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WSN (Wireless Sensor Network) has to be systemized to being resource effective and extensible. The concept of task scheduling requires less task completion time, less energy consumption and proper utilization of energy with fewer energy nodes. DAG (Directed Acyclic Graph) is considered for WSN organization as of the routing redundancy for the root. This research paper has aimed to enhance the performance of DAG in WSN. For the improvement, Cuckoo Search as an optimization algorithm and Support Vector Machine as a classification algorithm is utilized. For the evaluation of the proposed work, QoS parameters, such as Throughput, PDR (Packet Delivery Ratio), Energy Consumption, Network Lifetime and Delay are considered. The proposed algorithm stood very positive results for each QoS parameter. The results are compared on the base of with and without the proposed architecture.

WSN, DAG, Cuckoo Search, Support Vector Machine

[1]. Elson and A. Parker. Tinker, “A tool for designing data-centric sensor networks”, Information Processing in Sensor Networks, The Fifth International Conference, pp. 350-357, 2006.
[2]. A. Razaque, M. Abdulgader, C. Joshi, F. Amsaad and M. Chauhan, “P-LEACH: Energy efficient routing protocol for Wireless Sensor Networks,” 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, 2016, pp. 1-5.
[3]. R. Ramadan, K. Abdelghany, and H. El-Rewini, “SensDep: a design tool for the deployment of heterogeneous sensing devices”, In Second IEEE Work- shop on Dependability and Security in Sensor Networks and Systems, 2006.
[4]. KP Ferentinos and TA Tsiligiridis, “Heuristic dynamic clustering in wire- less sensor networks towards uniform sensing”, 15th IST Mobile & Wireless Communications Summit, Myconos, Greece, June, 2006.
[5]. Eduardo F. Nakamura, Antonio A. F. Loureiro, and Alejandro C. Frery, “Information fusion for wireless sensor networks: Methods, models, and classifications”, ACM Computing Survey, vol.39, 2007.
[6]. Vieira, Marcos Augusto M., Claudionor N. Coelho, D. C. Da Silva, and José Monteiro da Mata. "Survey on wireless sensor network devices." In Emerging Technologies and Factory Automation, 2003. Proceedings. ETFA`03. IEEE Conference, vol. 1, pp. 537-544. IEEE, 2003
[7]. Alasmary, W.; Weihua Zhuang, “The Mobility Impact in IEEE 802.11p Infra structureless Vehicular Networks,” in Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd , vol., no., pp.1-5, 6-9 Sept. 2010
[8]. Sandra Sendra, Jaime Lloret, Miguel García and José F. Toledo, “Power saving and energy optimization techniques for Wireless Sensor Networks”, IEEE, Vol.6 pp. 439-452, 2011.
[9]. Akyildiz, I.F.; Weilian Su; Sankarasubramaniam, Y.; Cayirci, E., “A survey on sensor networks,” in Communications Magazine, IEEE, vol.40, no.8, pp.102-114, Aug 2002.
[10]. S. Xu and T. Saadawi, “Does the IEEE802.11 MAC protocol work well in multi hop wireless ad hoc networks?", IEEE Commun. Mag., vol. 39, pp.130 -137 2001.
[11]. Toumpis, Stavros; Goldsmith, A.J., “Capacity regions for wireless ad hoc networks,” in Wireless Communications, IEEE Transactions on , vol.2, no.4, pp.736-748, July 2003.
[12]. M. Kaur and A. Sharma, “Routing in WSN network using neural Network (NN) and SPEED protocol,” 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India, 2016, pp. 161-167.
[13]. X. Wang, H. Cheng and Y. Yao, “Addressing-Based Routing Optimization for 6LoWPAN WSN in Vehicular Scenario,” in IEEE Sensors Journal, vol. 16, no. 10, pp. 3939-3947, May15, 2016.
[14]. U. Raza, A. Camerra, A. L. Murphy, T. Palpanas and G. P. Picco, “What does model-driven data acquisition really achieve in wireless sensor networks?,”2012 IEEE International Conference on Pervasive Computing and Communications, Lugano, 2012, pp. 85-94.
[15]. S. Ghosh, S. Mondal and U. Biswas, “Fuzzy C Means based Hierarchical Routing Protocol in WSN with Ant Colony Optimization,” 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Bangalore, India, 2016, pp. 348-354.
[16]. A. Sharma and P. Lakkadwala, “Performance comparison of reactive and proactive routing protocols in Wireless Sensor Network,” Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization, Noida, 2014, pp. 1-6.
[17]. B. Paul, K. A. Bhuiyan, K. Fatema and P. P. Das, “Analysis of AOMDV, AODV, DSR, and DSDV Routing Protocols for Wireless Sensor Network,” 2014 International Conference on Computational Intelligence and Communication Networks, Bhopal, 2014, pp. 364-369.
[18]. A. Komathi and M. Pushparani, “Trust performance of AODV, DSR and DSDV in wireless sensor networks,” Second International Conference on Current Trends In Engineering and Technology - ICCTET 2014, Coimbatore, 2014, pp. 423-425.
[19]. S. Li, W. Dai and S. Zhang, “NS2 Enabled Multi-Routing-Protocol Simulating of Wireless Mesh Networks,” 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, 2012, pp. 1-4.
[20]. H. Jin, L. Ren and Y. Ding, “A Hierarchical Wireless Routing Protocol for Multi-Channel Multi-Interface Ad Hoc Networks,” 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, 2011, pp. 1-4.
[21]. L. He, J. Huang and F. Yang, “A noval hybrid wireless routing protocol for WMNs,” 2010 International Conference on Electronics and Information Engineering, Kyoto, 2010, pp. V1-281-V1-285.
[22]. A. Rahimi and A. Harati, “CGSR features: Toward RGB-D image matching using color gradient description of geometrically stable regions,” 2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA), Rasht, 2015, pp. 1-6.
[23]. H. Raza, P. Nandal and S. Makker, “Selection of cluster-head using PSO in CGSR protocol,” 2010 International Conference on Methods and Models in Computer Science (ICM2CS-2010), New Delhi, 2010, pp. 91-94.
[24]. T. P. Venkatesan, P. Rajakumar and A. Pitchaikkannu, “Overview of Proactive Routing Protocols in MANET,” 2014 Fourth International Conference on Communication Systems and Network Technologies, Bhopal, 2014, pp. 173-177.
[25]. L. Mejaele and E. Oketch Ochola, “Effect of varying node mobility in the analysis of black hole attack on MANET reactive routing protocols,” 2016 Information Security for South Africa (ISSA), Johannesburg, 2016, pp. 62-68.
[26]. L. Mejaele and E. Oketch Ochola, “Effect of varying node mobility in the analysis of black hole attack on MANET reactive routing protocols,” 2016 Information Security for South Africa (ISSA), Johannesburg, 2016, pp. 62-68.
[27]. N. Bilandi and K. Kaur, “Performance analysis of propagation structures on DSR routing protocol in mobile ad-hoc networks,” 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 445-449.
[28]. B. K. Joshi and M. Soni, “Security assessment of AODV protocol under Wormhole and DOS attacks,” 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India, 2016, pp. 173-177.
[29]. A. Sharma and R. Kumar, “Performance comparison and detailed study of AODV, DSDV, DSR, TORA and OLSR routing protocols in ad hoc networks,” 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India, 2016, pp. 732-736.
[30]. K. Madhuri, N. K. Viswanath and P. U. Gayatri, “Performance evaluation of AODV under Black hole attack in MANET using NS2,” 2016 International Conference on ICT in Business Industry & Government (ICTBIG), Indore, 2016, pp. 1-3.
[31]. Y. Sun, F. Jiang, T. Liang and C. Phillips, “Enhanced Associativity-Based Multicast Routing protocol in CR Mobile Ad hoc Networks,” 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), Washington DC, 2014, pp. 1233-1237.
[32]. B. Preveze and A. Şafak, “Associativity Tick Averaged Associativity based Routing (ATAABR) for real time mobile networks,” 2009 International Conference on Electrical and Electronics Engineering - ELECO 2009, Bursa, 2009, pp. II-208-II-212.
[33]. R. Dube, C. D. Rais, Kuang-Yeh Wang and S. K. Tripathi, “Signal stability-based adaptive routing (SSA) for ad hoc mobile networks,” in IEEE Personal Communications, vol. 4, no. 1, pp. 36-45, Feb 1997.
[34]. A. Chandra and S. Thakur, “Performance evaluation of hybrid routing protocols against network layer attacks in MANET,” 2015 1st International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2015, pp. 239-244.
[35]. C. Y. Aung, P. H. J. Chong and R. J. Cai, “Hybrid opportunistic routing in highly dynamic MANET,” 2014 23rd International Conference on Computer Communication and Networks (ICCCN), Shanghai, 2014, pp. 1-6.
[36]. S. A. Alharthi and P. A. Johnson, “Threshold sensitive heterogeneous LEACH protocol for wireless sensor networks,”2016 24th Telecommunications Forum (TELFOR), Belgrade, 2016, pp. 1-4.

Mandeep Kaur, Balwinder Singh Sohi, "Efficient DAG Task Scheduling Algorithm for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.735-743, 2018.

Diabetes dataset is described by hyperglycemia happening because of abnormalities in insulin discharge which would thusly result in sporadic raise of glucose level. This overview exhibits an analytical investigation of a few algorithms which diagnosis and arranges Diabetes dataset information successfully. As of late, the effect of Diabetes dataset has expanded, as it were, particularly in creating nations like India. This is for the most part because of the irregularities in the sustenance habits of a few IT professionals. In this way, early diagnosis and order of this lethal malady has turned into a functioning region of research in the most recent decade. Various methods have been produced to manage his illness. Various grouping and arrangements strategies are accessible in the literature to envision fleeting information to recognizing patterns for controlling diabetes dataset. The multi-class ruling algorithms are broke down altogether to distinguish their focal points and limitations. The execution assessment of the multi-class ruling algorithms is completed to decide the best methodology. A best methodology among the multi-class ruling methodology is resolved and a solution is likewise proposed to enhance the general execution of diagnosis process.

Diabetes dataset, Classification, Gestational diabetes

[1] P. Radha, Dr. B. Srinivasan, “Predicting Diabetes by cosequencing the various Data Mining Classification Techniques”, IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 6, August 2014.
[2] Veena Vijayan V, Aswathy Ravikumar, “Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus”, International Journal of Computer Applications (0975 – 8887) Volume 95– No.17, June 2014.
[3] Aiswarya Iyer, S. Jeyalatha and Ronak Sumbaly, “Diagnosis Of Diabetes Using Classification Mining Techniques”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.1, January 2015.
[4] S.Neelamegam, Dr.E.Ramaraj, “Classification algorithm in Data mining: An Overview”, International Journal of P2P Network Trends and Technology (IJPTT) – Volume 4 Issue 8- Sep 2013.
[5] A.Ambica, Satyanarayana Gandi, Amarendra Kothalanka, “An Efficient Expert System For Diabetes By Naïve Bayesian Classifier”, International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013.
[6] V.Karthikeyani, I.Parvin Begum, K.Tajudin, I.Shahina Begam, “Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction”, International Journal of Computer Applications (0975 – 8887) Volume 60– No.12, December 2012.
[7] Rajesh, M., and J. M. Gnanasekar. “Congestion control in heterogeneous WANET using FRCC.” Journal of Chemical and Pharmaceutical Sciences ISSN 974 (2015): 2115.
[8] Rajesh, M., and J. M. Gnanasekar. “A systematic review of congestion control in ad hoc network.” International Journal of Engineering Inventions 3.11 (2014): 52-56.
[9] Rajesh, M., and J. M. Gnanasekar. “Annoyed Realm Outlook Taxonomy Using Twin Transfer Learning.” International Journal of Pure and Applied Mathematics 116.21 (2017) 547-558.
[10] Rajesh, M., and J. M. Gnanasekar. “Get-Up-And-Go Efficient memetic Algorithm Based Amalgam Routing Protocol.” International Journal of Pure and Applied Mathematics 116.21 (2017) 537- 547.

S. Thaiyalnayaki, J. Chockalingam, "A Multi-class Ruling Classification Technique using Diabetes Dataset," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.744-748, 2018.

Mobile node is a collection of mobile nodes which forms a temporary network. Some of the nodes in an ad hoc network may want access to an external network, such as internet. Different mechanisms have been proposed to integrate MANETs and the Internet. These mechanisms are differing based on gateway discovery mechanism, and Adhoc routing protocol. When MANET is connected to the Internet, it is important for the mobile nodes to detect an available gateway providing an access to the Internet. The objective of this paper is a survey on the Mobile Ad-hoc Network (MANET) routing protocols used in gateways. This article presents a survey of hybrid solutions for integrating MANETs with the Internet.

MANET, AODV, DSDV, Gateway, Routing, Hybrid protocol, OLSR

[1]. D. Saha et al., “A Mobility Support in IP: A Survey of Related Protocols,” IEEE Network, Nov./Dec. 2004.
[2]. Thomas R. Henderson, Boeing Phantom Works, “Host Mobility for IP Networks: A comparison,” IEEE Network, Nov./Dec. 2003.
[3]. D. Saha et al., “Mobility Support in IP: A Survey of Related Protocols,” IEEE Network, Nov./Dec. 2004.
[4]. D. Johnson, C. Perkins, and J. Arkko, “Mobility Support in IPv6,” RFC 3775, June 2004.
[5]. T. Narten, E. Nordmark, and W. Simpson, “IPv6 Neighbor Discovery,” RFC 2461, Dec. 1998.
[6]. B. Volz, “Dynamic Host Configuration Protocol for IPv6 (DHCPv6) Relay Agent Remote-ID Option,” RFC 4649, Aug. 2006.
[7]. S. Sesay, Z. Yang, and J. He, “A Survey on Mobile Ad Hoc Wireless Network,” Info. Tech. J., vol. 3, no. 2, 2004, pp.168–75.
[8]. C. E. Perkins and P. Bhagwat, “Highly Dynamic Destination Sequenced Distance-Vector Routing (DSDV) for Mobile Computers,” Proc. SIGCOM ’94 Conf. Commun. Architecture, Protocols and Applications, Aug. 1994, pp. 234–44.
[9]. T. Clausen and P. Jacquet, Eds., “Optimized Link State Routing Protocol (OLSR),” RFC 3626, Oct. 2003.
[10]. C. E. Perkins, E. M. Royer, “Ad hoc On-Demand Distance Vector (AODV) Routing,” RFC 3561, July 2003.
[11]. C.-C. Chiang, “Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel,” Proc. IEEE SICON ’97, Apr. 1997, pp. 197–211.
[12]. S. Murthy and J. J. Garcia-Luna-Aceves, “An Efficient RoutingProtocol for Wireless Networks,” ACM Mobile Networks andApp. J., special issue on Routing in Mobile Commun. Networks, vol. 1, no. 2,Oct. 1996, pp. 183–97.
[13]. U. Jonsson et al., “MIPMANET — Mobile IP for Mobile Ad Hoc Networks,” Proc. 1st Wksp. Mobile Ad hoc Network and Computing (MobiHOC’00), Boston, Massachusetts, Aug. 2000, pp. 75–85.
[14]. P. Ratanchandani and R. Kravets, “A Hybrid Approach to Internet Connectivity for Mobile Ad Hoc Networks,” Proc. IEEE WCNC 2003, vol. 3, Mar. 2003, pp. 1522–27.
[15]. B. A. Kock and J. R. Schmidt, “Dynamic Mobile IP Routers in Ad Hoc Networks,” IWWAN 2004, Int’l. Wksp. Wireless Ad-hoc Networks, OULU FINLAND, June 2004.
[16]. H. Lei and C.E. Perkins, “Ad Hoc Networking with Mobile IP,” Proc. 2nd European Pers. Mobile Commun. Conf. (EPMCC ’97), IEE, 1997, pp. 197–202.
[17]. E. M. Belding-Royer and C. E. Perkins, “Global Connectivity for IPv4 Mobile Ad hoc Networks,” IETF Internet draft, draft over manet-globalv4-00.txt, 14 Nov. 2001, work in progress, http://www.cs.ucsb.edu/~ebelding/txt/globalv4.txt
[18]. Y. Sun, E. Belding-Royer, and C. Perkins, “Internet Connectivity for Ad hoc Mobile Networks,” Int’l. J. Wireless Information Networks, special issue on Mobile Ad Hoc Networks (MANETs): Standards, Research, Applications, vol. 9, no. 2, Apr. 2002.
[19]. Y.-C. Tseng, C.-C. Shen, and W.-T. Chen, “Mobile IP and Ad Hoc Networks: An Integration and Implementation Experience,” IEEE Computer, vol. 36, no. 5, May 2003, pp. 48–55.
[20]. R. Wakikawa et al., “Global Connectivity for IPv6 Mobile Ad Hoc Networks,” IETF Internet draft, Feb. 2003, work in progress.
[21]. M. Benzaid et al., “Integration of Mobile-IP and OLSR for a Universal Mobility, Wireless Networks,” special issue on Ad-hoc Networking, Boston: Kluwer Academic Publishers, vol. 10, issue 4, July 2004, pp. 377–88.
[22]. B. Xie and A. Kumar, “Integrated Connectivity Framework for Internet and Ad hoc Networks,” 1st IEEE Int’l. Conf. Mobile Ad-hoc and Sensor Systems, Oct. 24–27, 2004, Fort Lauderdale, Florida, USA.
[23]. F. M. Abduljalil and S. K. Bodhe, “Integrated Routing Protocol (IRP) for Integration of Cellular IP and Mobile Ad Hoc Networks,” IEEE Int’l. Conf. Sensor Networks, Ubiquitous, and Trustworthy Computing, vol. 1, SUTC’06, Taiwan, 2006, pp. 312–15.

G. Ramesh, M.Geetha, "A Survey of Hybrid Routing Protocol for Interconnecting Mobile Ad Hoc Network and Internet," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.749-756, 2018.

Swarm intelligence is a cooperative behavior of collective systems like insects such as ant colony optimization (ACO), fish schooling, birds flocking, bee Colony Optimization (BCO) particle swarm optimization (PSO) and so on. In this paper, a hybrid performances for data organization and information extrapolation is recommended. The Honey Bee Mating Optimization algorithm and Artificial Neural Networks (HBMO-ANN) may also be considered as a distinctive swarm-based optimization, in which the exploration algorithm is encouraged by the development of real honey-bee marital and mimic the iterative mating process of honey bees and approaches to select applicable drones for mating progression through the fitness function enrichment for selection of superlative weights for hidden layers of Neural Network classifiers. Enhanced HBMO with Neural Network (EHBMO-NN) algorithm is now realistic to classify the data proficiently by training the neural network. The classification accuracy of EHBMO is much more compared with other algorithm such as Support Vector Clustering Algorithm (EHBMO-SVC). In this paper, enhanced honey-bee mating optimization algorithm is offered and verified. A developed way of Honey Bee Mating Optimization performance is combined with Neural Network which expands accuracy and moderate time delay in difficulty of various real world datasets.

Swarm intelligence, Honey Bee Mating Optimization Algorithm, Support Vector Clustering, Artificial Neural Networks

[1] “Association Rules Optimization using Artificial Bee Colony Algorithm with Mutation” International Journal of Computer Applications (0975 – 8887) Volume 116 – No. 13, April 2015/ 29 Manish Gupta Asst. Professor, Computer Science Dept. Vikrant Institute of Technology &Management Gwalior, India
[2] Yannis Marinakis1, Magdalene Marinaki2 and Nikolaos Matsatsinis1 “A Hybrid Clustering Algorithm based on Honey Bees Mating optimization and Greedy Randomized Adaptive Search Procedure”. International Journal of Applied Engineering Research, ISSN 0973-4562 Vol.10 No.82 (2015) © Research India Publications, httpwww.ripublication.comijaer.htm
[3] Kalyani. K and Chakravarthi.T “Swarm Intelligence Clustering Algorithm Based On Energy Sustainability to Cluster Association”. International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 3, Issue 9, December 2014
[4] K. Lenin and B. R. Reddy “Bumble Bees Mating Optimization (BBMO) Algorithm for Solving Optimal Reactive Power Dispatch Problem” International Journal of Electronics and Electrical Engineering Vol. 3, No. 4, August 2015 Jawaharlal Nehru Technological University, Kukatpally, Hyderabad 500 085, India.
[5]. K.Kalyani and T. Chakravarthi, “Load Profile Clustering: An Algorithmic Approach with Improved Replacement In Bee Optimization Algorithm”, ISSN: 2229-6956(ONLINE) ICTACT Journal On Soft Computing: JANUARY 2015, VOLUME: 05, ISSUE: 02.
[6]. K. Kalyani and T. Chakravarthi , “A Comparative Study On The Perceived Applicability of Honey Bee Mating Optimization Algorithm (HBMO) And Particle Swarm Optimization (PSO) Algorithm By Applying Three Factor Theory Among Researchers In Tamil Nadu” , ISSN: 2229-6956(ONLINE) ICTACT journal on soft computing, APRIL 2015, VOLUME: 05, ISSUE: 03s
[7]. Dr. T.Chakravarthi and K.Kalyani , “A Brief Survey Of Honey Bee Mating Optimization Algorithm
To Efficient Data Clustering” . ISSN (Print) : 0974-6846 ISSN (Online) : 0974- Indian Journal of Science
and Technology (IJST) , Vol 8(24), 59219, September 2015 (Scopus indexed)
[8]. Dr. T. Chakravarthi and k.kalyani “An Algorithmic Approach With Hbmo To Efficient Data Prediction Using Artificial Neural Network” . International Journal of Applied Engineering Research (IJAER), ISSN 0973-4562 Vol.10 No.82 (2015) © Research India Publications December-2015 ((Scopus indexed).
[9]. Omid Bozorg Haddad, and M. A. Mariño “Hbmo In Engineering Optimization “ Iran University of Science and Technology, Department of Civil engineering, Tehran, Iran Ninth International Water Technology Conference, IWTC9 2005, Sharm El-Sheikh, Egypt 1053.
[10] “L. Qingyong, S. Zhiping, S. Jun and S. Zhongzhi, “Swarm Intelligence Clustering Algorithm Based on Attractor”, Lecture Notes in Computer Science, Springer Link, Vol. 3621, 2005, pp. 496-504.
[11] “A Survey on Artificial Bee Colony Models for Numerical Optimizations and its Work in Image Segmentation and Data Classification”. Lavanya Gunasegaram and Srinivasan Subramanian Australian Journal of Basic and Applied Sciences ISSN: 1991-8178.
[12] “An Enhanced K Means Clustering using Improved Hopfield Artificial Neural Network and Genetic Algorithm”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-3, July 2013.
[13] Baris Yuce , Michael S. Packianather . “Honey Bees Inspired Optimization Method: The Bees Algorithm “Insects 2013, 4, 646-662; doi: 10.3390/insects4040646.
[14] “Training A Feed-Forward Neural Network With Artificial Bee Colony Based Backpropagation Method” International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 4, August 2012.
[15] Yuksel Celik1 and Erkan Ulker “An Improved Marriage in Hon y Bees Optimization Algorithm for Single Objective Unconstrained optimization” HindawiPublishing. The Scientific World Journal Volume 2013, Article ID 370172.
[16].UCI Machine Learning Repository //:archive.ics.uci.edu.ml//data sets.

K . Kalyani, T. Chakravarthi, "Swarm Approach Combined With Artificial Neural Networks to Constructive Data Organization and Information Extrapolation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.757-762, 2018.

The main purpose of this paper is exploring the fact that how to use a machine learning model in order to perform predictive maintenance on Automobile. Maintenance and Care play a key role in the smooth and safe running of your motorcycle. The goal is to predict when the automobile require service or maintenance. If the model runs successfully, it gives us enough data about determining what the problem is and not only providing the necessary solutions but also ordering the parts and scheduling the people necessary to repair it. The innovative solutions of Predictive Maintenance recursively monitor, evaluates and report the component and system conditions in the vehicle. Various techniques are discussed and tested, such as linear and quantile regression. The primary aim of the system is to increase the vehicle’s efficiency due to the observed and supervised driving behavior which is able to minimize the fuel consumptions and exhaust. Based on received data from the various connected vehicle and transmitting it to the cloud i.e. Azure where the processing of the data takes place, errors are predicted and fixed before time and with less damage of vehicle whereby reducing the overall cost of maintenance

Predictive maintenance, machine learning automobiles, 2-wheelers, Internet of Things, AZURE

[1]. Jong-Ho Shin and Hong-Bae Jun. “On condition based maintenance policy”. Journal of Computational Design and Engineering, 2(2): pp.119–127, 2015.
[2]. R. Kothamasu, S. H. Huang, and W. H. VerDuin.” System health monitoring and prognostics — a review of current paradigms and practices”. The International Journal of Advanced Manufacturing Technology, 28(9), pp. 1012–1024, 2006.
[3]. Ying Peng, Ming Dong, and Ming Jian Zuo. “Current status of machine prognostics in condition-based maintenance”: a review. The International Journal of Advanced Manufacturing Technology, pp. 297–313, 2010.
[4]. Xunyuan Yin, Zhaojian Li, Sirish L. Shah, Lisong Zhang, Changhong Wang , “Fuel Efficiency Modelling and Prediction for Automotive Vehicles: A Data-Driven Approach”, 2015 IEEE International Conference on Systems, Man, and Cybernetics, Chain ,pp 2527-2532, 2015.
[5]. Rohit Dhall, Vijender Solanki, “An IoT Based Predictive Connected Car Maintenance Approach”, International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, Nº3,pp. 16-22, 2017.
[6]. Gian Antonio Susto, Andrea Schirru, Simone Pampuri, Se´an McLoone Senior Member, IEEE, Alessandro Beghi Member, IEEE, “Machine Learning for Predictive Maintenance: a Multiple Classifier Approach”, IEEE Transactions on Industrial Informatics, 11(3), 812-820, pp.1-8 , 2014.
[7]. Emir Husni , Galuh Boy Hertantyo , Daniel Wahyu , Muhamad Agus Triawan , “Applied Internet Of Things (IoT):Car monitoring system using IBM BlueMix”, 2016 International Seminar on Intelligent Technology and Its Application©2016 IEEE, Indonesia, pp. 417-422, 2016.
[8] Robert H. Shumway and David S. Stoffer. “Time series analysis and its applications: with R examples. Springer”, New York,USA, 2nd [update];second; edition, 2006.

Prakash Patel, Pallavi Jain, Swapnil Bhambure, Yashraj Sen, N.F. Shaikh, "Predictive Maintenance Approach on Automobiles," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.763-767, 2018.

In recent years, software development in software industries is getting a transition from centralized version control systems (CVCSs) like subversion, mercurial, perforce, CVS etc. to decentralized version control systems (DVCSs) like Git due to a number of reasons like time, space, branching, merging, offline commits & builds and repository etc. Both centralized VCSs and distributed VCSs have gone through ample investigations in recent past but individually from the software developer’s point of view in a large commercial software industry. There has been a little focus on the transition across Git having a share of more than three-fourth of total VCS, and Subversion having a share of 13.5%. In this work transition process from Subversion VCS to Git VCS has been investigated.

Version control system, distributed VCS, centralized VCS, transition, branching, merging, time, space

[1]. N. B. Ruparelia. “The history of version control,” ACM SIGSOFT Software Engineering Notes vol. 35, no. 1, pp. 5-9, 2010.
[2]. B. De Alwis and J. Sillito, “Why are software projects moving from centralized to decentralized version control systems?” ICSE Workshop on Cooperative and Human Aspects on Software Engineering (CHASE`09), pp. 36-39, 2009.
[3]. D. Spinellis. “Git,” IEEE Software, vol. 29, no. 3, pp. 100-101, 2012.
[4]. https://www2.physics.ox.ac.uk/it-services/moving-projects-from-svn-to-git
[5]. https://stosb.com/static/talks/case_study_git_efl_linuxcon_eu_13.pdf
[6]. Loeliger, J., Matthew McCullough, “Version Control with Git: Powerful Tools and Techniques for Collaborative Software Development,” O’Reilly Media, Inc. Second Edition, 2009.
[7]. Tom De Nies, Sara Magliacane, Ruben Verborgh, Sam Coppens, Paul Groth, Erik Mannens, and Rik Van de Walle, “Git2PROV: Exposing Version Control System Content as W3C PROV,” Proc. 12th Int. Semantic Web Conf., pp. 1-4, Oct. 2013.
[8]. C. Brindescu, M. Codoban, S. Shmarkatiuk and D. Dig, "How Do Centralized and Distributed Version Control Systems Impact Software Changes?," Proc. 36th Int. Conf. on Software Engineering, Hyderabad, India, pp. 322-333, 2014.
[9]. Kıvanç Muşlu, Christian Bird, Nachiappan Nagappan, Christian Bird, “Transition from Centralized to Decentralized Version Control Systems: A Case Study on Reasons, Barriers, and Outcomes,” Proc. Int. Conf. on Software Engineering ICSE-2014, pp. 334-344, May 31- June 7, Hyderabad, India, 2014.
[10]. Christian Bird, Peter C. Rigby, Earl T. Barr, David J. Hamilton, Daniel M. German, Prem Devanbu, “The Promises and Perils of Mining Git,” Proc. 6th IEEE International Working Conference on Mining Software Repositories, Vancouver, BC, Canada, May 16-17, pp. 1-10, 2009
[11]. E. T. Barr, C. Bird, P. C. Rigby, A. Hindle, D. M. German and D. Premkumar, "Cohesive and Isolated Development with Branches," Proc. 15th International Conference on Fundamental Approaches to Software Engineering, Tallinn, Estonia, pp. 316-331, March 24 - April 01, 2012.

Monika Varshney, Azad Kumar Shrivastava, Alok Aggarwal, Adarsh Kumar, "Migration from Subversion to Git Version Control System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.768-771, 2018.

A Mobile Ad hoc Network (MANET) is an accumulation of wireless portable nodes shaping a system without utilizing any current infrastructure. The mobility characteristic of MANETs is an extremely critical one. The mobile nodes may follow different mobility patterns that may affect connectivity and performance. In MANET due to decentralize of resources every nodes have to perform the routing functionalities themselves. Communication in such type of network is challenge and complex. To overcome these problems Clustering is the best solution for wide, flexible and high portability Ad-Hoc System. It increases the potential of network and reduces the routing overhead for efficient routing in MANET. Different types of phases involve in Cluster are Designing of Cluster and Maintenance of Cluster. In Cluster designing, selecting proper cluster head is one of the primary issues of research. For Maintenance of cluster an efficient system is required so that cluster head can keep update all the data of cluster, due to change in cluster structure because of nodes mobility. This paper basically focused on the Weighted Metric algorithms in MANET

MANET,Cluster Head, Clustering

[1] Ilyas M, “Thе Hand book of Wirеlеss Nеtworks”, CRC Prеss, 2003
[2] Angionе G, Bеllavista P., Corradi, A., & Magistrеtti Е, “A k-hop Clustеring Protocol for Dеnsе Mobilе Ad-Hoc Nеtworks”, 26th IЕЕЕ Intеrnational Confеrеncе on Distributеd Computing Systеms Workshops (ICDCSW`06), Lisboa, Portugal, 2007
[3] Pеrkins C, “Ad hoc Nеtworks”, Addison-Wеslеy, 2001.
[4] Varadharajan, V., Shankaran, R., Hitchеns, M, “Sеcurity for Clustеr Basеd Ad hoc Nеtworks”, Computеr Communications 27(5), pp. 488–501, 2004.
[5] Amis A.D., R. Prakash, T.H.PVuong, D.T. Huynh, "Max-Min DClustеr Formation in Wirеlеss Ad Hoc Nеtworks". In procееdings of IЕЕЕ Confеrеncе on Computеr Communications (INFOCOM) Vol. 1. pp. 32-41, 2000.
[6] Basagni S, “Finding a Maximal Wеightеd Indеpеndеnt Sеt in Wirеlеss Nеtworks", Tеlеcomm. Systеms. 18(1/3):155-168, Sеptеmbеr 2001.
[7] S.T. Hedetniemi and R.C. Laskar, “Bibliography on domination in graphs and some basic definitions of domination parameters,” Discrete Mathematics, vol. 86 (1–3), pp. 257-277, 1990.
[8] M. Chatterjee, “WCA: A weighted clustering algorithm for mobile ad hoc networks,” in Cluster computing Journal, vol. 5 (2), 2002, pp. 193-204.
[9] S. Karunakaran and P. Thangaraj, “An Adaptive Weighted Cluster Based Routing (AWCBRP) Protocol for Mobile Ad-hoc Networks,” in WSEAS Transactions on Communications, vol. 7 (4), Stevens Point: WSEAS, 2008, pp. 248-257.
[10] F. Li, S. Zhang, X. Wang, X. Xue, H. Shen, “Vote-Based Clustering Algorithm in Mobile Ad Hoc Networks,” in Information Networking, vol. 3090, Springer Berlin Heidelberg, 2004, pp.13-23
[11] A. Karimi, F.Zarafshan, A. Afsharfarnia and S.A.R Al Haddad, “A Novel Clustering Algorithm for Mobile AdHoc Newtorks Based on Determination of Virutal Links’ Weight to Increase Network Stability” in The Scientific World Journal, vol. 2014, Iran: Hindawi, 2014.
[12] Zhaowen Xing, Le Gruenwald and K.K. Phang, “A Robust Clustering Algorithm for Mobile Ad Hoc Networks”, Handbook of Research on Next Generation Networks and Ubiquitous Computing, December 2008.
[13] Mohammad Shayesteh and Nima Karimi, “ An Innovative Clustering Algorithm for MANETs Based on Cluster Stability”, International Journal of Modeling and Optimization, Vol. 2, No. 3, June 2012.
[14] Mohammad Reza Monsed, Sam Jabbehdari and Farshad Safaei, “An Efficient Weight based Clustering Algorithm for MANET ”, JOURNAL OF COMPUTING VOLUME 3, ISSUE 1, JANUARY 2011 ISSN 2151-9617.
[15] Garg, A., Batra, N., Taneja, I., Bhatnagar, A., Yadav, A., & Kumar, S. (2017). “Cluster Formation based Comparison of Genetic Algorithm and Particle swarm Optimization Algorithm in Wireless Sensor Network”, International Journal of Scientific Research in Computer Science and Engineering Vol.5, Issue.2, pp.14-20, April (2017).

Sandeep Monga, J.L Rana, Jitendra Agarwal, "Review of Clustering Algorithm for Cluster Head Selection of MANET Using Weighted Metrics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.772-774, 2018.

In this research paper, the spite of action and reaction forces in case of Newton’s third law of motion there is a third force which plays vital role in the sustainability of any body. This force makes able the body to resist its shape in spite of the existing intermolecular forces. As long as the angle of responding the applying forces increases, value of reactionary forces decreases even with its components. Macro and Micro spectrum of air particle spreaded in the Universe protect the body as a plasma layer, angle β, force F give burn a new magnitude of force Ј which is plays decisive role and may be taken as the guiding factor to save the physical structure of a body. Values of this J =K(F+β)n where K= is the repetition of the frequency ranging from the whole number domain. n is supposed as the number of trial of any strike of any magnitude of a variable force during a certain moment provided no additional lubricants or viscous material is supplied during the application of these action and reaction of the forces

Force, Responding Force, Adhesive, Cohesive, Perpendicular, Third Force

[1]. Newton , Isaac Mathematical Principles of Natural Philosophy pp.19-20 , London, 1727 , translated by Andrew Motte from the Latin.
[2]. Newton: The Principia. Mathematical Principles of Natural Philosophy, University of California Press, Berkeley – Los Angeles – London, 1999. A new translation by B. Cohen and A. Whitman assisted by J. Budenz .
[3]. Taibu, R., Rudge, D., & Schuster, D. (2015). Textbook presentations of weight: Conceptualdifficulties and language ambiguities.Physical Review Special Topics –Physics EducationResearch,11,010117.doi:10.1103/PhysRevSTPER.11.010117.
[4]. Wilson, K. F., & Low,D. J. (2015). “On Second Thoughts":Changes of Mind as anIndicationof Competing KnowledgeStructures.American Journal of Physics,83(9), 802-808.doi:10.1119/1.4928131.
[5]. Stocklmayer,S.,Rayner,J. P.,& Gore,M. M. (2012). Changingthe Order of Newton`s Laws–Why& How the Third LawShould be First.ThePhysics Teacher,50(7), 406-409.doi:10.1119/1.4752043

Shobha Lal, Jitendra Joshi, "Possibilities of Existence of a Third Force J In Between Action and Reaction Forces of Newton," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.775-776, 2018.

This paper aims at security authentication for an unmanned surveillance system. The system takes the Face image, impressions of a person’s finger and images of eyes and prepares a database. A blended biometric approach is followed for calculating the weighted average of scores appraised from the three most trivial biometric traits, Face, Eye and Finger impressions. The features are extracted from the pre-processed images of iris, face and finger impressions.The details of a probing image are to be matched with the database we have .the individual details obtained after tallying are sent to the fusion module. This module consists of three major steps i.e., Pre-Processing, Discrete Wavelet Transformation and Image fusion. At the final phase the hidden key Analysis approach is followed to authenticate the subject under investigation.

Biometric Identity, IRIS Recognition, Finger print, Face Recognition, DWT, WAMS

[1] Hiren D. Joshi, “A Multimodal Biometric Authentication System for Person Identification and Verification using Fingerprint and Face Recognition” International Journal of Computer Applications (0975 – 8887) Volume 51– No.17, August 2012
[2] R. W. Picard, “Content access for image/video coding: “The Fourth Criterion”,” Tech. Rep. 295, MIT Media Lab, Perceptual Computing, Cambridge, MA, 1994.
[3] R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, ISBN 978-0-470-51706-2, 2009
[4] Perlovsky L. I. et.al., Neural Networks and Intellect: Using Model-Based Concepts. New York, NY: Oxford University Press; (3rd printing), 2001.
[5] Crawford, Mark. "Facial recognition progress report". SPIE Newsroom. Retrieved 2011-10- 06.
[6] Liu Y, Simon JD (February 2005). "Metal-ion interactions and the structural organization of Sepia eumelanin". Pigment Cell Res. 18 (1): 42–8. doi:10.1111/j.1600-0749.2004.00197.x. PMID 15649151.
[7] HRSID Iris Recognition | "more than 200 points that can be used for comparison, including rings, furrows and freckles" "Iris scanners `can be tricked`". "Biometric Identity Management System". UNHCR. Retrieved 2015-11-02.
[8] Wasserman, Philip (2005-12-26). "Solid-State Fingerprint Scanners - A Survey of Technologies" (PDF). Retrieved 2015-10-18.
[9] Setlak, Dale. "Advances in Biometric Fingerprint Technology are Driving Rapid Adoption in Consumer Marketplace". AuthenTec. Retrieved 4 November 2010. M. N. Do, M. Vetterli, (2005) “The contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091-2106.
[10] Liu, Z., Blasch, E., Xue, Z., Langaniere, R., and Wu, W., (2012). Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(1), 94-109.
[11] Martin, Zach (2011-03-23). "Biometric Trends: Will emerging modalities and mobile applications bring mass adoption?". SecureIDNews. Retrieved 2013-07-14
[12] "Biometric Identity Management System". UNHCR. Retrieved 2015-11-02.
[13] Wasserman, Philip (2005-12-26). "Solid-State Fingerprint Scanners - A Survey of Technologies" (PDF). Retrieved 2015-10-18.
[14] Hosseini, M.S.; Araabi, B.N.; Soltanian-Zadeh, H. (April 2010). "Pigment Melanin: Pattern for Iris Recognition". IEEE Trans Instrum Meas 59 (4): 792–804. doi:10.1109/TIM.2009.2037996

K. Divya, K. G. R. Narayan, V. Ramachandran, R. Eswariah, "A Blended Biometric Approach Using Matching Score Level Architecture," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.777-780, 2018.

To replace the present host-driven IP-based Internet architecture, Content-Centric Networking (CCN) is considered as conceivable substitution and is been developing worldwide. In CCN, the content name rather than IP address of host becomes the primary entity. Content is in this manner decoupled from its location. In addition to other things, this allows the cache to be ubiquitously present. One of the famous examples of CCN is Named Data Networking (NDN). To satisfy the incoming request for data, every node in NDN is permitted to have its own local cache. For productive huge scale content dissemination, NDN is been made a decent architecture because of this. This paper focuses on various caching techniques and explains many of them briefly. NS-3 based Named Data Networking simulator named as ndnSIM is used to assess proposed scheme.

NDN, CCN , CS, FIB, PIT, ndnSIM, NS-3

[1] Zhang, Lixia, et al. "Named data networking (ndn) project." Relatório Técnico NDN-0001, Xerox Palo Alto Research Center-PARC 157 (2010): 158. J. Pan, S. Paul, and R. Jain, “A survey of the research on future internet architectures,” IEEE Communications Magazine, vol. 49, no. 7, 2011.
[2] J. Pan, S. Paul, and R. Jain, “A survey of the research on future internet architectures,” IEEE Communications Magazine, vol. 49, no. 7, 2011.
[3] S. Guo, H. Xie, and G. Shi, "Collaborative Forwarding and Caching in Content-Centric Networks," pp. 41–55, 2012.
[4] Saxena, Divya, et al. "Named data networking: a survey." Computer Science Review 19 (2016): 15-55.
[5] J. Li, H. Wu, B. Liu, and J. Lu, “Effective Caching Schemes for Minimizing Inter-ISP Traffic in Named Data Networking,” pp. 580–587, 2012.
[6] H. Wu, J. Li, Y. Wang, and B. Liu, “EMC : The Effective Multi-path Caching Scheme for Named Data Networking,” 2013.
[7] J. Li, H. Wu, B. Liu, J. Lu, Y. Wang, and X. Wang, “Popularity-driven Coordinated Caching in Named Data Networking,” 2012.
[8] N. Aloulou, M. Ayari, M. F. Zhani, and L. Saidane, “A popularity-driven controller-based routing and cooperative caching for named data networks,” 2015 International Conference on the Network of the Future, NOF 2015, 2015.
[9] Yeh, Edmund, et al. "Vip: A framework for joint dynamic forwarding and caching in named data networks." Proceedings of the 1st ACM Conference on Information-Centric Networking. ACM, 2014.
[10] K. Cho, M. Lee, K. Park, T. T. Kwon, and Y. Choi, “WAVE : Popularity-based and Collaborative In-network Caching for Content-Oriented Networks,” pp. 1–6.
[11] N. Laoutaris, O. Telelis, V. Zissimopoulos, and I. Stavrakakis, "Distributed selfish replication," IEEE Transactions on Parallel and Distributed Systems, vol. 17, no. 12, pp. 1401–1413, 2006.
[12] X. Hu, C. Papadopoulos, J. Gong, and D. Massey, “Not So Cooperative Caching in Named Data Networking,” pp. 2263–2268, 2013.
[13] Rezazad, Mostafa, and Y. C. Tay. "A cache miss equation for partitioning an NDN content store." Proceedings of the 9th Asian Internet Engineering Conference. ACM, 2013.
[14] Wellem, Theophilus, Yu-Kuen Lai, and Wen-Yaw Chung. "2015 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS)."
[15] Y. Zeng and X. Hong, “A caching strategy in mobile ad hoc named data network,” Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011, pp. 805–809, 2011.
[16] C. Barakat, A. Kalla, D. Saucez, T. Turletti, and I. S. Antipolis-m, “Minimizing Bandwidth on Peering Links with Deflection in Named Data Networking.”
[17] M. Dehghan, B. Jiang, A. Dabirmoghaddam, and D. Towsley, “On the Analysis of Caches with Pending Interest Tables,” pp. 69–78.
[18] W. Dron, A. Leung, M. Uddin, S. Wang, T. Abdelzaher, R. Govindan, and J. Hancock, “Information-maximizing caching in Ad Hoc networks with named data networking,” Proceedings of the 2013 IEEE 2nd International Network Science Workshop, NSW 2013, pp. 90–93, 2013.
[19] Ran, Jianhua, et al. "On performance of cache policies in named data networking." International Conference on Advanced Computer Science and Electronics Information. 2013
[20] H. Dai, Y. Wang, H. Wu, J. Lu, and B. Liu, “Towards Line-Speed and Accurate On-line Popularity Monitoring on NDN Routers.”
[21] W. Gao, G. Cao, A. Iyengar, and M. Srivatsa, “Supporting cooperative caching in disruption tolerant networks,” Proceedings - International Conference on Distributed Computing Systems, pp. 151–161, 2011.
[22] H. Yan, D. Gao, W. Su, C. H. Foh, H. Zhang, and A. V. Vasilakos, “Caching Strategy Based on Hierarchical Cluster for Named Data Networking,” IEEE Access, vol. 5, no. c, pp. 8433–8443, 2017.
[23] V. Sourlas, L. Gkatzikis, P. Flegkas, and L. Tassiulas, “Distributed cache management in information-centric networks,” IEEE Transactions on Network and Service Management, vol. 10, no. 3, pp. 286–299, 2013.
[24] K. Mori, T. Kamimoto, and H. Shigeno, “Push-Based Traffic-Aware Cache Management in Named Data Networking,” 2015.
[25] H. Farahat, “Optimal Caching for Producer Mobility Support in Named Data Networks,” 2016.
[26] M. Faran, M. N. Dailey, R. Khan, and A. Tunpan, “Pre-caching : A proactive scheme for caching video tra ffi c in named data mesh networks,” vol. 87, no. March, pp. 116–130, 2017.
[27] Rao, Ying, et al. "Proactive caching for enhancing user-side mobility support in named data networking." Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2013 Seventh International Conference on. IEEE, 2013.
[28] Ramesh Bandaru and Rao S Basavala , "Information Leakage through Social Networking Websites leads to Lack of Privacy and Identity Theft Security Issues", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.1-7, 2013.
[29] Sanjay E. Pate, Bhojaraj H. Barhate, "A survey of Possible Attacks on Text & Graphical Password Authentication Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.77-80, 2018.

Ganesh Pakle, Neha Bais, Ramchandra Manthalkar, "A Survey on Caching in Named Data Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.781-787, 2018.