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With the exceptional challenge and expanding globalization in the money related markets, banking association must create client situated procedures so as to contend effectively in the focused financial condition. Client beat forecast goes for identifying clients with a high inclination to cut ties with an administration or an organization. An exact expectation enables an organization to take activities to the focusing on clients who are well on the way to beat, which can improve the productive utilization of the constrained assets and result in huge effect on business. The fundamental commitment of our work is to build up a client beat forecast model which helps banking and money related organizations to anticipate clients who are in all probability subject to stir. In this investigation we utilized the Decision Tree and Artificial Neural Networks to recognize the clients who are going to beat. In our test results demonstrates that Neural Network system model has showed signs of improvement exactness (86.52%) in contrasted with Decision Tree model (79.77%).

Churn, Stir, Decision Tree and Neural Networks

[1] Kiansing Ng and Huan Liu, “Customer Retention via Data Mining”, Artificial Intelligence Review, 14(6), 569-590, 2000
[2] Kirui, Clement, Li Hong, Wilson Cheruiyot, and Hillary Kirui. "Predicting customer churn in mobile telephony industry using probabilistic classifiers in data mining." IJCSI International Journal of Computer Science Issues 10, no. 2 (2013): 1694-0784.
[3] N. Lu, H. Lin, J. Lu and G. Zhang, "A Customer Churn Prediction Model in Telecom Industry Using Boosting," in IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1659-1665, May 2014. doi: 10.1109/TII.2012.2224355.
[4] Bagheri, Mahnaz, M. Valipour, and V. Amin. "The Bankruptcy Prediction in Tehran share holding using Neural Network and its Comparison with Logistic Regression." The Journal of mathematics and computer science 5, no. 3 (2012): 219-228.
[5] Ahna J.H, Hana S.P and Leeb Y.S, “Customer churn analysis: churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry”. Telecommunication policy, 30(10), 552-568, 2006
[6] Bose I & Chen X, “Hybrid models using Unsupervised clustering for prediction of customer churn”, Journal of organizational computing & Electronic Commerce, 19(2), 133-151, 2009
[7] A.O. Oyeniyi & A.B. Adeyemo,” Customer Churn Analysis in Banking Sector Using Data Mining Techniques”, African Journal of Computing & ICT, PP 165-174, Vol 8. No. 3 – September, 2015
[8] Hend Sayed, Manal A. Abdel-Fattah, Sherif Kholief, “Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study”, International Journal of Advanced Computer Science and Applications, PP 674-677Vol. 9, No. 11, 2018
[9] Jiri Pribil and Michaela Polejova , “A Churn Analysis Using Data Mining Techniques: Case of Electricity Distribution Company”, Proceedings of the World Congress on Engineering and Computer Science 2017 Vol I WCECS 2017, October 25-27, 2017, San Francisco, USA, ISBN: 978-988-14047-5-6
[10] J.Han and M.Kamber, “Data Mining Concepts and Techniques”, the Morgan Kaufmann series in Data Management Systems, 2nd Edition, Morgan Kaufmann, 2006

G. Ravi Kumar, K. Tirupathaiah, B. Krishna Reddy, "Client Churn Prediction of Banking and fund industry utilizing Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.842-846, 2019.

In this current technology driven society, internet has become a basic commodity for every individuals as well as organization. Due to the rapid increase of internet dependency of government offices, private company, or corporate sectors, security has become the main concern in all of these organizations. Attack over the network using stochastic approaches has created large chaos. The DDoS attack has created destruction and damages over the network since early 2000’s. DDoS is known for its ability to fade the identity of the source of attack because of multiple address and flooding mechanism. Preventing the attack from its original source is quite difficult. This floods the whole system making the system of the particular sector to be crippled and can be remedied by early detection of the attack. In this work we try to detect the different DDoS attack vectors and classify it. The nature and its mechanism are studied to identify the type of attack. We use scikit learn, a machine learning approach to detect different forms of attacks.

DDoS, vectors, Machine Learning, Confusion Matrix

[1] Krishna Modi, Prof. Abdul Quadir Md., “Detection and Prevention of DDoS Cloud using Double-TCP Mechanism and HMM-Architecture”, Vol.3,No.2,pp.113–120, April 2014,
[2] Amarpreet Singh, Priya Sharma, “A novel mechanism for detecting DOS attack in VANET using Enhanced Attacked Packet Detection Algorithm (EAPDA)”, IEEE Transaction, Proceedings of 2015 RAECS UIET Panjab University Chandigarh 21-22nd December 2015.
[3] Erwin Adi, “Distributed denial-of-service attacks against HTTP/2 services”, pp.79–86, 2016.
[4] Eric Perraud “Machine Learning Algorithm of Detection of DOS Attack on an Automotive Telmatic Unit” International Journal of Computer Networks & Communications (IJCNC) Vol.11, No.1, 27-43, January 2019
[5] Thwe Thwe Oo, Thandar Phyu, “Statistical Anomaly Detection of DDoS Attacks Using K-Nearest Neighbour”, International Journal of Computer & Communication Engineering Research (IJCCER) Volume.2, Issue.1 January 2014.
[6] Aqueel Sahi, D.Lai, Yan Li, Mohammed Diykh, “An Efficient DDoS TCP Flood Attack Detection and Prevention System in a Cloud Environment”,  IEEE Access, pp.1-13, Vol.5, April2017.
[7] Harshita, Ruchikaa Nayyar, “Detection of ICMP Flood DDOS Attack”, International Journal of Computer Science Trends and Technology (IJCST), Vol.5, Issue.2, pp.199-205,March-April2017.
[8] Munazza Shabbir, Muazzam A. Khan,Umair Shafiq Khan, Nazar A. Saqib “Detection and Prevention of Denial of Service Attacks in VANET”, International Conference on Computational Science and Computational Intelligence, pp.970-974, 2016.
[9] Opeyemi Osanaiye, Haibin Cai, Kim-Kwang Raymond Choo, Ali Dehghantanha, Zheng Xu and Mqhele Dlodlo, “Ensemble-based multi-filter feature selection method for DDOS Detection in Cloud Computing”, EURASIP Journal on Wireless Communication and Networking, pp.1-10, 2016.

Kunal Kumar Brahma, Satyajit Sarmah, Chandan Kalita, Rajdeep Ghosh, "Detection of Multi-Vector DDoS Attack," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.847-851, 2019.

Machine learning and Data Mining Techniques are mainly used for many Real world problems. The traditional methods include training the data and test .But it will not be applicable for real world scenario. Some of the reason may be the cost of training data and inability to get those. These drawbacks are giving rise to the concept known as Transfer Learning.It ensures that training data must be independent and distributed identically.Transfer Learning is considered as a solution to the insufficient training data.

Data Mining, Transfer Learning, Machine Learning

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[2] Dai, Wenyuan et al. “Translated Learning: Transfer Learning across Different Feature Spaces.” NIPS (2008).
[3]Mohsen Kaboli,”A Review of Transfer Learning Algorithms”,Technische Universität München, 2017
[4] Hinton G, Osindero S, Teh Y W. A Fast Learning Algorithm for Deep Belief Nets, J. Neural Computation, 2006, 18(7): 1527–1554
[5] Taghi M. Khoshgoftaar and DingDing Wang,” A survey of transfer learning”
[6] K. Driessens, J. Ramon, and T. Croonenborghs,”Transfer learning for reinforcement learning through goal and policy parametrization”.In ICML Workshop on Structural Knowledge Transfer for Machine Learning, 2006
[7] Rakesh Kumar Saini, "Data Mining tools and challenges for current market trends-A Review", International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.2, pp.11-14, 2019
[8]Madhusmita Dey, Swati Sucharita Barik,”Security Enhancement in ATMs through Helmet Detection using Inductive Transfer Learning”,IJSRET,Volume 7, Issue 4, April 2018
[9]Sinno Jialin Pan and Qiang Yang.”A survey on transfer learning. IEEE Transactions on knowledge and data engineering”, 22(10):1345–1359, 2010
[10]Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques for Building Intelligent Systems
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[15]S. J. Pan, V. W. Zheng, Q. Yang, and D. H. Hu, “Transfer learning for wifi-based indoor localization,” in Proceedings of the Workshop on Transfer Learning for Complex Task of the 23rd AAAI Conference on Artificial Intelligence, Chicago, Illinois, USA, July 2008
[16]K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell, “Text classification from labelled and unlabeled documents using EM ” Machine Learning, vol. 39, no. 2-3, pp. 103–134, 2000
[17]L. I. Kuncheva and J. J. Rodrłguez, “Classifier ensembles with a random linear oracle,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 4, pp. 500–508, 2007
[18]X. Zhu, “Semi-supervised learning literature survey,” University of Wisconsin–Madison, Tech. Rep. 1530, 2006
[19]H. DaumeIII and D. Marcu, “Domain adaptation for statistical classifiers,” Journal of Artificial Intelligence Research, vol. 26, pp. 101–126, 2006
[20]T. Evgeniou and M. Pontil, “Regularized multi-task learning,” in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, Washington, USA: ACM, August 2004, pp. 109–117
[21]J. Gao, W. Fan, J. Jiang, and J. Han, “Knowledge transfer via multiple model local structure mapping,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, Nevada: ACM, August 2008, pp. 283–291
[22]L. Mihalkova and R. J. Mooney, “Transfer learning by mapping with minimal target data,” in Proceedings of the AAAI-2008 Workshop on Transfer Learning for Complex Tasks, Chicago, Illinois, USA, July 2008
[23]S. Bickel, M. Bruckner, and T. Scheffer, “Discriminative learning for ¨ differing training and test distributions,” in Proceedings of the 24th international conference on Machine learning. New York, NY, USA: ACM, 2007, pp. 81–88
[24]W. Dai, Y. Chen, G.-R. Xue, Q. Yang, and Y. Yu, “Translated learning,” in Proceedings of 21st Annual Conference on Neural Information Processing Systems, 2008
[25]S. Ramachandran and R. J. Mooney, “Theory refinement of Bayesian networks with hidden variables,” in Proceedings of the 14th International Conference on Machine Learning, Madison, Wisconson, USA, July 1998, pp. 454–462
[26]X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. F. M. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach, D. J. Hand, and D. Steinberg, “Top 10 algorithms in data mining,” Knowl. Inf. Syst., vol. 14, no. 1, pp. 1–37, 2008
[27] Zehai Gao, Cunbao Ma, Zhiyu She, Xu Dong, "An Enhanced Deep Extreme Learning Machine for Integrated Modular Avionics Health State Estimation", Access IEEE, vol. 6, pp. 65813-65823, 2018
[28] Huan Liu, Zheng Liu, Haobin Dong, Jian Ge, Zhiwen Yuan, Jun Zhu, Haiyang Zhang, Xuming Zeng, "Recurrent Neural Network-Based Approach for Sparse Geomagnetic Data Interpolation and Reconstruction", Access IEEE, vol. 7, pp. 33173-33179, 2019.
[29]Dhar Gupta K., Pampana R., Vilalta R., Ishida E. E. O. and de Souza R. S. 2016 IEEE Symposium on Computational Intelligence and Data Mining (CIDM-16) (Athens, Greece) Automated Supernova Ia Classification Using Adaptive Learning Techniques

Swati Sucharita Barik, Mamata Garanayak, Sasmita Kumari Nayak, "Transfer Learning:Approaches and Methodologies," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.852-855, 2019.

Chatbots are computer programs that simulate intelligent human conversation. The development and creation of new applications have been possible because of the design and production of interactive chatbots that help in a better way for the progress of the people. This project will describe current efforts in the development of an intelligent Career Counselling Bot. Career counselling project is built using artificial intelligence algorithms that are used for analyzing user’s queries and understand the user’s message. It provides some valid result to the query of the user. The User can query any career related queries through the system. The user does not have to personally go to a career counsellor for the same. The System analyses the question and then answers to the query as if it is answered by the counsellor. This system helps the user to choose the right career that they should follow according to their interests and capabilities.

Chatbot, NLP, Artificial Intelligence, Machine Learning, Intent, Dialog Flow

[1]. Amazon Lex Author: Jeff Barr Year: 2017
[2]. Balbir Singh, Ajay Singh AIML Based Voice Enabled Artificial Intelligent Chatter-bot, International Journal of u- and e-Service, Science and Technology. -2017
[3].Dahiya M, Gupta ON. Formulation and in vitro evaluation of metoprolol tartrate microspheres. Bull. Pharm. Res. -2017
[4].Building Dialogue Structure from Discourse Tree of a Question Boris Galitsky Dmitry Ilvovsky Oracle Inc., Redwood Shores, CA USA National Research University Higher School of Economics, Moscow, Russia Boris.galitsky@oracle.com dilvosky@hse.ru-2017
[5].Andrew Thompson, J. Mao, "Statistical Pattern Recognition: A Review", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, Jan. 2000.
[6]. Lei Cui, Shaohan Huang, Furu Wei, Chuanqi Tan, Chaoqun Duan, and Ming Zhou Microsoft Research Asia {lecu,shaohanh, fewer, Bhutan, chads,mingzhou}@microsoft.com-2017
[7]. J Cahn Rodr´ıguez (2016). “NLAST: A natural language assistant for students”. In: Global Engineering Education Conference (EDUCON), 2016 IEEE. IEEE, pp. 709–713
[8]. Ranoliya, Bhavika R, Nidhi Raghuwanshi, and Sanjay Singh
.“Chatbot for University Related FAQs”. In: 2017 International Conference on Advances in Computing.- 2017.
[9].A Neural Chatbot with Personality Huyen Nguyen Computer Science Department Stanford University huyenn@stanford.edu David Morales Computer Science Department Stanford University mrlsdvd@stanford.edu Tessera Chin Computer Science Department Stanford University -2017
[10].New observations on the integrity, structure and physiology of flesh cells from fully ripened grape berry Natacha Fontes1,2, Manuela Côrte-Real2,3, and Hernâni Gerós1,2* 1 Centro de Investigação e de Tecnologias Agro-Ambientais e Biológicas (CITAB), Quinta de Prados, 5001-801 Vila Real, Portugal 2 Departamento de Biologia, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal 3 Centro de Biologia Molecular e Ambiental (CBMA), Campus de Gualtar, 4710-057 Braga, Portugal-2016
[11]. Rush, Alexander M., Sumit Chopra, and Jason Weston. "A neural attention model for abstractive sentence summarization." arXiv preprint arXiv:1509.00685 (2015).
[12].AIML Based Voice Enabled Artificial Intelligent Chatterbot, International Journal of u- and e-Service, Science and Technology Vol.8, No.2 (2017), pp.375-384

Akshansh Ohm, Bhavani K, "Chatbot for Career Guidance Using AI," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.856-860, 2019.

In this paper, the algebraic properties of two operations disjunction and conjunction from Lukasiewicz type over Pythagorean fuzzy matries are studied. Also, using the relation between disjunction and conjunction certain results are obtained using modal operators.

Intuitionistic Fuzzy Matrix, Pythagorean Fuzzy Set, Pythagorean, Fuzzy Matrix, Disjunction, Conjunction

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[3] Y.B.Im, E.B.Lee and S.W.Park, The determinant of square intuitionistic fuzzy matrices, Far East Journal of Mathematical Sciences, Vol.3, Issue.5 pp.789-796. 2001.
[4] M.Pal, S.K.Khan and A.K.Shyamal., Intuitionistic Fuzzy Matrices, Notes on Intuitionistic Fuzzy Sets, Vol.8, Issue.2, pp.51-62. 2002.
[5] P.Murugadas, S.Sriram, T.Muthuraji, Modal operators in intuitionistic fuzzy matrices, International Journal of Computer Application, Vol.90, Issue.17, pp.1-4. 2014.
[6] T.Muthuraji, S.Sriram and P.Murugadas, Decomposition of intuitionistic fuzzy matrices, Fuzzy Information and Engineering,Vol. 8 , pp. 345-354. 2016.
[7] T. Muththuraji and S. Sriram, Some Remarks on Lukasiwicz disjunction and conjunction operators On Intuitionistic Fuzzy Matrices, J. of advances in Mathematics, Vol.11, Issue.3, pp.5000-5006, 2015.
[8] T. Muthuraji, S. Sriram, Commutative monoids and monoid homomorphism on Lukasiwicz conjunction and disjunction operators over intuitionistic fuzzy matrices, Int. J. of Pure and Engg. Mathematics, Vol.3, Issue.2, pp.63-75, 2015.
[9] A.K.Shyamal and M.Pal, Distances between intuitionistic fuzzy matrices, V.U.J. Physical Sciences, Vol.8, pp.81-91.2002.
[10] I.Silambarasan and S.Sriram, Algebraic operations on Pythagorean fuzzy matrices, Mathematical Sciences International Research Journal, Vol.7, Issue.2, pp.406-414. 2018.
[11] M.G.Thomason, Convergence of powers of fuzzy matrix, J. Mathematical Analysis and Applications, Vol.57, pp.476-480.1977.
[12] D. Venkatesan and S. Sriram, Multiplicative operations of Intuitionistic Fuzzy Matrices, Annals of pure and Applied Mathematics, Vol.14, Issue.1, pp.173-181, 2017.
[13] D. Venkatesan and S. Sriram, Further multiplicative operations of Intuitionistic Fuzzy Matrices, Int. J. Fuzzy Mathematical Archive, Vol.12, Issue.2, pp.105-113, 2017.
[14] R.R.Yager, Pythagorean membership grades in multi-criteria decision making, IEEE Transactions on Fuzzy Systems, Vol.22, pp.958-965. 2014.
[15] X.L.Zhang and Z.S.Xu, Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets, Int. J.of Intelligent Systems, Vol.29 , pp. 1061-1078. 2014.

D. Venkatesan, S. Sriram, "On Lukasiewicz Disjunction and Conjunction of Pythagorean Fuzzy Matrices," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.861-865, 2019.

A wireless network is a multi-hop connection where each mobile node collaborates to form a network without using any infrastructure such as access points or base stations. It has important properties such as dynamic topology, restricted bandwidth and limited resources, which are major challenge to improve the use of energy resources which are the key aspects during the design of modern ad hoc network architecture. The nodes in the wireless ad hoc network have inadequate range of transport, and their processing and storage capacities in addition to their energy resources are inadequate. This resource constraint in ad hoc wireless networks creates significant challenges in effective routing to achieve better productivity in high-traffic network. This paper presents a review of the various issues and challenges in the power routing and optimization protocols during communication in the wireless network designed to improve the life span of the network for a longer period.

Energy Efficient Routing, Energy aware routing, Wireless Mobile Ad hoc Network

[1]. Y. Zhang, X. Zhang, S. Ning, J. Gao, Y.Liu, "Energy-efficient Multilevel Heterogeneous Routing Protocol for Wireless Sensor Networks", IEEE Access, pp. 1 - 1, 2019.
[2]. W. Sun, Z. Yang,X. Zhang, Yunhao Liu, "Energy-Efficient Neighbor Discovery in Mobile Ad hoc and Wireless Sensor Networks: A Survey", IEEE Comm. Surveys & Tutorials, Vol. 16, No. 3, 2014.
[3]. S. D. Misailovic, V. S. Sokolovic, G. B. Markovic, "Energy Efficient Multi-Hop Heed-Based Routing for Wireless Sensor Networks", 26th Telecommunications Forum (TELFOR), pp. 1 - 4, 2018.
[4]. C. Perkins, E. Royer and S. Das, "Ad hoc On Demand Distance Vector (AODV) Routing", draft-manet-ietf-aodv-8.txt, IETF, Work in Progress, March 2001.
[5]. D. B. Johnson, D A Maltz, J Broch, "DSR: The Dynamic Source Routing Protocol for Multi-hop Wireless Ad hoc Networks", 2001.
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[7]. Floriano, D. Rango, M. Fotino and Salvatore Marano, "EE- OLSR: Energy Efficient OLSR Routing Protocol For Mobile Ad-hoc Networks," in Proceedings of Military Communications (MILCOM`08), San Diego, CA, USA, November 17-19, 2008.
[8]. Y. Zhu, M. Huang, S. Chen, Yu Wang, "Energy-Efficient Topology Control in Cooperative Ad hoc Networks", IEEE Transactions On Parallel And Distributed Systems, Vol. 23, No. 8, August 2012.
[9]. Meng, Yuan, Feng & Tan, "Power adjusting algorithm: A new cross - layer power saving mechanism for mobile ad - hoc networks", Journal of Computer Sci ence and Technology, vol. 28, no. 1, pp. 42 - 53, 2013.
[10]. M. I Channa , A. H Jalbani , P. K. Harani, "A Survey of Power Aware Routing Schemes for Mobile Ad hoc Networks", International Journal of Computer Applications 58(11):48-50, November 2012.
[11]. S. Gopinath, N. Sureshkumar, et. al, "Energy Efficient Routing Protocol for MANET", IJCSI International Journal of Computer Science Issues, vol. 9, no. 2, March 2012.
[12]. C.-C. Chiang, H.-K. Wu, W. Liu, and M. Gerla, "Routing in Clustered Multihop Mobile Wireless Networks with Fading Channel", Proc. Singapore Int`l Conf. Networks (SICON`97), pp. 197-211, Apr. 1997.
[13]. P. Jacquet, P. Muhlethaler, T. Clausen, A. Laouiti, Qayyum and L.Viennot, "Optimized Link State Routing Protocol for Ad- hoc Networks", Proceedings. IEEE International Multi Topic Conference, 2001.
[14]. B. K. Pattanayak, M. K. Mishra, A. K. Jagadev, A. K. Nayak," Power Aware Ad hoc On- demand Distance Vector (PAAODV) Routing for MANETS", in Journal of Convergence Information Technology, Volume 6, Number 6, June 2011.
[15]. Z. J. Haas, M. R. Pearlman and P. Samar, "The Zone Routing Protocol (ZRP) for Ad hoc Networks," draft- ietf-manet-zone- zrp-04.txt, work in progress, July 2002.
[16]. K.-Ja Kim ,Hyun-Woo Koo , Sejong Univ., Seoul "Optimizing Power-Aware Routing using Zone Routing Protocol in MANET", Network and Parallel Computing Workshops, 2007 in IFIP international conference. Issue date:2007.
[17]. D. Feng, C. Jiang, G. Lim, L. J. Cimini, Jr., G. Feng, and G.Y. Li, "A survey of energy efficient wireless communications", IEEE Commun. Surveys Tuts., vol. 15, no. 1, pp. 167-178, 2013.
[18]. Z. Zhang, Guoqiang Mao, Brian D. O. Anderson, "Energy-Efficient Broadcast in Mobile Networks Subject to Channel Randomness", IEEE Transactions On Wireless Communications, Vol. 14, No. 6, June 2015.
[19]. T. Krishnamoorthy, A. D. Arivazhagan, "Energy Efficient Routing Protocol with Ad hoc on demand Distance Vector for MANET", IEEE 9th International Conference on Intelligent Systems and Control(ISCO) 2015.
[20]. R. Singh and S. Gupta, "EE-AODV: Energy Efficient AODV routing protocol by Optimizing route selection process", International Journal of Research in Computer and Comm. Technology, vol. 3, no. 1, January 2014.
[21]. Q. Cui et al., "Optimal Energy-Efficient Relay Deployment for the Bidirectional Relay Transmission Schemes", IEEE Trans. Veh. Technology, vol. 63, no. 6, pp. 2625-2641, July 2014.
[22]. Y. Li et al., "Multiple mobile data offloading through disruption tolerant networks", IEEE Transaction Mobile Computing, vol. 13, no. 7, pp. 1579-1596, Jul. 2013.
[23]. Y. Wang, Xiang-Yang Li, Wen-Zhan Song, Minsu Huang, and Teresa A. Dahlberg, "Energy-Efficient Localized Routing in Random Multihop Wireless Networks", IEEE Transactions On Parallel And Distributed Systems, Vol. 22, No. 8, August 2011.
[24]. H. Jun, M. H. Ammar, M. D. Corner, and E. W. Zegura, "Hierarchical power management in disruption tolerant networks using traffic-aware optimization", Elsevier Computer Commun., vol. 32, no. 16, pp. 1710- 1723, 2009.
[25]. Jinhua Z. and Xin Wang, "Model and Protocol for Energy-Efficient Routing over Mobile Ad hoc Networks", IEEE Transactions on Mobile Computing, Vol. 10, NO. 11, Nov. 2011.
[26]. J. E. Garcia, A. Kallel, K. Kyamakya, K. Jobmann, J.-C. Cano and P. Manzoni, "A Novel DSR-based Energy-ef?client Routing Algorithm for Mobile Ad-hoc Networks", IEEE in vehicular technology conference, 2003.
[27]. Y. chen, C. zhang and Z. liu, "Energy Efficient Routing Protocol Based on Energy of node and Stability of Topology", In 3 rd International conference on Information and computing 2010.
[28]. T. Hieng Tie, Chong Eng Tan and Sei Ping Lau, "Alternate Link Maximum Energy Level Ad-hoc Distance Vector Scheme for Energy Efficient Ad-hoc Networks Routing", In International Conference on Computer and Comm. Eng. (ICCCE 2010), Malaysia, 11-13 may, 2010.
[29]. J. Kuruvila, A. Nayak, and I. Stojmenovic, "Progress and Location Based Localized Power Aware Routing for Ad hoc and Sensor Wireless Networks", Int`l J. Distributed Sensor Networks, vol. 2, pp. 147-159, 2006.
[30]. M. Khabbazian, Ian F. Blake, V. K. Bhargava, "Local Broadcast Algorithms in Wireless Ad hoc Networks: Reducing the Number of Transmissions", IEEE Transactions On Mobile Computing, Vol. 11, No. 3, March 2012.
[31]. Y. Wang, Xiang-Yang Li, Wen-Zhan Song, Minsu Huang, and Teresa A. Dahlberg, "Energy-Efficient Localized Routing in Random Multihop Wireless Networks", IEEE Transactions On Parallel And Distributed Systems, Vol. 22, No. 8, August 2011.
[32]. K. Sharma, A. N. Patra, and C. Kumar, "An update based energy-efficient reactive routing protocol for mobile Ad hoc network", Int. J. Comput. Netw. Inf. Secur. (IJCNIS), vol. 5, no. 11, p. 17, 2013.
[33]. R. Alubady, M. Al-Samman, A. Habbal, S. Hassan, S. Arif, "Performance Analysis of Reactive and Proactive Routing Protocols in MANET", ARPN journal of engineering and applied sciences, vol. 10, no. 3, pp. 1819-6608, February 2015.
[34]. J. Zhu, X. Wang, "Model and Protocol for Energy-Efficient Routing over Mobile Ad Hoc Networks", IEEE Transactions on Mobile Computing Volume: 10, Issue: 11 Pages: 1546 - 1557, 2011.
[35]. Taha, R. Alsaqour, M. Uddin, M. Abdelhaq, T. Saba, "Energy Efficient Multipath Routing Protocol for Mobile Ad-Hoc Network Using the Fitness Function", IEEE Access, DOI: 10.1109/ACCESS 2017 2707537, Volume: 5 Pages: 10369 - 10381, 2017.

G. Rajeswarappa, S. Vasundra, "Energy Aware Routing Protocols for Wireless Mobile Ad hoc Networks: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.866-872, 2019.

Power crisis problem is one of the biggest challenge in-front of country. Increasing demand of fossil fuels and its constant depletion is the major concern that shifts our focus to use renewable energy sources which are sustainable to environment and mainly unlimited source of energy. Out of the some renewable energy sources solar energy is excellent alternative to use as it is not area specific. Besides its all advantages land acquisition is the major problem associated with solar power plant installed on land which is solve by liquid solar array concept. Most of solar project of government is in hot and dry region or higher radiation region which affects the efficiency of solar panel. Due to this innovative concept to set up liquid solar array on water bodies which prevents evaporation of water and growth of algae. Automatically cleaning of panel is done which increases panel efficiency and reduces manpower effort associated with cleaning it. Additionally Fresnel lens is used to concentrate sunlight on panel and monitoring is done by GSM and controller used here is Launchpad. Launchpad is also known as Msp430.

liquid solar array, GSM, launchpad, Fresnel lens,Msp430

[1] P. Sharma, B. Muni, D. Sen, “Design Parameters Of 10kw Floating Solar Power Plant”, International Advanced Research Journal in Science, Engineering and Technology (IARJSET) National Conference on Renewable Energy and Environment (NCREE-2015) IMS Engineering College, Ghaziabad Vol. 2, Special Issue 1, May 2015 .
[2] H. Akole, P. Jadhav, “floating solar plant”, In te proceedings of the International conference on emerging trend in engineering, technology, science and management ,India ISBN 978-93-86171-70-02.
[3] S. Ganguly, ,”Renewable Energy and Floating Solar Power Plants”, Vice President Projects, Vikram solar. Energetica india • AGO14.
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Siddhi Sandip Shinde, "Implementation of Efficient Liquid Solar Array System Using GSM and Launchpad," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.873-876, 2019.

Recognizable proof of the mango leaf malady is the primary objective to avert the misfortunes and nature of horticultural item. In India mango natural product harvest is broadly developed. So infection discovery and grouping of mango leaf is basic for maintainable farming. It`s impractical to rancher, to screen consistently the mango illness physically. It requires the over the top handling time, colossal measure of work, and some aptitude in the mango leaf ailments. To recognize and characterize the mango ailment we need quick programmed procedure so we use SVM classifier strategy. This paper shows predominantly five phases, viz picture securing, pre-handling, division, include extraction and SVM order. This paper is proposed to profit in the location and order of mango leaf infection utilizing bolster vector machine (SVM) classifier.

Image obtaining, pre-handling, Image division, SVM classifier

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[3] Amandeep Singh ,Maninder Lal Singh, “Automated Color Prediction of Paddy Crop Leaf using Image Processing”, International Conference on Technological Innovations in ICT for Agriculture and Rural Development (TIAR 2015), 2015.
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Sagar Gaikwad, Sagar Shinde, "Leaf Disease Detection using Digital Image Processing with SVM Classifier," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.877-881, 2019.

The quick response (QR) code was designed for storage information and high speed reading applications. With the wide application of QR code, the security problem of QR code is serious, such as information leakage and data tampering. The QR code contains secret message. In order to solve the QR information security problem, this paper proposed progressive visual secret sharing schemes for QR code message. In progressive visual secret sharing scheme the QR code message is divided into several parts called shares, which separately reveals no knowledge about the QR code message. QR code message can be revealed progressively by more and more shares one another. It improves the security of the data transmission and also improves the clarity of a secret image step by step.

Visual secret sharing scheme, QR code, Progressive visual cryptography scheme

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Komal S. Patil, Suhas B. Bhagate, "Progressive Visual Secret Sharing Scheme for QR Code Message," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.882-887, 2019.

Growth in the field of data mining rapidly increasing due to its well regulated techniques and efficient algorithms. At present, Genetic algorithm is the bustling research and it allocate the drastic modification in the field of data mining in terms of better optimization of result and the performance of different ventures effectively and efficiently. Because of their accuracy and efficiency, data mining algorithms attract and motivates to researchers to show interest in their technologies and large search space. Genetic algorithm works on bio-responsive operators to evaluate the fittest function in population by the Darwinism. This paper enumerates the enactment of genetic algorithm in frame of reference to data mining algorithms and techniques like decision tree and classification. The main objective of this paper is describes the application and benefits of different data mining techniques related to genetic algorithm

data mining, genetic algorithm, classification, decision tree

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Mariya Khatoon, Abhay Kumar Agarwal, "A Survey on Data Mining using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.888-891, 2019.