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Pole-Like Objects (PLOs) in the road environment located nearby the road boundary are important roadway assets. They play vital role in the road safety inspection and road planning. In present study a novel automated method for the detection of PLOs from Mobile Laser Scanner (MLS) point cloud data has been proposed. Proposed method includes four basic steps. Initially ground points are roughly filtered out from the input dataset to reduce the processing of un-necessary points; formerly local window search is performed at non-ground points to find out the concentrated point distribution. Principal Component Analysis (PCA) has been implemented at such concentrated distributed points for the identification of PLOs. In last step of proposed method knowledge based information are used for suppressing the false positives and rectifying the output. Proposed method has been tested on a MLS point cloud data of complex road environment and corresponding PLOs are detected having completeness and correctness of 91.48 % and 86.00 % respectively.

LiDAR, Pole Like Objects, PCA

[1] K.S. Yen, B. Ravani, T.A. Lasky, “LiDAR for Data Efficiency”, WSDOT Research Report, WA-RD 778.1, 2011.
[2] S.I. El-Halawany, D.D. Lichti, “Detection of road poles from mobile terrestrial laser scanner point cloud”, International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM), pp.1–6, 2011.
[3] C. Brenner, “Extraction of features from mobile laser scanning data for future driver assistance systems”, Advances in GIScience, Lecture Notes in Geoinformation and Cartography, Springer, pp.25-42, 2009.
[4] H. Yokoyama, H. Date, S. Kanai, H. Takeda, “Detection and Classification of Pole-like Objects from Mobile Laser Scanning Data of Urban Environments”, International Journal of CAD/CAM, Vol. 13, No. 2, pp.31-40, 2013.
[5] S. Pu, M. Rutzinger, G. Vosselman, S.O. Elberink, “Recognizing basic structures from mobile laser scanning data for road inventory studies”, ISPRS Journal of Photogrammetry and Remote Sensing 6(66), S28–S39, 2011.
[6] D. Li, S.O. Elberink, “Optimizing Detection of Road Furniture (Pole-Like Objects) in
Mobile Laser Scanner Data”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial
Information Sciences II-5/W2: pp. 163–168, 2013.
[7] Y.Z. Chen, H.J. Zhao, R. Shibasaki, “A mobile system combining laser scanners and cameras for urban spatial objects extraction”, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics 2, pp.1729-1733, 2007.
[8] A. Kukko, A. Jaakkola, M. Lehtomki, H. Kaartinen, Y. Chen, “Mobile mapping system and computing methods for modeling of road environment” Proceeding of the Urban Remote Sensing Joint Event , pp.331–338, 2009.
[9] M. Lehtomäki, A. Jaakkola, J. Hyyppa, A. Kukko, H. Kaartinen, “Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data”, Remote Sensing 2 (3), pp.641–664, 2010.
[10] K. Ishikawa, F. Tonomura, Y. Amano, T. Hashizume, “Recognition of Road Objects from 3D Mobile Mapping Data”, Proc. International Journal of CAD/CAM, vol. 13, No.2, pp.41-48, 2013.
[11] A. Golovinskiy, V. Kim, A. Funkhouser, “Shape-based recognition of 3D point clouds in urban environments”, Proceedings of the international conference on computer vision (ICCV), pp.2154–2161, 2009.
[12] S. Pu, M. Rutzinger, G. Vosselman, S.O. Elberink, “Recognizing basic structures from mobile laser scanning data for road inventory studies”, ISPRS Journal of Photogrammetry and Remote Sensing 6(66), S28–S39, 2011.

A. Husain, R.C. Vaishya, Md. Omar Sarif, "A Moving Window Search Method for Detection of Pole Like Objects Using Mobile Laser Scanner Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.1-6, 2018.

Advances in medical information technology have enabled healthcare industries to automatically collect huge amount of data through clinical laboratory examinations. Thyroid disease (TD) is a study of Endocrinology and is considered as one of the most common diseases that is frequently misunderstood and misdiagnosed. Machine learning techniques are increasingly introduced to construct the CAD systems owing to its strong capability of extracting complex relationships in the biomedical data. Feature selection is a technique to choose a subset of variables from the multidimensional data which can improve the classification accuracy in diversity datasets. In addition, the best feature subset selection method can reduce the cost of feature measurement. This work focuses on enhancing the wrapper based algorithms for feature selection.

Data Mining, Feature Selection, Wrapper Method, Genetic Algorithm, Ant Colony Optimization

[1] B. Srinivasan and K. Pavya, “A study on data mining prediction techniques in healthcare sector”, International Research Journal of Engineering and Technology, Vol.3, Issue.3, pp. 552-556, 2016.
[2] N. Sanchez-Marono, A. Alonso-Betanzos, and R.M. Calvo-Estevez, “A Wrapper Method for Feature Selection in Multiple Classes Datasets”, J. Cabestany et al. (Eds.): IWANN, pp. 456–463, 2009.
[3] B. Srinivasan and K. Pavya, “Diagnosis of Thyroid Disease Using Data Mining Techniques: A Study”, International Research Journal of Engineering and Technology, Vol.3, Issue.11, pp. 1191-1194, 2016.
[4] R. G. Osuna, “Pattern Analysis for Machine Olfaction: A Review”, IEEE SENSORS JOURNAL; pp.189-202, 2002.
[5] K. Pavya and B. Srinivasan, “Feature Selection Techniques in Data Mining: A Study”, International Journal of Scientific Development and Research (IJSDR), Vol.2, Issue.6, pp. 594-598,2017.
[6] K. Pavya, and B. Srinivasan " Enhancing Filter Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset”, International Journal of Advanced Research in Computer Science, Vol.8, Issue.9, pp. 184-188,2017.
[7] G. Chandrashekar , F. Sahin, “A survey on feature selection methods”, Computer and Electrical Engineering, pp.16-28,2014.
[8] Y. Saeys, T. Abeel and Y.V. Peer, “Robust Feature Selection Using Ensemble Feature Selection Techniques”, W. Daelemans et al.(Eds.): ECML PKDD, pp. 313–325, 2008.
[9] S. Lee , B. Schowe and V. Sivakumar, “Feature Selection for High-Dimensional Data with RapidMiner”, Technical Report of TU Dortmund University; 2011.
[10] J. Han and M. Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann publishers,Second Edition, 2008.
[11] L.Yu and H. Liu , “Efficient Feature Selection via Analysis of Relevance and Redundancy” , J.Machine Learning Research, Vol.10, Issue.5, pp. 1205-1224, 2004.
[12] S. Aravind , G. Michel, “Hybrid of Ant Colony optimization and Genetic Algorithm for Shortest Path in Wireless Mesh Networks”, Journal of Global Research in Computer Science,Vol.3, Issue.1, pp. 31-34, 2012.
[13] D. Guana, W. Yuana , Y.K. Leea , K. Najeebullaha, M.K. Rasela, “A Review of Ensemble Learning Based Feature Selection”, IETE Technical Review; 2014.
[14] A. Ozcift and A. Gulten “A Robust Multi-Class Feature Selection Strategy Based on Rotation Forest Ensemble Algorithm for Diagnosis”, J Med Syst; pp.941–949, 2012.
[15] Asha Gowda, Karegowda, M.A.Jayaram and A.S.Manujunath, “Feature Subset Selection Problem using Wrapper Approach in Supervised Learning”, International Journal of Computer Applications,Vol.1, Issue.7, pp. 13-17, 2010.
[16] B. Srinivasan and K. Pavya, “ A Comparative Study on Classification Algorithms in Data Mining”, International Journal of Innovative Science, Engineering & Technology, Vol. 3, Issue.3, pp. 415-418, 2016.

K. Pavya, B. Srinivasan, "Enhancing Wrapper Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.7-13, 2018.

The paper focuses on training and assessment of the social cultural awareness in Autism spectrum disorders (ASD) children. The ASD children studied were those who had gone through (a) model based on clinical treatment with all staffs are adequately trained, and (b) a concentrated treatment model involving special educator and parents with intensive supervision only. The research study is primarily based on analysis and examination of behavioral symptoms of children with ASD in India. The one to one ABA interventions to create social cultural awareness were given over a period of 3 years to ASD children. During the sessions with ABA interventions the major aim was to measure the child’s behavior towards social cultural awareness, using multimedia tools related to study of behavioral symptoms. Major difficulties faced during the 3 years of study was the absenteeism and irregularity of children due to factors like; children discontinuing the centers, family issues, change of educators etc. Remarkable growth in adaptive behavior was observed in ASD kids and the results showed substantial positive variances across age. Behavioral interventions given to ASD children can help in creating the social cultural awareness among them.

Autism Spectrum Disorders, ASD, Multimedia Tools, Applied Behavior Analysis,ABA, Intervention, Discrete Trials

[1] Grynszpan O,et al. “Innovative technology-based interventions for autism spectrum disorders: a meta-analysis. Autism” SageJournals, Vol 18, Issue 4, 346–361, 2013
[2] Megan L.E Clark, et al. “Professional and parental attitudes toward iPad application use in autism spectrum disorder Focus on Autism and Other Developmental Disabilities”,Sage journal, Vol 30, Issue 3,2014.
[3] Hopkins I M ,et al. “Avatar assistant: improving social skills in students with an ASD through a computer-based intervention”, Journal of Autism and Developmental Disorders, Vol 41, Issue 11, pp 1543–1555,2011
[4] Bedford R, et al. “Failure to learn from feedback underlies word-learning difficulties in toddlers at risk for autism.” Journal of Child Language, 40(1): 29–46, 2013.
[5] Cohen, H., et al., “Early intensive behavioral treatment: Replication of the UCLA Model in a community setting Developmental and Behavioral Paediatrics”, PubMed,27, 145–155 ,2006.
[6] Eikeseth, S., et al., “Intensity of supervision and outcome for preschool aged children receiving early and intensive behavioural interventions: A preliminary study”.Research in Autism Spectrum Disorders 3 67–73 ,2009.
[7] Hayward D., et al., “Assessing progress during treatment for young children with autism receiving intensive behavioural interventions Autism”,SagePub.,Vol.13,Issue6,613–633 ,2009.
[8] Eldevik, S., et al.,“Meta-analysis of Early Intensive Behavioral Intervention for children with autism”,J Clin Child AdolescPsychol Vol 38 ,Issue 3: 439–50 ,2009.
[9] Howard, J. S., et al., “A comparison of intensive behaviour analytic and eclectic treatment for young children with autism”, Research in Developmental Disabilities, 26, 359–383,2005.
[10] Tawseef A. S., et al., “Effect of WEKA Filters on the Performance of the NavieBayes Data Mining Algorithm on Arrhythmia and Parkinson’s Datasets”. International Journal of Computer Sciences and Engineering, Vol. 2 , Issue 5, PP(45-51) 2014.

R. Mishra, D. Bhatnagar, P.Chakrabarti, "Training and Assessment of Social Cultural Awareness in Autistic Kids in India," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.14-18, 2018.

Resource supervision and task scheduling are very important and complex problems in grid computing environment. Handle of such resources we need job scheduling and load balancing techniques which are responsible for efficient use of the grid resources, reduce job waiting time, access latency in a wise manner. After comprehensive investigation of an existing grid which involves a large number of CPU cluster, we observe that grid scheduling decisions can be significantly improved computation time if the characteristics of current usage patterns are understood. In this paper a new job scheduling algorithm, called Improved Dynamic Load Balancing (IDLB) is proposed. In the proposed algorithm the current scheduling is denoted as S* so the runtime delay is reduced by using Actual Latest Finish Time (ALFT). Finally, in this research the algorithm was simulated with the aid of OptorSim simulator and it was proved that our proposed algorithm provid an effective solution for resource management grid scheduling.

Grid Computing, Computational Grid, DLB, IDLB,Load Balance , Resource Management, Job Scheduling

[1] Garg SK, Buyya R, Siegel HJ (2010) Time and cost tradeoff management for scheduling parallel applications on utility Grids. Future Gener Comput Syst 26:1344–1355
[2] S. K. Patel, A.K. Sharma,“Grid Computing: Status of Technology In Current Perspective”, International Journal of Software & Hardware Research in Engineering, Vol.2, Issue.6, 2014.
[3] S. K. Patel, A.K. Sharma,“ Design and Implementation of an Efficient Resource Sharing algorithm for Grid Computing”, International Journal of Software & Hardware Research in Engineering, Vol.2, Issue.5, 2014.
[4] Foster, and C. Kesselman. 2003,”The Grid 2: Blueprint for a New Computing Infrastructure”, Morgan Kaufmann, USA.
[5] R. Buyya, D. Abramson, and S. Venugopal. 2005, “The Grid Economy”. Proceedings of the IEEE, pp. 698-714.
[6] S. K. Patel, A.K. Sharma,“Implementing job scheduling to optimize computational task in Grid Computing using PSO”, International Journal of Computer application, 2015.
[7] Thamarai Selvi, S., Ponsy, R. K., Bhama, S., Architha, S., Kaarunya, T., Vinothini, K.,( 2010). Scheduling In Virtualized Grid Environment Using Hybrid Approach, International Journal of Grid Computing & Applications (IJGCA) Vol.1, No.1.
[8] Somasundaram, K., Radhakrishnan, S., (2008). Node Allocation In Grid Computing Using Optimal Resource Constraint (ORC) Scheduling”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.6.
[9] Quan, L., Yeqing ,L.,( 2009). Grouping-Based Fine- grained Job Scheduling in Grid Computing, Vol.1, pp.556- 559, IEEE First International Workshop on Education Technology and Computer Science.
[10] Kaur,S., Kaur,S.(2013). Survey of Resource and Grouping Based Job Scheduling Algorithm in Grid Computing, IJCSMC, Vol. 2, Issue. 5, pg.214 – 218
[11] Srivastava, A., Rathore, R. & Sharma, R.(2013). High Compaction Coarse Grained Job Scheduling In Grid Computing, International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN 2249-6831 Vol. 3, Issue 2, 295-302.
[12] Nithiapidary , M.,(2005). A Dynamic Job Grouping- Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids
[13] Ang, T.F., . Ng, W.K., Ling, T.C., Por, L.Y., and Liew, C.S., (2009). A Bandwidth -Aware Job Grouping - Based Scheduling on Grid Environment. Information Technology Journal, 8: 372-377.
[14] Cameron, D. G., Schiaffino, R. C., Ferguson, J., Millar, P., Nicholson, C., Stockinger, K., and Zini, F., (2004). OptorSim v2.0 Installation and User Guide.
[15] S. K. Patel,“ Design And Development Of A New Technique Including Policies For Resource Sharing Management In Computational Grid System” , 2017.
[16] S. Parsa, R. Entezari-Maleki, RASA: A New Grid Task Scheduling Algorithm, JDCTA (2009) 91–99.
[17] A. Olteanu, F. Pop, C. Dobre, V. Cristea, A dynamic rescheduling algorithm for resource management in large scale dependable distributed systems, Comput. Math. Appl. 69 (9) (2012) 1409–1423.
[18] M. A. Vasile , P. Florin, “ Resource- Aware Hybrid Scheduling Algorithm in heterogeneous distributed Computing”, Futer Generation Computer System, 51 (2015), 61-67.

S.K. Patel, A.K. Sharma, "Optimization of Dynamic Resource Scheduling Algorithm in Grid Computing Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.19-26, 2018.

Clustering is an important technique in Mobile Wireless Sensor Networks to reduce the communication overhead in several cases and reduce the energy consumption. In this paper, we propose a new clustering algorithm, DMGEECA(Density Based Mean Grid Energy Efficient Clustering Algorithm for Mobile Wireless Sensor Networks). The objective of our proposed algorithm is to elect a Cluster Head and increase the number of Cluster Heads based on the density of nodes in the area to reduce the energy consumption and thereby increasing the network lifetime. Simulations are carried out to evaluate the performance of our clustering algorithm by comparing its performance with the previous work. The results of simulation demonstrate that our proposed clustering algorithm outperforms the other algorithms in terms of Network Lifetime, Energy Consumption.

MWSNs, Clustering, Grid, Density, Mobility, Energy Efficiency

[1] Shantala Devi Patil, Vijayakumar B P, “Clustering in Mobile Wireless Sensor Networks: A Review”, In the Proceedings of 1st International Conference on Innovations in Computing & Networking (ICICN16), May 2016.
[2] K. Juliet Catherine Angel, Dr. E. George Dharma Prakash Raj, “Clustering Algorithms in Mobile Wireless Sensor Networks – A Survey”, International. Journal of Engineering Research and Application, Vol. 7, Issue 12, ( Part -6), pp.17-21, December 2017.
[3] A. Garg, N. Batra, I. Taneja, A. Bhatnagar, A. Yadav, S. Kumar, "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, 2017
[4] Fatiha Djemili Tolba ; Wessam Ajib ; Abdellatif Obaid, “Distributed Clustering Algorithm for Mobile Wireless Sensors Networks”, SENSORS 2013, IEEE, 2013.
[5] Abhinav Gupta, Prabhdeep Singh, “Improving The Performance Of Mobile Wireless Sensor Networks Using Modified DBSCAN”, International Journal of Computer Sciences and Engineering (IJCSE), Vol -3, Issue 8, ,pp. 06-10, 2015.
[6] Kavita Gupta, Aarti Singh, Rashmi Singh, Saurabh Mukherjee, “An Improved Cluster Head Selection Algorithm for Mobile Wireless Sensor Networks”, Journal of Network Communications and Emerging Technologies (JNCET), Vol.5, Special Issue 2, December (2015).
[7] Dahane Amine, Berrached Nasr-Eddine, Loukil Abdelhamid, “A Distributed and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks”, ELSEVIER, Procedia Computer Science, Vol. 52, pp. 641-646, 2015.
[8] Rehman, E., Sher, M., Naqvi, S. H. A., Badar Khan, K., & Ullah, K, “Energy Efficient Secure Trust Based Clustering Algorithm for Mobile Wireless Sensor Network”, Journal of Computer Networks and Communications, Vol 2017, Article ID 1630673, 2017.
[9] K. Juliet Catherine Angel, Dr. E. George Dharma Prakash Raj, “EEECA: Enhanced Energy Efficient Clustering Algorithm for Mobile Wireless Sensor Networks”, 2017 World Congress on Computing and Communication Technologies (WCCCT), IEEEXPlore Digital Library, pp. 2-4, Feb 2017.
[10] K. Juliet Catherine Angel, Dr. E. George Dharma Prakash Raj, “GEECA: Grid Based Energy Efficient Clustering Algorithm for Mobile Wireless Sensor Networks”, Saudi Journal of Engineering and Technology (SJEAT), Vol-2, Iss-12, pp. 458-463, Dec, 2017.

K.J.C. Angel, E.G.D.P. Raj, "DMGEECA : Density Based Mean Grid Energy Efficient Clustering Algorithm For Mobile Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.27-33, 2018.

Wireless Body Area Networks (WBANs) are particular field of Wireless Sensor Networks (WSNs). It is the important building segments of forthcoming networks. Contemporary health care structure is one of the furthermost standard WBAN applications. In recent decades, study has focused on channel modeling, energy consumption strategy of well-organized medium access control (MAC) protocols. In this form of network, the number of sensors which are used in the network topology has been significantly reduced than existing WBAN. In this paper, we have evaluated TDMA based MAC protocol performance through several metrics. In this paper TDMA approach is used to avoid packet collision which leads to higher packet loss rate. Clock synchronization is the solution of problem like packet collision. After clocks of WBAN sensor nodes are synchronized, data can be transferred between sensor nodes and sink efficiently and rapidly.

TDMA, MAC protocol, Wireless body Area Network, Throughput.

[1] Mukherjee, S, Dolui, K & Datta, SK 2014, ‘Patient health management system using e-health monitoring architecture’, In Advance Computing Conference (IACC), 2014 IEEE International, pp. 400-405.
[2] Sun, X, Su, J, Wang, B & Liu, Q 2013, ‘Digital Watermarking Method for Data Integrity Protection in Wireless Sensor Networks’, International Journal of Security and Its Applications, vol. 7, no. 4.
[3] Zhu, L, Zhen Yang, Meng Li & Dan Liu 2013, ‘An Efficient Data Aggregation Protocol Concentrated on Data Integrity in Wireless Sensor Networks’, International Journal of Distributed Sensor Networks, vol. 2013.
[4] Wadhwa, N, Hussain, SZ & Rizvi, SAM 2013, ‘A Combined Method for Confidentiality, Integrity, Availability and Authentication (CMCIAA)’, Proceedings of the World Congress on Engineering, WCE 2013, London, U.K, vol. II.
[5] Dilbag Singh and Vijay Kumar. "Modified gain intervention filter based dehazing technique." Journal of Modern Optics 64, no. 20 (2017): 2165-2178.
[6] Chien-Ming Chen, Yue-Hsun Lin, Ya-Ching Lin & Hung-Min Sun 2012, ‘RCDA: Recoverable Concealed Data Aggregation for Data Integrity in Wireless Sensor Networks’, IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 4.
[7] Ruoyu Wu, Gail-JoonAhn & Hongxin Hu 2012, ‘Secure Sharing of Electronic Health Records in Clouds,’ 8th International Conference Conference on Collaborative Computing: Networking, Applications and Worksharing, Collaboratecom 2012 Pittsburgh, PA, United States.
[8] Dilbag Singh and Vijay Kumar. "Comprehensive survey on haze removal techniques." Multimedia Tools and Applications (2017): 1-26.
[9] Ed Gelbestein 2011, ‘Data Integrity-Information Security’s Poor Relation’, ISACA Journal, vol.6.
[10] Vladimir Oleshchuk & Rune Fensli 2010, ‘Remote Patient Monitoring Within a Future 5G Infrastructure,’ Wireless Personal Communication, Springer Science.
[11] Wenbo He, Liu, X, Nguyen, H, Nahrstedt, K & Abdelzaher, T 2008, ‘iPDA: An Integrity-Protecting Private Data Aggregation Scheme for Wireless Sensor Networks’, IEEE MILCOM, pp. 1-7.
[12] Chu, HT 2006, ‘A Ubiquitous Warning System for Asthma-Inducement’, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Taichung, pp. 186-191.
[13] Dilbag Singh and Vijay Kumar. "Dehazing of remote sensing images using improved restoration model based dark channel prior." The Imaging Science Journal 65, no. 5 (2017): 282-292.
[14] Wood, A, Virone, G, Doan, T, Cao, Q, Selvao, L, Wu, Y, Fang, L, He, Z, Lin, S & Stankovic, J 2006, ‘ALARM-NET:Wireless Sensor Networks for Assisted Living and Residential Monitoring’, Technical Report CS-2006-13, Department of Computer Science, University of Virginia.
[15] Zhao, YJ 2005, ‘A MEMS Viscometric Glucose Moni-toring Device’, The 13th IEEE International Conference on Solid-State Sensors, Actuators and Microsystems, Pittsburgh, pp. 1816-1819.
[16] Dilbag Singh and Vijay Kumar. "Dehazing of remote sensing images using fourth-order partial differential equations based trilateral filter." IET Computer Vision (2017).
[17] Ng, JWP, Lo, BPL, Wells, O, Sloman, M, Peters, N, Darzi, A, Toumazou, C & Yang, GZ 2004, ‘Ubiquitous Monitoring Environment for Wearable and Implantable Sensors (UbiMon)’, In Proceedings of 6th International Conference on Ubiquitous Computing (UbiComp’04), Nottingham, UK.
[18] Dilbag Singh and Vijay Kumar. "Defogging of road images using gain coefficient-based trilateral filter." Journal of Electronic Imaging 27, no. 1 (2018): 013004.
[19] Ishita Chakraborty, Prodipto Das, "Data Fusion in Wireless Sensor Network-A Survey", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp.9-15, 2017.
[20] Arjan Durresi, Vamsi Paruchuri, Rajgopal Kannan & Iyengar, SS 2004, ‘A Lightweight Protocol for Data Integrity in Sensor Networks’, IEEE, ISSNIP.
[21] Dilbag Singh and Vijay Kumar. "Single image haze removal using integrated dark and bright channel prior." Modern Physics Letters B (2018): 1850051.
[22] Lee, Chong Hyun, Jinho Bae, and Joon-Young Kim. "Novel Implant Device Tracking Algorithm for Wireless Health Monitoring in Wban." International Journal of Modern Physics B 25, no. 31 (2011): 4145-4148.
[23] Zargar, R. A., M. Arora, S. Chackrabarti, S. Ahmad, J. Kumar, and A. K. Hafiz. "Alcohol vapor sensing by cadmium-doped zinc oxide thick films based chemical sensor." Modern Physics Letters B 30, no. 12 (2016): 1650244.
[24] He, Yanan, Bo Zhang, and Jingling Shen. "Performance of terahertz metamaterials as high-sensitivity sensor." Modern Physics Letters B 31, no. 26 (2017): 1750240.
[25] Gaikwad, Sumedh, Gajanan Bodkhe, Megha Deshmukh, Harshada Patil, Arti Rushi, Mahendra D. Shirsat, Pankaj Koinkar, Yun-Hae Kim, and Ashok Mulchandani. "Chemiresistive sensor based on polythiophene-modified single-walled carbon nanotubes for detection of NO 2." Modern Physics Letters B 29, no. 06n07 (2015): 1540046.
[26] Li, Xiangyu, Liang Yin, Weiping Chen, Zhiqiang Gao, and Xiaowei Liu. "A high-resolution tunneling magneto-resistance sensor interface circuit." Modern Physics Letters B 31, no. 04 (2017): 1750030.
[27] Yarn, Kao-Feng. "High sensitive hydrogen sensor by Pd/oxide/InGaP MOS structure." Modern physics letters B 20, no. 28 (2006): 1781-1787.
[28] Sakya, G. and Sharma, V., 2013, January. Performance analysis of SMAC protocol in wireless sensor networks using network simulator (Ns-2). In International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (pp. 42-51). Springer, Berlin, Heidelberg.

Pallvi, Sunil Kumar Gupta, Rajeev Kumar Bedi, "An Improved Energy Efficient TDMA based MAC Protocol for WBAN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.34-39, 2018.

A traffic-aware routing in VANET is a prime step in transmitting the long data for applications. Researchers’ address that the traditionally used routing protocols employed in Mobile Ad Hoc Networks are not suitable for routing in VANET, as VANETs differ from MANETs in the mobility model and environment. The demand to develop a traffic-aware protocol in VANET initiated to propose a routing protocol, termed as Adaptive Autoregressive Whale Optimization algorithm (Adaptive-ARW). The main goal of the proposed algorithm is to select the optimal path for performing routing in VANETs, for which the traffic required to be predicted. For predicting the traffic in the road segment, Exponential Weighed Moving Average (EWMA) is employed that predicts the traffic based on the average vehicle speed and the average traffic density. The minimum values of average speed and vehicles average traffic density to the less traffic density. Using the predicted traffic, the routing paths are generated, and the optimal paths are selected using the proposed algorithm that exhibits adaptive property. The analysis of the proposed algorithm provides the End-to-End delay, distance, average traffic density, and throughput of 2.938, 2.08, 0.0095, and 0.1354, respectively.

Exponential Weighed Moving Average (EWMA), End-to-End Delay (EED), Whale Optimization algorithm (WOA), Autoregressive Model, Adaptive property

[1] Mayouf, Y. RafidBahar, M. Ismail, N.F. Abdullah, A.W.A. Wahab, O.A. Mahdi, S. Khan, and K.K.R. Choo. "Efficient and Stable Routing Algorithm Based on User Mobility and Node Density in Urban Vehicular Network" , PloS one, vol.11, no.11, 2016.
[2] A. Ibrahim, A. Ahmed; A.Gani, S.A. Hamid; S. Khan, N. Guizani, KwangmanKo, "Intersection-based Distance and Traffic-Aware Routing (IDTAR) protocol for smart vehicular communication", In Proceedings of the 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp.489 - 493, 2017.
[3] H.T. Cheng, H. Shan, W. Zhuang, “Infotainment and road safety servicesupport in vehicular networking: From a communication perspective”, Mechanical Systems and Signal Processing, vol.25, no.6,pp.2020−2038, 2011.
[4] Y. Ding, Y. Liu, X. Gong, W. Wang, "Road traffic and geography topology based opportunistic routing for VANETs", The Journal of China Universities of Posts and Telecommunications, vol.21, no.4, pp.32-39, August 2014.
[5] Kosch, Timo, Adler, Christian, Eichler, Stephan, Schroth, Christoph, Strassberger, Markus,"The Scalability Problem of Vehicular Ad Hoc Networks and How to Solve it", IEEE Wireless Communications Magazine, vol.13, no.5, pp.22-28, 2006.
[6] Khekare, S. Ganesh. and V. Apeksha, Sakhare, "A smart city framework for intelligent traffic system using VANET", In Proceedings of the IEEE International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), pp. 302-305, 2013.
[7] Mamta Devi, Dr Rakesh Kumar, and Er Nidhi Bhatla, "Secure and Enhanced Vehicular Ad-Hoc Networks Using DSR Protocol and BFOA Algorithm," International Journal on Computer Science and Engineering (IJCSE), Vol. 9 No.8, pp. 506-516, August 2017.
[8] M. Jerbi, S.M. Senouci., R. Meraihi, and Y.G. Doudane,"An improved vehicular ad hoc routing protocol for city environments", In Proceedings of the IEEE International Conference on Communications (ICC 07), pp. 3972–3979, June 2007.
[9] C. Liu, Y. Shu, O. Yang, Z. Xia and R. Xia, “SDR: A Stable Direction-Based Routing for Vehicular Ad Hoc Networks”, Wireless Personal Communication, vol. 73, no. 3, pp. 1298-1308, 2013.
[10] C. Li, L. Wang, Y. He, C. Zhao, H. Lin, and L. Zhu, "A link state aware geographic routing protocol for vehicular ad hoc networks", EURASIP Journal on Wireless Communications and Networking, pp.176, 2014.
[11] C.C. Lo, and Y.H. Kuo, “Traffic-aware routing protocol with cooperative coverage-oriented information collection method for VANET", IET Communications, vol.11, no.3, pp.444 - 450, 2017.
[12] Y. Zhu, Y. Qiu, Y. Wu, M. Gao, B. Li and Y. Hu, “On adaptive routing in urban vehicular networks”, In Proceedings of IEEE Global Communication Conference, pp. 1593-1598, 2013.
[13] D. Kumari, N. Venkatramana, S.B. Srikantaiah, J. Moodabidri, "SCGRP: SDN-enabled connectivity-aware geographical routing protocol of VANETs for urban environment", IET Networks, vol.6, no.5, pp.102 - 111, 2017.
[14] S. Mirjalili, A. Lewis, "The Whale Optimization Algorithm", Advances in Engineering Software, vol. 95, pp. 51–67, 2016.
[15] M. Balachandra, K.V. Prema, K. Makkithaya, "Multiconstrained and multipath QoS aware routing protocol for MANETs", Wireless Networks, vol. 20, no.8, pp. 2395–2408, November 2014.
[16] A.K. Yadav, S. Tripathi, "QMRPRNS: Design of QoS multicast routing protocol using reliable node selection scheme for MANETs", Peer-to-Peer Networking and Applications, pp. 1–13, Feb 2016.
[17] R.F. Engle, S. Manganelli, "CAViaR: Conditional Value at Risk by Quantile Regression", Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pp. 367-381, October 1999.
[18] M.A. Togou, AbdelhakimHafid, and LyesKhoukhi, “SCRP: Stable CDS-Based Routing Protocol for Urban Vehicular Ad Hoc Networks”, IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 5, pp. 1298-1307, 2015.
[19] B. Wu, C. Qian, W. Ni, S. Fan, "The improvement of glow worm swarm optimization for continuous optimization problems", Expert Systems with Applications, vol. 39, no. 7, pp. 6335–6342, 2012.

Deepak Rewadkar, Dharmpal Doye, "Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.40-49, 2018.

In wireless technology, the devices communicate with each other without physical connections. It gives together merits and demerits when evaluated to a wireless technology. In this research work, we have studied the DSDV, AODV, DSR and ZRP routing protocol comparison. The encryption technique has implemented the security in the mesh networks. We have implemented the secret key encryption algorithm. This algorithm uses a dissimilar key for encoding and decoding. The decoding key cannot derive from the encoding key. Main focus on this research work as to identify the wormhole attacker node in the network with the help of Secret Encryption Algorithm and prevention using Data Ant Colony Optimization algorithm. We have used the MATLAB 2013a simulation tool with SCRIPT Language. We calculated the performance parameters, i.e. throughput, packet delivery rate, probability distribution vs. time and delay vs. Frame error rate [ms].

Wireless Mesh Network, Data Ant Colony optimization, Secret Encryption Algorithm, Wormhole attack and routing protocol.

[1]. Torjemen, N.,Zhioua, G.S.M., and Tabbane, N., (2015),"A handover decision algorithm from LTE-advanced to Wireless Mesh Network." In Communications and Networking (COMNET), 2015 5th International Conference on, pp. 1-6. IEEE.
[2]. S. Mewada, UK. Singh and P. Sharma, "A Novel Security Based Model for Wireless Mesh Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.1, pp.11-15, 2013.
[3]. Wu, P., Li, Y., and Liu, X., (2015),"Capacity-based channel assignment scheme in multi-radio multi-channel wireless mesh networks." Chinese Journal of Electronics 24, no. 2: 419-425.
[4]. Xing, S., Huang, H., and Wang, R., (2015), "Load balanced coding aware multipath routing for wireless mesh networks." Chinese Journal of Electronics 24, no. 1: 8-12.
[5]. Aggeliki, S., and Chatzimisios, P., (2013),"A survey on security and privacy issues in wireless mesh networks." Security and Communication Networks.
[6]. Umesh Kumar Singh, Jalaj Patidar , Kailash Chandra Phuleriya, “On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks”, International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.11-15, 2015.
[7]. Kato, N., Nishigama,H., and Miura, R., (2015),"Toward fair maximization of energy efficiency in multiple us-aided networks: a game-theoretic methodology." IEEE Transactions on Wireless Communications 14, no. 1 305-316.
[8]. Moh, A.N.A., and Borhanuddin, M.,(2015),"Optimum QoS resource allocation algorithm for video traffic over wireless mesh networks based on IEEE 802.11 s." In Communications (MICC), 2015 IEEE 12th Malaysia International Conference on, pp. 102-106. IEEE.
[9]. Wang, j., and Lu, k., (2016),"ULNC: An Untraceable Linear Network Coding Mechanism for Mobile Devices in Wireless Mesh Networks." IEEE Transactions on Vehicular Technology 65, no. 9: 7621-7633.
[10]. Sbeiti, M., Goddemeier, N., and Wietfeld, C., (2016),"PASER: Secure and Efficient Routing Approach for Airborne Mesh Networks" IEEE Transactions on Wireless Communications 15, no. 3 : 1950-1964.
[11]. Akilarasu,G., and Shalinie, S.M., (2016),"Worm hole free routing and DoS attack defense in wireless mesh networks." Wireless Networks: 1-10.
[12]. Mayuri, A.V.R., and Subramanyam, M.V., (2015),"MPGA: QOS adequacy latitude aware cooperative spectrum sensing in Cognitive Wireless Mesh Networks by Meticulous Progression based GA." In Power, Control, Communication and Computational Technologies for Sustainable Growth (PCCCTSG), Conference on, pp. 318-325. IEEE.
[13]. Akyildiz, I.F., and Wang, X., (2005),"A survey on wireless mesh networks." IEEE Communications magazine 43, no. 9: S23-S30.
[14]. Neeraj Paliwal , "Relevance Multipath Forwarding in Intended Wireless Mesh Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.2, pp.5-10, 2015
[15]. Soufiane, J., Haqiq, A., and Nassereddin, B., (2015),"Fairness and differentiation of services in wireless mesh network." In Information and Communication Technologies (WICT), 2015 5th World Congress on, pp. 59-66. IEEE.
[16]. Islam, M.J., Nurain,N., and Raghunathan, V., (2016),"Channel Assignment Techniques for Multi-Radio Wireless Mesh Networks: A Survey." IEEE Communications Surveys & Tutorials 18, no. 2: 988-1017.
[17]. Wang, X., Zhang, j., and Zhang, X., (2015),"Monitor Node Selection Algorithm Based on Mutual Information in Wireless Mesh Networks." In Smart City/Social Com/Sustain Com (Smart City), 2015 IEEE International Conference on, pp. 349-353. IEEE.

Paramjit Kaur, RakeshKumar and Harinder Kaur, "An Improved Security Network Life Based on Data Ant Colony Optimization Method Used in Wireless Mesh Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.50-56, 2018.

VANETs are susceptible to safety threats due to cumulative dependence upon transmission, computing, and control mechanisms. Therefore, securing the end to end communication in VANETs becomes a major area of research. Many researchers have proposed several security protocols so far to improve the integrity, confidentiality, nonrepudiation, access control, etc. to provide secure VANETs to its users. Therefore, the overall goals of security protocols of VANETs are to recognize malicious nodes in the network by using suitable mechanism. In this work trustworthiness of VANETs has been improved by using some well-known adaptive Neuro-fuzzy system tools to detect the attacks in more efficient manner. Adaptive Neuro-fuzzy system tools have been used frequently to monitor the behavior of VANETs nodes and evaluate some malicious nodes based upon already developed model using historical knowledge of the same network. Since, training of the model is based upon the various features of VANETs nodes therefore, it is able to monitor the attack even in complex environment. Extensive analysis indicates that the proposed protocol outperforms others in terms of Packet Loss Ratio, Throughput, End to End Delay and Average Download Delay.

VANET, Adaptive neuro-fuzzy system, Attacks, Malicious nodes

[1] Al-Kahtani, Mohammed Saeed. "Survey on security attacks in Vehicular Ad hoc Networks (VANETs)." In Signal Processing and Communication Systems (ICSPCS), 6th International Conference on IEEE , pp. 1-9, 2012.
[2] Bibhu, Vimal, Roshan Kumar, Balwant Singh Kumar, and Dhirendra Kumar Singh. "Performance analysis of black hole attack in VANET." International Journal Of Computer Network and Information Security, vol. 4, no. 11, pp. 47, 2012.
[3] C. Lai, K. Zhang, N. Cheng, H. Li and X. Shen, "SIRC: A Secure Incentive Scheme for Reliable Cooperative Downloading in Highway VANETs," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1559-1574, June 2017.
[4] Chinnasamy, A., S. Prakash, and P. Selvakumari. "Enhance trust based routing techniques against sinkhole attack in AODV based VANET." International Journal of Computer Applications, vol. 65, no. 15, 2013.
[5] Doaa Al-Terri, Hadi Otrok, Hassan Barada, Mahmoud Al-Qutayri, Yousof Al Hammadi, “Cooperative based tit-for-tat strategies to retaliate against greedy behavior in VANETs”, Computer Communications, Vol.104, Pages 108-118, 2017.
[6] Guowei Wu, Jie Wang, Yongchuan Wang, Lin Yao, “Pollution Attack Resistance Dissemination in VANETs Based on Network Coding”, Procedia Computer Science, Vol. 83, Pages 131-138, 2016.
[7] Hamssa Hasrouny, Abed Ellatif Samhat, Carole Bassil, Anis Laouiti, “VANet security challenges and solutions: A survey”, Vehicular Communications, Vol. 7, Pages 7-20, 2017.
[8] Jay Rupareliya, Sunil Vithlani, Chirag Gohel, “Securing VANET by Preventing Attacker Node Using Watchdog and Bayesian Network Theory”, Procedia Computer Science, Vol. 79, Pages 649-656, 2016.
[9] Kaur, Harbir, Sanjay Batish, and Arvind Kakaria. "An approach to detect the wormhole attack in vehicular adhoc networks." International Journal of Smart Sensors and Ad Hoc Networks (IJSSAN), pp. 86-89, 2012.
[10] K. Zaidi, M. B. Milojevic, V. Rakocevic, A. Nallanathan and M. Rajarajan, "Host-Based Intrusion Detection for VANETs: A Statistical Approach to Rogue Node Detection," in IEEE Transactions on Vehicular Technology, vol. 65, no. 8, pp. 6703-6714, Aug. 2016.
[11] Kim, Yeongkwun, Injoo Kim, and Charlie Y. Shim. "A taxonomy for DOS attacks in VANET." In Communications and Information Technologies (ISCIT), 2014 14th International Symposium on, IEEE, pp. 26-27., 2014.
[12] Lo, Nai-Wei, and Hsiao-Chien Tsai. "Illusion attack on vanet applications-a message plausibility problem." In Globecom Workshops, IEEE, pp. 1-8., 2007.
[13] Lyamin, Nikita, Alexey Vinel, Magnus Jonsson, and Jonathan Loo. "Real-time detection of denial-of-service attacks in IEEE 802.11 p vehicular networks." IEEE Communications letters, vol. 18, no. 1, pp: 110-113, 2014.
[14] Muhammad Mohsin Mehdi, Imran Raza, Syed Asad Hussain, “A game theory based trust model for Vehicular Ad hoc Networks (VANETs)”, Computer Networks, Vol. 121, Pages 152-172, 2017.
[15] Qamas Gul Khan Safi, Senlin Luo, Chao Wei, Limin Pan, Qianrou Chen, “PIaaS: Cloud-oriented secure and privacy-conscious parking information as a service using VANETs”, Computer Networks, Volume 124, Pages 33-45, 2017.
[16] Quyoom, Abdul, Raja Ali, Devki Nandan Gouttam, and Harish Sharma. "A novel mechanism of detection of denial of service attack (DoS) in VANET using Malicious and Irrelevant Packet Detection Algorithm (MIPDA)." In Computing, Communication & Automation (ICCCA), 2015 International Conference on, IEEE, pp. 414-419, 2015.
[17] R. Muthumeenakshi, T.R. Reshmi, K. Murugan, “Extended 3PAKE authentication scheme for value-added services in VANETs”, Computers & Electrical Engineering, Vol. 59, Pages 27-38, 2017.
[18] Raghad Baiad, Omar Alhussein, Hadi Otrok, Sami Muhaidat, “Novel cross layer detection schemes to detect blackhole attack against QoS-OLSR protocol in VANET”, Vehicular Communications, Vol. 5, pp. 9-17, 2016.
[19] Raw, Ram Shringar, Manish Kumar, and Nanhay Singh. "Security challenges, issues and their solutions for VANET." International Journal of Network Security & Its Applications, vol. 5, no. 5, pp. 95, 2013.
[20] D.Rewadkar, D.Doye, "Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET", International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.303-312, 2018.

Sahil Nayyar, Anita Suman, Parveen Kumar, "Adaptive Neuro-fuzzy System based Attack Detection Techniques for VANETS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.57-64, 2018.

K-means clustering is one of the top 10 algorithms in the field data mining and knowledge discovery. The uniform effect in the k-means clustering reveals that, the imbalance nature of the data source hampered the performance in terms of efficient knowledge discovery. In this paper, we proposed a novel clustering algorithm known as Precise Reduction Sampling K-means (PRS_K-means) for efficient handling of imbalance data and reducing the uniform effect. The experiments shows that the algorithm can not only give attention to different instances of sub clusters for identify the intrinsic properties of the instances for clustering; and it performs better than K-means in terms of reduction in error rate and has higher accuracy and recall rate for improved performance.

Data Mining, Knowledge Discovery, Clustering, K-means, imbalance data, uniform effect, under sampling, PRS_K-means

[1] Prateeksha Tomar, Amit Kumar Manjhvar, "Clustering Classification for Diabetic Patients using K-Means and M-Tree prediction model", International Journal of Scientific Research in Multidisciplinary Studies , Vol.3, Issue.6, pp.48-53, 2017
[2] Hui Xiong, Junjie Wu, and Jian Chen,” K-Means Clustering Versus Validation Measures: A Data-Distribution Perspective”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 39, NO. 2, APRIL 2009.
[3] Abhishek kumar K and Sadhana,: SURVEY ON K-MEANS CLUSTERING ALGORITHM”, International Journal of Modern Trends in Engineering and Research (IJMTER) Volume 04, Issue 4, [April– 2017]
[4] Farhad Pourkamali-Anaraki and Stephen Becker, “Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means”,
[5] Fabon Dzogan, Christophe Marsala, Marie-Jeanne Lesot and Maria Rifqi,” An ellipsoidal K-means for document clustering”, 2012 IEEE 12th International Conference on Data Mining
[6] Kaile Zhou, Shanlin Yang,” Exploring the uniform effect of FCM clustering: A data distribution Perspective”, Knowledge-Based Systems 96 (2016) 76–83
[7] Jaya Rama Krishnaiah VV, Ramchand H Rao K, Satya Prasad R (2012) Entropy Based Mean Clustering: An Enhanced Clustering Approach. J Comput Sci Syst Biol 5: 062-067. doi:10.4172/jcsb.1000091
[8] Hartono, O S Sitompul, Tulus and E B Nababan,: Optimization Model of K-Means Clustering Using Artificial Neural Networks to Handle Class Imbalance Problem”, IOP Conf. Series: Materials Science and Engineering 288 (2017) 012075
[9] Md. Akmol Hussain, Akbar Sheikh Akbari, Ahmad Ghaffari, “Colour Constancy using K-means Clustering Algorithm”, 2016 9th International Conference on Developments in eSystems Engineering.
[10] Junjie Wu, Hui Xiong and Jian Chen,” Adapting the Right Measures for K-means Clustering”,
[11] Richard Nock and Frank Nielsen,” On Weighting Clustering”, EEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 28, NO. 8, AUGUST 2006
[12] Wu.J,”The Uniform Effect of K-means Clustering”, J. Wu, Advances in K-means Clustering, Springer Theses, DOI:10.1007/978-3-642-29807-3_2, © Springer-Verlag Berlin Heidelberg 2012.
[13] HamiltonA. Asuncion D. Newman. (2007). UCI Repository of Machine Learning Database (School of Information and Computer Science, Irvine, CA: Univ. of California [Online]. Available: http://www.ics.uci.edu/∼mlearn/MLRepository.html
[14] Witten, I.H. and Frank, E. (2005) Data Mining: Practical machine learning tools and techniques. 2nd edition Morgan Kaufmann, San Francisco.

Shaik.Nagul, R.Kiran Kumar, "An Effective K-means approach for Imbalance data clustering using Precise Reduction Sampling," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.65-70, 2018.