Volume-07 , Special Issue-16 , May - 2019 Go Back

Today’s E-commerce development growths are very high in all fields such as online purchase, education, medical etc., the trend BtoC Business to Customer has been changed to CtoB Customer to Business, many customers like and preferred to buy the products on online shopping, due to time constrain, traffic, tracking system, discount, and also one advantages which is available in online not in traditional shopping, it’s very easy to compare with other products. Reviews play a vital role to customers and merchants, using the reviews merchants are trying to give the best quality product, best price and discount to the customers, so they can improve the profit and increase the number of customers. Customers while purchasing the product, it is very difficult and impossible to read all the reviews. There are many algorithms available to recommend and rank the product to the customer, but if the input given to the system is incorrect then the output will not be in accurate manner as per the user request, this survey overview the different aspect extraction techniques and approaches. And also identified the research gaps and propose a recommendation system for online purchase using customer reviews, the proposed system has four phases i. Pre-processing ii. Aspect identification (explicit and implicit) iii. Semantic classification and Aspect polarity identification iv. Efficient Product Aspect Based Ranking.

Social Networks; Customer Reviews; Sentiment classification; Aspect Polarity

[1] Ciprian-Daniel Neagu, Mircea Negoita and Vasile Palade, “Aspccts of Integration of Explicitand Implicit Knowledge in Connectionist Expert Systems” 1999 IEEE.
[2] Thomas Y. Lee, Simon Li, and Ran Wei [2] proposed “Needs-Centric Searching and Ranking Based on Customer Reviews” 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services 2008 IEEE.
[3] Bangzuo Zhang, Yu Guan, Haichao Sun, Qingchao Liu and Jun Kong “Survey of User Behaviors as Implicit Feedback” International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE) 2010 IEEE.
[4] Jianxing Yu, Zheng-Jun Zha, MengWang and Tat-Seng Chua “Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1496–1505, Portland, Oregon, Association for Computational Linguistics. June 19-24, 2011.
[5] Xing Hu, Sukanya Manna and Brian N. Truong “Product Aspect Identification: Analyzing Role of Different Classifiers” 2014 IEEE.
[6] R. Suganya “Identifying and Ranking Product Aspects based on Consumer reviews” International Journal for Research in Applied Science & EngineeringTechnology (IJRASET) Volume 3 Issue I, January 2015.
[7] Charushila Patil , Prof. P. M. Chawan, Priyamvada Chauhan and Sonali Wankhede “A Survey on Product Aspect Ranking” International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Vol. 4, Issue 12, December 2015.
[8] Namrata R. Bhamre and Nitin N. Patil “Aspect Rating Analysis Based Product Ranking” International Conference on Global Trends in Signal Processing, Information Computing and Communication, 2016 IEEE.
[9] Saif A. Ahmad Ali Alrababah, Keng Hoon Gan and Tien-Ping Tan “Product aspect ranking using sentiment analysis and TOPSIS” Third International Conference on Information Retrieval and Knowledge Management, 2016 IEEE.
[10] Madhuri Shirsat and Nilesh Vani “Product Aspect Ranking based on IDR/EDR Opinion” 2016 ijcsit.
[11] Neha M Toshniwal and DV Gore “Ranking of Products on the Basis of Aspects A Probabilistic approach” 2016 IJCST.
[12] R Sivashankari and B Valarmathi “An Empirical Semi-Supervised Machine Learning Approach on Extracting and Ranking Document Level Multi-Word Product Names Using Improved C-value Approach, 2016 IEEE.
[13] Saif A. Ahmad Ali Alrababah, Keng Hoon Gan and Tien-Ping Tan [13] “Comparative analysis of MCDM methods for Product Aspect Ranking: TOPSIS and VIKOR, 2017 8th International Conference on Information and Communication Systems (ICICS).
[14] Blety Babu Alengadan and Shamsuddin S Khan “A Proposed System for Modifying Aspect Based Opinion Mining for Ranking of Products” 2017, IEEE.
[15] T.Sangeetha, N.Balaganesh and K.Muneeswaran “Aspects based Opinion Mining from Online Reviews for Product Recommendation” 2017, IEEE.
[16] A. Jenifer Jothi Mary and L. Arockiam “A FRAMEWORK FOR ASPECT BASED SENTIMENT ANALYSIS USING FUZZY LOGIC” ICTACT JOURNAL ON SOFT COMPUTING, JANUARY 2018, VOLUME: 08, ISSUE: 02
[17] Anjali A. Dudhe and Sachin R. Sakhare “TEACHER RANKING SYSTEM TO RANK OF TEACHER AS PER SPECIFIC DOMAIN” ICTACT JOURNAL ON SOFT COMPUTING, JANUARY 2018, VOLUME: 08, ISSUE: 02
[18] Yan Fang, Xinyue Xiao, Xiaoyu Wang and Huiqing Lan “Customized Bundle Recommendation by Association Rules of Product Categories for Online Supermarkets” 2018 IEEE.
[19] Karthik.R.V, Sannasi Ganapathy and Arputharaj Kannan “A Recommendation System for Online PurchaseUsing Feature and Product Ranking” August 2018, IEEE.
[20] Toqir A, Rana and Yu-N Cheah “Aspect extraction in sentiment analysis: comparative analysis and survey” Artif Intell Rev (2016) 46:459-483, Springer.

Zafar Ali Khan, R Mahalakshmi, "Product Aspect extraction in opinion mining: a Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.113-116, 2019.

High power consumption in Cloud Data center leads to high emissions of carbon which is unsuitable for environment. Energy consumption in the data center can be reduced by balancing load among active physical nodes and minimizing the number of active servers which are lightly loaded. Static lower and upper thresholds are not suitable for dynamically changing resource usage of physical machines. Dynamic Threshold based load balancing algorithms are proposed: i)Upper threshold dynamically varied based on CPU utilization and the Lower Threshold is predefined. ii) The average utilization of all the machines in the datacenter is used to define Upper Threshold. System is monitored at regular intervals and whenever server load goes above the Upper Threshold or lower than Lower Threshold, system identified as in imbalance state and Virtual Machine migration is initiated for load balance and for server consolidation. Simulation results shows the proposed schemes can improve resource utilization and energy.

VM Migration, Load Balancing, Server consolidation, Dynamic Threshold

[1]. Datacenter Dynamics 2012, Global Census.
[2] H. Jin et al., “Live virtual machine migration with adaptive memory compression”, Proceeding of the IEEE international conference on cluster computing, pp. 1-10, 2012.
[2]. H. Jin et al., “Live migration of virtual machine based on full system trace and replay”, Proceeding of the 18th ACM 2009.
[3]. R. Nathuji and K. Schwan, “Virtual power: Coordinated power management in virtualized enterprise systems”, Proceeding of the ACM SIGOPS Symp. on Op.Sys. Principles, ACM press, pp.265-278, Dec. 2007.
[4]. A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing”, Future Generation Computer Systems, pp.755- 768, May. 2012.
[5]. A. Beloglazov and R.Buyya. "Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers”, Proceedings of the 8th international workshop on middleware for grids, clouds and e-science ACM, pp. 4, 2010.
[6]. A. Beloglazov and R. Buyya, “Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers”, Concurrency and Computation: Practice and Experience(CCPE), Wiley Press, New York, USA, vol. 24, no. 13, pp. 1397-1420, Sep. 2012
[7]. Amir Varasteh and Maziar Goudarzi, ”Server Consolidation Techniques in Virtualized Data Centers: A Survey”, IEEE Systems, VOL. 11, NO. 2, June 2017.
[8]. C.C. Lin, P.Liu and J.J Wu, ”Energy-efficient Virtual Machine provision Algorithms for cloud systems”, Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference, 2011,81-88.
[10]. Shingo Takeda and Toshinori Takemura, ”A Rank based VM Consolidation Method for Power Saving in Datacenters”, IPSJ Online Transactions 3(2):88-96, January 2010.
[11]. Liting Hu, Hai Jin, Xianjie Xiong and Haikun Liu, ”Magnet: A novel scheduling policy for power reduction in cluster with virtual machines”, 2008 IEEE International Conference on Cluster Computing, 13-22.
[12]. Geetha Megharaj, Mohan G. Kabadi, “Server
Consolidation through Virtual Machine Task Migration to achieve Green Cloud”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 3, March 2018.
[13]. A Jain et al., “A Threshold Band Based Model for Automatic Load Balancing in Cloud Environment”, in proc. of IEEE International Conference on Cloud Computing in Emerging Markets, pp 1-7, 2013.
[14]. J. M. Galloway, K. L. Smith, and S. S. Vrbsky, “Power aware load balancing for cloud computing”, Proc. the World Congress on Engineering and Computer Science, 2011, 19-21.
[15]. Shivani Gupta, Damodar Tiwari, Shailendra Singh,
“Energy Efficient Dynamic Threshold Based Load Balancing Technique in Cloud Computing Environment”, International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, 1023-1026
[16]. R. N. Calheiros, R. Buyya, and A. Beloglazov,
“CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, Software: Practice and Experience, Wiley Press, pp.23-50, Jan. 2011.

Geetha Megharaj, Mohan G. Kabadi, "Dynamic Threshold Based Load Balancing and Server Consolidation in Cloud", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.117-121, 2019.

Software Defined Networks are the new standard in networking. ONF [Open Networking Foundation] contributes a high level architecture for SDN. It has three layers, they are Infrastructure layer, control layer and application layer.[1] From the ONF we gets a well-defined definition for SDN which is as follows, “In the SDN architecture, the control and data planes are decoupled, network intelligence and state are logically centralized, and the underlying network infrastructure is abstracted from the applications” [2]. The network security in the SDN architecture is improved by the centralized control over the network and controls the traffic in run time. This paper analyse and produce the importance and effectives of the SDN architecture for future networking.

SDN , ONF, Network security

[1] M. D. Yosr Jarraya, Taous Madi, “A survey and a layered taxonomy of software-defined networking,” IEEE Commun. Surveys & Tutorials, vol. 16, no. 4, pp. 1955–1980, Fourth Quarter 2014.
[2] S. Sezer, S. Scott-Hayward, P.-K. Chouhan, B. Fraser, D. Lake, J. Finnegan, N. Viljoen, M. Miller, and N. Rao, “Are we ready for SDN? implementation challenges for software-defined networks,” IEEE Commun. Mag., vol. 51, no. 7, July 2013.
[3] S. Namal, I. Ahmad, S. Saud, M. Jokinen, and A. Gurtov, “Implementation of OpenFlow based cognitive radio network architecture: SDNR,” Wireless Networks, pp. 1–15, 2015.
[4] M. D. Yosr Jarraya, Taous Madi, “A survey and a layered taxonomy of software-defined networking,” IEEE Commun. Surveys & Tutorials, vol. 16, no. 4, pp. 1955–1980, Fourth Quarter 2014.
[5] A. T. Campbell, I. Katzela, K. Miki, and J. Vicente. “Open signaling for ATM, internet and mobile networks (OPENSIG’98).” ACM SIGCOMM Computer Communication Review 29.1 (1999): 97–108.
[6] J. E. Van der Merwe, S. Rooney, I. Leslie, and S. Crosby. “The tempest‐a practical framework for network programmability.” IEEE Network 12.3 (1998): 20–28.
[7] N. Shalaby, Y. Gottlieb, M. Wawrzoniak, and L. Peterson. “Snow on silk: A nodeOS in the Linux kernel.” Active Networks. Springer, Berlin (2002): 1–19.
[8] NIST: National Vulnerability database, http://web.nvd. nist.gov/view/vuln/ detail ? vulnId=CVE-S2009-3733; 2011.
[9] D. Goodin, “Webhost Hack Wipes Out Data for 100,000 Sites ”,http:// www.theregister.co.uk/ 2009 /06/ 08/ webhost_attack, 2009
[10] C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel, M. Rajarajan,“A Survey of Intrusion Detection Techniques in Cloud”, Journal of Network and Computer Applications 36 (2013), pp. 42-57.
[11] S. Shin and G. Gu, “Attacking software-defined networks: a first feasibility study,” in Proceedings of the second ACM SIGCOMM workshop on Hot topics in software defined networking. ACM, 2013, pp. 165–166.
[12] X. Wen, Y. Chen, C. Hu, C. Shi, and Y. Wang, “Towards a secure controller platform for OpenFlow applications,” in Proceedings of the second ACM SIGCOMM workshop on Hot topics in software defined networking. ACM, 2013, pp. 171–172.
[13] T. Tsou, H. Yin, H. Xie, and D. Lopez, “Use-Cases for ALTO with Software Defined Networks,” 2012.
[14] Advanced message queuing protocol. [Online]. Available: http://www.amqp.org
[15] Y. Hu, W. Wang, X. Gong, X. Que, and S. Cheng, “On reliabilityoptimized controller placement for software-defined networks,” Communications, China, vol. 11, no. 2, pp. 38–54, Feb 2014.
[16] security enforcement kernel for OpenFlow networks,” in Proceedings of the First Workshop on Hot Topics in Software Defined Networks, ser. HotSDN ’12. ACM, 2012, pp. 121–126.
[17] A. Khurshid, W. Zhou, M. Caesar, and P. B. Godfrey, “Veriflow: Verifying Network-wide Invariants in Real Time,” SIGCOMM Comput.Commun. Rev., vol. 42, no. 4, pp. 467–472, Sep. 2012.
[18] A. Tootoonchian and Y. Ganjali, “HyperFlow: A distributed control plane for OpenFlow,” in Proceedings of the 2010 internet network management conference on Research on enterprise networking. USENIX Association, 2010, pp. 3–3.
[19] S. Shin, V. Yegneswaran, P. Porras, and G. Gu, “Avant-guard: Scalable and vigilant switch flow management in software-defined networks,” in Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, ser. CCS ’13. ACM, 2013, pp. 413–424.
[20] A. Tootoonchian, S. Gorbunov, Y. Ganjali, M. Casado, and R. Sherwood, “On controller performance in software-defined networks,” in USENIX Workshop on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services (Hot-ICE), 2012.

B. Parvathi Devi, V. Vallinayagi, "Effectiveness of Security in Software Defined Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.122-125, 2019.

Agriculture industry is the backbone of any emerging country, in developing countries like India the bulk of the rural population mostly depend on agriculture as their main basis of revenue. Several major crops grown in India are rice, wheat, maize, jowar etc. The primary focus of this paper is to present an efficient method to detect quality of rice grains and also to detect the dissimilar varieties of rice grains via various image processing methodologies. Commercialization of rice grains is mainly dependent on the quality analysis of rice grains which is further based on size of the grain kernel (full, half or broken) and rice varieties are recognized by human inspection. Results of the inspections may change depending on the expertise of inspectors and decisions made by inspectors according to external parameters. Furthermore, the decision making abilities of quality check inspectors are dependent on their personal characteristics such as exhaustion, annoyance, partiality etc. and identification of dissimilar varieties is carried out by applying traditional methods which are erroneous and consumes more time. Digital image processing techniques will help overcome the above issues, which offers quick, economical, and reliable solutions.

Agriculture, Grain Kernel, Image processing, Feature extraction, Grain counting, classification of rice grains

[1] Gurpreet Kaur, Bhupinder Verma. “Measurement standards based grading of rice kernels by separating touching kernels for embedded imaging applications”. International Journal of Electronics, Communication& Instrumentation Engineering Research and Development (IJECIERD) Vol. 3, Issue 1,127-134 Mar-2013.
[2] Jagdeep Singh Aulakh, V.K. Banga. “Grading of rice grains by image processing”, International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 4, June – 2012.
[3] Chetna V. Maheshwari, Kavindra R. Jain, Chintan K. Modi. “Non-destructive Quality Analysis of Indian Basmati Oryza Sativa SSP Indica (Rice) Using Image Processing”. IEEE – 2012.
[4] D.M. Hobson, R.M. Carter, Y .Yan. “Characterizations and Identification of Rice Grains through Digital Image Analysis”, IEEE-2007.
[5] R.Kiruthika, S.Muruganand, Azha Periasamy “Matching of Different Rice grains Using Digital Image processing”. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 2, Issue 7, 2013.
[6] Chaugule, A.: Application of image processing in seed technology: A survey. In: International Journal of Emerging Technology and Advanced Engineering, 2(4), (2012)
[7] L.A.I.Pabamalie,H.L.Premaratne,“A Grain Quality Classification System,” University of Colombo School of Computing, Sri Lanka, 2010 IEEE.
[8] Nandini Sidnal, Uttam V. Patil and Pankaja Patil “Grading and Quality Testing of Food Grains Using NeuralNetwork”, IJRET,Volume: 02 ,Nov -2013.
[9] Original website: “Food safety and standards authority of India”, Available at http://www.fssai.gov.in/
[10] Narendra V G, Hareesh K S . “Prospects of computer vision automated grading and sorting systems in agricultural and food products for quality evaluation”. International journal of computer applications (0975 – 8887), Manipal Institute of technology, Karnataka, 2010.
[11] K. S. Srinivasan and D. Ebenezer. “A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises”, IEEE, Vol. 14, No. 3,March 2007.
[12] Rafael C.Gonzalez and Richard E. woods, “Digital Image Processing”, PearsonEducation, Second Edition, 2005.
[13] www.mathworks.in.

Mrutyunjaya M S, Arulmurugan R, "An Efficient Image Processing Methodology to Assess Quality in Food Grains", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.126-128, 2019.

A critical increment for utilization of proximal/remote hyper spectral imaging frameworks to contemplate plant properties, types, and conditions. Various budgetary and ecological advantages of utilizing such frameworks have been the driving constrain inside this development. This paper is worried about the examination of hyper spectral information for identifying plant sicknesses and stress conditions and ordering crop types by methods for cutting edge machine learning strategies. Primary commitment of the work lies in the utilization of an inventive order system for the examination, in which versatile component choice, curiosity recognition, and troupe learning are coordinated. Three hyper spectral datasets and a non-imaging hyper spectral dataset were utilized in the assessment of the proposed structure. Show critical upgrades accomplished by the proposed technique contrasted with the utilization of exact ghastly lists and existing arrangement techniques.

Hyper spectral Data imaging, ND, plant monitoring, remote sensing, SVM

[1] A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Shake, "Imaging spectrometry for earth remote detecting," Science, vol. 228, no. 4704, pp. 1147– 1153,1985.
[2] P. L. M. Geladi, H. F. Grahn, and J. E. Burger, "Multivariate pictures, hyper spectral imaging: Background and gear," in Techniques and Uses of Hyperspectral Image Analysis. New York, NY, USA: Wiley, 2007, pp. 1– 15.
[3] G. ElMasry and D.- W. Sun, "Section 1: Principles of hyper spectral imaging innovation," in Hyperspectral Imaging for Food Quality Analysis and Control, D.- W. Sun, Ed. San Diego, CA, USA: Academic, 2010, pp. 3– 43.
[4] J. Campbell and R. Wynne, Introduction to Remote Sensing, fifth ed. New York, NY, USA: Guilford Publications, 2011.
[5] A. Cracknell and L. Hayes, Introduction to Remote Sensing, second ed. Boca Raton, FL, USA: CRC Press, 2007.
[6] Y. Zhang, D. C. Butcher, and E. S. Staab, "Hearty hyper spectral visionbased characterization for multi-season weed mapping," ISPRS J. Photogramm. Remote Sens., vol. 69, pp. 65– 73, 2012.
[7] H. Liu, S.- H. Lee, and J. S. Chahl, "Advancement of a proximal machine vision framework for off-season weed mapping in broadacre no-culturing fallows," J. Comput. Sci., vol. 9, no. 12, pp. 1803– 1821, 2013.
[8] P. Geladi, E. Bengtsson, K. Esbensen, and H. Grahn, "Picture examination in science I. Properties of pictures, greylevel activities, the multivariate picture," Trends Anal. Chem., vol. 11, no. 1, pp. 41– 53, 1992.
[9] G. Lu and B. Fei, "Therapeutic hyper spectral imaging: An audit," J. Biomed. Select., vol. 19, no. 1, 2014, Art. no. 010 901.
[10] C. Duchesne, J. Liu, and J. MacGregor, "Multivariate picture examination in the process ventures: An audit," Chemometrics Intell. Lab. Syst., vol. 117, pp. 116– 128, 2012.
[11] E. Barrett and L. Curtis, Introduction to Environmental Remote Sensing. Dordrecht, The Netherlands: Springer, 1992.
[12] J. Qin, "Section 5: Hyperspectral imaging instruments," in Hyperspectral Imaging for Food Quality Analysis and Control, D.- W. Sun, Ed. San Diego, CA, USA: Academic, 2010, pp. 129– 172.
[13] I. Herrmann, U. Shapira, S. Kinast, A. Karnieli, and D. J. Bonfil, "Groundlevel hyper spectral symbolism for recognizing weeds in wheat fields," Precis. Agribusiness, vol. 14, no. 6, pp. 637– 659, 2013.
[14] A. AlSuwaidi, "Dubaisat-2 mission outline," SPIE Remote Sens., vol. 8533, 2012, Art. no. 85330W.
[15] A. AlRais, A. AlSuwaidi, and H. Ghedira, "Dubaisat-1: Mission outline, advancement status and future applications," in Proc. IEEE Int. Geosci. Remote Sens. Symp., vol. 5, Jul. 2009, pp. V-196– V-199.
[16] A. AlSuwaidi, C. Veys, M. Hussey, B. Lament, and H. Yin, "Hyperspectral choice based calculation for plant characterization," in Proc. IEEE Int. Conf. Imag. Syst. Techn., Oct. 2016, pp. 395– 400.
[17] A. AlSuwaidi, C. Veys, M. Hussey, B. Lament, and H. Yin, "Hyperspectral highlight choice outfit for plant arrangement," in Proc. Hyperspectr. Imag. Appl., Session 4-12.00, Oct. 2016.
[18] A. M. Martinez and A. C. Kak, "PCA versus LDA," IEEE Trans. Example Butt-centric. Mach. Intell., vol. 23, no. 2, pp. 228– 233, Feb. 2001.
[19] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, second ed. New York, NY, USA: Wiley-Interscience, 2000.
[20] L. Jiang, B. Zhu, and Y. Tao, "Section 3: Hyperspectral picture arrangement techniques," in Hyperspectral Imaging for Food Quality Analysis also, Control, D.- W. Sun, Ed. San Diego, CA, USA: Academic, 2010, pp. 79– 98.
[21] H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Information Mining. Norwell, MA, USA: Kluwer, 1998.
[22] L. C. Molina, L. Belanche, and A. Nebot, "Highlight choice calculations: An overview and exploratory assessment," in Proc. IEEE Int. Conf. Information Mining, 2002, pp. 306– 313.
[23] K. Kira and L. A. Rendell, "The element choice issue: Traditional strategies and another calculation," in Proc. tenth Nat. Conf. Artif. Intell., 1992, pp. 129– 134.
[24] M. Robnik-Sikonja and I. Kononenko, "Hypothetical and experimental investigation ˇ of Relieff and RRelieff," Mach. Learn., vol. 53, no. 1, pp. 23– 69, 2003.
[25] M. A. Lobby and L. A. Smith, "Highlight determination for machine learning: Contrasting a relationship based channel approach with the wrapper," in Proc. twelfth Int. Florida Artif. Intell. Res. Soc. Conf., 1999, pp. 235– 239.
[26] I. H. Witten, E. Forthcoming, and M. A. Corridor, "Section 8: Ensemble learning," in Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems), I. H. Witten, E. Forthcoming, and M. A. Corridor, Eds., third ed. Boston, MA, USA: Morgan Kaufmann, 2011, pp. 351– 373.
[27] T. G. Dietterich, "Machine-learning research: Four ebb and flow headings," Artif. Intell. Mag., vol. 18, pp. 97– 136, 1997.
[28] R. Polikar, "Group based frameworks in basic leadership," IEEE Circuits Syst. Mag., vol. 6, no. 3, pp. 21– 45, Third Quart. 2006.
[29] L. O. Jimenez, A. Spirits Morell, and A. Creus, "Arrangement of hyperdimensional information dependent on highlight and choice combination approaches utilizing projection interest, lion`s share casting a ballot, and neural systems," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1360– 1366, May 1999.
[30] U. G. Mangai, S. Samanta, S. Das, and P. R. Chowdhury, "An overview of choice combination and highlight combination techniques for example arrangement," IETE Tech. Rev., vol. 27, no. 4, pp. 293– 307, 2010.
[31] Y. Zhong, Q. Cao, J. Zhao, A. Mama, B. Zhao, and L. Zhang, "Ideal choice combination for urban land-use/arrive cover grouping dependent on versatile differential development utilizing hyper spectral and LiDAR information," Remote Sens., vol. 9, no. 8, 2017, Art. no. 868.
[32] P. Ghamisi, J. Square, Y. Chen, J. Li, and A. J. Square, "Progressed otherworldly classifiers for hyper spectral pictures: An audit," IEEE Geosci. Remote Sens. Mag., vol. 5, no. 1, pp. 8– 32, Mar. 2017.
[33] F. Melgani and L. Bruzzone, "Characterization of hyper spectral remote detecting pictures with help vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778– 1790, Aug. 2004.
[34] P. Ghamisi, A. R. Ali, M. S. Couceiro, and J. A. Benediktsson, "An epic developmental swarm fluffy bunching approach for hyper spectral symbolism," IEEE J. Select. Subjects Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2447– 2456, Jun. 2015.
[35] B. M. Shahshahani and D. A. Landgrebe, "The impact of unlabeled examples in decreasing the little example measure issue and alleviating the Hughes marvel," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 5, pp. 1087– 1095, Sep. 1994.
[36] C. Persello and L. Bruzzone, "Dynamic and semisupervised learning for the characterization of remote detecting pictures," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 6937– 6956, Nov. 2014.
[37] L. Hamel, "Curiosity location," in Knowledge Discovery With Support Vector Machines. New York, NY, USA: Wiley, 2009, pp. 209– 217.
[38] M. A. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko, "An audit of curiosity discovery," Signal Process., vol. 99, pp. 215– 249, 2014.
[39] L. Clifton, D. A. Clifton, Y. Zhang, P. Watkinson, L. Tarassenko, and H. Yin, "Probabilistic curiosity discovery with help vector machines," IEEE Trans. Reliab., vol. 63, no. 2, pp. 455– 467, Jun. 2014.
[40] F. de Morsier, D. Tuia, M. Borgeaud, V. Gass, and J. P. Thiran, "Semisupervised oddity identification utilizing SVM whole arrangement way," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 4, pp. 1939– 1950, Apr. 2013.
[41] D. A. Clifton, L. A. Clifton, P. R. Railing, and L. Tarassenko, "Computerized curiosity discovery in modern frameworks," in Advances of Computational Intelligence in Industrial Systems, Y. Liu, A. Sun, H. T. Loh, W. F. Lu, and E.- P. Lim, Eds. Berlin, Germany: Springer, 2008, pp. 269– 296.
[42] W. Fisher, "Advancement in remote detecting (1972– 1976)," Photogrammetria, vol. 32, pp. 33– 72, 1976.
[43] F. A. Kruse, J. W. Boardman, and J. F. Huntington, "Examination of airborne hyper spectral information and EO-1 Hyperion for mineral mapping," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 6, pp. 1388– 1400, Jun. 2003.
[44] H. Breit, T. Fritz, U. Balss, M. Lachaise, A. Niedermeier, and M. Vonavka, "TerraSAR-X SAR preparing and items," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 2, pp. 727– 740, Feb. 2010.
[45] A. AlSuwaidi, A. AlRais, and H. Ghedira, "Building up a satellite-based device to screen dust a dust storms in the UAE," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jul. 2010, pp. 1434– 1437.
[46] A. Merentitis, C. Debes, and R. Heremans, "Group learning in hyper spectral picture arrangement: Toward choosing a great predisposition change tradeoff," IEEE J. Select. Points Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1089– 1102, Apr. 2014.
[47] J. Xia, J. Chanussot, P. Du, and X. He, "Turn based help vector machine group in arrangement of hyper spectral information with restricted preparing tests," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 3, pp. 1519– 1531, Mar. 2016.
[48] K. H. Holland, D. W. Sheep, and J. S. Schepers, "Radiometry of proximal dynamic optical sensors (AOS) for farming detecting," IEEE J. Select. Points Appl. Earth Observ. Remote Sens., vol. 5, no. 6, pp. 1793– 1802, Dec. 2012.
[49] D. A. Sims and J. A. Gamon, "Connections between leaf shade content also, unearthly reflectance over a wide scope of animal varieties, leaf structures and formative stages," Remote Sens. Environ., vol. 81, no. 23, pp. 337– 354, 2002.
[50] G. Rondeaux, M. Steven, and F. Baret, "Improvement of soil-balanced vegetation lists," Remote Sens. Environ., vol. 55, no. 2, pp. 95– 107, 1996.
[51] A. Gitelson and M. N. Merzlyak, "Phantom reflectance changes related with harvest time senescence of Aesculus hippocastanum L. what`s more, Acer platanoides L. leaves. ghostly highlights and connection to chlorophyll estimation," J. Plant Physiol., vol. 143, no. 3, pp. 286– 292, 1994.
[52] T. P. Dawson and P. J. Curran, "another method for adding the reflectance red edge position," Int. J. Remote Sens., vol. 19, no. 11, pp. 2133– 2139, 1998.
[53] A.- K. Mahlein, T. Rumpf, P. Welke, H.- W. Dehne, L. Plmer, U. Steiner, what`s more, E.- C. Oerke, "Advancement of ghostly files for distinguishing and recognizing plant ailments," Remote Sens. Environ., vol. 128, pp. 21– 30, 2013.
[54] J. Penuelas, J. Pinol, R. Ogaya, and I. Filella, "Estimation of plant water fixation by the reflectance water list WI (R900/R970)," Int. J. Remote Sens., vol. 18, no. 13, pp. 2869– 2875, 1997.
[55] B.- C. Gao, "NDWI—A standardized distinction water list for remote detecting of vegetation fluid water from space," Remote Sens. Environ., vol. 58, no. 3, pp. 257– 266, 1996.
[56] T. Rumpf, A.- K. Mahlein, U. Steiner, E.- C. Oerke, H.- W. Dehne, and L. Plmer, "Early discovery and order of plant ailments with help vector machines dependent on hyper spectral reflectance," Comput. Electron. Farming, vol. 74, no. 1, pp. 91– 99, 2010.
[57] A. Apan, A. Held, S. Phinn, and J. Markley, "Recognizing sugarcane orange rust malady utilizing EO-1 Hyperion hyper spectral symbolism," Int. J. Remote Sens., vol. 25, no. 2, pp. 489– 498, 2004.
[58] S. L. Ustin, D. A. Roberts, M. Gardner, and P. Dennison, "Assessment of the capability of Hyperion information to appraise out of control fire risk in the Santa Ynez front range, Santa Barbara, California," in Proc. IEEE Int. Geosci. Remote Sens. Symp., vol. 2, 2002, pp. 796– 798.
[59] I. Guyon and A. Elisseeff, "A prologue to variable and highlight determination," J. Mach. Learn. Res., vol. 3, pp. 1157– 1182, Mar. 2003.
[60] H. Liu and L. Yu, "Toward incorporating highlight choice calculations for characterization and bunching," IEEE Trans. Knowl. Information Eng., vol. 17, no. 4, pp. 491– 502, Apr. 2005.
[61] A. M. De Silva and P. H. W. Leong, "Highlight determination," in GrammarBased Feature Generation for Time-Series Prediction. Singapore: Springer, 2015, pp. 13– 24.
[62] G. Dark colored, A. Pocock, M.- J. Zhao, and M. Lujn, "Restrictive probability boost: A binding together structure for data theoretic element choice," J. Mach. Learn. Res., vol. 13, pp. 27– 66, 2012.
[63] J. Reiche, C. M. Souza, D. H. Hoekman, J. Verbesselt, H. Persaud, and M. Herold, "Highlight level combination of multi-fleeting ALOS PALSAR and Landsat information for mapping and observing of tropical deforestation and backwoods debasement," IEEE J. Select. Themes Appl. Earth Observ. Remote Sens., vol. 6, no. 5, pp. 2159– 2173, Oct. 2013.
[64] W. Liao, A. Piurica, R. Bellens, S. Gautama, and W. Philips, "Summed up diagram based combination of hyper spectral and LiDAR information utilizing morphological highlights," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 3, pp. 552– 556, Blemish. 2015.
[65] X. Bai, C. Liu, P. Ren, J. Zhou, H. Zhao, and Y. Su, "Article arrangement by means of highlight combination based underestimated parts," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 1, pp. 8– 12, Jan. 2015.
[66] G. Dark colored, J. Wyatt, R. Harris, and X. Yao, "Decent variety creation techniques: An overview and categorisation," J. Inf. Combination, vol. 6, pp. 5– 20, 2005.
[67] S. Prasad and L. M. Bruce, "Choice combination with certainty based weight task for hyper spectral target acknowledgment," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1448– 1456, May 2008.
[68] A. C. Turlapaty, H. K. Goru, and B. Gokaraju, "An element subset based choice combination approach for scene grouping utilizing shading, otherworldly, and surface insights," in Proc. IEEE seventh Int. Adv. Comput. Conf., Jan. 2017, pp. 624– 628.
[69] D. M. Duty and R. P. Duin, "Bolster vector information portrayal," Mach. Learn., vol. 54, no. 1, pp. 45– 66, 2004.
[70] V. Chandola, A. Banerjee, and V. Kumar, "Oddity identification: A review," ACM Comput. Surv., vol. 41, no. 3, pp. 15:1– 15:58, Jul. 2009.
[71] C. M. Cleric, Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer, 2006.
[72] V. Hautamaki, I. Karkkainen, and P. Franti, "Anomaly identification utilizing Knearest neighbor diagram," in Proc. seventh Int. Conf. Example Recog., vol. 3, Aug. 2004, pp. 430– 433. [73] B. Scholkopf, R. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, ¨"Bolster vector technique for oddity identification," in Proc. twelfth Int. Conf. Neural Inf. Process. Syst., 1999, pp. 582– 588.
[74] B. Scholkopf, J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. ¨ Williamson, "Evaluating the help of a high-dimensional dispersion," Neural Comput., vol. 13, no. 7, pp. 1443– 1471, Jul. 2001.
[75] T. M. Cover and J. A. Thomas, Elements of Information Theory (Wiley Arrangement in Telecommunications and Signal Processing). Hoboken, NJ, USA: Wiley-Interscience, 2006.
[76] S. Kulkarni and G. Harman, An Elementary Introduction to Statistical Learning Theory, first ed. New York, NY, USA: Wiley, 2011.
[77] G. Camps-Valls and L. Bruzzone, "Bit based techniques for hyper spectral picture characterization," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351– 1362, Jun. 2005.
[78] A.- K. Mahlein, U. Steiner, H.- W. Dehne, and E.- C. Oerke, "Ghostly marks of sugar beet leaves for the location and separation of illnesses," Summary. Horticulture, vol. 11, no. 4, pp. 413– 431, 2010.
[79] D. H. Cultivate, K. Amano, and S. M. C. Nascimento, "Shading steadiness in regular scenes clarified by worldwide picture insights," Vis. Neurosci., vol. 23, nos. 3– 4, pp. 341– 349, 2006.
[80] D. H. Cultivate, K. Amano, S. M. C. Nascimento, and M. J. Cultivate, "Recurrence of metamerism in common scenes," J. Pick. Soc. Amer. A, vol. 23, no. 10, pp. 2359– 2372, Oct. 2006.
[81] A.- K. Mahlein, U. Steiner, C. Hillnhutter, H.- W. Dehne, and E.- C. Oerke, ¨ "Hyperspectral imaging for little scale examination of indications caused by various sugar beet illnesses," Plant Methods, vol. 8, no. 1, 2012, Workmanship. no. 3.
[82] A.- K. Mahlein, S. Hammersley, E.- C. Oerke, H.- W. Dehne, H. Goldbach, furthermore, B. Lament, "Supplemental blue LED lighting exhibit to enhance the flag quality in hyper spectral imaging of plants," Sensors, vol. 15, no. 6, pp. 12834– 12840, 2015.
[83] G. A. Carter, "Proportions of leaf reflectances in thin wavebands as pointers of plant pressure," Int. J. Remote Sens., vol. 15, no. 3, pp. 697– 703, 1994.
[84] C. Daughtry, C. Walthall, M. Kim, E. de Colstoun, and J. McMurtrey III, "Assessing corn leaf chlorophyll fixation from leaf and shade reflectance," Remote Sens. Environ., vol. 74, no. 2, pp. 229– 239, 2000.
[85] D. Haboudane, J. R. Mill operator, N. Tremblay, P. J. Zarco-Tejada, and L. Dextraze, "Coordinated tight band vegetation lists for forecast of edit chlorophyll content for application to accuracy agribusiness," Remote Sens. Environ., vol. 81, no. 23, pp. 416– 426, 2002.
[86] J. Gamon, J. Peuelas, and C. Field, "A limited waveband phantom record that tracks diurnal changes in photosynthetic effectiveness," Remote Sens. Environ., vol. 41, no. 1, pp. 35– 44, 1992.
[87] G.- Z. Li and J. Y. Yang, "Highlight determination for group learning and its application," in Machine Learning in Bioinformatics. New York, NY, USA: Wiley, 2008, pp. 135– 155.

Anuj Rapaka, Arul Murgan Ramu, "Feature Extraction Techniques in Hyper Spectral Data Sets for Classification Purpose", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.129-135, 2019.

The application of artificial intelligence involves the areas such as artificial intelligence, expert system, artificial neural network, fuzzy logic, image processing, natural language processing, speech recognition, robotics etc. Though these areas are not separate, at times two or more applications are contributes to enrich the library services. In this article, the authors have explored the various possible applications of artificial intelligence as mentioned above. In addition, authors explain the possible areas where few of these applications can be implemented which enhances the quality of services and thereby create the potential impact of AI on library services.

Artificial Intelligence, Academic Libraries, Expert systems, Robotics, Machine Learning

[1] A. A. Asefeh Asemi, "Artificial Intelligence (AI) application in Library Systems in Iran: A taxonomy study," Library Philosophy and Practice, vol. 2, March-April 2018.
[2] S. S. Mogali, "Artificial Intelligence and its applications in Libraries," 2014.
[3] J. Charles W. Bailey, "Intelligent library systems: artificial intelligence technology and library automation systems," Advances in Library Automation and Networking, vol. 4, pp. 1-23, 1991.
[4] W. Kristin, "Libraries in an Artificially Intelligent World," Public Libraries, 2016. [Online]. Available: http://publiclibrariesonli ne.org/2016/02/libraries-in-an-artificially-intelligent-world/ [Accessed Feb 2016].
[5] B. Johnson, "Libraries in the age of artificial intelligence," Computers in Libraries, vol. 38, 2018.
[6] G. Liu, "The application of intelligent agents in libraries: a survey," Electronic library and information systems, vol. 45, pp. 78-97, 2011.
[7] N. Jacknis, "The AI-Enhanced Library," [Online]. Available: https://medium.com/@ NormanJacknis/the-ai-enhanced-library-a34d96fffdfe [Accessed 2019 Jun 2017].
[8] Merriam-webster, "Definition of Artificial Intelligence," 2019. [Online]. Available: https://www.merriam-webster.com/dictionary/artificial+intelligence. [Accessed Feb 2019].

S. Vijayakumar, K.N. Sheshadri, "Applications of Artificial Intelligence in Academic Libraries", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.136-140, 2019.

The introduction of the Cognitive Approach of Computing would lead to a comprehensive and better decision-making system. This approach of cognitive computing on the Internet of Things is called Cognitive IoT or CIoT. This paper focuses on various different kinds of applications of Cognitive Computing in the field of IoT. The goal of Cognitive IoT is to make computers understand people by trying to recreate how the neocortex in our brain works to make them think like humans. These fields are interdependent as the computer requires numerous data points. With a collection of other devices performing the same task, their results can be used as information for the system to learn. The Internet of Things` concept of a "system of systems" helps Cognitive Computing`s core plan to simulate the human brain move forward.

Cognitive Computing, Internet of Things, Artificial Intelligence, Machine Learning, Big Data Analysis

[1] Charles Roe, 8 May 2014, A Brief History of Cognitive Computing https://www.dataversity.net/brief-history-cognitive-computing/
[2] Amit Fisher n.d, Cognitive IoT: What is Watson IoT? https://resources.sei.cmu.edu/asset_files/Presentation/2016_017_001_454718.pdf
[3] Sky Matthews, 24 March 2016, What is Cognitive IoT? https://www.ibmbigdatahub.com/blog/what-cognitive-iot
[4] Dr. Harald Raetzsch, Cognitive Computing and Internet of Things, https://swisscognitive.ch/2017/10/10/cognitivenews-cognitive-computing-and-the-internet-of-things/
[5] Dominique Ginard and Vlad Trifa, Hello, World Wide Web of Things, https://internetofthings.technicacuriosa.com/2017/03/09/hello-world-wide-web-of-things/
[6] Wikipedia, World Wide Web, https://en.wikipedia.org/wiki/World_Wide_Web
[7] M. Alhuessein, G. Muhammad, M. S. Hossain, S. U. Amin, “Cognitive IoT-Cloud Integration for Smart Healthcare: Case Study for Epileptic Seizure Detection and Monitoring”, Mobile Netw Appl (2018), Vol. 23, No.,6, pp 1624-1635, 2018.
[8] Priya Dilani, 28 December 2018, The role of AI and IoT in Future of Insurance Industry, https://www.analyticsinsight.net/the-role-of-ai-and-iot-in-future-of-insurance-industry/
[9] Kamalika Some, 23 September 2018, How analytics insures the insurance industry? https://www.analyticsinsight.net/how-analytics-insures-the-insurance-industry/
[10] Colin Bristow, 28 November 2016, Six ways cognitive computing will impact banks, https://blogs.sas.com/content/hiddeninsights/2016/11/28/six-ways-cognitive-computing-will-impact-banks/
[11] Doug Drinkwater, 6 December 2017, Eight examples of how iot is improving retail banking, https://internetofbusiness.com/eight-examples-iot-retail-banking/
[12] 5 ways iot will help banking in 2018 and beyond, https://datafloq.com/read/5-ways-iot-will-help-banking-2018-beyond/4825
[13] Cognitive computing and education sector, http://www.aftabhussain.com/cognitive_computing.html
[14] Leticia Lafuente Lopez, 2 October 2016, Watson: cognitive computing in education, https://elearningindustry.com/watson-cognitive-computing-in-education
[15] Fueling global climate research with cognitive computing applications, https://www.nextplatform.com/microsite-landing/hpe-solutions/fueling-global-climate-research-cognitive-computing-applications/
[16] Protecting the environment, https://www.ibm.com/watson/advantage-reports/ai-social-good-sustainability.html
[17] Mrzimmerman, 27 May 2016, How cognitive computing can drive sustainability, https://www.ibm.com/blogs/think/2016/05/cognitive-sustainability/
[18] Rodrigo Arias, Knud Lasse Lueth, Abhay Rastogi, 21 January 2018, The effect of the internet of things on sustainability, https://www.weforum.org/agenda/2018/01/effect-technology-sustainability-sdgs-internet-things-iot/
[19] Laura Cox, 31 July 2018, 5 ways the lot is supporting sustainability, https://disruptionhub.com/5-ways-iot-supports-sustainability/
[20] Dennis Gerson, 4 March 2014, IBM moves cognitive computing out of the lab and into the kitchen and food truck, https://www.ibm.com/blogs/insights-on-business/consumer-products/ibm-moves-cognitive-computing-out-of-the-lab-and-into-the-kitchen-and-food-truck/
[21] Cognitive computing …. For farming? , https://www.tyguspartners.com/blog/2016/7/13/cognitive-computingfor-farming
[22] N. Mishra, C. Lin, H. Chang, “A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective”, Vol. 11, No.,10, 2015.
[23] C. Savaglio, G. Fortino, M. Zhou, “Towards interoperable, cognitive and automatic IoT systems: An Agent-based approach”, 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), 2017.
[24] K. J. Sharma, V. Ramamohan, “Prospects of Congitive Computing Research”, International Journal of Science, Engineering, and Technology Research (IJSETR), Vol. 5, No.,2, pp 552-562, 2016.

Ashutosh Rao Chawan Umesh Rao Chawan, Parikshith Vallish, "A Study on Applications of Cognitive Computing in the Internet of Things", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.141-147, 2019.

In mobile computing to determine the future location of Mobile user movement is used to manage communication and to provide the Quality of location based services. Mobile computing surroundings are considered by wireless links are not faster and comparatively below privileged hosts are battery powers were inadequate , inclined to frequent interruptions. Mobile hosts (MHs) caching a data in a wireless network helps to slove the problems related with slow, inadequate bandwidth wireless links, by plummeting preserving bandwidth. Battery power is preserved by plummeting the number of up-link requests. There are some problems due to incomplete wireless communication, partial client source, suspension of client and unobstructed mobility. So it is problematic to uphold the data cache and pre-fetch in mobile computing. Three key techniques, namely, FLA (Future Location Agent), PLA (Preceding Location agent) and CLA (Contemporary Location agent) have been designed and proposed the future mobile user location using the user movement history. These methods use log and thread harmonization Classical for data caching and prefetching conservation in site founded queries .This method consequences in abridged server workload saving of wireless bandwidth and abridged network traffic.

mobile computing , Datacache , mobile cache , mobile agent

[1] D. Lee, B. Zheng, and W.-C. Lee, “Data Management in Location- Dependent Information Services,” IEEE Pervasive Computing, vol. 1, no. 3, pp. 65-72, July-Sept. 2002.
[2] B. Zheng, J. Xu, and D.L. Lee, “Cache Invalidation and Replacement Strategies for Location-Dependent Data in Mobile Environments,” IEEE Trans. Computers, vol. 15, no. 10, pp. 1141- 1153, Oct. 2002.
[3] B. Zheng and D.L. Lee, “Processing Location-Dependent Queries in a Multi-Cell Wireless Environment,” Proc. Second ACM Int’l Workshop Data Eng. for Wireless and Mobile Access, 2001.
[4] B. Zheng, J. Xu, W.-C. Lee, and D.L. Lee, “On Semantic Caching and Query Scheduling for Mobile Nearest-Neighbor Search,” Wireless Networks, vol. 10, no. 6, pp. 653-664, Dec. 2004.
[5] X. Gao and A. Hurson, “Location Dependent Query Proxy,” Proc -ACM Int’l Symp. Applied Computing, pp. 1120-1124, 2005.
[6] X. Gao, J. Sustersic, and A.R. Hurson, “Window Query Processing with Proxy Cache,” Proc. Seventh IEEE Int’l Conf. Mobile Data Management, 2006.
[7] K.C. Lee, J. Schiffman, B. Zheng, and W.-C. Lee, “Valid Scope Computation for Location-Dependent Spatial Query in Mobile Broadcast Environments,” Proc. 17th ACM Conf. Information and Knowledge Management, pp. 1231-1240, 2008.
[8] T. Camp, J. Boleng, and V. Davies, “A Survey of Mobility Models for Ad Hoc Network Research,” Wireless Comm. Mobile Computing, vol. 2, no. 5, pp. 483-502, Sept. 2002.
[9] S.-C. Lo, G. Lee, W.-T. Chen, and J.-C. Liu, “Architecture for Mobility and Qos Support in All-Ip Wireless Networks,” IEEE J. Selected Areas Comm., vol. 22, no. 4, pp. 691-705, May 2004.
[10] F.P. Tso, J. Teng, W. Jia, and D. Xuan, “Mobility: A Double-Edged Sword for Hspa Networks: A Large-Scale Test on Hong Kong Mobile Hspa Networks,” Proc. MobiHoc Conf., pp. 81-90, 2010.

G. Shanmugarthinam, "Mobile user location Prediction in cellular network using Agent Technique", International Journal of Computer Sciences and Engineering, Vol.07, Issue.16, pp.148-154, 2019.