Volume-7 , Issue-5 , May - 2019 Go Back

Availability of seats in trains to the passengers nowadays have become a major concern as the number of passengers is increasing day by day. There are certain areas where passengers wish to get seats in chunks but are not able to get due to our current running system, as it fails to allocate request in parts. As a result, seats are going vacant because our system allocates seats as per availability in one shot only on the passenger’s request. This paper describes the optimization of the seats in chunks using iterative method approach that they be fairly distributed across passengers on their wish. This technique serves two purposes, namely, resource optimization and revenue generation. If implemented on the existing railway system, will directly increase the railway revenue. It can be a great contribution to our government and travelers.

Resource Optimization, Iterative method, Seat Allocation

[1] D Guo, X Qu, M Wu and K Wu,“A Modified Iterative Alternating Direction Minimization Algorithm for Impulse Noise Removal in Images”, Journal of AppliedMathematics, July 2014.
[2] G.S. Sooch,A Bagchi, “A New Iterative Procedure for Deconvolution of Seismic Ground Motion in Dam-Reservoir-Foundation Systems”, Journal of Applied Mathematics, September 2014.
[3] R. Witula, E Hetmaniok, D Slota, A Zielonka,“Application of the Picard’s iterative method for the solution of one-phase Stefan problem”, Archives of Foundry Engineering, Vol. 10, Issue 4, 2010.
[4] S Bi,Q Wang,“Fractal Image Coding Based on a Fitting Surface”, Journal of Applied Mathematics, August 2014.
[5] M Grau-Sanchez, A Grau, M Noguera, “On the computational efficiency index and some iterative methods for solving systems of nonlinear equations”, Journal of Computational and Applied Mathematics, 236 (2011) 1259-1266.
[6] J Boyar, K.S Larsen, “The Seat Reservation Problem”, Odense University, Campusvej 55, DK-5230 Odense M, Denmark.
[7] J.S Kohrt, K.S Larsen, “On-line Seat Reservations via Off-line Seating Arrangements", International Journal of Foundations of Computer Science, April 2005.
[8] T Clausen, A.N Hjorth, M Nielsen, D Pisinger, “The Off-line Group Seat Reservation Problem”, DIKU, University of Copenhagen, 2007.
[9] B Yun, L Jun, M Min-Shu, M Ling-yun, “Seatinventory control methods for Chinese passenger railways”, J. Cent. South Univ.(2014) 21: 1672-1682.
[10] W Fan, J Wang, W.H. Ip, “Multi-leg Seat Inventory Control Based on EMSU and Virtual Bucket”, International Journal of Engineering Business Management, 2014.

A. Solanki, H. Patidar, "Railway Seat Allocation System Using Iterative Method," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.815-823, 2019.

Frequent imagination by cryptosystem designers that secrets will be manipulated in closed reliable computing environments. Unfortunately, computers and micro systems leak information about the operations they process. This paper examines self-organising neural network to securely transfer data through a given network. We also discuss approaches for building cryptosystems that can operate securely in existing system that leaks.

Cryptography, code generation, key management, self-organizing neural networks, encryption, decryption (key words)

[1] Manel Dridi, Mohamed Ali Hajjaji, Belgacem Bouallegue, Abdellatif Mtibaa Cryptography of medical images based on a combination between chaotic and neural network IET Image processing,volume 10 isssue 11.
[2] Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. (references)
[3] R. M. Gray, “Vector quantization,” IEEE Acoust., Speech, Signal Processing Mag., pp. 9–31, Apr. 1984.
[4] [2] A. Gersho and Robert M. Gray, Vector Quantization and Signal Compression. London: Kluwer, 1992.
[5] J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[6] I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
[7] K. Elissa, “Title of paper if known,” unpublished.
[8] R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.
[9] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
[10] M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.[10] M. Hu, J. Liu, and D. Luo, “Adaptive digital watermarking using neural network techniques”, in Proc. Of the 37th Annual Carnahan on intelligent control and Automation, vol. 5, pp. 4066-4069, 2004.
[11] Z. W. N. Wang, and B. Shi, “A novel bind watermarking scheme based on neural network in wavelet domain”, in Proc. Of the 6th World Congress on Intelligent Control and Automation, vol. 1, pp. 3024-3027, 2006.
[12] W. Kinzel, and I. Kanter, “Neural Cryptography”, in Proc. 9th Int’l Conf. on Neural information Processing (ICONIP’02), vol. 3, pp. 1351-1354,2002.
[13] E. Kilen, R. Mislovaty, I. Kanter, A. Ruttor, and W.Kinzel, “Synchronization of neural network by mutual learning and its application to cryptography”, in Proc. NIPS, pp. 689-696, 2004.
[14] R. M. Jogdand, Sahana S. Bisalapur, “Design of an efficient neural key generation”, International Journal of Artificial Intelligence and Application (IJAIA), vol. 2, No.1, 60-69, 2011.
[15] Ajit Singh, Aartinandal, “Neural cryptography for secret key exchange and encryption with AES”,International Journal of Advanced researched in computer science and software Engineering, vol. 3, issue. 5,376-381,2013.
[16] A. Ruttor, W. Kinzel, I. Kanter, “Neural cryptography with queries”, J. stat. Mech., P01009, 2005

Anaswara Venunadh, Shruthi N, Mannar Mannan, "Privacy Concern Code Generation Using Crypto Neural Scheme," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.824-828, 2019.

In recent years the biometric authentication systems are gaining popularity and became the integral part of security systems in every organization. Now a day’s spoof fingerprint detection is very important. There are several techniques proposed to tackle this problem. Liveness Detection is the method to detect real fingerprints. Since the emergence of deep learning the efficiency to solve this problem has been increased. In this paper we proposed a Convolution Neural Network (CNN) model which achieves average classification accuracy of around 93.12% on LivDet 2009, 85.16% on LivDet 2011, 86.76% on LivDet 2013, 82.20% on LivDet 2015 dataset.

Convolutional Neural Network(CNN), Fingerprint Livness Detetction, Deep Learning, Livdet Dataset

[1] R. F. Nogueira, R. D. A. Lotufo, and R. C. Machado, “Fingerprint liveness detection using convolutional neural networks”, IEEE Trans.Inf. Forensics Security, vol. 11, no. 6, pp. 12061213, Jun. 2016.
[2] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma,Z. Huang, A. Karpa-thy, A. Khosla, M. Bernstein et al., “Imagenet large scale visual recognition challenge,International Journal of Computer Vision”, pp. 142, 2014.
[3] P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis”, in null.IEEE, 2003, p. 958.
[4] G.L. Marcialis, A. Lewicke, B. Tan, P Coli, F. Roli, D. Grimberg, A. Congiu, A.Tidu, S Schuckers, and the LivDet 2009 Group, “First International Fingerprint Liveness Detection CompetitionLivDet 2009”, Proceedings of ICIAP, Sept 2009
[5] D Yambay, L Ghiani, P Denti, G L Marcialis, F Roli, S Schuckers, LivDet 2011 “Fingerprint Liveness Detection Competition 2011”, Biometrics (ICB), 2012 5th IAPR International Conference on, pp. 208 215, 2012.
[6] L. Ghiani, D. Yambay, V. Mura, S. Tocco, G.L. Marcialis, F. Roli, and S. Schuckers, LivDet 2013 – “Fingerprint Liveness Detection Competition 2013”, 6th IAPR/IEEE Int. Conf. on Biometrics, June, 4-7, 2013, Madrid (Spain).
[7] V. Mura, L. Ghiani, G. L. Marcialis, F. Roli, D. A. Yambay, and S. A. Schuckers. LivDet 2015 _”fingerprint liveness detection competition 2015”. In IEEE 7th International Conference on Biometrics Theory, Applications and Systems, pages 16, 2015.

A.M. Chougule, M.A. Shah, "Use of Convolutional Neural Network for Fingerprint Liveness Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.829-832, 2019.

This paper describes the importance of wireshark as a sniffing tool in a computer network. Any throttle in the performance of a network can prove to be a serious concern for network administrators, often leading to huge loss of resources. Many times the cause behind service disruptions like sudden terminal shutdown, connectivity loss, performance degradation etc go undetected because of unawareness about traffic analyzing tools or not knowing exactly why a disruption has occured and is often concluded due to poor network architecture. However, sometimes the cause behind such service disruptions could be due to external attacks which attempt to bring our web server down, send false ARP reply packets or infect our network with malware to form a part of a botnet. The first step towards taking proper action in all these cases is to determine the source of the attack and here, wireshark can be used to monitor and map network traffic. This paper shows how wireshark can prove to be extremely beneficial in such scenarios and accentuates how various local area network attacks like ARP poisoning,DOS attack,MAC flooding and DNS spoofing can be detected using wireshark and also provides some mitigation techniques for these attacks.

Wireshark, LAN Attacks, Packet Sniffers, TCP/IP, Switch, Hub, Server

[1] C. Sanders, Practical Packet Analysis With Wireshark. .
[2] S. Mishra, L. Jena, and A. Pradhan, “Networking Devices and Topologies: A Succinct Study,” 2012.
[3] S. Hijazi and M. S. Obaidat, “Address resolution protocol spoofing attacks and security approaches: A survey,” Secur. Priv., p. e49, Dec. 2018.
[4] D. Bruschi, A. Ornaghi, and E. Rosti, “S-ARP: a secure address resolution protocol,” in 19th Annual Computer Security Applications Conference, 2003. Proceedings., pp. 66–74.
[5] M. Hamedi, Insider Attack and Cyber Security, vol. 39, no. 2. Boston, MA: Springer US, 2008.
[6] S. Pavithirakini, D. D. M. M. Bandara, C. N. Gunawardhana, K. K. S. Perera, B. G. M. M. Abeyrathne, and D. Dhammearatchi, “Improve the Capabilities of Wireshark as a tool for Intrusion Detection in DOS Attacks,” Int. J. Sci. Res. Publ., vol. 6, no. 4, p. 378, 2016.
[7] “Denial of service (DoS) attack prevention through random access channel resource reallocation,” Dec. 2010.
[8] R. Droms, “Dynamic Host Configuration Protocol,” Mar. 1997.
[9] X. Gu and R. Hunt, “Wireless LAN Attacks and Vulnerabilities,” Networks and Communication Systems. ACTA Press.
[10] L. Senecal, “Understanding and preventing attacks at layer 2 of the OSI reference model,” in 4th Annual Communication Networks and Services Research Conference (CNSR’06), 2006, p. 1 pp.
[11] J. Biswas and A. Ashutosh, “An Insight in to Network Traffic Analysis using Packet Sniffer,” Int. J. Comput. Appl., vol. 94, no. 11, pp. 39–44, 2014.
[12] S. Naaz and F. A. Badroo, “Investigating DHCP and DNS Protocols Using Wireshark Investigating DHCP and DNS Protocols Using Wireshark,” no. May 2017, pp. 0–8, 2016.

Haroon Iqbal, Sameena Naaz, "Wireshark as a Tool for Detection of Various LAN Attacks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.833-837, 2019.

Handwritten character recognition is currently a under research field. A lot research is getting done in this field in which the point of interest is to receive as higher accuracy as possible in a distorted writing. That is as we know the way of writing of different person is different, so to recognize every writing with a greater accuracy is the point of concern. In this paper we proposed a method for different languages handwritten character recognition. The main focus is to train the model with pre-set data and then using that trained model to test the handwritten character passed to it. In our proposed method we used MATLAB to design our code, in this the model can be trained on runtime also.

Handwritten Character, Character Recognition, Feature Extraction, Neural Networks, Image Recognition, Offline Character Recognition.

[1] M. Kumar, M. K. Jindal, and R. K. Sharma,“Classification of characters and grading writers in offline handwritten Gurmukhi script,” Proc. Int. Conf. Image Inf. Process., ICIIP 2011.
[2] U. Pal, R. Jayadevan, and N. Sharma, “Handwriting Recognition in Indian Regional Scripts,” ACM Trans. Asian Lang. Inf. Process., 2012.
[3] E. Kavallieratou and S. Stamatatos, “Discrimination of machine-printed from handwritten text using simple structural characteristics,” Proc. 17th Int. Conf. Pattern Recognition, Vol.1, p. 437–440, ICPR 2004.
[4] A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognit. Lett., vol. 31, no. 8, pp. 651– 666, 2010.
[5] M. Kumar, M. K. Jindal, and R. K. Sharma, “Review onOCR for handwritten indian scripts character recognition,” Commun. Comput. Inf. Sci., vol. 205 CCIS, pp. 268–276, 2011.
[6] K. Singh Siddharth, M. Jangid, R. Dhir, and R. Rani,“Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional DistributionFeatures.” International Journal on Computer Science and Engineering (IJCSE) 3, no. 06, 2011.
[7] D. Sharma and P. Jhajj, “Recognition of IsolatedHandwritten Characters of Gurumukhi Script usingNeocognitron,” Int. J. Comput. Appl., vol. 4, no. 8, pp. 9–17, 2010.
[8] Due Trier, A. K. Jain, and T. Taxt, “Feature extraction methods for character recognition-A survey,” Pattern Recognit., vol. 29, no. 4, pp. 641–662, 1996.
[9] M. K. Mahto, K. Bhatia, and R. K. Sharma, “CombinedHorizontal and Vertical Projection Feature ExtractionTechnique for Gurmukhi Handwritten Character Recognition,” In International Conference on Advances in Computer Engineering and Applications (ICACEA),pp. 59–65, 2015.
[10] C. L. Liu and C. Y. Suen, “A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters,” Pattern Recognit., vol. 42, no. 12, pp. 3287–3295, 2009.
[11] H. Ma and D. Doermann, “Word Level ScriptIdentification for Scanned Document Images,” SPIE Conf. Doc. Recognit. Retr., pp. 124–135, 2004.
[12] Zheng, Y., Liu, C. and Ding, X., "Single-character type identification." In Document Recognition and Retrieval IX, Vol. 4670, pp. 49-57, December 2001.
[13] Zhou, L., Lu, Y., & Tan, C. L. "Bangla/English script identification based on analysis of connected component profiles." In International Workshop on Document Analysis Systems, pp. 243-254, 2006.
[14] S. Haboubi, S. Maddouri, N. Ellouze, “Diff´ erenciation de documents textes Arabe et Latin par filtre de Gabor”, 2007. wwd
[15] S. Mozaffari and P. Bahar, “Farsi/arabic handwritten from machine-printed words discrimination,” In Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR, 2012.
[16] G. Muhammad, M. H. Al-Hammadi, M. Hussain, and G. Bebis, “Image forgery detection using steerable pyramid transform and local binary pattern,” Mach. Vis. Appl., vol. 25, no. 4, pp. 985–995, 2014.
[17] S. D. Connell and A. K. Jain, “Recognition ofUnconstrained On-Line Devanagari Characters,” ICPR,1–4, 2000.
[18] A. F. R. Rahman, R. Rahman, and M. C. Fairhurst, “Recognition of handwritten Bengali characters: A novel multistage approach,” Pattern Recognit., 35(5), 997-1006, 2002.
[19] H. Ma and D. Doermann, “Adaptive Hindi OCR Using Generalized Hausdorff Image Comparison”, ACM Transactions on Asian Language Information Processing (TALIP), 2(3), 193-218, 2003.
[20] U. Pal, K. Roy, and F. Kimura, “A Lexicon-Driven Handwritten City-Name Recognition Scheme for Indian Postal Automation,” IEICE transactions on information and systems, no. 5, pp. 1146–1158, 2009.
[21] V. Deepu, S. Madhvanath, and A. G. Ramakrishnan,“Principal component analysis for online handwritten character recognition,” Pattern Recognit. (ICPR), 17th Int. Conf., vol. 2, no. 2, p. 327–330 Vol.2, 2004.
[22] N. Sharma and U. Pal, “Recognition of off-line handwritten devnagari characters using quadratic classifier,” Comput. Vision, Graph. …, pp. 805–816, 2006.
[23] U. Bhattacharya, M. Shridhar, and S. K. Parui, “OnRecognition of Handwritten,” Comput. Vision, Graph.,817–828, 2006.
[24] M. A. Rahman and A. El Saddik, “Modified syntactic method to recognize Bengali handwritten characters,”IEEE Trans. Instrum. Meas., vol. 56, no. 6, pp. 2623– 2632, 2007.
[26] U. Pal, T. Wakabayashi, and F. Kimura, “Handwritten bangla compound character recognition using gradient feature,” Proc. - 10th Int. Conf. Inf. Technol. ICIT 200A. Sharma, R. Kumar, and R. K. Sharma, “Online Handwritten Gurmukhi Character Recognition Using Elastic Matching.” Image and Signal Processing, 2008. CISP`08. Congress on. Vol. 2. IEEE, 2008.
[27] D. Singh, M. Dutta, and S. H. Singh, “Neural Network Based Handwritten Hindi Character Recognition System.” In Proceedings of the 2nd Bangalore Annual Compute Conference 2009.
[28] N. Das, B. Das, R. Sarkar, S. Basu, M. Kundu, and M. Nasipuri, “Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier,”J. Comput., vol. 2, no. 2, pp. 2151–9617, 2010.
[29] M. Kumar, “Offline handwritten Gurmukhi character recognition : Study of different feature-classifier combinations,” In Proceeding of the workshop on Document Analysis and Recognition, pp. 94-99, ACM, 2012.
[30] S. Iamsa-at and P. Horata, “Handwritten Character Recognition Using Histograms of Oriented Gradient Features in Deep Learning of Artificial Neural Network,” Int. Conf. IT Converg. Secur. 2013., no. 1, 1–5, 2013.
[31] E. Hassan, “Exploiting multimedia content: A machine learning based approach,” Electron. Lett. Comput. Vis. Image Anal., vol. 13, no. 2, p. 69, 2014.
[32] N. Dalal, B. Triggs, N. Dalal, B. Triggs, O. Gradients, and D. Cordelia, “Histograms of Oriented Gradients for Human Detection”, Computer Vision and Pattern Recognition, (CVPR), IEEE Computer Society Conference on. Vol. 1. 2005.
[33] J. Arróspide, L. Salgado, and M. Camplani, “Image-based on-road vehicle detection using cost-effective Histograms of Oriented Gradients,” J. Vis. Commun. Image Represent., 2013.
[34] Q. Zhu, S. Avidan, M. C. Yeh, and K. T. Cheng, “Fast Human Detection Using a Cascade of Histograms of Oriented Gradients,” IEEE Conf. Comput. Vis. Pattern Recognit., vol. 2, pp. 1491–1498, 2006.
[35] A. Bosch, A. Zisserman, and X. Munoz, “Representing shape with a spatial pyramid kernel,” in Proceedings of the 6th ACM international conference on Image and video retrieval - CIVR ’07, 2007.
[36] D. Salvi, J. Zhou, J. Waggoner, and S. Wang, “Handwritten Text Segmentation using Average Longest Path Algorithm.” In Applications of Computer Vision (WACV), 2013 IEEE Workshop on, pp. 505-512. IEEE, 2013
[37] Y. Bai, L. Guo, L. Jin, and Q. Huang, “A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition,” in Proceedings - International Conference on Image Processing, ICIP, 2009.
[38] C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
[39] V. N. Vapnik, “An overview of statistical learning theory.,” IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 988–99, 1999.
[40] C.-C. Chang and C.-J. Lin, “Libsvm,” ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1–27, 2011.
[41] B. B. Chaudhuri and A. Majumdar, “Curvelet-based multi SVM recognizer for offline handwritten bangla: Amajor Indian script,” in Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2007.
[42] A. Dhurandhar, K. Shankarnarayanan, and R. Jawale, “Robust pattern recognition scheme for devanagari script,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005

Hemant Yadav, Sapna Jain, "Offline Handwritten Character Recognition using Neural Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.838-845, 2019.

An algorithm is a sequence of computational steps that transform input data into output. Here an algorithmic approach on the process of graph labeling specially on a particular graph labeling called equitable antimagic graphs (EAG), is adopted. A graph labeling is a process in which labels-numbers or labels, have been assigned for vertices, edges or even both, subject to certain conditions. Further equitable antimagic graphs are graphs which follows a special kind of labeling called equitable antimagic labeling. There are many algorithms used to ease the process of labeling. This paper concentrates on the development and the exposition of an algorithm for analyzing some of the graphs for the equitable antimagic property. This algorithmic approach is mainly dealt to list out equitable antimagic graphs generated from a fixed number of vertices, say n and number of edges say m.

Labeling, Antimagic labeling, Graph Algorithm, Equitable antimagic labeling, Equitable antimagic graph

[1] G. Chartrand and Lesniak, Graphs and Digraphs, CRC Press, Boca Raton, 2005.
[2] A. Rosa, “On certain valuations of the vertices of a graph”, Theory of Graphs, Internat. Symp., pp. 349 – 355, 1966.
[3] J. A. Gallian, “A dynamic survey of graph labeling”, Electron. J. Combin, # DS6, 2017.
[4] N. Hartsfield and G. Ringel, Pearls in Graph Theory, Academic Press, San Diego, 1990.
[5] I. S. Hamid and S. A. Kumar, “Equitable irregular edge-weighting of graphs”. SUT J. Math., 46, pp. 79 – 91, 2010.
[6] A. Puthussery and I. S. Hamid, “Equitable antimagic labeling of graphs”, submitted to Discrete Applied Mathematics, 2019.
[7] T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms”, MIT Press, 1990.
[8] A. Levitin, “Introduction to the Design & Analysis of Algorithms”, Pearson, 2012.
[9] N. Choudhary, S. Agarwal, G. Lavania, "Smart Voting System through Facial Recognition", International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp.7-10, 2019.
[10] A. Puthussery, I. S. Hamid and M. Thomas, “Algorithmic approach for equitable antimagic labeling of complete graphs”, communicated Springer proceedings of International Conference of Emerging Trends in Graph Theory, 2019.

A. Puthussery, I. S. Hamid, A. Anitha, "An Equitable Antimagic Labeling of Graphs: Algorithmic Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.846-851, 2019.

Learning to make machine learning agents work on visual inputs has been a hurdle that researchers have faced since the early days of machine learning. There are currently techniques like deep learning and reinforcement learning which can be used in multi-feature environments. These techniques have stood their ground for a long time and have proven to be efficient. Therefore combining these fundamental concepts in order to realize a bigger goal is the best way to get best out of both. The Deep Learning Agent is to be designed using traditional machine learning methods like deep learning, reinforcement learning and deep Q-learning. Hence the agent is able to make the highest rewarding decision the will maximize the agent skill and make the learning process worth-while and efficient

Reinforcement Learning, Deep Learning, Game Bot, Agent, Action, Environment, Reward

[1] M.A.J.Bourassa and L.Massey, “Artificial Intelligence In Games A Survey Of The State Of The Art”, Defence Research &Development Organization Canada.(DRDC). Technical Memorandum, pp.3-40. DRDC Ottawa TM 2012-084 August, 2012.
[2] Robert Loftin, Michel.L.Littman, Jeff Huang, “A Strategy Aware Technique for Learning Behaviours from Discrete human feedback”, Berckely Brown Association for the Advancement of Artificial Intelligence, pp 1-5. June, 2014
[3] Jacob Schrum, Risto Miikkulakainen. “Constructing Game Agents through Simulated Evolution.”, In Encyclopedia of Computer Graphics and Games, pp.1-10 March, 2016 Springer.
[4] Jason Rennie, Andrew McCallum. “Using Reinforcement Learning To Spider The Web Efficiently”, ICML proceedings of sixteenth International Conference on Machine Learning, pp.334-345. June 27-30, 2009.
[5] Philip Hingston Senior Member IEEE. “A Turing Test For Computer Game Bots”, IEEE Transactions On Computational Intelligence in AI in Games, pp.1-18. September, 2009.
[6] Matthew, Peter Stone. “Deep Reinforcement Learning In Parameterized Action Space”, ICLR International Conference on Learning Representations, pp.143-155. Feb 2016.
[7] Igor. V. Karpov, Jacob Schkrum, Risto Miikkulakainen. In Philip.F.Hingston,“Belivable Bot Navigation via Playback of Human Traces", Believable Bots, pp.151-170. 2012. Springer.
[8] N.S.Lele, “Image Classification Using Convolutional Neural Network”. International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.3, pp.22-26, June, 2018.
[9] A. Deepa, E. Chandra Blessie. “Input Analysis for Accreditation Prediction in Higher Education Sector by Using Gradient Boosting Algorithm”. International Journal of Scientific Research in Network Security and Communication. Vol-6, Issue-3, June, 2018.

Durgaram Borker, Teslin Jacob, "Game Solving Through Deep Learning Agents," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.852-855, 2019.

In the rapidly developing world of technology where a new concept or a plausible implementation of a long-lost concept is born and is deployed on the cloud for various benefits that it offers. With the rapidly growing and ever-increasing dependency on cloud, it is of the most importance to ensure stability from a developer standpoint and it is also very important that it is dynamically scalable and fault-tolerant and in a hope to achieve the same, here is a project set to fulfill the necessities of the modern application which could benefit from the autoscaling resource optimized clusterization. This is set to be achieved by using the help of a popular containerization platform called Docker. Using a container translates to better isolated environments which are independent of their operational state and in-turn better provides robust security and generally contributes to the fault tolerant nature of an infrastructure adding Kubernetes in the mix not only makes the deployment highly robust and scalable it also makes deployment simpler for the developer mainly because of its declarative instruction advantage over Docker CLI or even KubeCTL’s Imperative instruction type. Majorly optimizing the efforts of developer’s deployment rather than the build from scratch approach that was previously used extensively. The implementation is designed with fluidity in mind and its main intention is to provide a seamless experience regardless the operations being carried out in the background. These background operations may include spawning of new nodes or pods, re-creation of deteriorated or erroneous containers (PODS) inside the nodes.

Scalable, Fault-Tolerant, Containerization, Docker , Clusterization, Robust, Kubernetes, pods,CLI

[1] Jonathan Baier, “Getting Started with Kubernetes”, PACKT Publishing, 2015
[2] Publishing Docker images, https://www.howtoforge.com/tutorial/building-and-publishing-custom-docker-images. (as on April - 2019).
[3] Kubernetes Concepts and Fundamentals, https://kubernetes.io/docs/concepts/ (as on April - 2019).
[4] Docker- Stories, Accelerate digital transformation with docker, https://www.hub.docker.com
[5] Multi Author, “YAML”, Tutorials Point, 2018
[6] Docker INC, “Introduction to Docker” , Docker Fundamentals 2cb8348, 2014
[7] Amazon Web Services Route 53 Documentation, https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/welcome-domain-registration.html (as on April - 2019).
[8] Kubernetes Encryption before REST, https://kubernetes.io/docs/tasks/administer-cluster/encrypt-data/ (as on April - 2019).
[9] Leila Vayghan, Mohamed Saied, Maria Toeroe, Ferhat Khendek, “Kubernetes as an Availability Manager for Microservice Applications”, Natural Sciences and Engineering Research Council of Canada (NSERC) and Ericsson , October 2018.

Mahesh G Prasad, S. Vignesh, "Orchestrated Clusteres using Kubernetes on Cloud Web Services," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.856-860, 2019.

Every year 19 million people approximately die from heart disease worldwide. A heart patient shows several symptoms and it is very tough to attribute them to the heart disease in so many steps of disease progression. Data mining, as an answer to extract a hidden pattern from the clinical dataset, are applied to a database in this analysis. All available algorithms in classification technique are compared to each other to achieve the highest accuracy. To further increase the correctness of the solution, the dataset is preprocessed by different unsupervised and supervised algorithms. The two important tasks which are needed for the development of classifier come under data mining and they are clustering and classification. In K-means clustering the initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. Methods to enhance the k-means clustering algorithm are discussed. With the help of these methods efficiency, accuracy and performance are improved. So, to improve the performance of clusters the Normalization which is a pre-processing stage is used to enhance the Euclidean distance by calculating more nearer centers, which result in a reduced number of iterations which will reduce the computational time as compared to k-means clustering. Finally, the classifiers are developed with Logistic regression by using the data extracted by K-Means Clustering. The techniques adopted in the design of classifier perform relatively well in terms of classification results better compared to clustering techniques.

Data mining, Classification techniques, K-means clustering, Neural Networks, Logistic Regression

[1] de Carvalho Junior, Helton Hugo, et al. "A heart disease recognition embedded system with fuzzy cluster algorithm." Computer methods and programs in biomedicine 110.3 (2013): 447-454.
[2] Mirmozaffari, Mirpouya, Alireza Alinezhad, and Azadeh Gilanpour. "Data Mining Apriori Algorithm for Heart Disease Prediction." Int`l Journal of Computing, Communications & Instrumentation Engg (IJCCIE) 4.1 (2017).
[3] Khaing, Hnin Wint. "Data mining based fragmentation and prediction of medical data." Computer Research and Development (ICCRD), 2011 3rd International Conference on. Vol. 2. IEEE, 2011.
[4] Patel, Ajad, Sonali Gandhi, Swetha Shetty, and Bhanu Tekwani. "Heart Disease Prediction Using Data Mining." (2017).
[5] Wghmode, Mr Amol A., Mr Darpan Sawant, and Deven D. Ketkar. "Heart Disease Prediction Using Data mining Techniques." Heart Disease (2017).
[6] Vijayashree, J., and N. Ch SrimanNarayanaIyengar. "Heart disease prediction system using data mining and hybrid intelligent techniques: A review." Int. J. Bio-Sci. Biotechnol 8 (2016): 139-148.
[7] Singla, Meenu, and Kawaljeet Singh. "Heart Disease Prediction System using Data Mining Clustering Techniques."
[8] Cp, Prathibhamol, Anjana Suresh, and Gopika Suresh. "Prediction of cardiac arrhythmia type using clustering and regression approach (P-CA-CRA)." Advances in Computing, Communications and Informatics (ICACCI), 2017 International Conference on. IEEE, 2017.
[9] Banu, NK Salma, and Suma Swamy. "Prediction of heart disease at early stage using data mining and big data analytics: A survey." Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 016 International Conference on. IEEE, 2016.
[10] Mane, Tejaswini U. "Smart heart disease prediction system using Improved K-means and ID3 on big data." Data Management, Analytics and Innovation (ICDMAI), 2017 International Conference on. IEEE, 2017.
[11] Senthil Kumar, B., and Dr Gunavathi R. "A Survey on Data Mining Approaches to Diabetes Disease Diagnosis and Prognosis." IJARCCE 5 (2016): 463-467.
[12] Kumar, B. Senthil, and R. Gunavathi. "Comparative and Analysis of Classification Problems." Journal of Network Communications and Emerging Technologies (JNCET) www. jncet. org 7.8 (2017).
[13] G. Kesavaraj, Dr. S.Sukumaran,”A Study on Classification Techniques in Data Mining”,IEEE-31661.
[14] F.U.Siddiqui, N.A.Mat Isa, “Optimized k-means clustering algorithm for image segmentation”, School of Electrical and electronic engineering, university Sains Malaysia, 14300, Nibong Tebel, Penang, Malaysia, 2012.
[15] Shital A. Raut and S. R. Sathe, “A Modified Fastmap KMeans Clustering Algorithm for Large Scale Gene Expression Datasets”, International Journal of Bioscience, Biochemistry and Bioinformatics, Vol. 1, No. 4, page 120- 124, November 2011.
[16] Tapas kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, Angela Y.Wu,”An Efficient k-Means Clustering Algorithm: Analysis and Implememtation”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.24, No.7,2002.
[17] Kapil Joshi, Himanshu Gupta, Prashant Chaudhary, Punit Sharma,” Survey on Different enhanced K-means Clustering Algorithm”, International Journal Of Engineering Trends And Technology, Vol. 27 ,No. 4-September 2015.
[18] Adil Fahad, Najlaa Alshatri, Zahir Tari, Addullah Alamri, Ibrahim Khalil, albert Y.Zomaya, Sebti Foufou, Abdelaziz Bouras, “A survey of Clustering Algorithms for Big Data: Taxonomy And empirical Analysis”, IEEE TTANSACTION ON Emerging Topics in Computing.
[19] Jiawei Han, Micheline kamber, Jian pei, “Data Mining Concepts and Techniques”, Third Edition, © 2012, Elsevier Inc.
[20] K.Rajalakshmi,Dr.S.S.Dhenakaran,N.Roobin“Comparative Analysis of K-Means Algorithm in Disease Prediction”, International Journal of Science, Engineering and Technology Research (IJSETR), Vol. 4, Issue 7, July 2015. [
[21] BV Sumana, T.Santhanam,”Prediction of diseases by Cascading Clustering and Classification”,International Conference on Advances in Electronics , Computers and Communication(ICAECC),2014.
[22] K. R. Lakshmi, M. V. Krishna and S. P. Kumar, “Performance Comparison of Data Mining Techniques for Predicting of Heart Disease Survivability,” International Journal of Scientific and Research Publications, ISSN 2250-3153, Vol.3, Issue.6, June 2013.
[23] M. Kumari, R. Vohra and A. Arora, “Prediction of Diabetes Using Bayesian Network,” International Journal of Computer Science and Information Technologies, Vol. 5 (4), 5174-5178, 2014. M. A. Banu, B. Gomathy, “Disease forecasting system using data mining methods,” in IEEE International Conference on Intelligent Computing Applications (ICICA`14), pp. 130-133, 2014. https://doi.org/10.1109/icica.2014.36
[24] B. Bahrami, and M. H. Shirvani, “Prediction and Diagnosis of Heart Disease by Data Mining Techniques,” Journal of Multidisciplinary Engineering Science and Technology (JMEST), ISSN: 3159-0040, Vol. 2, Issue 2, February 2015.
[25] I. H. Witten and E. Frank, “Data Mining Practical Machine Learning Tools and Techniques,” Morgan Kaufman Publishers, 2005
[26] R. Jyoti, G. Preeti, “Analysis of Data Mining Techniques for Diagnosing Heart Disease,” International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 5, ISSUE. 7, July 2015.
[27] K. Manimekalai, “prediction of heart disease using data mining techniques,” IJIRCCE, Vol.4, Issue 2, February 2016.
[28] A. Durgadevi and K. Saravanapriya, “comparative study of data mining classification algorithm in heart disease prediction,” international journal of recent research in mathematics computer science and information technology, Vol.2, Issue 2, March 2016.
[29] O. Y. Atkov, “Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters,” Elsevier, 2012.
[30] R. Alizadehsani, J. Habibi, M. Hosseini, R. Boghrati, A. Ghandeharioun, B. Bahadorian, Z. Alizadehsani, “A Data Mining Approach for Diagnosis of Coronary Artery Disease, ” Elsevier, 2013.
[31] S. U. Amin, K. Agarwal, and R. Beg, “Genetic neural network based data mining in prediction of heart disease using risk factors,” presented at the IEEE Conference on Information & Communication Technologies, 2013.
[32] Priyanka, Sana Khan, Tulsi Kour “Investigation on Smart Health Care Using Data Mining Methods” International Journal Of Scientific Research in Computer Sciences and Engineering 2016.

Reetu Singh, E. Rajesh, "Prediction of Heart Disease by Clustering and Classification Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.861-866, 2019.

Nowadays, biometric technology has attracted lots of researcher’s attention all over the world. Biometric based authentication provides the high-level security and confidentiality. Finger vein is one of the most accepted biometric traits for person identification. Finger veins are internal features of human body hence the effective security is guaranteed. These vein patterns are unique for each person so they are widely suitable for authentication. Feature extraction is the most important process of finger vein authentication. An efficient feature extraction technique which can improve the accuracy of the finger vein recognition. Further, various finger vein based feature extraction techniques are analyzed and discussed. In this survey, the feature extraction methods are categorized into following groups such as local binary-based methods, dimensionality reduction-based methods, minutiae-based methods and vein pattern based methods. Finally we concluded with the comparative analysis of different methods along with their Equal Error Rate (EER) and recognition rate (RR).

Finger-vein,Feature extraction, Authentication, Identification

[1] J Hashimoto,“Finger vein authentication technology and its future”, Symposium on VLSI Circuits, IEEE, pp. 5-8, 2006.
[2] Song Xie, Liyong Fang, Ziqian Wang, Zhaochun Ma, Jingyuan Li “Review of personal identification based on near infrared Vein imaging of finger”, 2nd International Conference on Image, Vision and Computing, IEEE, pp.978-1-5090-6238-6, 2017.
[3] Lu Yang, Gongping Yang, Yilong Yin, Lizhen Zhou, “A survey of finger vein recognition”,Springer International Publishing Switzerland, pp. 234–243, 2014.
[4] D Mulyono,H S Jinn,"A study of finger vein biometric for personal identification", International Symposium on Biometrics and Security Technologies, IEEE, pp. 1-8, 2008.
[5] F Tagkalakis,D Vlachakis,V Megalooikonomou,A Skodras, "A novel approach to finger vein authentication",International Symposium on Biomedical Imaging, IEEE, pp. 659-662, 2017.
[6] E Ting,M Z Ibrahim,“A review of finger vein recognition system”, e-ISSN: 2289-8131, vol.10, no.1-9, 2018.
[7] Rahul Dev, Ruqaiya Khanam, “Review on Finger Vein Feature Extraction Methods“,International Conference on Computing, Communication and Automation (ICCCA2017),ISBN:978-1-5090-6471, IEEE, 2017.
[8] J Yang, X Li , “Efficient finger vein localization and recognition”, 20th International Conference on Pattern Recognition (ICPR), IEEE, pp. 1148-1151, 2010.
[9] Z Liu, “Finger vein recognition with manifold learning”, Journal of Network and Computer Applications, vol.33, no.3, pp.275–282, 2010.
[10] F Guan,K Wang,Q Yang,”A study of two direction weighted (2D)2LDA for finger vein recognition”, 4th International Congress on Image and Signal Processing (CISP), IEEE, pp.860-864, 2011.
[11] S Damavandinejadmonfared, “Finger vein recognition using linear Kernel Entropy Component Analysis”, International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, pp. 249-252, 2012.
[12] N Miura,A Nagasaka, “Feature extraction of finger-vein pattern based on repeated line tracking and its application to personal identification”. Machine Vision and Applications, Springer, vol.15, no.4, 194-203, 2004.
[13] Bhagyashree Bersa, Ramesh Kumar Mohapatra, “Extraction of segmented vein patterns using repeated line tracking algorithm” 3rd International Conference on Sensing, Signal Processing and Security (ICSSS),IEEE, pp. 978-1-5090-4929-5, 2017.
[14] T Liu, J B Xie, W Yan, P Q Li, H Z Lu, “An algorithm for finger-vein segmentation based on modified repeated line tracking”, The Imaging Science Journal 61(6), 491–502, 2013.
[15] N Miura,A Nagasaka,T Miyatake, “Extraction of finger-vein patterns using maximum curvature points in image profiles”. IEICE Transactions on Information and Systems, vol.E90-D, no.8, pp.1185–1194, 2007.
[16] Q Guo and B Qiao, "Research on the finger vein image capture and finger edge extraction",International Conference on Mechatronics and Automation (ICMA), IEEE, pp. 275-279, 2017.
[17] L Yang, G Yang, Y Yin, X Xi, "Finger vein recognition with anatomy structure analysis," in IEEE Transactions on Circuits and Systems for Video Technology, vol.28, no.8, pp. 1892-1905, 2018.
[18] Lee, E C,H Jung, D Kim,“New finger biometric method using near infrared imaging sensors”, vol.11,no.3, pp. 2319–2333, 2011.
[19] B A Rosdi,C W Shing,S A Suandi,“Finger vein recognition using local line binary pattern”,Sensors, vol.11, no.12, pp. 11357-11371,2011.
[20] X J Meng,G P Yang,Y L Yin,R Y Xiao, “Finger vein recognition based on local directional code”,Sensors, vol.12, no.11,pp.14937-14952, 2012.
[21] G P Yang,R Y Xiao,Y L Yin,L Yang, “Finger vein recognition based on personalized weight maps”, Sensors, vol.13, no.12, pp.12093-12112, 2013.
[22] G P Yang,X M Xi,Y L Yin, “Finger vein recognition based on a personalized best bitmap”,Sensors, vol.12, no.2,pp.1738-1757, 2012.
[23] J D Wu,C T Liu, “Finger-vein pattern identification using SVM and neural network technique”, Expert Systems with Applications, vol.38, no.11, pp.14284-14289, 2011.
[24] J D Wu,C T Liu,“Finger-vein pattern identification using principal component analysis and the neural network technique”, Expert Systems with Applications, vol.38, no.5, Elsevier, pp.5423-5427, 2011.
[25] G P Yang,X M Xi,Y L Yin, “Finger vein recognition based on (2D)2 PCA and metric learning”,Journal of Bio Medicine and Biotechnology, vol.12, pp.1-9, 2012.
[26] B Huang,Y Dai,R Li,D Tang,W Li,“Finger-vein authentication based on wide line detector and pattern normalization”, in Proc. 20th International Conference Pattern Recognition,(ICPR), pp, 1269–1272, 2010.
[27] R Ananadha Jothi,V Palanisamy,"Analysis of Fingerprint Minutiae Extraction and Matching an Algorithm " International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Volume- 3, Special Issue 20,pp: 398-410, April 2016.
[28] Preethy Prabhakar, Tony Thomas,” Finger Vein Identification Based On Minutiae Feature Extraction With Spurious Minutiae Removal” 3rd International Conference on Advances in Computing and Communications,IEEE,pp.978-0-7695-5033-6, 2013.
[29] Y Lu, “Finger vein recognition using histogram of competitive Gabor responses”, 22nd International Conference on Pattern Recognition (ICPR), IEEE, pp. 25-43, 2014.
[30] W Song,T Kim,H C Kim,J H Choi,H J Kong,S R Lee, “A finger-vein verification system using mean curvature”,Pattern Recognition Letter, vol.32, no.11, pp.1541-1547, 2011.
[31] A Kumar,Y B Zhou, “Human identification using finger images”, IEEE Transactions on Image Process, vol.21, no.4, pp.2228–2244,2012.
[32] M Kono,H Ueki,S Umemura, “A new method for the identification of individuals by using of vein pattern matching of a finger”, In: Proceedings of the 5th Symposium on Pattern Measurement, pp.9–12,2000
[33] T Yanagawa,S Aoki,T Ohyama, “Human finger vein images are diverse and its patterns are useful for personal identification, MHF Preprint Series, Kyushu University, pp. 1–7, 2007.
[34] A Perez Vega,CM Travieso,JB Alonso,“Biometric personal identification system based on patterns created by finger veins”, International Work Conference on Bio-inspired Intelligence (IWOBI), IEEE, pp.65 -70, 2014.
[35] C Liu, Y Kim, "An efficient finger-vein extraction algorithm based on random forest regression with efficient local binary patterns”, International Conference on Image Processing (ICIP), IEEE,pp. 3141-3145, 2016.
[36] H F Qin,C B Yu,L Qin, “Region growth–based feature extraction method for finger-vein recognition”,Optical Engineering, vol.5, no.5, 057208-057208, 2011.
[37] R Anandha Jothi, V Palanisamy, “Performance Enhancement of Minutiae Extraction Using Frequency and Spatial Domain Filters”, International Journal of Pure and Applied Mathematics Volume-118 issue-7 page no-647-654 ISSN No-1314-3395.
[38] R Anandha Jothi ,V Palanisamy, J Nithyapriya, ”Evaluation of fingerprint minutiae on ridge structure using Gabor and closed hull filter”, Computational Vision and Bio Inspired Computing. Springer (2018) (in press).
[39] P Abirami,R Anandha Jothi,V Palanisamy, “A Survey on Biometric E-Voting System Using Retina”, International Journal of Pure and Applied Mathematics, vol.118,no.7,647-654, 2018.
[40] R Suganya,R Anandha Jothi,V Palanisamy,”A Survey on Security Methodologies in E-Voting System.”,International Journal of Pure and Applied Mathematics, vol.118,no.7, 647-654,2018.
[41] Roshani L Jain, Lubdha M Bendale, Gayatri D Patil, “Image Enhancement Using Different Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.73-76, 2018.
[42] Nitin Tiwari , “An Overview and Analysis Based on Biometric Framework Technique and Fingerprint Biometric Technology”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.69-74, 2017.
[43] 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, Issue.1, pp.7-11, 2018.
[44] V Davis, S Devane, "Diagnosis of Brain Hemorrhage Using Artificial Neural Network", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.1, pp. 20-23, 2017.

G. Thenmozhi, R. Anandha Jothi, V. Palanisamy, "Comparative Analysis of Finger Vein Pattern Feature Extraction Techniques: An Overview," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.867-872, 2019.