., & Guida, L. A service-oriented perspective on blockchain smart contracts. IEEE Internet Computing, 2019, 23(1), pp. 46-53.
[6]. Eyal, I., Gencer, A. E., Sirer, E. G., & Van Renesse, R. Bitcoin-ng: A scalable blockchain protocol. In 13th (USENIX) Symposium on Networked Systems Design and Implementation (NSDI), 2016, pp. 45-59.
[7]. Gervais, A., Karame, G. O., Wüst, K., Glykantzis, V., Ritzdorf, H., & Capkun, S., On the security and performance of proof of work blockchains. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016, October pp. 3-16.
[8]. Schrijvers, O., Bonneau, J., Boneh, D, & Roughgarden, T, Incentive compatibility of bitcoin mining pool reward functions. In International Conference on Financial Cryptography and Data Security, 2016, February, pp. 477-498. Springer, Berlin, Heidelberg.
[9]. Wang, W., Hoang, D. T., Hu, P., Xiong, Z., Niyato, D., Wang, P., ... & Kim, D. I.. A survey on consensus mechanisms and mining strategy management in blockchain networks. 2019,IEEE Access, 7, 22328-22370.
[10]. Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H., Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 2018, 14(4), pp. 352-375.
[11]. Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017, June). An overview of blockchain technology: Architecture, consensus, and future trends. In 2017 IEEE International Congress on Big Data (BigData Congress), 2017, pp. 557-564.
[12]. Divyakant Meva, “Issues and Challenges with Blockchain: A Survey”, in the International Journal of Computer Science and Engineering, 2018, Vol.-6, Issue-12, E-ISSN: 2347-2693.

R. Thamarai Selvi, Abigail Christina Fernandez, "A Modified Consensus Algorithm with a Diminutive of Proof of Longevity - Augmenting the Effectuation of Blockchain," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.60-65, 2019.

In today’s world there are a large amount of data which need to be processed with big databases. In recent years, increase plethora of companies has adopted different-different types of non-relational database. The goal of this research is to implement techniques to retrieve big database for the big datasets and investigate the performance of the big database techniques on CPU utilization and high-performance computing software. It attempts to use NoSQL database to replace the relational database. In this research mainly focuses on the new technology of NoSQL database i.e. MongoDB, HadoopDB. Performance comparison of two big data techniques is carried out. The result found that Aggregation technique consumes less execution time than MapReduce technique and more efficient with MongoDB database where as MapReduce technique has less efficient with HadoopDB. Aggregation technique also produces fine relevant information results with less CPU utilization. The result also shows that MongoDB has the capability to switch SQL databases as compare to HadoopDB.

Big Data, MongoDB, HadoopDB, Aggregation, MapReduce

[1] Kirti, M Pardeep, “Database for unstructured, semistructured data- NoSQL”, International journal of advanced research in computer engineering & technology, Vol. 4, Issue.2, pp. 466-469, 2015.
[2] A Ait-Mlouk, F Gharnati, T Agouti, “Application of big data analysis with decision tree for road accident”, Indian Journal of Science Technology, Vol. 10, Issue.29, pp. 1-10, 2017.
[3] N Rajyaguru, M Vinay, “A comparative study of big data on mobile computing”, Indian Journal of Science and Technology, Vol. 10, Issue.21, pp. 1-7, 2017.
[4] A Kamilaris, A Kartakoullis, B X. F Prenafeta, “A review on the big data analysis in agriculture”, Computer and Electronics in Agriculture, Vol. 143, pp. 23-27, 2017.
[5] Dean J and Ghemawat S (2008) MapReduce: Simplified Data Processing on Large Clusters. 137-150.
[6] Dede E, Govindaraju M, Gunter D, Canon R S, Ramakrishan L (2013) Performance evaluation of a MongoDB and hadoop platform for scientific data analysis. 4th Workshop on Scientific Cloud Computing, ACM, pp. 13-20.
[7] Nunan D, Domenico M D (2013) Market research and the ethics of big data. International journal of market research, 55(4):505-520.
[8] Ozarkar K, Rajani R (2014) Optimization technique for efficient dynamic query forms with NoSQL. International journal of science and research, 3(11):2041-2044.
[9] Bhosale H S, Gadekar D P (2014) A review paper on big data and hadoop. International Journal of Scientific and Research Publications, 4(10):1-7.
[10] A D Arasteh, D Mohammadpur, M Meghdadi, “MapReduce based implementation of aggregate functions on Cassandra”, International journal of electronics communication and computer technology, Vol. 4, Issue.3, pp. 604-609, 2014.
[11] R Zuech, M T Khoshgoftaar and R Wald, “Intrusion detection and big heterogeneous data a survey”, Journal of Big Data, Vol.2, Issue.3, pp. 2-41, 2015.
[12] Z Mo, Y Li, “Research of big data based on the views of technology and application”, American journal of industrial and business management, Vol.5, pp. 192-197, 2015.
[13] V S Thiyagarajan, A Ayyasamy, “Privacy preserving over big data through Vssfa and Map-Reduce framework in cloud environment”, Indian Journal of Wireless Personal Communication, Vol. 97, Issue.4, pp. 6239-63, 2017.
[14] K Abouelmehdi, H A Beni and H Khaloufi, “Big healthcare data: preserving security and privacy”, Journal of Big Data, Vol. 5, pp. 1-18, 2018.
[15] M S A Khan, H Jamshed, S Bano, N M Anwar, “Big data management in connected world of Internet of things”, Indian Journal of Science Technology, Vol. 10, Issue.29, pp. 1-9, 2017.
[16] V. M A Martin, K David, A Vignesh, “Big Data and its challenges”, International journal of scientific research in computer science, engineering and information technology, Vol. 3, Issue.3, pp. 533-538, 2018.
[17] M Chevalier, M E Malki, A Kopliku, O Teste, R Tournier, “Implementing Multidimensional Data Warehouses into NoSQL”, ICEIS, Vol. 1, pp. 172-183, 2015.
[18] L Kumar, S Rajawat, K Joshi, “Comparative analysis of NoSQL (MongoDB) with MySQL Database”, International Journal of Modern Trends in Engineering and Research, Vol.2, Issue. 5, pp. 120-127, 2015.

S. Puri, L. Jain, O.P. Gupta, "Analysis of Techniques to Retrieve Big Database," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.66-71, 2019.

With the increase in the mortality rate due to Breast cancer among young women folks, different techniques are developed for the early detection of breast abnormality. Thermal Infrared Imaging is one such modality that made use of thermal camera for the detection of the dreadful disease. This research work presents the use of bilateral breast geometrical analysis on the breast thermal signatures collected from Kidwai Institute of Oncology, Bangalore. The analysis has been performed on 70 bilateral breast thermal signatures. Breast thermal signatures have been captured at distances 1m, 1.5m and less than 1.5m. An algorithm has been implemented based on Digital Image Processing techniques. ROI processing has been performed on suitable palette. After detecting contour of breast area, edge linking has been implemented using Parabolic Hough Transform. Obtained results are correlated with ground truth mammography reports. It has been observed that out of 70 bilateral images, 21 have shown asymmetry which matches with ground truth. The analysis gives 77% sensitivity and 60.4% specificity. The distance between subject and camera also shows the effect on sensitivity. It is observed that the images taken at 1.5m distance are more apt for analysis purpose.

Breast asymmetry, thermal imaging, data acquisition, Canny edge detector, Hough Transform, BIRADS, Matlab, SmartView, ROI (Region of interest)

[1] Deborah Kennedy, Tanya Lee and Dugald Seely, “A comparative review of thermography as a breast screening technique”, Integrative Cancer therapies, Volume 08 Number 12009 09-16, 2008 Sage Publications.
[2] Kantilal P. Rane, Rupesh R. Joshi, L.A. Chaudhari, “IR Imaging: A new approach for Breast Cancer Detection”, Internationals of advanced electronics and communication system. 2277-7318,15th-16th February, 2014.
[3] V. Umadevi, S. Suresh and S.V. Raghavan, “Improved infrared thermography based image construction for biomedical applications using Markov chain monte carlo method”, 31st annual international conference IEEE EBMS Minneapolis, Minnesota, USA, September 2-6, 2009.
[4] F.J. Gonzalez, “Thermal simulation of breast tumors”, Instrumentation Revista Mexicana De Fisica, 53(4), 2007, pp323-326.
[5] Francisco Javier Gonzalez, “Non-invasive estimation of the metabolic heat production of breast tumors using digital infrared imaging”, Quantitative InfraRed Thermography Journal, 8:2, 139-148, 2011.
[6] R. Nithya, B. Santhi, “Comparative study on feature extraction method for breast cancer classification”, Journal of Theoretical and Applied Information Technology 30th November 2011. Vol. 33 No.2
[7] V. Umadevi, S.V. Raghavan, and Sandeep Jaipurkar, “Framework for estimating tumour parameters using thermal imaging”, Indian J Med Res. 2011 Nov; 134(5): 725–731.
[8] Qi H, Kuruganti PT, Liu Z., “Early detection of breast cancer using thermal texture maps” IEEE Symposium on Biomedical Imaging: Macro to Nano, Washington D.C.: 2002. Jul 7-12.
[9] Paruch M, Majchrzak E, “Identification of tumor region parameters using evolutionary algorithm and multiple reciprocity boundary element method” Eng Appl Artif Intell. 2007;20:647–55.
[10] Mittal M, Pidaparti RM, “Breast tumor simulation and parameters estimation using evolutionary algorithms”, Modell Simul Eng. 2001:756436–41. Article.
[11] Hairong Qi, Jonathan F. Head, “Asymmetry Analysis Using Automatic Segmentation and Classification for Breast Cancer Detection in Thermograms”, DOI: 10.1109/IEMBS.2001.1017386 Conference: Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, Volume: 3
[12] U. Rajendra Acharya & E. Y. K. Ng & Jen-Hong Tan & S. Vinitha Sree, “Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine”, J Med Syst (2012) 36:1503–1510 DOI 10.1007/s10916-010-9611-z.
[13] Pragati Kapoor, S. V. A. V. Prasad & Seema Patni, “Image Segmentation and Asymmetry Analysis of Breast Thermograms for Tumor Detection”, International Journal of Computer Applications (0975 – 8887) Volume 50 – No.9, July 2012.
[14] Phani Teja Kuruganti, Hairong Qi, “Asymmetry Analysis in Breast Cancer Detection Using Thermal Infrared Images”, Proceedings 01 the Second Joint EMBSiBMES Conference Houston, TX USA October 23-26.2002.
[15] Tiago B.Borchartt, AuraConci, RitaC.F.Lima, RogerResmini, AngelSanchez, “Breast thermography from an image processing viewpoint: A survey”, Signal Processing Volume 93, Issue 10, October 2013, Pages 2785-280.
[16] CHEN Bao-ping, MA Zeng-qiang,“Automated Image Segmentation and Asymmetry Analysis for Breast Using Infrared Images” 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing.
[17] Mona A. S. Ali, Gehad Ismail Sayed, Tarek Gaber, Aboul Ella Hassanien, Vaclav Snasel, Lincoln F. Silva, “Detection of Breast Abnormalities of Thermograms based on a New Segmentation Method”, Proceedings of the Federated Conference on Computer Science and Information Systems pp. 255–261 DOI: 10.15439/2015F318 ACSIS, Vol. 5
[18] Dave Tahmoush and Hanan Samet, “Using Image Similarity and Asymmetry to Detect Breast Cancer”, Proc. of SPIE Vol. 6144 61441S-6.

Hidangmayum Bebina, Joshi Manisha Shivaram, Aradhana Katke, Umadevi V, "Bilateral Breast Geometry Analysis –A Preliminary Tool for Detection of Breast Abnormality," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.72-77, 2019.

The position of an individual on Earth is of great importance and can have enormous applications such as Route Recommendation, Driving Navigation, Vehicular Turn Prediction, Travel Pattern Similarity, Pattern Mining, Route Planning, Social Networks, Vehicular ad-hoc networks and so on. Trying to figure out where you are and where you are going is probably one of man’s oldest pastimes. Route Prediction is also dealing with the same thing. There are many attributes, for example, temporal attributes, and transportation means, which can also be used for predicting next optimal point towards the destination. Over the years, all kinds of technologies have tried to simplify this task such as Landmark Techniques, Dead Reckoning Technique, Celestial Techniques, OMEGA Technique, LORAN Techniques, Satellite Navigation Technique and so on. This paper gives a detailed survey of some recent algorithms of route prediction, the attributes handled by them and the methods used by them.

Data Mining, Route Prediction, Probabilistic Model, Route Predictor Systems, GPS, Route Pattern, Map Matching, Spatial Database, Trajectory

[1] Ling Chen, Mingqi Lv, Qian Ye, Gencai Chen and John Woodward, “A personal route prediction system based on trajectory data mining,” Information Sciences, vol. 181, pp. 1264-1284, Apr. 2011.
[2] J. M. Kim, H. Baek and Y. T. Park, “Probabilistic graphical model based personal route prediction in mobile environment,” Applied Mathematics and Information Sciences, vol. 6, No. 2S, pp. 651S-659S, Jan 2012.
[3] Mingqi Lv, Yinglong Li, Zhenming Yuan and Qihui Wang, “Route Pattern Mining From Personal Trajectory Data”, Journal of Information Science and Engineering, vol. 31, pp. 147-164, 2015.
[4] Seongwon Min, Jong-Yong Lee and Kye-Dong Jung, “Real-time path prediction and grid-based path modeling method using gps,” International Journal of Applied Engineering Research, Vol. 12, No. 20, pp. 9997-10001, 2017.
[5] Vishnu Shankar Tiwari and Arti Arya, “Horizontally scalable probabilistic generalized suffix tree (PGST) based route prediction
using map data and GPS traces,” Journal of Big Data, vol. 4, No. 23, 2017.
[6] Vishnu Shankar Tiwari and Arti Arya, “Distributed Context Tree Weighting (CTW) for route prediction,” Open Geospatial Data, Software and Standards, vol. 3, No 10, 2018.
[7] Shun Taguchi, Satoshi Koide and Takayoshi Yoshimura, “Online Map Matching With Route Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, No. 1 pp. 338-347, Jan. 2018.
[8] Sudhir Kumar Adhlaka, Neelam Duhan, Komal Kumar Bhatia and Himanshu Sharma, “Route Prediction Techniques using GPS Traces and Spatial Data,” Proceedings of INDIACom IEEE International Conference on “Computing for Sustainable Global Development”, BVICAM, New Delhi, pp. 1475-1481, 13th-15th March, 2019.
[9] Y. Deguchi, “HEV charge/discharge control system based on car navigation information,” Proceedings of SAE Convergence International Congress & Exposition on Transportation Electronics, pp. 1–4, 2004.
[10] Ron Begleiter , Ran El-Yaniv and Golan Yona, “On Prediction Using Variable Order Markov Models,” Journal of Artificial Intelligence Research, vol. 22, pp. 385-421, Apr. 2004.
[11] W.H. Lee, S.S. Tseng, W.Y. Shieh, “Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system,” Information Sciences, vol. 180, No. 1, pp. 62-70, Jan 2010.
[12] K. Torkkola, K. Zhang, H. Li, H. Zhang, C. Schreiner and M. Gardner, “Traffic advisories based on route prediction,” Proceedings of Workshop on Mobile Interaction with the Real World, pp. 33–36, 2007.
[13] J. Froehlich and J. Krumm, “Route prediction from trip observations,” in Proc. of the World Congress on Society of Automotive Engineers (SAE), Apr. 2008.
[14] Claire F. Minett, A. Maria Salomons, Winnie Daamen, Bart van Arem and Sjon Kuijpers, “Eco-routing: Comparing the fuel consumption of different routes between an origin and destination using field test speed profiles and synthetic speed profiles,” 2011 IEEE Forum on Integrated and Sustainable Transportation System, Jun. 2011.
[15] E. D. Tate and S.P. Boyd, “Finding Ultimate Limits of Performance for Hybrid Electric Vehicles,” SAE Transactions - Journal of Passenger Car - Mechanical Systems,vol 109, 2001.
[16] U. C. Satish, P. Kondikoppa, S. Park, M. Patil, and R. Shah, “Mapreduce based parallel suffix tree construction for human genome,” in Proc. of the IEEE 20th International Conference on Parallel and Distributed Systems (ICPADS 2014), Taiwan, pp. 664-670, 2014.
[17] E. Mansour, A. Allam, S. Skiadopoulo, and P. Kalnis, “ERA: efficient serial and parallel suffix tree construction for very long strings,” Proc VLDB Endow (PVLDB), vol. 5, no. 1, pp. 49-60, 2011.
[18] J. Yuan, Y. Zheng, X. Xie, and G. Sun, “T-Drive: Enhance driving directions with taxi drivers’ intelligence,” IEEE Transactions on Knowledge and Data Engineering, Vol. 25, pp. 220-232, 2013.
[19] Y. Takeuchi and M. Sugimoto, “CityVoyager: An outdoor recommendation system based on user location history,” Ubiquitous Intelligence and Computing, Vol. 4159, pp. 625-636, 2006.
[20] A. Karbassi and M. Barth, “Vehicle route prediction and time of arrival estimation techniques for improved transportation system management,” in Proceedings of Intelligent Vehicles Symposium, pp. 511-516, 2003.
[21] Y. Zheng and X. Xie, “Learning travel recommendations from usergenerated GPS traces,” ACM Trans. Intell. Syst. Technol., vol. 2, no. 1, 2011.
[22] J. Yuan et al., “T-drive: Driving directions based on taxi trajectories,” in Proc. 18th ACM SIGSPATIAL Int. Conf. Adv. Geograph. Inf. Syst., pp. 99–108, 2010.
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[24] A. Ghoting and K. Makarychev, “Indexing genomic sequences on the IBM blue gene,” in Proc. of the Conference on High-Performance Computing Networking,” Storage and Analysis (SC), pp. 1-11, 2009.
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Dimple Singh, Pritam, Neelam Duhan, Komal Kumar Bhatia, "A Comprehensive Study of Route Prediction Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.78-85, 2019.

The master card project is all about integrating the currently available provisions by government of India along with few enhancements like linking DNA (Deoxyribonucleic Acid) of Humans with their permanent account number (PAN), QR code which assists a person in various ways like knowing his/her details, payments etc. and Integrated chip which can be read in a device when scanned. These are the provisions of the project which are partially taken from the existing services of government of India like Aadhar and few are additionally added from my end to improve effective administration. It helps the citizens of India to effectively connect with the government and avail its services. It helps the government of India to reduce corruption, ensures its schemes are implemented in proper way (delivering services to right means of people), It helps Indian judicial services to sort out pending cares and in giving judgment quickly likewise things etc. It creates a platform for government of India to bring all the citizens of India on one page and administer them effectively.

ATM magnetic strip, integrated chip, QR Code, DNA Number, PAN Number

[1] Increased crime rate in recent time in banking sector, inefficient government schemes like Demonetization made to think for such a solution

Pasupuleti Janakiramayya, "Master Card," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.86-90, 2019.

RSA algorithm is an efficient algorithm for preventing unauthorized access over the network. But there are some drawbacks of RSA algorithm such as its high computational time. In this work we are reducing the computational time of RSA algorithm and increasing security of RSA algorithm. In this work we are modifying security of RSA algorithm by using three prime numbers instead of two as used in RSA algorithm. For reducing computational time of RSA algorithm to each character, multiple characters are merged together to form a merged unit. For merging each character cantor’s pairing algorithm has been used. The merged unit is now encrypted to the network. To the receiver side cipher text is received. After decryption, the merged data unit is received to the receiver side. After going to cantor’s unpairing algorithm individual characters of merged data unit are displayed to the receiver side .The highlight of this work is it increases efficacy of RSA cryptosystem. This modified work reduces computational time of RSA algorithm, even increases security of this algorithm. Even In this work we are hiding cipher text in the terms of DNA sequences. So that it is very difficult for intruders to get a real DNAsequence.

DNA steganography, Cryptography, RSA, public, key, private key, pairing and unpairing algorithm

[1]. DNA based cryptographic technique for data hiding in DNA media Samiha Marwan, Ahmed Shawish, Khaled Nagaty.
[2]. Atul kahate, Cryptography and network security (TMH) RSA algorithm.
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[4]. Vivek Choudhary1 and Mr. N. Praveen2 “Enhanced RSA Cryptosystem Based on Three Prime Numbers” 1 Post Graduate Scholar, Department of Computer Science & Engineering, SRM University, Chennai, Tamilnadu, India 2 Assistant Professor, Department of Computer Science & Engineering, SRM University, Chennai, Tamilnadu, India
[5]. An Elegant Pairing Function”, Matthew Szudzik, Wolfram Research, Inc. NKS 2006 Wolfram Science Conference

Harsh Sahay, "Modified RSA Cryptosystem with Data Hiding Technique in the Terms of DNA Sequences," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.91-94, 2019.

Software testing issued to evaluate a trait or potential of system and conclude that whether it meets necessary prospects. The most reasonably demanding part of testing is to plan of test cases. These days, UML has been broadly used for object oriented modeling and design. UML matamodel is used to describe structural and behavioural aspects of an architecture. However to recognize this performance is still hard, because the size of automatically generated model diagrams tends to be huge. To overcome this problem Software visualization model based slicing procedure has been developed. Model based slicing is a coherent advance to extract and recognize appropriate model parts or associated elements across diverse model views. On the basis of slicing criteria an original procedure has proposed to extort the sub- model from a big model diagrams. The planned methodology use the concept of model based slicing to slice the sequence diagram to extract the desired hunk. In the presented approach UML, conversion of UML into XML, Java DOM API for parsing and slicing has been used. Then Extracted Sequence Diagram has been generated by using the Editor. After that test case generation is performed.

Model Based Slicing, Sequence Diagram, Parsing, Slicing, UML

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Venus Grover, Jitender Kumar, "Slicing based on UML Diagram & Test Case Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.95-101, 2019.

As going deep under the water nothing can be seen properly as well as it is difficult to identify any substance residing or present under water. This survey basically focuses on the detection of the underwater image which are taken through various self-ruling submerged vehicles and remotely controlled vehicles, in order to improve the quality of the pictures. The factors include the low contrast, blur, non-uniform lighting and faded colors. This paper analyzed an image enhancement technique alon

Offshore, underwater image restoration, under water imaging, underwater optical model

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Martina Martin, Nishchol Mishra, "Object Detection and Filtering Techniques of Underwater Images : A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.102-107, 2019.