Volume-6 , Issue-7 , Jul - 2018 Go Back

The detection and correction of document skew is one in all the foremost vital document image analysis steps. Projection profiles have a few applications in record picture process and that they consider flat and vertical lines being adjusted to the tomahawks. The proposed system works in two phases. In the first phase system find the skewed angle from the input text document and in the second phase system correct the sleekness in the given document on line by line basis. At the last every line output line is combined to obtain the final output. The proposed system is experimented on approx 30 text documents for testing. The accuracy of algorithm is around 97%.

Skew Detection, Skew Correction, Profile Projection Technique

[1] Darko Brodić & Zoran N. Milivojević 2016 Text Skew Detection Using Combined Entropy Algorithm, ITC-Journal of Information Technology And Control, 46: PP 308-318.
[2] A. Papandreou, B. Gatos, G. Louloudis and N. Stamatopoulos 2013 Document Image Skew Estimation Contest, ICDAR-Published in the 12th International Conference on Document Analysis and Recognition, PP 1444-1448.
[3] B. Aditya Vighnesh,Abhishek Kumar,Abhishek Kumar 2013 Skew Detection in Handwritten Documents IJCA- International Journal of Computer Applications, 69:PP 17-18
[4] Bishakha jain, Mrinaljit Borah 2014 A Comparison Paper on Skew Detection of Scanned Document Images Based on Horizontal and Vertical Projection Profile Analysis, IJSRP-International Journal of Scientific and Research Publications 4: PP 1-4.
[5] Danil Korchagin 2012 Automatic Time Skew Detection and Correction, IJCEE- International Journal of Computer and Electrical Engineering , 4: PP 684-687.
[6] Deepak Kumar, Dalwinder Singh 2012 A Review of Scanned Document Skew Detection and Correction Techniques, IJCST-International Journal of Computer Science Trends and Technology, 3: PP 251-254
[7] Jinal Patel,Anup Shah,Dr. Hatal Patel 2015 Skew Angle Detection and Correction using Randon Transform, IJEECS-International Journal of Electronics, Electrical and Computational System, 4: PP 1-6
[8] Jung Gap Kuk,Nam Ik Cho 2009 Feature Based Binarization of Document Images Degraded by Uneven Light Condition, IEEE-Published in 10th International Conference on Document Analysis and Recognition, PP 1-5
[9] Lipi Shah, Ripal Patel, Shreyal Patel, Jay Maniar 2014 Skew Detection and Correction for Gujarati Printed and Handwritten Character using Linear Regression, IJARCSSE- International Journal of Advanced Research in Computer Science and Software Engineering, 4: PP 642-648
[10] Loveleen Kaur, Simpel Jindal 2011 Skew Detection Technique for Various Scripts, IJSER-International Journal of Scientific & Engineering Research 2: PP 1-3
[11] Mandip Kaur, Simpel Jindal 2013 An Integrated Skew Detection and Correction Using Fast Fourier Transform and DCT, IJSTR-International Journal Of Scientific & Technology Research 2: PP 164-169
[12] Mamatha Hosalli Ramappa and Srikantamurthy Krishnamurthy 2012 Skew Detection, Correction and Segmentation of Handwritten Kannada Document, IJAST-International Journal of Advanced Science and Technology 48: PP 71-88

Rubani, Jyoti Rani, "Skew Detection and Correction in Text Document Image using Projection Profile Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.986-990, 2018.

Mobile Cognitive Ad Hoc Networks is the one of the cognitive radio networks which is the advanced networking technologies for spectrum scarcity problem constrain in the Federal Communication Commission (FCC). The cognitive radio networks are the cognitive network which abject its network parameters with respect to network environment. In cognitive radio network two users namely the primary and secondary or cognitive users will access the available spectrum to communicate each other when the spectrum is accessed by the primary user the secondary user must leave the spectrum and access the spectrum when spectrum holes available for transmission. So routing is challenging issues in cognitive networks and it is very challenging in mobile cognitive networks due to the node mobility, primary user interface and spectrum scarcity. A delay minimized routing protocol is proposed for minimum delay route selection between the source and destination, which is improved version of AODV. The numerical and ns2 simulation results for the proposed protocol significantly state that delay minimized routing protocol (DMR) is better in terms of average end-to-end delay and average throughput.

Primary users (PU), delay minimized routing protocol (DMR), spectrum, cognitive radio networks, cognitive users, Mobile Cognitive Ad-Hoc Networks, Dynamic Spectrum Access(DSA), Federal Communication Commission(FCC)

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a survey,” Computer Networks, vol. 50, no. 13, pp. 2127-2159, 2006.
[6]Feilong Tang, Can Tang, Yanqin Yang, Laurence T. Yang, Tong Zhou, Jie Li, Minyi Guo,” Delay-Minimized Routing in Mobile Cognitive Networks for Time-Critical Applications” IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 13, NO. 3, JUNE 2017.
[7] Guo-Mei Zhu, Ian F. Akyildiz, Geng-Sheng (G.S.) Kuo.” STOD-RP: A Spectrum-Tree Based On-DemandRouting Protocol for Multi-Hop Cognitive Radio Networks,” 978-1-4244-2324-8/08/$25.00 © 2008 IEEE.
[8] Hsien-Po Shiang and Mihaela van der Schaar,” Distributed Resource Management in Multi-hop Cognitive
Radio Networks for Delay Sensitive Transmission,” This work was supported by ONR.
[9] Suyang Ju and Joseph B. Evans,” Cognitive Multipath Multi-Channel
Routing Protocol for Mobile Ad-Hoc Networks,” 978-1-4244-5638-3/10/$26.00 ©2010 IEEE
[10] L. Indhumathi, R. Vadivel,” Adaptive Delay Tolerant Routing Protocol (ADTRP) for Cognitive Radio Mobile Ad Hoc Networks,” International Journal of Computer Applications (0975 – 8887) Volume 128 – No.6, October 2015
[11] S. Selvakanmani and Dr. M. Sumathi,” OVERVIEW AND LITERATURE SURVEY ON ROUTING PROTOCOLS FOR MOBILE COGNITIVE RADIO AD HOC NETWORKS,” Natarajan Meghanathan, et al. (Eds): SIPM, FCST, ITCA, WSE, ACSIT, CS & IT 06, pp. 235–249, 2012. © CS & IT-CSCP 2012 DOI : 10.5121/csit.2012.2323
[12] Sunita Barve, Kanchan Hadawale,” LINK PREDICTION-BASED ADAPTIVE ROUTING FOR COGNITIVE RADIO MOBILE AD-HOC NETWORKS,” ISSN (Online): 2347-2812, Volume-1, Issue -3, 2013

Banala Revanth, M. Sakthivel, "Delay-Minimized Routing Protocol for Mobile Cognitive Ad-Hoc Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.991-996, 2018.

Cloud computing is a comprehensive new approach on how processing administrations are created and used. Cloud computing is an achievement of different kinds of administrations which has pulled in numerous clients in the present situation. The most appealing administration of distributed computing is Information outsourcing, because of this the information proprietors can have any size of information on the cloud server and clients can get to the information from cloud server when required. The new model of information outsourcing likewise faces the new security challenges. However, clients may not completely believe the cloud specialist organizations (CSPs) in light of the fact that occasionally they may be untrustworthy. It is hard to decide if the CSPs meet the client`s desires for information security. In this way, to effectively keep up the respectability of cloud information, numerous evaluating plans have been proposed. Some current trustworthiness strategies can serve for statically chronicled information and some inspecting methods can be utilized for the progressively refreshed information. In this paper, we have dissected different existing information uprightness evaluating plans alongside their results.

Third Party Auditor (TPA), Cloud Service Provider(CSPs), Information Outsourcing, Proof of Retrievability (POR), Provable Data Possession (PDP).

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Auditing Scheme in Cloud Computing”, InternationalJournal of Advances in Computer Science and Cloud Computing, ISSN: 2321-4058, Vol. 2, Issue: 1, MAY 2014.
[3] C. Wang, S. M. Chow, Q. Wang, K. Ren and W. Lou, ʺPrivacy‐Preserving Public Auditing for Secure Cloud
Storage, ʺ vol. 62, IEEE Trans. on Computers, no. 2, pp. 362‐375, 2013.
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584‐597, 2007.
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Privacy Preserving Public Auditing in Cloud Storage”, (manuscript submitted for publication), 2017.
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[7] C. Wang, Q. Wang, K. Ren and W. Lou, “Privacy‐Preserving Public Auditing for Data Storage Security in Cloud Computing”, Proc. IEEE INFOCOM,pp1‐9, 2010.
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[10] M. S. Giri, B. Gaur, D. Tomar, “A Survey on Data Integrity Techniques in Cloud Computing”, Vol. 122,
No. 2, International Journal of Computer Applications (0975 – 8887), July 2015.
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Journal of Computer Applications (0975 – 8887), International Conference on Internet of Things, Next Generation Networks and Cloud Computing, 2016.
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PP, Issue: 99, IEEE Transactions on Service Computing, Manuscript ID, DEC 2016.
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Clouds”, Vol. 2 Issue 8, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), AUG 2013.
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International Journal of Engg Research and Tech (IJERT), ISSN: 2278-0181, NOV 2012.
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Clouds”, Vol. 6, no. 2, IEEE Trans. on Services Computing, pp. 227–238, 2013.
[19] Q. Wang, C. Wang, K. Ren, W. Lou and J. Li,‘‘Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing’’, Vol. 22, no. 5, IEEE Trans. on Parallel and Distributed Systems, pp. 847‐859, 2011.
[20] A P Shirahatti, P S Khanagoudar, “Preserving Integrity of Data and Public Auditing for Data Storage Security in Cloud Computing”, IMACST, Vol. 3, Number 3,JUN 2012.
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[22] Syed Rizvi, Katie and Abdul, “Cloud Data Integrity Using a Designated Public Verifier,” in 2015 IEEE 17th
International Conference on High Performance Computing and Communications (HPCC), International Symposium on Cyberspace Safety and Security (CSS) and International Conference on Embedded Software and System (ICESS).

[23] S Lins, S Schneider, and A Sunyaev, “Trust is Good,Control is Better: Creating Secure Clouds by
Continuous Auditing”, Vol. PP, Issue: 99 IEEE Transactions on Cloud Computing, TCC-2015-10-0378, JAN 2016.
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Data Integrity in Cloud Computing”, Vol. 3, Issue 11,ISSN (Print): 2319-5940, International Journal of Advanced Research in Computer and Communication Engineering, NOV 2014.
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[26] Wang, Huaqun. "Identity-Based Distributed Provable Data Possession in Multicloud Storage", Services Computing, IEEE Transactions on 8.2 (2015): 328-340.
[27] S. Pearson, "Toward Accountability in the Cloud", Vol. 15, no. 4, IEEE Internet Computing, pp. 64–69, 2011.
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2010.
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[30] S. N. Poornima, R. S. Ponmagal, “Secure PreservingPublic Auditing for Regenerating Code Based On Cloud Storage”, International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353, Vol. 21, Issue: 4, APR 2016.

[31] K. Chen, J. M. Chang, T. Hou, “Multithreading in Java: Performance and Scalability on Multicore Systems”, Vol. 60, IEEE Transactions on Computers, NO. 11, NOV 2011.
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[33] Tengfei Tu, Lu Rao, Hua Zhang, Qiaoyan Wen, and Jia Xiao “Privacy-Preserving Outsourced Auditing Scheme forDynamic Data Storage in Cloud” Security and Communication Networks
Volume 2017, Article ID 4603237, 17 pages

[34] Baoyuan Kang, Jiaqiang Wang, and Dongyang Shao “Certificateless Public Auditing with Privacy Preserving forCloud-Assisted Wireless Body Area Networks” Hindawi,Mobile Information Systems
Volume 2017, Article ID 2925465, 5 pages
[35] Shubham Singha, Surmila Thokchomb “ Public integrity auditing for shared dynamic cloud data ” 6th International Conference on Smart Computing and Communications, ICSCC 2017, 7-8 December 2017, Kurukshetra, India.

[36] Santhosh Kumar, Latha Parthiban “ Cloud Data Integrity Auditing Over Dynamic Data for Multiple Users” International Journal of Intelligent Engineering and Systems, Vol.10, No.5, 2017,pp.23

L.Ramesh, R.A.Roseline, "A Survey on Cloud Computing Security and Data Integrity Auditing Schemes in Cloud Platform," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.997-1001, 2018.

Mobile Cloud Computing is a technological platform through which we can share resources like platform, software application, infrastructure, business processes and so on using mobile phones. It is a platform which combines the advantages of Mobile Computing and Cloud Computing and also it suffers from the common drawbacks of Mobile Computing (such as battery utilization) and Cloud Computing (such as privacy and security). In this technology, a user needs not to worry about the high configuration of the mobile phone because all the computations are done at the cloud not on the mobile device. In this paper, we present a survey on the research which has been done on MCC including challenges and open research issues.

Mobile Computing, Cloud Computing, Security, Offloading, Virtualization

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[7] G. Gao, W. Zhang, Y. Wen, Z. Wang and W. Zhu”,Towards cost-efficient video transcoding in media cloud: Insights learned from user viewing patterns”,IEEE Transactions on Multimedia, 17(8), pp.1286-1296, 2015.
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[9] G. Nan,.Z. Mao, M. Yu, M. Li, H. Wang and Y.Zhang, “Stackelberg game for bandwidth allocation in cloud-based wireless live-streaming social networks”,IEEE Systems Journal, 8(1), pp. 256-267. 2014.
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[11] K. E Psannis, S. Xinogalos and A. Sifaleras”,Convergence of Internet of things and mobile cloud computing”,Journal Systems Science & Control Engineering, 2(1), pp. 476-483. 2014.
[12] A. Botta, W. De Donato, V. Persico and A. Pescapé”,Integration of cloud computing and internet of things: a survey “,Future Generation Computer Systems, 56, pp. 684-700, 2016.
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[14] X. Chen, L, Jiao, W. Li, and X. Fu”, Efficient multi-user computation offloading for mobile-edge cloud computing”, IEEE/ACM Transactions on Networking, 24(5), pp. 2795-2808. 2016.
[15] Y. Jun, L. Shan and X. Lizhen”,Research of cache mechanism in mobile data management”,in IEEE Conference on Web Information Systems and Applications (WISA), pp. 159-162, November 2012.
[16] H. Mao, N. Xiao, W. Shi and Y. Lu”,Wukong: A cloud-oriented file service for mobile Internet devices”,Journal of Parallel and Distributed Computing, 72(2), pp. 171-184, 2012.
[17] Cho, E.,Myers, S. A., & Leskovec, J. (2011). Friendship and mobility: user movement in location-based social networks. In ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1082–1090.
[18] Stuedi, P., Mohomed, I.,&Terry, D. (2010).Wherestore: Location-based data storage for mobile devices interacting with the cloud. In ACM workshop on mobile cloud computing and services: Social networks and beyond, pp. 1–8.
[19] Rai, A., Bhagwan, R., & Guha, S. Generalized resource allocation for the cloud. In ACM symposium on cloud computing, pp. 15:1–15:12.
[20] He, Y., Elnikety, S., Larus, J., & Yan, C. (2012). Zeta: Scheduling interactive services with partial execution. In ACM symposium on cloud computing, pp. 1–14.
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[22] V. Odelu, A.K. Das, Y.S. Rao, S. Kumari, M. K. Khan and K. K. R Choo,“ Pairing-based CP-ABE with constant-size ciphertexts and secret keys for cloud environment, ” Computer Standards & Interfaces, May 2016.
[23] M.Qiu, K. Gai, B.Thuraisingham, L. Tao, and H. Zhao, H”, Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry”, Future Generation Computer Systems, February 2016.
[24] M.Sookhak, A. Gani, M. K. Khan and R. Buyya,“Dynamic remote data auditing for securing big data storage in cloud computing”,Information Sciences, 380, pp. 101-116. 2017.
[25] Y. Yu, Y. Li, M. H. Au,., W. Susilo, K. K. R. Choo and X Zhang,“Public cloud data auditing with practical key update and zero knowledge privacy, ” in Australasian Conference on Information Security and Privacy, pp. 389-405, July 2016.
[26] M. Louk, and H. Lim”,Homomorphic encryption in mobile multi cloud computing”,in IEEE International Conference on Information Networking (ICOIN), pp. 493-497, January 2015.
[27] H. Wang, S. Wu, M. Chen and W. Wang,“Security protection between users and the mobile media cloud”,IEEE Communications Magazine, 52(3), pp. 73-79, 2014.
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[29] S. K.Pasupuleti, S. Ramalingam and R. Buyya, “An efficient and secure privacy preserving approach for outsourced data of resource constrained mobile devices in cloud computing”, Journal of Network and Computer Applications, 64, pp. 12-22. 2016.
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[33] N. M. Dhanya and G. Kousalya,“Adaptive and Secure Application Partitioning for Offloading in Mobile Cloud Computing,“in International Symposium on Security in Computing and Communication, pp. 45-53, August 2015.
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[35] J.Tan, R.Gandhi and P. Narasimhan”,STOVE: Strict, Observable, Verifiable Data and Execution Models for Untrusted Applications”,in 6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 644-649, December 2014.
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[37] S. Dey, S. Sampalli and Q. Ye,“A context-adaptive security framework for mobile cloud computing”, in 11th International Conference on Moile Ad-hoc and Sensor Networks (MSN), pp. 89-95, December 2015.
[38] S.Singh, P. K.Sharma and J. H. Park,“A Security Model for Protecting Virtualization in Cloud Computing”,in International Conference on Computer Science and its Applications, pp. 385-388, December 2016
[39] D.Sgandurra and E. Lupu”,Evolution of attacks, threat models, and solutions for virtualized systems”,ACM Computing Surveys (CSUR), 48(3), pp. 46. 2016.
[40] N. Paladi, C. Gehrmann and A. Michalas, “Providing user security guarantees in public infrastructure clouds”,IEEE Transactions on Cloud Computing, pp.1-1, 2016.
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[43] J. Imgraben, A.Engelbrecht, and K. K. R Choo,“Always connected, but are smart mobile users getting more security savvy? A survey of smart mobile device users”,Behaviour & Information Technology, 33(12), pp.1347-1360, 2014.

Santosh Kumar Sharma, Saurabh Jha, Nayan Chitransh, "A Survey on Challenges and Its Possible Solutions in Mobile Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1002-1005, 2018.

Tourism plays an important role in the economies of many countries. It is important to provide correct and relevant information to the tourist. Visitors are not always given a guided tour at the museums and other places of importance. It is infeasible to provide personal human guides to each visitor due to shortage of personnel and language constraints. Not just in tourism, providing location guidance is important in many areas like schools, colleges, companies, hospitals, exhibitions to guide people and provide them with relevant information. Audio tours using handheld devices is a solution to this problem. But traditional audio tour devices are time based, where information is displayed based on time and user has to pause and play accordingly; or they are interactive devices where users have to choose the current location to receive relevant information. This lead to development of location aware audio tour systems. But these systems rely on GPS signals for location information which is highly unreliable in indoor locations as GPS signals does not pierce through the solid walls or structures. The proposed system uses Radio Frequency Identification (RFID) which relies on radio waves. Each location or artefact is embedded with an NRF24L01 module, which is a radio frequency transceiver with limited range, that acts as a transmitter and the handheld unit given to the user also has an NRF24L01 module that acts as our receiver. Based on the signal received by the receiver unit relevant information is given via text, images and audio.

Location Aware, nRF, Raspberry Pi (RPi), Arduino, Radio Waves

[1] Neeraj Chhabra, “Comparative Analysis of different Wireless Technologies”, International Journal of Scientific Research in Network Security and Communication, Vol. 1, No. 5, 2013.
[2] M.V.Moreno, J.L.Hernandez, and A.F.Skarmeta, “A New LocationAware Authorization Mechanism for Indoor Environments”, In the Proceedings of the 2014 28th International Conference on Advanced Information Networking and Applications, Victoria, pp.791 – 796,2014.
[3] KiBeom Kang, JeongWoo Jwa and SangDon Earl Park, “Smart Audio Tour Guide System using TTS”, International Journal of Applied Engineering Research, Vol. 12, No. 20, pp. 9846-9852, 2017.
[4] B.H.S. Jaleleddine, “Advance remote control home appliance switching system using radio frequency and Bluetooth”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, No.4, pp.60-62, 2017.
[5] K.Somalatha and V.R.Kavitha, “IoT Based Smart Museum using Bluetooth Low Energy” In 2017 3rd International Conference on Advances in Electrical. Electronics, Information, Communication and Bio_Informatics.
[6] Mr.Sagar Patil, Ms.Shraddha Limbekar, Ms.Amruta Mane and Ms.Netra Ponis, “Smart Guide – an approach to the Smart Museum using Android”, International Research Journal of Engineering and Technology, Vol. 5, 2018.

K.S. Sampada, Nithin Mathew, Pavana .A, S. Megha, Shubh Mehta, "Location Aware Audio Tour using NRF," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1006-1009, 2018.

One of the major challenges in e-learning development is search and discovery of an appropriate learning content, among the distributed content repositories according to the contextual and personal interests of the learner. The technique like conventional keyword-based search appears to be not an efficient approach for searching the resources on the web. According to several researchers semantic web-based educational system has been the promising interaction environment for the next generation e-learning system. The major purpose of this paper is to explore the importance of integrating the Semantic Web technologies into e-learning domain and presents the current state of the art in the area of the semantic web technologies and their integration within the e- learning system. It also discusses some of the important issues that need significant improvement in intelligent e-learning systems.

Metadata, Semantic-Web, E-Learning, Learning Object, Ontology, Issues in E-learning

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Kalla Madhusudhana, "The Need of Semantic Web Technologies Integration with Web-based Educational System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1010-1013, 2018.

Cloud computing has become an important phenomena in computing and internet era. Cloud computing has enabled service providers to completely present their services in cloud platform. The main challenge is to fully utilize those resources in such a way so that system performance has increased and energy utilization has decreased. In this paper, we presented a combined strategy that allows more than two users to schedule the task. Experimentation shows that our proposed strategy increases the success rate by significantly decreasing the energy consumption and increases the cloud processor performance. The purposed criteria are shown by comparing it with traditional algorithm.

Cloud Computing, Job Scheduling in Cloud for performance improvement, combined strategy

[1]Yintian, Wang, RuanaRao,”A Round Robin with multiple feedback job scheduler in hadoop”,IEEE International Conference, Shangai Ziotang University, 2014 China.
[2] Xiuhua Li,Chunsheng Zhu, “ Job scheduling for cloud computing integrated with wireless network” ,IEEE 6th International conference, 2014, Vancouver, Canada.
[3] Alaka Ananth ,”Game theoretic strategies for scheduling the jobs,” IEEE 5th International Conference, Suratkal,2014 India.
[4]Cheng Dazhao, "Resource And Deadline-Aware Job Scheduling In Dynamic Hadoop Clusters" IEEE International Parallel and Distributed Processing Symposium (IPDPS),2015.
[5]Abhishek Gupta, "A Theoretical Comparison Of Job Scheduling Algorithms In Cloud Computing Environment, "IEEE International Conference on Next Generation Computing Technologies (NGCT),2015.
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[10]Chen ,Huangning, and Wenzhong Guo, "Real-Time Task Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization." in Cloud Computing and Big Data, Springer International Publishing, pp. 141-152.
[11] Mishra, Manoj Kumar, Prithviraj Mohanty, and G. B. Mund, "A Modified Grouping-Based Job Scheduling in Computational Grid" IEEE International Conference (NUiCONE) ,2011
[12] Tang Wei, "Adaptive metric-aware job scheduling for production of supercomputers", Workshops In parallel processing system (ICPPW), 41st IEEE International Conference,2012 .
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Karambir Bidhan, Charul, "A Combined Strategy For Performance Enhancement In Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1014-1017, 2018.

Data storing and sharing becomes a most important exceptionally attractive service supplied by cloud computing platforms because of its convenience cost effective platform and more economy. Data owner to store and outsourcing their data in the cloud and through which provide the data access to the user However, outsourcing data to a third-party administrative control entails serious security concerns. Cloud client upload Data leakage may be occur due to attacks by other users and machines in the cloud. Data leakage is and data privacy strategies an ongoing problem in the field of cloud security. The proposed work identifies security and privacy issues for secure data management in cloud environment. The proposed system provides a novel effective scheme that is named as HSDS-DP (High Secure Data Share with dynamic policy update), which is a new technique for data privacy with improved security features. The proposal has three main contributions such as, Threshold Secret Sharing, Dynamic access control update, and key managers. The proposed method Client receives public keys from all Key Managers (KM), afterwards client generate random symmetric-key for perform encryption, Symmetric key are protected by the public key. Encrypted data and keys are uploaded in the cloud. For accessing the file client should download encrypted entire key share and encrypted data file from cloud. Accessing the data client send share of key to the Key Managers, so that client will receives backs decrypted share. Proposed Dynamic Access Policy Update Scheme so client dynamically updates the data access making request to cloud server. The results reveal that proposed can be effectively used for security of outsourced data by employing key management, access control, and file dynamic access policy updating process. Our proposed scheme can prevent cheating and Data leakage problem in public cloud infrastructure.

Data security, Access Control, Secret Sharing, Cloud computing, Semi-Trusted Third Party, key management

[1].Ali, Mazhar, Samee U. Khan, and Athanasios V. Vasilakos. "Security in cloud computing: Opportunities and challenges." Information sciences 305 (2015): 357-383.
[2]. Chandankere, Rekha, and Masrath Begum. "Secure data sharing in an untrusted cloud." Int. J. Eng. Res. Appl 5 (2015): 49-54.
[3]. Chugh, Sonam, and Sateesh Kumar Peddoju. "Access control based data security in cloud computing." International Journal of Engineering Research and Applications (IJERA) 2.3 (2016): 2589-2593.
[4]. Chou, Te-Shun. "Security threats on cloud computing vulnerabilities." International Journal of Computer Science & Information Technology 5.3 (2015): 79.
[5]. H. Takabi, J. B. D. Joshi, and G. Ahn, "Security and privacy challenges in cloud computing environments” IEEE Security and Privacy, Vol. 8, No. 6, 2014,pp. 24-31.
[6] . K. Yang, X. Jia, K. Ren, B. Zhang, and R. Xie, “Dac: Effective data access control for multi authority cloud storage systems,” Information Forensics and Security, IEEE Transactions on, vol. 8, no. 11, pp. 1790–1801, 2013.
[7] . Yang, Kan, Xiaohua Jia, and Kui Ren. "Secure and verifiable policy update outsourcing for big data access control in the cloud." IEEE Transactions on Parallel and Distributed Systems 26.12 (2015): 3461-3470.
[8] . Chen, Yanli, Lingling Song, and Geng Yang. "Attribute-based access control for multi-authority systems with constant size ciphertext in cloud computing." China Commun 13.2 (2016): 146-162.

P. Jayasree, V. Saravanan, "High Secure and dynamic Access Control Scheme for Big Data Storage in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1018-1022, 2018.

The rapid growth of Web and Social Media Website brought about the need for sentiment analysis and opinion mining. Sentiment analysis and Opinion mining aims to explore the opinions or sentiments of customer reviews found in different social media platforms through deep learning technique. Deep learning is found to be more efficient to overcome the challenges faced by sentiment analysis and can handle the multiplicities involved. Deep Learning can perform sentiment analysis on any unstructured data with minimal restrictions and with no specific manual feature engineering. This paper proposes a sentiment analysis algorithm for the analysis of customer reviews by applying deep learning algorithm like Autoencoder Neural Network. Sentiment classification using deep learning promises to perform much better than the traditional supervised algorithms like Naive Bayes and SVM, with minimal constraints on the task or data for sentiment analysis.

Opinion Mining, Sentiment Analysis, Sentiment Classification, Deep Learning

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[3] Deep Learning for Sentiment Analysis: A Survey, Lei Zhang, Shuai Wang, Bing Liu, 2018
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M. Lal, A. Jain, M. Avatade, "Sentiment Analysis on Customer Reviews using Deep Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1023-1024, 2018.

Delivering a quality education to the students is a very important objective of any academic. And it is also important for an institute to examine their students continuously for their development. In this paper, an e-learning management system is developed where a student and teaching staff can register themselves. After successful registration, the student is permitted to again register for new courses available. After course registration, the system allows the student to start his/her learning from the beginning or else the student can start with any module. This course consists of modules, also called lessons. After completing all the modules an overall multiple choice questions test is conducted based on the whole course and the student is awarded a certificate based on the results. Here teaching staff will be able to upload study materials, update the course and questions. The facility to view mark list and the number of qualified and disqualified candidates are also available. Administrator here is responsible for adding and deleting the students, staff, and courses.

Discussion Forums, e-Learning, Knowledge Sharing, Learning Management Systems

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P. Hari Tejaswi, "ELMS: E-Learning Management System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1025-1032, 2018.