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Docking process of UAV Autonomous Refueling is a critical issue during the docking phase of autonomous aerial refueling (AAR), and the successful docking between the probe and drogue need higher probability for an aerial refueling system. To cope with this issue, a novel and effective model based on the theory of stochastic process crossing target area is proposed. In order to ensure its accurate and easy application, according to prior information and assumptions for the movements of the probe related to the drogue, the probe-drogue docking success probability is converted to the probability of the probe located in the circle area of drogue. The temporal and spatial characteristics of the pointing error have been considered which makes the model of the docking success probability more accord with the actual situation. simulations were conducted to demonstrate the effectiveness of the proposed method. This model provides theoretical support for the design and verification of AAR’s control system.

Autonomous aerial refueling, UAV, stochastic process, docking success probability

[1] J. L. Hansen, J. E. Murray, N. V. Campos, “The NASA Dryden AAR project: a flight test approach to an aerial refueling system” , Proceedings of the Collection of Technical Papers - AIAA Atmospheric Flight Mechanics Conference, pp. 477–495, Reston, Virginia, USA, August 2004.
[2] M. Q. Hu, P. Liu, X. Nie, R. X. Zhou, “Influence of air turbulence on the movement of hose-drogue”, Flight Dynamics, vol. 28, No. 5, pp. 20–23, 2010.
[3] M.B. Giri, P. kulkarni, S. Bullock, and A.Doshi, “Agricultural Environmental Sensing Application Using Wireless Sensor Network for Automated Drip Irrigation”, International Journalof Computer Sciences and Engineering, vol. 8(6), pp. 14–35, 2016.
[4] A.Omanakuttan,D. Sreedhar,A. Manoj and A. Achankunju, “GPS and GSM Based Engine Locking System Using Smart Password”, International Journalof Computer Sciences and Engineering, vol. 5(4), pp. 57–61, 2017
[5] L. Y. Zhang, H. Zhang, Y. Yang, L. Huang, “Dynamics modeling and simulation of docking process in aerial refueling”, Acta Aeronautica et Astronautica Sinica, vol. 33, No. 7, pp. 1347– 1354, 2012.
[6] J. Zhang, S. Z. Yuan, Q. Q. Gong, “Modeling and control of shaking motion of aerial refueling hose-drogue”, Journal of System Simulation, vol. 28, No. 2, pp. 388–395, 2016.
[7] J. P. Nalepka and J. L. Hinchman, “Automated aerial refueling: extending the effectiveness of UAVs”, Proceedings of the AIAA Modeling and Simulation Technologies Conference and Exhibit, San Francisco, Calif, USA, pp. 2005–6005.2005
[8] H. T. Wang, X. M. Dong, H. F. Dou, J. P. Xue, “Dynamic modeling and characteristics analysis of hose-paradrogue aerial refueling system”, Journal of Beijing University of Aeronautics and Astronautics, vol. 40, No. 1, pp. 92–98, 2014.
[9] P. R. Thomas, U. Bhandari, S. Bullock, T. S. Richardson, “Advances in air to air refuelling”, Progress in Aerospace Sciences, vol. 71, No. 3,pp. 14–35, 2014.
[10] K. Ro , J. W. Kamman, “Modeling and simulation of hose-paradrogue aerial refueling systems” ,Journal of Guidance, Control, and Dynamics, vol. 33, No. 1, pp. 53–63, 2010.
[11] H Wang, X Dong, J Xue, J Liu, “Dynamic modeling of a hose-drogue aerial refueling system and integral sliding mode backstepping control for the hose whipping phenomenon”, Chinese journal of aeronautics, Vol.42, No.6, pp.61-70, 2014.
[12] Z Liu, J Liu, W He, “Modeling and vibration control of a flexible aerial refueling hose with variable lengths and input constraint”, Automatica, Vol.77, Issue.3, pp.302-310, 2017.
[13] Rachita Dahama, Kevin W. Sowerby, Gerard B. Rowe, “Outage Probability Estimation for Licensed Systems”, IEEE 69th Vehicular Technology Conference, Barcelona, Spain ,2009
[14] A. Dogan, W. Blake, C. Haag, “Bow wave effect in aerial refueling: computational analysis and modeling”, Journal of Aircraft, vol. 50, no. 6, pp. 1856–1868, 2013.
[15] X. H. Dai, Z. B. Wei, Q. Quan, “Modeling and simulation of bow wave effect in probe and drogue aerial refueling”, Chinese Journal of Aeronautics, vol. 29, no. 2, pp. 448–461, 2016..
[16] John Valasek, Kiran Gunnam, Jennifer Kimmett, John L. Junkins, Declan Hughes, Monish D. Tandale. "Vision-Based Sensor and Navigation System for Autonomous Air Refueling", Journal of Guidance, Control, and Dynamics, Vol. 28, No. 5, pp. 979-989. 2005.
[17] Xufeng Wang, Jianmin Li, Xingwei Kong, Xinmin Dong, Bo Zhang, “An Approach to Mathematical Modeling and Estimation of Probe-Drogue Docking Success Probability for UAV Autonomous Aerial Refuelin”, International Journal of Aerospace Engineering, Vol.2017, pp.1-14, 2017.
[18] X. F. Wang, X. M. Dong, X. W. Kong, J. M. Li, B. Zhang, “Drogue detection for autonomous aerial refueling based on convolutional neural networks”, Chinese Journal of Aeronautics, vol. 30, no. 1, pp. 380–390, 2017.
[19] H. W. Xie, H. L. Wang, “Binocular vision-based short-range navigation method for autonomous aerial refueling”, Journal of Beijing University of Aeronautics and Astronautics, vol. 37, no. 2, pp. 206–209, 2011.
[20] P.R. Thomas, U. Bhandari, S. Bullock, T. S. Richardson, and J. L. Du Bois, “Advances in air to air refuelling”, Progress in Aerospace Sciences, vol. 71, pp. 14–35, 2014.

Xiangmin Wang, Jun Wang, "Modeling of Probe-Drogue Docking Success Probability for UAV Autonomous Refuelling," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1-6, 2018.

This paper manages the issue of Multiprocessor scheduling Problem is one of the most challenging problems in distributed computing system. Many researchers solved the multiprocessor scheduling problem as static. But in this paper uses the dynamic multiprocessor scheduling problem which is an advanced area. Dynamic allocation strategies can be connected to huge arrangements of genuine applications that can be planned in a way that takes into account deterministic execution. In the first place, here defines the Dynamic Multiprocessor scheduling, which is an optimization problem, after that it optimizes the execution time of various tasks assigned to the processors with a Krill Herd Algorithm (KHA). In recent times, a robust meta-heuristic optimization algorithm, known as Krill Herd, which is used for global optimization to enhance the execution of the multiprocessor scheduling problem but other traditional algorithms stuck in local optimization. In this paper with the end goal of comparison, contemporary methodologies utilizing Genetic Algorithm (GA), Bacteria Foraging Optimization (BFO) and Genetic based Bacteria Foraging (GBF) found in the literature. Here, it demonstrates the better performance of Krill Herd Algorithm with the above mentioned methods by simulation process.

Multiprocessor scheduling, Optimization problem, Krill Herd Algorithm (KHA)

[1] Sasmita Kumari Nayak, Sasmita Kumari Padhy, Siba Prasada Panigrahi, “A Novel algorithm for dynamic task scheduling”, Future Generation Computer Systems, 2012, Volume 28, Issue 5, Pages 709-717.
[2] Fatma A. Omara, Mona M. Arafa, Genetic algorithms for multiprocessor scheduling problem, Journal of Parallel and Distributed Computing 70 (1) (2010) 13–22.
[3] Orhan Engin, Gülşad Ceran, Mustafa K. Yilmaz, An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems, Applied Soft Computing 11 (3) (2011) 3056–3065.
[4] Savaş Balin, Non-identical parallel machine scheduling using genetic algorithm, Expert Systems with Applications 38 (6) (2011) 6814–6821.
[5] Tzu-Chiang Chiang, Po-Yin Chang, and Yueh-Min Huang, “Multi-Processor Tasks with Resource and Timing Constraints Using Particle Swarm Optimization”, IJCSNS International Journal of Computer Science and Network Security, Vol.6 No.4 (2006), pp. 71-77.
[6] Holland J. Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press; 1975.
[7] Al-Tabtabai H, Alex PA. Using genetic algorithms to solve optimization problems in construction. Eng Constr Archit Manage 1999;6(2):121–32.
[8] Grierson DE, Khajehpour S. Method for conceptual design applied to office buildings. J Comput Civil Eng 2002;16(2):83–103.
[9] Joglekar A, Tungare M. Genetic algorithms and their use in the design of evolvable hardware. http://www.manastungare.com/ articles/genetic/genetic-algorithms.pdf; 2003, accessed on May 20, 2004, 15 p.
[10] Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
[11] Gandomi AH, Yang X-S, Talatahari S, Deb S (2012) Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput Math Appl 63(1): 191–200. doi:10.1016/j.camwa.2011.11.010
[12] Khazraee S, Jahanmiri A, Ghorayshi S (2011) Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution. Neural Comput Appl 20(2):239–248. doi:10.1007/s00521-010- 0364-x
[13] Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948
[14] Chen D, Zhao C, Zhang H (2011) An improved cooperative particle swarm optimization and its application. Neural Comput Appl 20(2):171–182. doi:10.1007/s00521-010-0503-4
[15] Talatahari S, Kheirollahi M, Farahmandpour C, Gandomi AH (2012) A multi-stage particle swarm for optimum design of truss structures. Neural Comput Appl. doi:10.1007/s00521-012-1072-5
[16] Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part II: Geotechnical and Earthquake Engineering Problems. Neural Comput Appl 21 (1):189–201
[17] Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239. doi:10.1016/j.ins.2011.07.026
[18] Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702–713
[19] Wang G, Guo L, Duan H, Liu L, Wang H (2012) Dynamic deployment of wireless sensor networks by biogeography based optimization algorithm. J Sens Actuat Netw 1(2):86–96. doi: 10.3390/jsan1020086
[20] Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483
[21] Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2012) Bat algorithm for constrained optimization tasks. Neural Comput Appl. doi:10.1007/s00521-012-1028-9
[22] Gandomi AH, Yang X-S, Alavi AH (2012) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput Ger. doi:10.1007/s00366-011-0241-y
[23] Gandomi AH, Talatahari S, Yang XS, Deb S (2012) Design optimization of truss structures using cuckoo search algorithm. Struct Des Tall Spec. doi:10.1002/tal.1033
[24] Wang G, Guo L, Duan H, Wang H, Liu L, ShaoM(2012) A hybrid meta-heuristic DE/CS algorithm for UCAV three-dimension path planning. Sci World J 2012:1–11. doi:10.1100/2012/583973
[25] Yang X-S, Sadat Hosseini SS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12(3):1180–1186. doi:10.1016/j.asoc.2011.09.017
[26] Gandomi AH, Yang X-S, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23–24):2325–2336. doi:10.1016/j.compstruc.2011.08.002
[27] Talatahari S, Gandomi AH, Yun GJ (2012) Optimum design of tower structures using Firefly Algorithm. Struct Des Tall Spec
[28] Wang G, Guo L, Duan H, Liu L, Wang H (2012) A modified firefly algorithm for UCAV path planning. Int J Hybrid Inf Technol 5(3):123–144
[29] Gandomi AH, Alavi AH (2012) Krill Herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 17(12):4831–4845. doi:10.1016/j.cnsns.2012.05.010
[30] Wang Chen, Yan-jun Shi, Hong-fei Teng, Xiao-ping Lan, Li-chen Hu, An efficient hybrid algorithm for resource-constrained project scheduling, Information Sciences 180 (6) (2010) 1031–1039.
[31] Peng-Yeng YinT, Shiuh-Sheng Yu, Pei-Pei Wang, Yi-Te Wang, “A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems”, science direct, Computer Standards & Interfaces 28 (2006) 441–450
[32] S.N. Sivanandam. "Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimisation", International Journal of Bio-Inspired Computation, 2009.
[33] Hofmann EE, Haskell AGE, Klinck JM, Lascara CM. Lagrangian modelling studies of Antarctic krill (Euphasia superba) swarm formation. ICES J Mar Sci 2004;61:617–31.
[34] Mani Ashouri, Seyed Mehdi Hosseini, “Application of Krill herd and Water cycle algorithms on Dynamic Economic Load Dispatch Problem”, IJIEEB, 2014, (4), pp: 12-19.
[35] Wang, G., Guo, L., Wang, H., Duan, H., Liu, L., Li, J.: Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput & Applic 24, 853-871 (2014).

S.K. Nayak, C.S. Panda, S.K. Padhy, "Multiprocessor Scheduling using Krill Herd Algorithm (KHA)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.7-17, 2018.

Multiple Input Multiple Output (MIMO) systems have been widely used in an area of wireless cellular communication system, providing the both increased capacity and reliability. However, the use of multiple antennas in mobile terminals may not be very practical due to limited space and other implementation issues. In this paper, cooperative MIMO has been used in a way to optimise the implementation and working of conventional MIMO systems in terms of BER and Spectral Efficiency while maintaining a minimal number of antennas on each handset. Cooperative MIMO with V-BLAST transmission over Rayleigh flat fading channels and amplify and forward protocol with one relay node for modulation techniques like BPSK, QPSK, QAM using various decoding techniques has been analysed. Decoding algorithms like ZF, MMSE and ML have been analysed with respect to their BER performances. Since, there is throughput loss in cooperative MIMO due to extra resources required for relaying, adaptive modulation has been used with C-MIMO to meet the demands for high data rates in Long Term Evolution Network.

Amplify and Forward (AF), MRC (Maximal Ratio Combining), V-BLAST (Vertical Bell Laboratories Layered Space-Time), Zero Forcing (ZF), Minimum Mean Square Error (MMSE), Maximum Likelihood (ML), BER, Spectral Efficiency (SE), BPSK, QPSK

[1] P. K. Kharat And J. D. Gavade, "Cooperative Communication: New Trend in Wireless Communication," Int.J. Future Generation Communication Networking, Vol. 6, Iss.5, Pp. 157-166, 2013
[2] S. Medhn, "Cooperative MIMO System for WiMAX Technology," MSc. thesis, Dept. Elect. Eng and Comp., University of Addis Ababa, Ethiopia, 2012.
[3] M. M. Fadoul, M. B. Morsin, C.Y. Leow and A.A. Etengusing, “Amplify-And-Forward Relay for Coverage Extension in Indoor Environments.” Journal of Theoretical and Applied Information Technology, Vol.91. No.2, 30th September 2016.
[4] T. M. Cover and A. E. Gamal, “Capacity Theorems for The Relay Channel”, IEEE Trans. On Information Theory, Vol. 25, No. 5, Pp. 572–584, Sept. 1979
[5] X. B. Yu, S. H. Leung, and W. K. Wong, "Performance analysis of variable-power adaptive modulation with antenna selection over Rayleigh fading channels,” IET Communication., vol. 5, Iss. 10, pp. 1394–1404, Jul. 2011.
[6] W. Lin., "Performance Analysis of Cooperative Networks with Random Decode-and-Forward Relaying," Proc. 10th IEEE Int. Conf. High Performance Computer. and Communication, Dalian, pp. 526-531, Sept. 2008.
[7] M. Sterna, “Towards systems beyond 3G based on adaptive OFDMA transmission,” Proc. IEEE, vol. 95, Iss. 12, pp. 2432-2455, Dec. 2007.
[8] S. H. O. Salih and M. M. A. Suliman, "Implementation of adaptive modulation and coding techniques using Matlab," Proc. 53rd International Symposium ELMAR-2011, Zadar, Croatia, pp. 137-139, 14-16 September 2011
[9] D. M. Jimenez, G. Gomez, J. F. Paris and J. T. Entrambasaguas, "Joint Adaptive Modulation and MIMO Transmission for Non-Ideal OFDMA Cellular Systems," IEEE Globecom Workshops, University of Malaga, Spain, pp. 1-5, 2009.
[10] X. Yu, S. H. Leung, W. H. Mow and W. K. Wong, "Performance of Variable-Power Adaptive Modulation with Space Time Coding and Imperfect CSI in MIMO Systems," IEEE Trans. Veh. Technol., vol. 58, Iss. 4, pp. 2115-2120, May 2009.
[11] A. Tesegaye, "Adaptive Modulation based OFDM System in Doubly Selective Channel," MSc. thesis, Dept. Elect. Eng and Comp., University of Addis Ababa, Ethiopia, 2012.
[12] E. Yohannes, "Adaptive Modulation-based MIMO systems for fading channels in LTE," MSc. thesis, Dept. Elect. Eng and Comp., University of Addis Ababa, Ethiopia, 2012.
[13] R.O. Abolade and Z. K. Adeyemo, "Adaptive Modulation Performance In MIMO- OSTBC Over Rayleigh Fading Channel,” Int. J. Eng. & Computer Science, vol.12, Iss. 6, Apr. 2013.
[14] E. C. Strinati, S. Yang and J. C. Belfiore, "Adaptive Modulation and Coding for Hybrid Cooperative Networks," Int. Conf. Communications, Glasgow, pp. 4191-4195, 2007.
[15] S. Hussain, A. Azim, J.H. Park, “Energy efficient virtual MIMO communication for wireless sensor networks,” Telecommun. Syst.42, pp. 139–140, 2009.
[16] S.K. Jayaweera, “Virtual MIMO-based cooperative communication for energy-constrained wireless sensor networks,” IEEE Trans. Wireless Commun. 5, pp. 984–989, 2006.
[17] W. Chen, Y. Yuan, C. Xu, K. Liu, Z. Yang, “Virtual MIMO protocol based on clustering for wireless sensor network,” in: Proc. IEEE Symp. Computer and Communications, Cartagena, Murcia, Spain, pp. 335–340, 2005.
[18] J. Liu, “Energy-efficient cross-layer design of cooperative MIMO multi-hop wireless sensor networks using column generation,” Wireless Personal Commun. J. 66, pp. 185–205, 2012
[19] M.R. Islam, J. Kim, “Energy efficient cooperative MIMO in wireless sensor network,” in: Proc. IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Sydney, Australia, pp. 505–510, 2008.
[20] Amol B. Suryawanshi and Baljit Kaur Saini, “Survey on Various Routing Protocols in Ad-hoc Networks”, Int. J. Sci. Res. in Network Security and Communication, Volume-5, Issue-3, June 2017
[21] G. Sharma, A. Kumar, “REVIN: Reduced Energy Virtuous Immune Network for WSN”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.1-8, April (2017)
[22] V. Prasad1, V.S. Sunsan,” Multi path dynamic routing for data integrity and delay Minimization differentiated services in wireless sensor network”, International Journal of Scientific Research in. Computer Science and Engineering Vol.4, Issue.4, pp.20-23, August (2016)

Sukhreet Kaur, Amita Soni, "Novel Adaptive Cooperative-MIMO for LTE Technology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.18-26, 2018.

Wireless sensor networks have large number of sensor nodes, which sense and transmit data to the sink. Network lifetime is one of the key challenge for these networks due to limited capacity batteries of sensor nodes. Clustering techniques, being the prominent way to prolong network lifetime through data aggregation, are taken up in this work. DEEC and its variants improve network performance up to certain extent, still have scope for further improvement. EDDEEC, being the recent variant of DEEC, dynamically adjusts the CHs selection probability and selects the suitable CHs. Stability period, duration for which all network nodes are alive, is a more concrete performance parameter than network lifetime for reliable communication over the network. In this paper, an improved dynamic clustering technique, improved Enhanced Developed Distributed Energy Efficient Clustering protocol, is proposed and evaluated for various performance parameters. The results are analyzed and compared with relevant protocols, EDEEC and EDDEEC. Proposed technique iEDDEEC dynamically elects cluster-heads based on selection probability and threshold value of different nodes. The probability of a node to become cluster-head is decided with the ratio between residual energy of each node and average energy of the network, while threshold depends upon the current network state. Simulation results shows that iEDDEEC protocol extends the stability period of underlying network with improved throughput and energy dissipation.

Clustering; Energy; heterogeneous; stability period; wireless sensor networks

[1] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey”, Computer Networks, Vol.52, Issue.12, pp.2292–2330, 2008.
[2] J. Yan, M. Zhou, and Z. Ding, “Recent Advances in Energy-Efficient Routing Protocols for Wireless Sensor Networks”, Middle-East J. Sci. Res., Vol.4, pp.5673–5686, 2016.
[3] K. Akkaya and M. Younis, “A survey on routing protocols for wireless sensor networks”, Ad Hoc Networks, Vol.3, Issue.3, pp. 325–349, 2005.
[4] W.B. Heinzelman, A.P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks”, IEEE Trans. Wirel. Commun., Vol.1, Issue.4, pp.660–670, 2002.
[5] G. Smaragdakis, I. Matta and A. Bestavros, “SEP: A stable election protocol for clustered heterogeneous wireless sensor networks”, Second Int. Work. Sens. Actor Netw. Protoc. Appl. (SANPA 2004), pp.1–11, 2004.
[6] L. Qing, Q. Zhu, and M. Wang, “Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks”, Comput. Commun., Vol.29, Issue.12, pp.2230–2237, 2006.
[7] B. Elbhiri, S. Rachid, S. Fkihi, and D. Aboutajdine, “Developed Distributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks”, 2010 5th Int. Symp. I/V Commun. Mob. Networks, ISIVC 2010, pp.1–4, 2010.
[8] P. Saini and A. K. Sharma, “E-DEEC - Enhanced distributed energy efficient clustering scheme for heterogeneous WSN”, 2010 1st Int. Conf. Parallel, Distrib. Grid Comput. PDGC - 2010, pp.205–210, 2010.
[9] N. Javaid et al., “An energy-efficient distributed clustering algorithm for heterogeneous WSNs”, Eurasip J. Wirel. Commun. Netw., Vol.15, Issue.1, pp.1–11, 2015.
[10] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks”, Proc. 33rd Annu. Hawaii Int. Conf. Syst. Sci., Vol.1, Issue.11, pp.10-15, 2000.
[11] D. Kumar, T. C. Aseri, and R. B. Patel, “EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks”, Comput. Commun., Vol.32, Issue.4, pp.662–667, 2009.

G. Kaur, S. Sharma, "iEDDEEC: Improved Enhanced Developed Distributed Energy Efficient Clustering Protocol for Heterogeneous Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.27-36, 2018.

Abstract— In the world of network security, hetnet security is upcoming task in the era of evolution of fifth generation around 2020. The mobile device usage to handle cellular data which needs high level of security is increasing day by day.This data is used for various applications in different fields extensively, hence the attacks in an attempt to break security breach is also increasing. It is essential to provide security for network formed by small cells like picocell, femtocell and microcell. This paper deals with one of the approach to provide security to heterogeneous network from black hole kind of attack based on performance evolution using simulation of various network parameters in matlab to provide end to end authentication and security. The security to femtocell or picocell in hetnet not only help to send data at high speed in millimeter range but also increase coverage, capacity and efficiency of the network and reduce power requirement up to the greater extent. The heterogeneous network security solution focuses on how data level and network level security can be achieved by improving hetnet parameters to reduce the level of security breach and to achieve desired security.

HetNet, Communication node, Attacks, Routing Protocol, Security

[1] Rose Hu,Yi Qian,Sastri Kota, Giovanni Giambene, “Hetnets- a new paradigm for increasing cellular capacity and coverage” IEEE Journel and Magazine, Vol-18,Issue-3,pp-8-9,June-2011.
[2] Pradeepkumar Sharma, Shivlal Mewada,Pratiksha Nigam,”Investigation based performance of Black and Gray Hole Attack in Mobile Adhoc Network” International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue 4, pp.8-11, Sep.-2013.
[3] Leena Pal,Pradeep Sharma,Netram Kaurav,and Shivlal Mewda “Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc –Networks”, International Journal of Scientific Research in Network Security and Communication, Vol.1, No.5, pp.1-4, Nov. 2013.
[4] Elamathi N, Dr. S. Jayshree, “Security For Green Communication in Heterogeneous Wireless Networks” International Journel of Innovative Research in Computer and Communication Engineering, Vol. 2, Jan. 2014.
[5] Abderrahmane Baadache, Alibelmehdi, “ Struggling against simple and cooperative blackhole attacks in multihop wireless adhoc networks” ScienceDirect Journel of Computer Networks, Vol-73, pp-173-184,November 2014.
[6] Meenakshi Sharma, GNDE RC Jalandhar, “Future Trends in the Cell Site Planning For Capacity Enhancement” International Journel of Engineering Research and Management Technology, Vol. 2, March 2015.
[7] Sumant Kumohapatra, BiswaRanjan Swain, Pravanjan Das, “Comprehensive Survey of Possible Security Issues on 4G Networks” International Journal of Network Security and Its Applications, Vol. 7, No.2, March 2015.
[8] Xianbin Wang, Peng Hao, Lajos Hanzo, “Physical Layer Authentication for Wireless Enhancement: Current Challenges and Future Developments”, IEEE Journel and Communications Magazine, 2016.
[9] Javed Ahmad Shaheen, “ Wireless Network Architecture an Overview and Security Issues on 4G” International Journel of Future Green Communication and Networking, Vol. 10, No. 1, pp 15-24, 2017.
[10] Lin C., Lee B., “Exploration of Routing Protocols in Wireless Mesh Network”, In the Proceedings of the 2015 IEEE Symposium on Colossal Big Data Analysis and Networking Security, Canada, pp.111-117, 2015.
[11] Muhammad Omer Farooq Cormac J Sreenan and Kenneth N. Brown,“Research Challenges in 5G Networks : a Hetnets Perspective” International ICIN conference, Paris, March 2016.
[12] Makato Ando, Miao Zhang, Jiro Hirokawa, Kei Saka Guchi, Toru Taniguchi, Makoto Noda, Akira Yamaguchi, “ Demonstration of mmWave Systems and Networks for the Hetnet in 5G Mobile Communication” URSI International Symposium on Electromagnetics Theory (EMTS), 2016.
[13] Hakima Chaouchi, Maryline Laurent-Maknavicius , “Wireless and Mobile Network Security:Security in On the shelf and Emerging Technologies.” JohnWiley and Sons Publication, UK, pp. 409-435, 2010.
[14] Kan Zheng, Long Zhao, Jie Mei, Mischa Dohler, Wei xiang, Yuexing Peng, “10Gbs/s, HetsNets with Millimeter-Wave Communications Access and Networking – Challenges and Protocols.” IEEE Communications Magazine , Jan. 2015.
[15] Silvere Mavoungou, Georges Kaddoum, Mostafa Taha, Georges Matar, “ Survey on Threats and Attacks on Mobile Networks” IEEE Access, Vol. 4, 2016.

D.V. Chikhale, S.B. Deosarkar, "Hetnet Security Solution for Black Hole Attack In Millimeter Range Mobile Communication," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.37-42, 2018.

Stock index prediction is one of the important tasks in the domain of computational finance. A number of tools have been developed by various groups of researchers and are being used by many analysts to identify the future price index. However, due to the high degree of non-linearity of the problem and surrounded by many optimal solutions, this paper proposes Radial Basis Function Neural Networks (RBFNNs) learning using Genetic Algorithm (GA) to predict the stock price index and at the same time the connection weights between the layers and thresholds are optimised using GA. Further, potential indicators are used to make the model robust in terms of its efficiency and accuracy. The accuracy is compared to MLP-BP and GA models. Finally, the experimental results show that the optimized RBFNNs model is the optimum model in comparison to other conventional models.

Stock Index Prediction, RBF Neural Network, Genetic Algorithm, Real coding

[1] S. Chen, C.F.N. Cowan and P.M. Gant, “Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks”, J. IEEE Transactions on Neural Networks, Vol. 3, pp.302-308, 1991.
[2] Y. Chao-gang, Y. Yi-bin, W. Jian-ping, N. Jamal and Y. Jia, “Determining Heating Pipe Temperature in Greenhouse Using Proportional Integral Plus Feed-forward Control and Radial Basis Function Neural Networks”, Journal of Zhejiang University-SCIENCE A, Vol. 6, No.4, pp. 265-269, 2005.
[3] J.H. Holland, “Adaptation in Natural and Artificial Systems”, University of Michigan Press, Ann Arbor, 1975.
[4] P. S. Mishra and S. Dehuri, “Potential Indictors for Stock Index Prediction: A Perspective”, International Journal of Electronic Finance, Vol. 6, No. 2, pp. 157-183, 2012, doi:10.1504/IJEF.2012.048465.
[5] P. S. Mishra and S. Dehuri, “Potential Indicators based Neural Networks for Cash Forecasting of an ATM, International Journal of Information Systems and Social Change, Vol. 5, No.4, pp. 41-57, 2014, doi:10.4018/ijissc.2014100103.
[6] A. Brabazon, “Financial time series modeling using neural networks: An assessment of the utility of a stacking methodology”, in proceedings of AICS 2002, Lecture Notes in Artificial Intelligence(2464),(Eds,) O’Neill et al., Springer,pp.137-144, 2002b.
[7] K. Nygren, “Stock Prediction—A Neural Network Approach”, Master’s Thesis, Royal Institute of Technology, KTH, Sweden, 2004.
[8] E. Altman, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”, The Journal of Finance, Vol. 23, pp.589-609, 1968.
[9] E. Altman, “Corporate financial distress-A complete guide to predicting, avoiding and dealing with bankruptcy”, New York: John Wiley, 1983.
[10] J. Ohlson, “Financial ratios and the probabilistic prediction of bankruptcy”, Journal of Accounting Research, Vol. 18, No.1, pp.109-131, 1980.
[11] M.E. Zmijewski,“Methodological Issues Related to the Estimated of Financial Distress Prediction Model”, Journal of Accounting Research, Vol.22, No.1, pp.59-82,1984.
[12] R. Barniv, A. Agarwal and R. Leach, “Predicting the outcome following bankruptcy filing: A three-state classification using neural networks”, Intelligent Systems in Accounting, Finance and management, Vol.6, pp. 177-194, 1997.
[13] J. Boritz and D. Kennedy, “Effectiveness of neural network types for prediction of business failure”, Expert Systems with Applications, Vol.9, pp.503-512, 1995.
[14] A. Brabazon and M. O’Neill, M, “Diagnosing Corporate Stability using Grammatical Evolution”, International Journal of Applied Mathematics and Computer Science, Vol. 14, No. 3 pp.363-374,2004.
[15] F. Varetto, “Genetic algorithms in the analysis of insolvency risk”, Journal of Banking and Finance, Vol. 22, No.10, pp.1421-1439, 1998.
[16] A.M. Colin, “Genetic algorithms for financial modeling”, In Deboeck, G.J. (Eds.), Trading on the Edge, New York: John Wiley, pp. 148-173, 1994.
[17] G.J. Deboeck, “Using GAs to optimize a trading system”, In Deboeck, G.J (Eds.), Trading on the Edge, New York: John Wiley, pp. 174-188, 1994.
[18] S. Mahfoud and G. Mani, "Financial forecasting using genetic algorithms", Applied Artificial Intelligence, Vol. 10, No.6, pp. 543-565, 1996.
[19] E. Rutan,, “Experiments with optimal stock screens”, Proceedings of the 3rdInternationalConference on Artificial Intelligence Applications on Wall Street, pp.262-273,1993.
[20] J. Kingdom and K. Feldman, “Genetic algorithms for bankruptcy prediction”, Search Space Research Report, No.01-95, Search Space Ltd., London, 1995.
[21] R. Walker, E. Haaasdijk and M. Gerrets, “Credit evaluation using a genetic algorithm”, In Coonatilake, S. and Treleaven, P. (Eds.), Intelligent Systems for Financial and Business, Chichester, John Wiley, pp.35-59, 1995.
[22] N. Packard, “A genetic learning algorithm for the analysis of complex data”, Complex Systems, Vol. 4, pp.543-572, 1990.
[23] M. Durairaj and T. Sathyavathi, “Applying Rough Set Theory for Medical Informatics Data Analysis”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, No.5,pp.1-8, 2013.
[24] L. Davis, “Handbook of Genetic Algorithms”, Van Nostrand Reinhold, New-York, 1991.
[25] D.E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, MA: Addison-Wesley, 1989.
[26] A. Brabazon and M. O’ Neill, “Biologically Inspired Algorithms for Financial Modelling”, Springer, Berlin, Germany, 2006.
[27] D.S. Broomhead and D. Lowe, “Multivariable functional interpolation and adaptive networks”, Complex System, Vol. 2, pp.321-355, 1988.
[28] J. Park and I. W. Sandberg, “Universal Approximation using Radial-Basis-Function Networks”, Neural Computation, Vol. 3, pp.246-257, 1991.

P.S. Mishra, "Optimization of the Radial Basis Function Neural Networks Using Genetic Algorithm for Stock Index Prediction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.43-51, 2018.

In the agriculture economy, understanding of spatial crop growing pattern is significant to agricultural structure adjustment and regional food safety policy. The phenlogical profile of crop can reflect a real trend of crop growth and therefore have been used to interpret seasonal crop growing patterns. Accurate identification of potato growing areas from other crop is not so easy because of their similar characteristics in the proposed study area. This study proposed a method to precisely predict the spatial potato crop growing pattern in the potato bowl districts of West Bengal by using 16-day composite MODIS NDVI data (MOD13Q1) in the potato cropping year of 2012-13 and 2013-14. Based on time series NDVI data and vast knowledge of field investigation a threshold value was set to build decision trees to pick up the potato crop as well as to eliminate the other crops. As a result, the potato crop area was successfully segregated from the multi-temporal NDVI data. Both predicted potato growing areas derived from MODIS NDVI data and the actual potato growing area is deployed for evaluation and the results give a satisfactory accuracy in both potato cropping year of 2012-13 and 2013-14. This result demonstrated that MODIS NDVI data are potentially good data source for spatial potato crop growing area extraction.

Potato Crop, Crop Phenology, MODIS, NDVI, Decision Trees

[1] A. Huete, K. Didan, T. Miura, E. Rodriguez, P. E, X. Gao, and L. G. Ferreira, “Overview of the radiometric and biophysical performance of the MODIS vegetation indices”, Remote Sensing of Environment, vol. 83, pp. 195–213, 2002.
[2] A. Chitradevi, S. Vijayalakshmi, “Random Forest for Multitemporal and Multiscale Classification of Remote Sensing Satellite Imagery”, International Journal of Computer Sciences and Engineering, Vol. 4, Issue. 2, pp. 59-65, 2016.
[3] B.D. Wardlow, J.H. Kastens, S.L. Egbert, “Using USDA crop progress data for the evaluation of greenup onset date calculated from MODIS 250-meter data”, Photogrammetric Engineering and Remote Sensing, vol. 72, pp. 1225−1234, 2006.
[4] B.D. Wardlow, S.L. Egbert and J.H. Kastens, “Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains”, Remote Sensing of Environment, vol. 108, pp. 290−310, 2007.
[5] B.D. Wardlow and S.L. Egbert, “Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains”, Remote Sens. Environ., vol. 112, no. 3, pp. 1096–1116, 2008.
[6] B. Huang, H. Zhang, H. Song, J. Wang and C. Song, “fusion of remote-sensing imagery: generating simultaneously high-resolution synthetic spatial–temporal–spectral earth observations”, Remote Sens. Lett., vol. 4, pp. 561–569, 2013.
[7] C.O. Justice, J.B.G. Townshend, E.F. Vermote, E. Masuoka, R.E. Wolfe, N. Saleous, D.P. Roy and J.T. Morisette, “An overview of MODIS land data processing and product status”, Remote Sens. Environ, vol. 83, pp. 3–15, 2002.
[8] G. Hmimina, E. Dufrêne, J.Y. Pontailler, N. Delpierre, M. Aubinet, B. Caquet, A. De Grandcourt, B. Burban, C. Flechard, A. Granier, P. Gross, B. Heinesch, B. Longdoz, C. Moureaux, J.M. Ourcival, S. Rambal, L. Saint André, and K. Soudani, “Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements”, Remote Sens. Environ., vol. 132, pp. 145–158, 2013.
[9] H. Zhang, J. Chen, B. Huang, H. Song and Y. Li, “Reconstructing seasonal variation of Landsat vegetation index related to leaf area index by fusing with MODIS data”, IEEE J. Select. Topics Appl. Earth Observ. Remote Sens, vol. 1, pp. 1–11, 2013.
[10] M. Zhang, Z. Qin, X. Liu and S. Ustin, “Detection of stress in tomatoes indiced by late blight disease in California, USA, using hyperspectral remote sensing”, International Journal of Applied Earth Observation and Geoinformation, vol. 4, no. 4, pp. 295-310, 2003.
[11] Ramesh K.N, Meenavathi M.B, "Agriculture Crop Area mapping in images acquired using Low Altitude Remote Sensing", International Journal of Computer Sciences and Engineering, Vol.6, Issue. 1, pp. 55-62, 2018.
[12] T. Sakamoto, M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka, and H. Ohno, “A crop phenology detection method using time-series MODIS data”, Remote Sens. Environ., vol. 96, no. 3–4, pp. 366–374, 2005.
[13] T. Sakamoto, N. Van Nguyen, H. Ohno, N. Ishitsuka, and M. Yokozawa, “Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers”, Remote Sens. Environ., vol. 100, no. 1, pp. 1–16, 2006.
[14] W.B. Wu, P. Yang, H.J. Tang, Q.B. Zhou, Z.X. Chen, and R. Shibasaki, “Characterizing Spatial Patterns of Phenology in Cropland of China Based on Remotely Sensed Data”, Agric. Sci. China, vol. 9, no. 1, pp. 101–112, 2010.
[15] X. Zhang, M. A. Friedl, C. Schaaf, “Global Vegetation Phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of Global Patterns and Comparison with in situ Measurements”, Journal of Geo-physical Research, vol. 111: G04017, 2006.

Ramprasad Kundu, Dibyendu Dutta, Abhisek Chakrabarty, Manoj Kumar Nanda, "Spatial Growth Pattern of Potato in West Bengal using Multi-temporal MODIS NDVI Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.52-59, 2018.

Temperature affects the smallest details of our daily life from what we wear to how we get to work to what we eat for lunch. Seldom can we go even a day without needing to know what the temperature is or will be. And we know that these days the temperature has been rising steadily around us and across the globe as well, thus we intended to make a study on Global temperature. The data set which we used in this paper is from the National Oceanic and Atmospheric Administration (NOAA). We have the Global temperature anomaly with respect to land and ocean from the year 1880 to 2017. Statistical techniques like Descriptive Statistics to summarize the data, Cluster Analysis to form clusters of the years that show similar kind of temperature variation, Correlation Analysis to understand the related variation between Land and Ocean temperature anomaly were carried out. Further Double Exponential Smoothing (Holt) model and ARIMA model is fitted to forecast the Land and Ocean temperature anomaly using the training set and there after the accuracy of the forecasted models has been compared by using Mean Absolute Error (MAE) and Mean Absolute Percent Error (MAPE). Finally, the model that has more accuracy is used to forecast the temperature anomaly for the year 2018 and 2019.

Temperature anomaly, Chernoff face, Clusters, ARIMA and Holt model

[1] P. Romilly,”Time Series Modelling of Global Mean Temperature for Managerial Decision Making”, Environmental Management, Vol.76, pp 61–70, 2005.
[2] M. Wakaura, Y. Ogta, “A Time Series Analysis on the Seasonality of Air Temperature Anomalies”, Meteorological Applications, Vol.14, pp. 425-43, 2007.
[3] Akbarizan , M. Marizal, M. Soleh, Hertina, M. Abdi, R. Yendra, A. Fudholi,“Utilization of Holt’s forecasting Model for Zakat Collection in Indonesia”, American Journal of Applied Science,Vol.13, Issue 12, pp 1342-1346, 2016.
[4] R. A. Johnson, D.W. Wichern, “ Applied Multivariate Statistical Analysis”, Prentice Hall, U.S.A., pp 267-286, 2007.
[5] J. Han, M. Kamber, “Data mining-Concepts and Techniques”, Morgan Kaufman Publishers, U.S.A., pp 444-525, 2002.
[6] G.E.P. Box, G.M. Jenkins, “Time Series Analysis: Forecasting and Control”, Holden-day, San Franscisco, USA, pp 211-245, 1976.
[7] R. H. Humway, D.S. Stoffer, “Time Series Analysis And its Applications”, Springer, Fourth edition, New York, pp 116-148, 2016.
[8] C .Chatfield, “The Analysis of Time Series An Introduction”, Chapman and Hall, London, pp 123-186,1996.

S. Harsha, S. M. Fernandes, "Statistical Analysis on Global Temperature Anomalies," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.60-68, 2018.

Educational data mining has gained importance for discovering the useful information from the student databases. It is observed that there is a lack of performance of the students during campus selection in technical institutions. Hence the problem highlighted in this research work is: “What factors are responsible for placement of some students but why not others during campus selection of technical institutions?” The objective of this research work is related to the prediction and discovery of the factors for student placement using the data mining techniques and tool. The methodology used in this research work involves four main stages to achieve the required objectives. They are Data Collection, Pre-processing, Classification and Interpretation of Result. The Classification algorithms used in this research paper include decision tree, Naive Bayes, Neural Network (Multilayer perceptron) and Sequential Minimal Optimisation. It has been found that Naive Bayes algorithm works best in student placement prediction with maximum accuracy. The identification of attributes is done using output decision tree model. After such findings, a classification system model is proposed which depicts the stages of pre-processing, attribute selection, classification, factor identification, factor improvement and placement prediction. It may also be applied at any institute where placement prediction is required before-hand to increase the chances of campus selection irrespective of courses. The classification model can be applied to the problems related to student placement at technical institutions.

Educational Data Mining, Placement chance prediction, Classification Algorithms, Attribute selection, Student Performance.

[1] C. Anuradha, T. Velmurugan, “A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance”, International Journal of Science and Technology, Vol. 8, Issue 15, 2015.
[2] K.P. Chaudhari, R.A. Sharma, S.S. Jha, R.J. Bari, “Student Performance Prediction System using Data Mining Approach”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 6, Issue 3, 2017.
[3] H. Hamsa, S. Indiradevi, J.J. Kizhakkethottam, “Student Academic Performance Prediction Model Using Decision tree and Fuzzy Genetic Algorithm”, Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology, 2016.
[4] R.R. Kabra, R.S. Bichkar, “Student`s Performance Prediction Using Genetic Algorithm”, International Journal of Computer Engineering and Applications, Vol. VI, Issue III, 2014.
[5] A. Katare, S. Dubey, “A Comparative Study of Classification Algorithms in EDM using 2 Level Classification for Predicting Student’s Performance”, International Journal of Computer Applications, Vol. 165, Issue 9, 2017.
[6] A. Mueen, B. Zafar, U. Manzoor, “Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques”, I. J. Modern Education and Computer Science, 2016.
[7] M. Mayilvaganan, D. Kalpanadevi, “Comparison of Classification Techniques for predicting the performance of Students Academic Environment”, International Conference on Communication and Network Technologies, 2014.
[8] A. Nichat, A.B. Raut, “Predicting and Analysis of student Performance Using Decision Tree Technique”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5,Issue 4, 2017.
[9] S. Rana, R. Garg, “Evaluation of Student`s Performance of an Institute Using Clustering Algorithms”, International Journal of Applied Engineering Research, Vol. 11, 2016.
[10] P. Revathy, P. Kalaiarasi, J. Kavitha, D.A. Madhumita, “Data Mining Approach for Suggesting Higher Education Courses Based on Student’s Performance”, International Journal of Science and Technoledge, Vol. 3, Issue 3, 2015.
[11] C. Romero, “Educational Data Mining: A Review of the State of the Art”, IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews, Vol. 40, 2010.
[12] A.A. Saa, “Educational Data Mining and Students’ Performance Prediction”, International Journal of Advanced Computer Science and Applications, Vol. 7, 2016.
[13] R. Anupriya, P. Saranya, R. Deepika, “Mining Health Data in Multimodal Data Series for Disease Prediction”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issues 2, pp. 96-99, 2018.
[14] M. Fernandes, “Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, Issue 1, pp. 19-23, 2017.

M. Shukla, A. K. Malviya, "Analysis and Comparison of Classification Algorithms for Student Placement Prediction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.69-81, 2018.

Enormous news contents are getting generated today which keeps on growing to a great extent. The Archiving of news items has become a tedious and challenging task because of its rich quantity. It also creates problems for journalists to find proper content using current search tools. Personalized news extraction helps the journalist to find the right news content without browsing through irrelevant news items. Semantic Web techniques improve personalizing the news content for information extraction. The proposed Ontology-based news extraction framework provides a high degree of semantically similar news contents for a search query. That helps the journalist to develop a news story within a short span of time. The Proposed framework is evaluated using YouTube – 8M dataset and results are positive. The lack of local knowledge concepts incorporated with the underlying ontology used in this framework can be addressed in the future enhancement. Further researches are needed to include the priority listing of news contents extracted for the same search query on geographical and news value specific.

Information Extraction, Knowledge Management, Ontology, Personalization, Semantic web techniques

[1] Shine K George, V P Jagathy Raj, G Santhosh Kumar, “Ontology based framework for news extraction in visual media”, In:Proceedings of IEEE international conference on Data Science & Engineering (ICDSE) ,pp. 220-222, 2012
[2] Berners-Lee, T. Hendler, J. Lassila, “The Semantic Web.Scientific American, pp. 29-37, 2001
[3] Daya.C.Wimalasuriya, Dejing Dou,“Ontology based information extraction: an introduction and a survey of current approaches”, Journal of Information Science, Vol. 36, No. 3. pp.306-323,2010
[4] F. Wu and D. S. Weld, “Autonomously semantifying wikipedia”. In:Proceedings of the Sixteenth ACM Conference on Information andKnowledge Management, pp. 41-50, 2007
[5] J. Kietz, A. Maedche, and R.Volz, “A method for semi-automaticontology acquisition from a corporate intranet”, In: Proceedings ofthe EKAW`00 Workshop on Ontologies and Text, 2000
[6] P.Cimiano, S. Handschuh, and S. Staab, “Towards the self annotating web”, In: Proceedings of the 13th InternationalConference on World Wide Web, 2004.
[7] D. Maynard, W. Peters, and Y. Li, “Metrics for evaluation ofontology-based information extraction”,. In: Proceedings of the WWW 2006 Workshop on Evaluation of Ontologies for the Web,(ACM, New York, 2006)
[8] S. Abu-El-Haija, N. Kothari, J. Lee, P. Natsev, G. Toderici,B. Varadarajan, and S. Vijayanarasimhan. YouTube-8M: Alarge-scale video classification benchmark. arXiv preprint,arXiv:1609.08675, 2016
[9] Anna Rozeva1, Silvia Zerkova1 “Assessing Semantic Similarity of Texts – Methods andAlgorithms”, Proceedings of the 43rd International Conference Applications of Mathematics in Engineering and EconomicsAIP Conf. Proc. 1910, 060012 ,2017
[10] Chelsea Boling, “Semantic Similarity of Documents Using Latent Semantic Analysis”, Proceedings of the National Conference On Undergraduate Research (NCUR) 2014 University of Kentucky, Lexington, KY April 3-5, 2014
[11] Apurva Dube, Pradnya Gotmare, “Semantics Based Document Clustering”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.25-30, August 2017
[12] M.Chahbar, A.Elhore, Y.Askane, “PERO2: Machine Teaching based on a Normalized Ontological Knowledge Base”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.63-74, October 2017

Shine. K. George, Jagathy Raj V. P, "Personalized Visual News Extraction and Archival Framework," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.82-85, 2018.