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A collection of inter-related files on one or more web servers is known as Web, while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web pages, web links, objects on the web and web logs. Web mining is used to understand the customer behavior, evaluate a particular website based on the information, which is stored in web log files. Web mining is generated by making use of the data mining techniques, classification, clustering, and association rules. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. With the flood of information on the Web, Web mining is a new research issue, which draws great interest from many communities. Currently, there is no agreement about Web mining yet. It needs more discussion among researchers in order to define what it is exactly. In this paper, we have studied the basic concepts of web, web mining, classification, processes, the taxonomy and the function of Web mining.

Web Mining, Taxonomy, web structure mining

[1] Mr. Dushyant B.Rathod, Dr.Samrat Khanna, “A Review on Emerging Trends of Web Mining and its Applications” ISSN: 2321-9939
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[5] R. Lokeshkumar1, R. Sindhuja2, Dr. P. Sengottuvelan, “A Survey on Pre-processing of Web Log Filein Web Usage Mining to Improve the Quality of Data” International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 8, August 2014.
[6] AshirrK Kashyap, Iflah Naseem, Dheeraj Mandloi, "Web Mining an Approach to Evaluate the Web", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.79-85, 2017
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[8] Sonia Sharma, Munishwar Rai, "Customer Behaviour Analysis using Web Usage Mining", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.47-50, 2017
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[10] Jaideep Srivastava, “Web Mining: Accomplishments & Future Directions”, University of Minnesota USA, srivasta@cs.umn.edu,
[11] Md. Zahid Hasan, Khawja Jakaria Ahmad Chisty and Nur-E-Zaman Ayshik, “Research Challenge in Web Data Mining”, International Journal of Computer Science and Telecommunications Volume 3, Issue 7, July 2012
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[13] Ananthi.J,” A Survey Web Content Mining Methods and Applications for Information Extraction from Online Shopping Sites”, International Journal of Computer Science and Information Technologies, Vol. 5 (3), 2014.
[14] http://www.slideshare.net/Tommy96/web-mining-tutorial R. Lokeshkumar1, R. Sindhuja2, Dr. P. Sengottuvelan, “A Survey on Pre-processing of Web Log File in Web Usage Mining to Improve the Quality of Data” International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 8, August 2014.
[15] Alberto Sillitti, Marco Scotto, Giancarlo Succi, Tullio Vernazza,” News Miner: a Tool for Information Retrieval”
[17] Mamta M. Hegde, Prof. M.V.Phatak, “Developing an approach for hyperlink analysis with noise reduction using Web Structure Mining”, International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May2012.

P.Joseph Charles, I.Carol, Barna Bass, "Research Issues on Web Mining", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.43-48, 2018.

To improve the performance of access control in cloud environment, different approaches has been discussed earlier. Towards the problem of access restriction, an efficient trust based access control mechanism is presented in this paper. The method maintains the access details of various services of different users in different time window. The time window access logs has various information about the service access and their status of completion. Using the log available, the method estimates the frequency of services being accessed. Also with the status of the services access, the method computes the trust factor. The frequency measures has been computed for different time window for each service. Based on the value of frequency measures, the method generates fuzzy rules. Based on the fuzzy rules generated, the trust factor has been estimated for any user. The method produces efficient results in access restriction and reduces the time complexity as well.

Cloud Environment, Access Control, Fuzzy Rules, Frequency Analysis, Time Orient Approach

[1]. Wei Teng ; Geng Yang Attribute-based Access Control with Constant-size Ciphertext in Cloud Computing, IEEE Transactions on Cloud Computing ( Volume: PP, Issue: 99 ), Page(s): 1 – 1, 2015.
[2]. Jun Luo, A Novel Role-based Access Control Model in Cloud Environments, Journal International Journal of Computational Intelligence Systems Volume 9, 2016 - Issue 1, 2016.
[3]. Lixia Xie and Chong Wang, Cloud Multidomain Access Control Model Based on Role and Trust-Degree, Hindawi, Journal of Electrical and Computer Engineering Volume 2016 (2016).
[4]. Kan Yang, Time-Domain Attribute-Based Access Control for Cloud-Based Video Content Sharing: A Cryptographic Approach, IEEE Transactions on Multimedia, Vol 18, Issue: 5, May 2016.
[5]. Jongkil Kim, Surya Nepal, A Cryptographically Enforced Access Control with a Flexible User Revocation on Untrusted Cloud Storage, Data Science and Engineering , Volume 1, Issue 3, pp 149–160, 2016.
[6]. Mehdi Sookhaka,, F. Richard Yua, Attribute-based data access control in mobile cloud computing: Taxonomy and open issues, Elsevier, Future Generation Computer Systems 72 (2017) 273–287.
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[8]. Nicolai Paladi, Providing User Security Guarantees in Public Infrastructure Clouds, IEEE Transaction on Cloud Computing, Vol. 5, Issue 3, 2017.
[9]. Jesus Luna, Quantitative Reasoning about Cloud Security Using Service Level Agreements, IEEE Transaction on Cloud Computing, Vol. 3, Issue 5, 2017.
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[11]. Kei Fan, Privacy protection based access control scheme in cloud-based services, IEEE Transaction on China Communications, vol. 14, Issue 3, 2017.
[12]. Sakshi kathuria, "A Survey on Security Provided by Multi-Clouds in Cloud Computing", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.1, pp.23-27, 2018.
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J. Persis Jessintha, R. Anbuselvi, "Time Window Based Frequency Analysis for Efficient Access Control in Cloud Using Fuzzy Logic", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.49-53, 2018.

The combinatorial optimization problem is a collection of problems which need a sample amount of time and effort to be solved. Vast difficulties have been occurring to solving these types of problem that there is no exact formula to solve the problem. Each feasible solution works on some order and the size of the probability increases algorithmically as the number of the problem also increases dynamically. This paper discusses about N–Queen problem, it is also a type of NP – hard problem. Many researchers have proposed various methods and algorithms for this problem. Henceforth, Genetic Algorithm is one kind of famous algorithm for solving NP hard problems. This paper mainly focuses on the review work of genetic algorithm to solve the N -Queen Problems (NPQ).

N–Queen Problem, NP-hard problem, Genetic algorithm, Heuristic algorithm

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S. Sathyapriya, R. Stephen, V.S.Joe Irudayaraj, "Survey on N-Queen Problem with Genetic Algorithm", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.54-58, 2018.

Deduplication is a task of identifying one or more records in repository that represents same object or entity. The problem is that the same data may be represented in different way in every database. While merging the databases, duplicates occur despite different schemas, writing styles or misspellings. They are called as replicas. Removing replicas from the reposi¬tories provides high quality information and saves processing time. With the development of cloud computing through virtualization technology, creation of VMs rapidly increasing, this in turn increases data centres. Backup in virtualized environments takes the snapshot of VM called VM image and moved to backup device. Data is duplicated by VMs for many purposes like backup, fault tolerance, consistency, disaster recovery, high availability, etc., these results in unnecessary consumption of resources, such as network bandwidth and storage space. Data Deduplication is a process of detecting and removing duplicate data thus the amount of data, energy consumption and network bandwidth is reduced. This paper describes Deduplication methods for large scale databases (Big data) and several Deduplication techniques like Extreme Binning, MAD2, and Multi-level Deduplication where Deduplication is performed in backup services. The paper also describes Cloud spider, Liquid Deduplication techniques for VM images in Big Data extracted from cloud environment, their comparison based on several factors.

Deduplication, Big data, Cloud, Live Virtual Machine Migration, Cloud spider, MAD2, Extreme Binning, Liquid, SAFE, Multi-Level Deduplication.

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A.Sahaya Jenitha, V.Sinthu Janita Prakash, "Deduplicates In Big Data: A Technical Survey", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.59-65, 2018.

Cloud computing is a modern technology that provides all types of resources to the users with the help of internet. Resource management is the key factor that decides the performance of the cloud. Scheduling plays a prominent role in managing resources in the cloud. Scheduling also affects the power consumption of the data center. The cost of providing services increases when in appropriate scheduling is used. The environmental pollution increases by the emission of carbon. In this paper scheduling algorithms in the perspective of scheduling metrics such as throughput, response time, Resource utilization, fault tolerance and performance are presented.

Resource scheduling, Cloud computing, Energy, Throughput, Response time

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Volume 2016 (2016), Article ID 3896065, 14
[36] Bibhav Raj, Pratyush Ranjan, Naela Rizvi, Prashant Pranav ,andSanchita Paul “Improvised Bat Algorithm for LoadBalancing-Based Task Scheduling”Progress in Intelligent Computing Techniques: Theory, Practice, and Applications pp 521-530 (2018)
[37] Narander Kumar and Diksha Shukla”Load Balancing Mechanism Using Fuzzy Row Penalty Method in Cloud ComputingEnvironment”Information and Communication Technology for Sustainable Development pp 365-373(2018)

V.A. Jane, B.J. Hubert Shanthan, L. Arockiam, "A Survey of Algorithms for Scheduling in the Cloud: In a metric Perspective", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.66-70, 2018.

Mobile Ad hoc Networks (MANET) are self configuring, transportation less, dynamic wireless networks in which the nodes are resource constrained. Intrusion Detection Systems (IDS) are used in MANETs to monitor actions so as to detect any intrusion in the otherwise vulnerable network. In this paper, we present efficient schemes for analyzing and optimizing the time length for which the intrusion detection systems require to remain active in a mobile ad hoc network. A probabilistic model is proposed that makes use of help between IDSs among neighbourhood nodes to reduce their unit active time. Usually, an IDS has to run all the time on every join to oversee the network behaviour.

[1] S. Zeadally, R. Hunt, Y-S. Chen, A. Irwin and A. Hassan, “Vehicular ad hoc networks (VANETS): status, results, and challenges,” Telecommunication Systems, vol. 50, no. 4, pp. 217-241, 2012.
[2] S. K. Bhoi and P. M. Khilar, ”Vehicular communication: a survey”, IET Networks, vol. 3, no. 3, pp. 204 - 217, 2014.
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[4] C. Manikopoulos and L. Ling, ”Architecture of the Mobile Ad-hoc Network Security (MANS) System,” Proc. IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3122- 3127, October 2003.
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[6] A. Partwardan, J. Parker, A. Joshi, M. Iorga and T. Karygiannis, ”Secure Routing and Intrusion Detection in Ad-hoc Networks,” Proc. 3rd IEEE International Conference on Pervasive Computing and Communications, Hawaii Island, Hawaii, March 8-12, 2005.
[7] N. Marchang and R. Datta, ”Lightweight Trust-based Routing Protocol for Mobile Ad Hoc Networks,” IET Information Security, vol. 6, no. 4, pp. 77-83, 2012.
[8] M. Hadded, R. Zagrouba, A. Laouiti, P. Muhlethaler, and L. A. Saidane, “A multi-objective genetic algorithm-based adaptive weighted clustering protocol in vanet,” in Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, pp. 994–1002.
[9] Y. Peng, Z. Abichar, and J. M. Chang, “Roadside-aided routing (RAR) in vehicular networks,” in IEEE International Conference on Communications, 2006, pp. 3602–3607.
[10] N. Wisitpongphan, O. K. Tonguz, J. S. Parikh, P. Mudalige, F. Bai, and V. Sadekar, “Broadcast storm mitigation techniques in vehicular ad hoc networks,” IEEE Wireless Communications, vol. 14, no. 6, pp. 84–94, 2007.
[11] I. Tal and G.-M. Muntean, “User-oriented cluster-based solution for multimedia content delivery over vanets,” in IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, 2012, pp. 1–5.
[12] Y. Shi, L. H. Zou, and S. Z. Chen, “A mobility pattern aware clustering mechanism for mobile vehicular networks,” in Applied Mechanics and Materials, vol. 130, 2012, pp. 317–320.
[13] C. S. Jensen, D. Lin, and B. C. Ooi, “Continuous Clustering of Moving Objects,” IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. 9, pp. 1161–1174, 2007.
[14] J. Bernsen and D. Manivannan, “Unicast routing protocols for vehicular ad hoc networks: A critical comparison and classification,” Pervasive and Mobile Computing, vol. 5, no. 1, pp. 1–18, 2009.
[15] K. Jagadeesh, S. S. Sathya, G. B. Laxmi, and B. B. Ramesh, “A survey on routing protocols and its issues in vanet,” International Journal of Computer Applications, vol. 28, no. 4, pp. 38–44, 2011.
[16] S. Singh and S. Agrawal, “Vanet routing protocols: Issues and challenges,” in Engineering and Computational Sciences (RAECS), 2014 Recent Advances in, 2014, pp. 1–5.

S. Sathiyavani , "A Novel Approach for Efficient Usage of Intrusion Detection System in Mobile Ad Hoc Networks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.71-75, 2018.

The RPL routing protocol published in RFC 6550 was designed for efficient and reliable data collection in low power and lossy networks. It constructs a Destination Oriented Direction Acyclic Graph (DODAG) for data forwarding. However, due to the load imbalance the parent nodes face heavier workload, bottle neck and hot spot problems. Such load imbalance will result in the parent nodes quickly exhausting their energy, and shorten the overall network lifetime. There are attempts to load balance the RPL network using LB-OF algorithm and Child Node Count (CNC) object. Load balance using LB-OF and CNC provides load balance of the DODAG but fails in terms of load balancing network resources, i.e., bandwidth. In this paper, we propose Bandwidth Allocation based load balancing algorithm in RPL named (BA-LBRPL). BA-LBRPL removes the deficiency faced in LB-OF and CNC and enhances resource load balancing in low power and lossy environments. Thus, BA-LBRPL is efficient theoretically in thwarting bottleneck, fast energy depletion of parents and improve life of the network

Internet of Things, RPL, Load Balancing, Bandwidth Allocation

[1] Z. Sheng, S. Yang, Y. Yu, A. Vasilakos, J. McCann, and K. Leung, “A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities”, IEEE Wirel. Commun., vol. 20, no. 6, pp. 91–98, 2013.
[2] S. Sivagurunathan et al (2016), “Connectivity Framework for smart Devices”(ed) by Zaigham Mahmood, Computer and Communications Network, Springer, 307-331, 2016
[3] T. Winter, P. Thubert, A. R. Corporation, and R. Kelsey, “RPL: Routing Protocol for Low Power and Lossy Networks,” pp. 1–157.
[4] H.-S. Kim, H. Kim, J. Paek, and S. Bahk, “Load Balancing under Heavy Traffic in RPL Routing Protocol for Low Power and Lossy Networks,” IEEE Trans. Mob. Comput., vol. 1233, no. c, pp. 1–1, 2016.
[5] Marwa Mamdough et al, “RPL Load balancing via minimum degree spanning tree”, IEEE transaction, 2016
[6] X. Liu, J. Guo, G. Bhatti, P. Orlik, and K. Parsons, “Load Balanced Routing for Low Power and Lossy Networks.”
[7] O. Iova, F. Theoleyre, and T. Noel, “Exploiting multiple parents in RPL to improve both the network lifetime and its stability,” IEEE Int. Conf. Commun., vol. 2015–Septe, pp. 610–616, 2015.
[8] Quan Le, Thu Ngo-Quynh, Thomas Magedanz et al, “RPL based multipath Routing protocols doe Internet of Things”, IEEE Xplore, 2014
[9] Minkeun Ha, Kiwoong Kwon, Daeyoung Kim, Peng-Yong Kong, “Dynamic and Distributed Load Balancing Scheme in Multi-gateway based 6LoWPAN”, IEEE International Conference on Green Computing, 2015
[10] B. G. Mamoun Qasem, Ahmed Al-Dubai, Imed Romdhani, “Load Balancing Objective Function in RPL,” . Internet Draft, February, pp. 1–10, 2017.
[11] O Gnawali, P Levis, “The Minimum Rank with Hysteresis Objective Functione”, RFC, pp. 1–13, 2012.

[12] J Hou, R Jadhav, Z Luo, “Optimization of Parent node delection in RPL based Networks”, Internet Draft, pp.1-11, 2017

A. Sebastian, S. Sivagurunathan, "Bandwidth Allocation based Load Balancing for RPL (BA-LBRPL)", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.76-80, 2018.

Facial Recognition is a primary branch of Pattern Recognition used to distinguish faces based on feature variation. All the objects in the universe has a proper arrangement termed as “pattern”, thus every human face has a unique pattern. Those patterns are identified or verified by Recognition process. Especially Facial Recognition done manually is a trouble-free process whereas recognizing a face using computers is a knotty task because of illumination, occlusions, facial expression and pose. The input for the recognition face can be in any digital sources such as image or video from the face database. Therefore the key features of the face such as brows, eyes, nose and mouth serves as input. In order to achieve utmost accuracy in facial recognition in digital images different feature extraction techniques are used in Facial Recognition. Feature based and Appearance based complexities like feature extraction and illumination for automated computer vision recognition is discussed. This paper evaluates feature extraction techniques in a broad perspective.

Facial Recognition, Feature Extraction, Illumination, Appearance Based, Recognition Rate

[1] Xiaoxing Li et al., “Adapting geometric attributes for expression-invariant 3D face recognition”, IEEE International Conference on Shape Modeling and Applications (SMI), 2007, pp 21- 32.
[2] Md. Sarfaraz Jalil, Joy Bhattacharya, “A Survey on Various Facial Expression Techniques”, International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015, pp 1212-1214.
[3] Jian Yang, David Zhang et al., “Two -Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 1, January 2004, pp 131-137.
[4] Baback Moghaddam et al., USA, “Bayesian face recognition”, The Journal of Pattern recognition society, Pattern Recognition 33 (2000), pp 1771-1782.
[5] Priyanka Sharma et al., Chandigarh, “Classification in Pattern Recognition: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 4, April 2013.
[6] Zhong Jin,et al., “Face recognition based on the uncorrelated Discriminant transformation” , The Journal of Pattern recognition society, Pattern Recognition 34 (2001), pp 1405-1416.
[7] Aruni Roy Chowdhury et al., “One-to-many face recognition with bilinear CNNs” in Computer Vision and Pattern Recognition, 28 Mar 2016.
[8] Roman et al., Poland, “Rough set methods in feature selection and recognition”, Pattern recognition letters, Pattern Recognition Letters 24 (2003), pp 833–849.
[9] Zhanwei Chenet al., “Towards a face recognition method based on uncorrelated discriminant sparse preserving projection”, Springer, Science + Business Media New York 2015.
[10] Rahimeh Rouhi et al., “A Review On Feature Extraction Techniques In Face Recognition”, Signal & Image Processing: An International Journal (SIPIJ) Vol.3, No.6, December 2012.
[11] Thomas et al., England, “Face Recognition: A Comparison of Appearance-Based Approaches” Proc. VIIth Digital Image Computing: Techniques and Applications, Dec 2003, Sydney.
[12] Jorg orts in Face Recognition Techniques, The University of Wisconsin madison in ECE533 – Image Processing Project.
[13] Timo Ahonen et al., “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 28, No. 12, December 2006, pp 2037 - 2041.
[14] Li-Fen Chen et al., “A new LDA-based face recognition system which can solve the small sample size problem”, Pattern Recognition 33 (2000), pp 1713-1726.
[15] Rajkiran et al., “An improved face recognition technique based on Modular PCA approach”, Pattern Recognition Letter 25 (2004), pp 429-436.
[16] King-Sun Fu et al., “Pattern Recognition and Image Processing”, IEEE Transactions On Computers, Vol. C-25, No. 12, December 1976, pp 1136 - 1346.
[17] M. Parisa Beham et al., “Face Recognition Using Appearance Based Approach: A Literature Survey” Proceedings published in International Journal of Computer Applications (IJCA), pp 16 - 21.
[18] M A Rabbani et al., “A Different Approach to Appearance –based Statistical Method for Face Recognition Using Median”, International Journal of Computer Science and Network Security, VOL.7 No.4, April 2007, pp 262 – 267.
[19] Aisha Azeem et al., Pakistan, “A Survey: Face Recognition Techniques under Partial Occlusion” in International Arab Journal of Information Technology, January 2014.
[20] Divyarajsinh et al., “Face Recognition Methods & Applications”, International Journal of Computer Technology & Applications, Vol 4 (1), pp 84-86.

M. Merlin Steffi, J. John Raybin Jose, "Comparative Analysis of Facial Recognition involving Feature Extraction Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.81-86, 2018.

Cloud computing is a distinctively different environment, that has captured many hearts and it has emerged as a powerful computing environment with the provision of high standard data storage mechanism and sharing the data efficiently among multiple users across the globe. As it is dynamic in nature, it offers innumerable advantages such as flexibility, resource pooling, elasticity, and scalability, etc. One of the important features of cloud computing is the multitenant environment, which enables outsourcing data into the server; however many security challenges incorporated with this are unauthorized access, data privacy, malicious attacks, and threats. Accessing the data from the server plays a very important role in cloud computing environment. This paper analysis several data access control schemes have been described to ensure the data access as convenient and efficient as possible. Access control is a security technique that defines the access policy, and it can be used to legalize who or what can use various resources in a computing environment. It is necessary to have tightly controlled system to access the data securely and the data access risk must be addressed. This survey explores myriad ways of Cloud Data Access Control Techniques and its challenges.

Cloud Computing; Access Control System; Security Technique; Authentication

[1] Mell Peter, and Tim Grance, "The NIST Definition of Cloud Computing.”, pp. 20-23, 2011.
[2] Arockiam, L. and Monikandan, S. and Parthasarathy G. “Cloud Computing: A Survey”, International Journal of Internet Computing, Volume 1, No. 2, pp.26-33, 2011.
[3] M. Kallahalla, E. Riedel, R. Swaminathan, Q. Wang, and K. Fu, “Plutus: Scalable Secure File Sharing on Untrusted Storage,” Proc. USENIX Conf. File and Storage Technologies, San Francisco, CA., pp. 29-42, 2003.
[4] H. Lin, Z. Yan and R. Kantola, "CDController: A Cloud Data Access Control System Based on Reputation," IEEE International Conference on Computer and Information Technology (CIT), Helsinki, pp. 223-230,2017.
[5] Chase M., “Multi-authority Attribute Based Encryption”, In: Vadhan S.P. (eds) Theory of Cryptography. TCC 2007. Lecture Notes in Computer Science, vol 4392. Springer, Berlin, Heidelberg, 2007.
[6] J. Bethencourt, A. Sahai and B. Waters, "Ciphertext-Policy Attribute-Based Encryption," 2007 IEEE Symposium on Security and Privacy (SP `07), Berkeley, CA, pp. 321-334, 2007.
[7] X. Liu, Y. Xia, S. Jiang, F. Xia and Y. Wang, "Hierarchical Attribute-Based Access Control with Authentication for Outsourced Data in Cloud Computing", IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Melbourne, VIC, pp. 477-484, 2013.
[8] J. Li et al., "Fine-Grained Data Access Control Systems with User Accountability in Cloud Computing", IEEE Second International Conference on Cloud Computing Technology and Science, Indianapolis, IN, pp. 89-96, 2010.
[9] Amit Sahai and Brent Waters. Fuzzy Identity-Based Encryption. EUROCRYPT’ 05, LNCS 3494, Springer, pp. 457-473, 2005.
[10] S. Ruj, A. Nayak and I. Stojmenovic, "DACC: Distributed Access Control in Clouds”, IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications, Changsha, , pp. 91-98, 2011.
[11] Z. Yan, X. Li, M. Wang and A. V. Vasilakos, "Flexible Data Access Control Based on Trust and Reputation in Cloud Computing”, IEEE Transactions on Cloud Computing, vol. 5, no. 3, pp. 485-498, 2017.
[12] L. Zhou, V. Varadharajan and M. Hitchens, "Achieving Secure Role-Based Access Control on Encrypted Data in Cloud Storage”, IEEE Transactions on Information Forensics and Security, vol. 8, no. 12, 2013, pp. 1947-1960, 2013.
[13] I. El Ghoubach, F. Mrabti and R. Ben Abbou, "Efficient secure and privacy preserving data access control scheme for multi-authority personal health record systems in cloud computing," 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, 2016, pp. 174-179, 2016.
[14] Mustapha Ben Saidi, Anas Abou Elkalam, Abderrahim Marzouk, “TOrBAC: A Trust Organization Based Access Control Model for Cloud Computing Systems”, International Journal of Soft Computing and Engineering (IJSCE) Volume-2, Issue-4, ISSN: 2231-2307, pp.122-130, 2012.
[15] Dongwan Shin, Ying Wang, and William Claycomb. "A Policy-based Decentralized Authorization Management Framework for Cloud Computing", ACM Symposium on Applied Computing (SAC 12), Riva del Garda (Trento), Italy, 26-30, 2012.
[16] S. Fugkeaw and H. Sato, "CLOUD-CAT: A collaborative access control tool for data outsourced in cloud computing", Tenth International Conference on Digital Information Management (ICDIM), Jeju, pp. 243-248, 2015.
[17] M. S. Ferdous, A. Margheri, F. Paci, M. Yang and V. Sassone, "Decentralised Runtime Monitoring for Access Control Systems in Cloud Federations,” IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, pp. 2632-2633, 2017.
[18] J. Hu, L. Chen, Y. Wang and S. H. Chen, "Data Security Access Control Model of Cloud Computing", International Conference on Computer Sciences and Applications, Wuhan, pp. 29-34, 2013.
[19] W. Tian, H. Xu, M. Komi and J. Zhang, "Secure and flexible data sharing via ciphertext retrieval for cloud computing", IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), Macau, pp. 161-166, 2017.
[20] F. Khan, H. Li and L. Zhang, "Owner Specified Excessive Access Control for Attribute Based Encryption," in IEEE Access, vol. 4, pp. 8967-8976, 2016.
[21] R. Aluvalu and L. Muddana, "A dynamic attribute-based risk aware access control model (DA-RAAC) for cloud computing”, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, pp. 1-5, 2016.
[22] S. Kattimani and S. Pachouly, "A robust and verifiable threshold multi-authority access control system in public cloud storage", 2016 International Conference on Computing Communication Control and automation (ICCUBEA), Pune, pp. 1-4, 2016.
[23] S. Yu, C. Wang, K. Ren and W. Lou, "Achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing," IEEE INFOCOM, San Diego, CA, pp. 1-9, 2010.
[24] R. Ahuja and S. K. Mohanty, "A Scalable Attribute-Based Access Control Scheme with Flexible Delegation cum Sharing of Access Privileges for Cloud Storage," IEEE Transactions on Cloud Computing, vol 14. no. 8, pp. 1-14, 2015.
[25] S. Rizvi and J. Mitchell, "A Semi-distributed Access Control Management Scheme for Securing Cloud Environment", IEEE 8th International Conference on Cloud Computing, New York City, NY, pp. 501-507, 2015.
[26] P. P. Gutal, R. S. Kothe and S. B. Jahveri, "Efficient hierarchical cloud storage data access structure with KDC”, IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), Pune, pp. 328-332, 2016.
[27] Wang, Guojun, Qin Liu, and Jie Wu. “Hierarchical attribute-based encryption for fine-grained access control in cloud storage services”, 17th ACM conference on Computer and Communications Security,Chicago, Illinois, USA, pp.735-737, 2010.
[28] Z. Shen, J. Shu and W. Xue, "Keyword search with access control over encrypted data in cloud computing," IEEE 22nd International Symposium of Quality of Service (IWQoS), Hong Kong, pp. 87-92, 2014.
[29] M. Auxilia and K. Raja, "A semantic-based access control for ensuring data security in cloud computing”, International Conference on Radar, Communication and Computing (ICRCC), Tiruvannamalai, pp. 171-175, 2012.
[30] Cloud Security Alliance, “Security Guidance for critical areas of focus in Cloud Computing V.30,” CSA, Tech, Rep.,2003.
[31] Arockiam.L. and Monikandan.S, “Data Security and privacy in Cloud Storage using Hybrid Symmetric Encryption Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Volume 2.No. 8, pp.3064-3070, 2013.

S.S. Manikandasaran, S. Sudha, "Data Access Control Techniques and Security Challenges in Cloud Computing: A Survey", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.87-95, 2018.

Text mining has become an important research area. It deals with machine supported analysis of text. The unstructured texts which contains massive amount of information cannot simply be used for further processing by the computer and knowledge from unstructured text completed by using text mining. It uses the techniques from information retrieval, information extraction as well as natural language processing and connects them with the algorithms and methods of KDD, data mining, machine learning and statistics. In this paper we have discussed briefly about the text mining process and the techniques used in the text mining.

Text Mining, Data Mining, Text Preprocessing, Text Transformation, Clustering

[1] Priyanka patil T. and S.I. Nipanikar, “Survey on scene text detection and text recognition”, (IJARCCE) International Journal of Advanced Research in Computer and Communication Engineering. ISSN (e):2278-1021, Vol.5, Issue 3, March 2016.
[2] Priyanka Muchhadiya and Rajkot .C, “Detection and localization of texts from natural scene images: a hybrid approach” (RHIMRJ), Research HUB: International Multidisciplinary Research Journal . ISSN 2349-7637(e) Volume 02, Number 7 (July 2015), pp. 1-7.
[3] Kumuda T. and Basavaraj L, “Hybrid approach to extract text in natural scene images” – International Journal of Computer Applications (IJOCA).ISSN 0975-8887 vol.142, No.10 (May 2016), PP.18-22.
[4] Miss. Poonam B. Kadam, Mrs.Latika R. and Desai, “A hybrid approach to detect and recognize texts in images”,(IOSRJEN) , IOSR Journal of Engineering . ISSN 2250-3021 Volume 04, Number 7 (July 2014), pp. 19-20.
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[6] Q.Guo and W.D.S.Yu, "A novel approach to the text mining ", 2010.
[7] J.Uma Mahesh and S.Lalitha, "Data mining feature clustering algorithms in text classification" in International conference on computer science and information technology, ISBN: 978-93-81693-5, 2012
[8] Haralampos Karanikas and Manchester (2005), "Knowledge discovery in text and text mining software", center for research in information management.
[9] V.Gupta, G.S Lehal, " A survey of text mining technique and applications" in journal of emerging technologies in web intelligence, 2009.
[10] Liritano S and Ruffolo M (2011), "Managing the knowledge contained in electronic documents: a clustering method for text mining", IEEE 455-458.
[11] Setu madhavi and Krishna R (2008), "Experiments on supervised learning algorithms for text categorization", international conference, IEEE computer society 1-8.
[12] Yi Feng Pan, Xinwen Hot, and Cheng, “A hybrid approach to detect and localize text in natural scene images”, (IEEE), Volume 20, Number 3 (March 2011), pp. 800-813.

S Murugan, R. Karthika, "A Literature Review on Text Mining Techniques and Methods", International Journal of Computer Sciences and Engineering, Vol.06, Issue.02, pp.96-99, 2018.