Volume-06 , Special Issue-03 , Apr - 2018 Go Back

The cloud platforms are becoming popular day by day with the advent of more customers every second, flooding the cloud environment with millions of requests thereby making the processing of such requests a major challenge to be handled. The major goal of cloud computing is to provide requested resources efficiently and effectively which can be achieved by distributing the load in a balanced manner among various nodes leaving the network in an optimal condition. In this research paper, much optimized load balancing scheme has been proposed to schedule the tasks in the cloud environment. The scheme has been designed to calculate the load on the list of available Virtual Machines (VM), considering the CPU utilization and usage as a metric for calculation of load. The proposed scheme modifies the already existing Active Monitoring Load balancing algorithm and merges the advantages of Active Monitoring Load Balancing and dynamic Round Robin scheduling techniques.

Cloud computing; resource management; load balancing; virtual machines

[1] S. Kapoor, “Cluster Based Load Balancing in Cloud Computing,” Ieee, 2015.
[2] S. Hamid, H. Madni, M. Shafie, A. Latiff, Y. Coulibaly, and M. Abdulhamid, “Recent advancements in resource allocation techniques for cloud computing environment : a systematic review,” Cluster Comput., 2016.
[3] S. Singh and I. Chana, “Cloud resource provisioning: survey, status and future research directions,” Knowl. Inf. Syst., vol. 49, no. 3, pp. 1005–1069, 2016.
[4] S. Singh and I. Chana, “A Survey on Resource Scheduling in Cloud Computing :,” J. Grid Comput., pp. 217–264, 2016.
[5] M. Xu and W. Tian, “A survey on load balancing algorithms for virtual machines placement in cloud computing,” no. February, pp. 1–16, 2017.
[6] Y. W. Qiu and J. I. G. Hwang, “A Two-Level Load Balancing Method with Dynamic Strategy for Cloud Computing,” Proc. - 2016 IEEE 14th Int. Conf. Dependable, Auton. Secur. Comput. DASC 2016, 2016 IEEE 14th Int. Conf. Pervasive Intell. Comput. PICom 2016, 2016 IEEE 2nd Int. Conf. Big Data Intell. Comput. DataCom 2016 2016 IEEE Cyber Sci. Technol. Congr. CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016, pp. 565–571, 2016.
[7] N. K. Chien, N. H. Son, and H. Dac Loc, “Load balancing algorithm based on estimating finish time of services in cloud computing,” 2016 18th Int. Conf. Adv. Commun. Technol., pp. 1–1, 2016.
[8] U. Thakkar, “A Novel Approach for Dynamic Selection of Load Balancing Algorithms in Cloud Computing,” pp. 1–4, 2016.
[9] Y. Gao, “Energy-aware Load Balancing in Heterogeneous Cloud Data Centers.”
[10] S. G. Damanal and G. R. M. Reddy, “Optimal Load Balancing in Cloud Computing By Efficient Utilization of Virtual Machines,” 2014.
[11] S. G. Domanal and G. R. M. Reddy, “Load Balancing in Cloud Computing Using Modified Throttled Algorithm.”
[12] D. Malhotra, Bhagyalakshmi, “A Review : Different Improvised Throttled Load Balancing Algorithms in Cloud Computing Environment,” Int. J. Eng. Technol. Manag. Appl. Sci., vol. 5, no. 7, pp. 409–416, 2017.
[13] R. Panwar and B. Mallick, “Load Balancing in Cloud Computing Using Dynamic Load Management Algorithm,” Ieee, pp. 773–778, 2015.
[14] S. Elmougy, S. Sarhan, and M. Joundy, “A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique,” J. Cloud Comput. Springer, 2017.

Bhagyalakshmi, D.Malhotra, "AMRRHC: Active Monitoring Round Robin with Holding Capacity Load Balancing Algorithm", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.56-60, 2018.

The cognitive radio technology is one of the best candidates to handle the problem of the scarcity of the radio spectrum. The basic principle of cognitive radio is that it allows to secondary users to use the free (idle) primary channels. The cognitive radio networks do not only face the traditional security attacks, but also new security attacks due to the new characteristics of the cognitive radio technology. A number of the major news attacks in the Cognitive Radio Networks (CRNs) are the primary user emulation, spectrum sensing data forgery, and jamming attacks. In wireless networks, security is a challenging aspect. In the CRN, it is much more difficult because Cognitive Radios (CR) performs the various functions such as sensing the radio spectrum, managing the spectrum, spectrum mobility and sharing the spectrum. To perform all these functions efficiently without any attack, security mechanisms are required to implement. In this paper, major security attacks in the cognitive radio networks are discussed. Also, the various countermeasures to these security attacks in CRN are described.

Cognitive Radio (CR); Cognitive Radio Networks (CRNs); Primary Users (PUs); Spectrum Sensing Data forgery (SSDF) Secondary Users (SUs).

[1] Akyildiz, I.F., Yeol, L.W., Vuran, M.C., Shantidev, M., “Next generation /dynamic spectrum access/cognitive radio wireless networks a survey”, Elsevier Computer Network, vol:50, pp no. 2127-2159, 2006.
[2] Salem, T.M., Abd El-kader S.M., Ramadan, S.M., Abdel-Mageed, M.Z., “Opportunistic Spectrum Access in Cognitive Radio Ad Hoc Networks” IJCSI International Journal of Computer Science Issues, vol. 11(1), pp no-41-50, 2014.
[3] Rawat, D. B., Song,M., Shetty,S., “ Dynamic Spectrum Access for Wireless Networks”. Springer, 2015.
[4] Chen, R., Park, J.M., “Ensuring Trustworthy Spectrum Sensing in Cognitive Radio Networks”, IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR), Reston, pp no.110-119, 2006.
[5] Alahmadi, A., Abdelhakim, M., Ren, J., Li, T., “Defense against primary user emulation attacks in cognitive radio networks using advanced encryption standard,” Information Forensics and Security, IEEE Transactions on, vol: 9(5), pp no. 772-781, 2014.
[6] Haykin, S., "Cognitive radio: Brain-Empowered wireless communication," IEEE journal on selected areas in communication, vol: 25, pp no. 201-220, 2005.
[7] Zou, Y., Wang. X., Shen, Z., “Physical-Layer Security with Multiuser Scheduling in Cognitive Radio Networks,” IEEE Transactions on Communications, vol:61(12), pp no.5103-5113, 2013.
[8] Tafazzoli, S., Berangi, R., “Cognitive Radio Handover in Cellular Networks”, IJCSI International Journal of Computer Science Issues, vol:11(2),no 1, pp no 1694-0784, 2014.
[9] Mathur, C., Subbalakshmi, K., “Security Issues in Cognitive Radio Networks, Cognitive Networks: Towards Self-Aware Networks”, Wiley, pp no. 284-293, 2007.
[10] Zhihui Shu; Yi Qian; Song Ci, “On physical layer security for cognitive radio networks,” Network, IEEE, vol:27, no.3, pp no.28-33,2013.
[11] Xu, W., Trappe, W., Zhang, Y., Wood, T., “The Feasibility of Launching and Detecting Jamming Attacks in Wireless Networks”, ACM MobiHoc, pp no.46-57, 2005.
[12] Babu, B.R., Tripathi, M., Gaur,M.S., Gopalani, D., Jat,D.S., “Cognitive Radio Ad-Hoc Networks: Attacks and Its Impact” IEEE conference on Emerging Trends in Networks and Computer Communications (ETNCC), pp no. 125-130,2015.
[13] Zhu, L., Zhou, H., “Two Types of Attacks against Cognitive Radio Network MAC Protocols”, International Conference on Computer Science and Software Engineering, vol:4, pp no.1110-111, 2008,.
[14] Mathur, C.N., Subbalakshmi, K.P.,"Digital signatures for centralized DSA network," in First IEEE workshop on cognitive radio networks, 2007.
[15] Huang, L., Xie, L., Yu, H., Wang, W., Yao, Y. “Anti-PUE Attack Based on Joint Position Verification in Cognitive Radio Networks”, International Conference on Communications and Mobile Computing (CMC), vol: l, no. 2, pp no. 169-17, 2010.
[16] Zhao, C., Wang, W., Huang, L., Yao, Y., “Anti-PUE Attack Base on the Transmitter Fingerprint Identification in Cognitive Radio”, International Conference on Wireless Communications, Networking and Mobile Computing, pp no. 1-5, 2009.
[17] Pandharipande, A., , Kim, J.M., Mazzarese, D., Ji, B., “IEEE P802.22 Wireless RANs: Technology Proposal Package for IEEE 802.22”, IEEE WG on WRANs, 2005.
[18] Chen, R., Park, J.M., Hou, Y.T., Reed, J.H., “Toward Secure Distributed Spectrum Sensing in Cognitive Radio Networks”, IEEE Communications Magazine, vol.46, no.4, pp no. 50-55, 2008.
[19] Kaligineedi, P., Khabbazian, M., Bhargava, V.K., “Secure Cooperative Sensing Techniques for Cognitive Radio Systems”, IEEE International Conference on Communications, pp no.3406-3410, 2008.
[20] Wang, W., Li, H., Sun, Y., Han, Z., “Attack-Proof Collaborative Spectrum Sensing in Cognitive Radio Networks”, Conference on Information Sciences and Systems, 2009.
[21] Sodagari, S., Attar, A., Leung, V., Bilen, S., “Denial of service attacks in Cognitive radio networks through channel eviction triggering”, IEEE Global Telecommunication, pp no. 610, 2010.
[22] Karlof, C., Wagner, D., “Secure Routing in Wireless Networks: Attacks and Countermeasures”, Ad Hoc Networks, vol:1, pp no.293-315, 2009.

Jagsir Singh, Jaswinder Singh, "Analysis of the various Security Attacks and Countermeasures in Cognitive Radio Network", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.61-65, 2018.

This paper aims to highlight the immense capacity of the use of Information and communication technology for the teaching of English literature. In the time of fast digitalization, one wonders why should the teaching of a traditional subject like English literature by use of digital methods be left behind when these digital tools are an intrinsic part of teaching technical subjects like engineering and other applied sciences. This paper throws some light on the practical relevance of the use of various tools of information and communication technology in classroom teaching the otherwise passive classroom teaching of literature can be instantly transformed to an active and live experience by extensive use of these tools. Increased use of information and communication technology will help in better understanding and lively exchange of information in the otherwise repetitive and prosaic traditional teaching methodology this paper proposes to use information and communication technology by digital aids like online educational videos, 3D Animation, Films, interactive media, language labs, e-libraries and various other browsers. The aim is not the replacement of the teacher but to supplement the teacher in order to reinforce and enhance traditional methods of teaching English literature.

Learning,ICT,Digital Tools,Traditional Teaching

[1] Prof.G.Kour, “The Use ofInformation & Communication Technology (ICT) in Teaching English Literature and Language for Enhancing the Learning Efficiency in Students”, International Journal of Advanced Research in Computer Sciences., Vol. 7, 2016.
[2] https://en.wikipedia.org/wiki/Novel
[3] Margaret Anne Doody, “The True Story of the Novel”, New Brunswick, NJ: Rutgers University Press”, 1996, rept. 1997, p. 1. Retrieved 25 April 2014.
[4] https://en.wikipedia.org/wiki/Literary_criticism

P. Kaul, J. Kaur, "The Relevance of Information and Communication Technology in Teaching of English Literature for both Pleasure And Education", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.66-68, 2018.

Social Network provides different applications like Facebook, Twitter, Skype and Instagram through which different users can use them and share their thoughts, images, videos and feelings with their friends. It is very difficult for a user to recommend friends to other new users. In this paper a survey of existing friend recommendation techniques such as Match maker, content based and geographical based recommendation has been presented after that this paper provides mechanism how a friend will be recommended to new user in Facebook social network.

Social Network, Recommendation, Facebook, Content based recommendation, and community

[1] F. Celli, “Unsupervised Personality Recognition for Social Network Sites,” in ICDS 2012, The Sixth International Conference on Digital Society, no. c, 2012, pp. 59–62.
[2] Y. Bachrach, M. Kosinski, T. Graepel, P. Kohli, and D. Stillwell, “Personality and Patterns of Facebook Usage,” in proceedings of the 3rd annual ACM web science conference, 2012, pp. 24–32.
[3] J. Staiano, F. Pianesi, B. Lepri, and A. Pentland, “Friends don t Lie - Inferring Personality Traits from Social Network Structure,” in Proceedings of the 2012 ACM conference on ubiquitous computing, 2012, pp. 321–330.
[4] Pran Dev, Jyoti, Dr. Kulvinder Singh and Dr. Sanjeev Dhawan, “A Naive Algorithmic Approach for Detection of Users’ with Unusual Behavior in online Social Networks” International Journal of Software and Web Sciences (IJSWS), ISSN: 2279-0071pp: 37-41,2015.
[5] Ekta, Sanjeev Dhawan and Kulvinder Singh, “Feature Extraction and Content Investigation of Facebook User’s using Netvizz and Gephi”, Advances in Computer Science and Information Technology (ACSIT), ACSIT 2016, pp. 262-265.
[6] Kyungmin Kim, Taehun Kimand Soon J. Hyun, “Friend Recommendation using Offline and Online Social Information for Face-to-Face Interactions”, IEEE 2016, pp: 1-5.
[7] Fenghua Li, Yuanyuan He, Ben Niu, Hui Li and Hanyi Wang, “Match-MORE: An Efficient Private Matching Scheme Using Friends-of-Friends’ Recommendation”, 2016 IEEE International Conference on Computing, Networking and Communications, Communications and Information Security, pp: 1-6.
[8] Sanjeev Dhawan and ShiviGoel, “Analysis of Pattern of Information Revelation and Site Use Behavior in Social Networking Sites”,International Journal of Computer Applications Technology and Research 2014 ISSN: 2319–8656 pp: 42 – 44.
[9]Sanjeev Dhawan, Kulvinder Singh and Jyoti, “High Rating Recent Preferences Based Recommendation System”,4thInternational Conference on Eco-friendly Computing and Communication Systems 2015,pp: 259-264.
[10]Chi Zhang, “A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks”, ieee transactions on dependable and secure computing, vol. 12, no. 4, july/august 2015.
[11] P. Lin, P.-C. Chung, and Y. Fang, “P2P-iSN: A peer-to-peer architecture for heterogeneous social networks,” IEEE Netw., vol. 28, no. 1, pp. 56– 64, Jan./Feb. 2014.
[12] RuturajDhekane, BrionVibber, Talash: Friend Finding In Federated Social Networks Hyderabad, India, 2011.
[13]T. H.-J. Kim, A. Yamada, V. Gligor, J. Hong, and A. Perrig, “RelationGram: Tie-strength visualization for user-controlled online identity authentication,” in Proc. 17th Int. Conf. Financial Cryptography Data Security, 2013, pp. 69–77.
[14]A.Squicciarini, F. Paci, and S. Sundareswaran, “PriMa: A comprehensive approach to privacy protection in social network sites,” Ann. Telecommun., vol. 69, nos. 1/2, pp. 21–36, 2014.

Sanjeev Dhawan, Kulvinder Singh and Honey Gupta, "User Behavior Based Friend Recommendation in Facebook Social Networks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.69-73, 2018.

With the flooding of a huge amount of data on the web with reference to the text, a technique to condense this data in summary form is very important so that the users can have access to relevant information regardless of enormous content on the web that is available to the user. This content could be informative, relevant or even important to the user or could be purely irrelevant. Text summarization techniques help in reducing the time and effort of the user looking for content about a particular topic on the internet by summarizing the content of the documents and the user by only looking at the summarized content can decide whether the document is relevant or irrelevant. Thus, automatic text summarization techniques play a key role in information retrieval from the web. In this paper, a study of various text summarization techniques has been conducted based on parameters like a number of documents, content, output, language, availability of training data etc. Also, the summary evaluation processes i.e. intrinsic and extrinsic are discussed. Extractive approaches for text summarization are also discussed and the recent work done in each of these approaches is compared and contrasted.

Text Summarization, Extractive summarization, Intrinsic Evaluation, Extrinsic Evaluation

[1] H. P. Luhn, “The Automatic Creation of Literature Abstracts,” IBM J. Res. Dev., vol. 2, no. 2, pp. 159–165, 1958.
[2] M. Gambhir and V. Gupta, “Recent automatic text summarization techniques :,” Artif. Intell. Rev., vol. 47, no. 1, pp. 1–66, 2017.
[3] M. Hassel, “Evaluation of Automatic Text Summarization.”
[4] “Extractive Summarization - Google Search.” [Online]. Available: https://www.google.co.in/search?q=Extractive+Summarization&oq=Extractive+&aqs=chrome.1.69i57j69i59j69i61l3j0.7516j0j7&sourceid=chrome&ie=UTF-8. [Accessed: 25-Feb-2018].
[5] V. K. Gupta and T. J. Siddiqui, “Multi-document summarization using sentence clustering,” in 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), 2012, pp. 1–5.
[6] Xiao-Ying Liu, Yi-Ming Zhou, and Ruo-Shi Zheng, “Measuring semantic similarity within sentences,” in 2008 International Conference on Machine Learning and Cybernetics, 2008, pp. 2558–2562.
[7] R. Ferreira et al., “Assessing sentence scoring techniques for extractive text summarization,” 2013.
[8] A. N. Gulati and S. D. Sawarkar, “A novel technique for multidocument Hindi text summarization,” 2017 Int. Conf. Nascent Technol. Eng. ICNTE 2017 - Proc., 2017.
[9] L. Yang, X. Cai, S. Pan, H. Dai, and D. Mu, “Multi-document summarization based on sentence cluster using non-negative matrix factorization,” J. Intell. Fuzzy Syst., vol. 33, no. 3, pp. 1867–1879, 2017.
[10] G. Yang, D. Wen, Kinshuk, N.-S. Chen, and E. Sutinen, “A novel contextual topic model for multi-document summarization,” Expert Syst. Appl., vol. 42, no. 3, pp. 1340–1352, Feb. 2015.
[11] Z. Wu et al., “A topic modeling based approach to novel document automatic summarization,” Expert Syst. Appl., vol. 84, pp. 12–23, 2017.
[12] E. Baralis, L. Cagliero, N. Mahoto, and A. Fiori, “GRAPHSUM: Discovering correlations among multiple terms for graph-based summarization,” 2013.
[13] S. alZahir, Q. Fatima, and M. Cenek, “New graph-based text summarization method,” in 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), 2015, pp. 396–401.
[14] C. Fang, D. Mu, Z. Deng, and Z. Wu, “Word-sentence co-ranking for automatic extractive text summarization,” Expert Syst. Appl., vol. 72, pp. 189–195, Apr. 2017.
[15] M. A. Fattah and F. Ren, “GA, MR, FFNN, PNN and GMM based models for automatic text summarization,” Comput. Speech Lang., vol. 23, no. 1, pp. 126–144, Jan. 2009.
[16] L. Yang, X. Cai, Y. Zhang, and P. Shi, “Enhancing sentence-level clustering with ranking-based clustering framework for theme-based summarization,” Inf. Sci. (Ny)., vol. 260, pp. 37–50, Mar. 2014.
[17] W. Li, W.-K. Ng, Y. Liu, and K.-L. Ong, “Enhancing the Effectiveness of Clustering with Spectra Analysis,” IEEE Trans. Knowl. Data Eng., vol. 19, no. 7, pp. 887–902, Jul. 2007.
[18] N. Desai and P. Shah, “AUTOMATIC TEXT SUMMARIZATION USING SUPERVISED MACHINE LEARNING TECHNIQUE FOR HINDI LANGAUGE.”

S. Gandotra, B. Arora, "Extractive Approaches for Automatic Text Summarization", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.40-80, 2018.

Cloud computing works in a distributed environment that is very complex in nature. Due to its large size and high complexity, emergence of fault can happen anywhere and anytime. Since cloud is a distributed platform, so it is more prone to errors and failures. In such an environment, avoiding a failure is difficult and identifying the source of failure is also very complex. Fault in cloud may lead to loss of critical data or trust loss of the customer in the organization. Data failure may be due to some corrupted data, missing source file or some other flaw in the data. Different fault tolerance techniques are used to achieve the robustness of the system. These techniques maintain the robustness and dependability of the cloud network. Many Researchers have employed the different techniques for self healing, aging detection and rejuvenation of virtual machine. This research paper presents the various proactive fault tolerance mechanisms that reduces the consumption of computing resources and enhances service availability to large extent.

cloud computing, self healing, aging detection, rejuvenation

[1] Soma Pratibha , S. Sowvarnica ,” Survey of Failures and Fault Tolerance in Cloud”,Second International Conference On Computing and Communications Technologies (ICCCT’17), 2017.
[2] Youssef M.Essa ,” A Survey of Cloud Computing Fault Tolerance: Techniques and Implementation”, International Journal of Computer Applications (0975 – 8887), Volume 138 – No.13, March 2016.
[3] Harpreet kaur, Amritpalkaur,” A survey on Fault tolerance techniques in Cloud Computing”, International Journal of Science, Engineering and Technology, April 20, 2015.
[4] I.M Umesh, Dr. G N Srinivasan, Matheus Torquato,”Software Rejuvenation Model for Cloud Computing Platform” International Journal of Applied Engineering Research, Volume 12, Number 19 (2017) ,pp. 8332-8337.
[5] Yuanshun Dai, Yanping Xiang, and Gewei Zhang, ” Self-healing and Hybrid Diagnosis in Cloud Computing”, CloudCom 2009, LNCS 5931, pp. 45–56, 2009.
[6] Engelmann, G. R. Vallee, T. Naughton, and S. L.Scott, “Proactive fault tolerance using preemptive migration”, Euromicro International Conference on Parallel, Distributed, and network‐based Processing (PDP), pages 252–257, 2009.
[7] Avinash Nidumbur, ”high-performance computing”, [online]available: https://www.ibm.com/blogs/cloudcomputing/2016/06/high-performance-computing-cloud/
[8] virtual machine monitor[online] , available :
https://www.techopedia.com/definition/717/virtual-machine-monitor-vmm
[9] Niloofar Khanghahi and Reza Ravanmehr,” CloudComputing Performance Evaluation: issues and challenges”, International Journal on Cloud Computing: Services and Architecture (IJCCSA), Vol.3, No.5, October 2013
[10] Rachel Householder, Scott Arnold, Robert Green,” On Cloud-based Oversubscription”, International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 8- Feb 2014.
[11] Danilo Ardagna, Giuliano Casale, Michele Ciavotta, Juan F Pérez and Weikun Wang,” Quality-of-service in cloud computing: modeling techniques and their applications ”, Journal of Internet Services and Applications 2014

Atul Kumar, Deepti Malhotra, "Study of Various Proactive Fault Tolerance Techniques in Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.81-87, 2018.

Social Networks are networks which provides platform to different users to share their thoughts and make new friends also recommend some products, movies and friends to their friends or any other new users. In today’s environment it is very difficult to suggest a friend to watch what kind of movie on the basis of their interest. To overcome this kind of problem in this paper an attempt has been made to propose a mechanism to recommend a movie to friends based on their interest. The proposed mechanism is assessed using weka tool. This paper is divided into six sections. In section i brief introduction of social networks and recommendation has been discussed, in section ii existing recommendation techniques with their challenges has been presented, section iii covers modern recommendation techniques after that in section iv challenges and issues of different recommendation techniques has been studied in section v proposed mechanism has been presented section vi covers results and analysis of proposed work with weka tool.

Online Social Networks, Recommendation, Collaborative filtering, Rating and Weka

[1]B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web, ser. WWW ’01. New York, NY, USA: ACM, 2001, pp. 285–295.
[2]G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. on Knowl. and Data Eng., vol. 17, pp. 734–749, June 2005.
[3]M. van Setten, S. Pokraev, and J. Koolwaaij, “Context-aware recommendations in the mobile tourist application compass,” in Adaptive Hypermedia and Adaptive Web-Based Systems, ser. Lecture Notes in Computer Science, P. De Bra and W. Nejdl, Eds. Springer Berlin / Heidelberg, 2004, vol. 3137, pp. 515–548.
[4]G. Adomavicius and A. Tuzhilin, “Context-aware recommender systems,” in Recommender Systems Handbook: A Complete Guide for Research Scientists and Practitioners. Springer, 2010.
[5] Sanjeev Dhawan, Kulvinder Singh and Deepika Sehrawat, “Emotion Mining Techniques in Social Networking Sites”, “International Journal of Information & Computation Technology”, ISSN 0974-2239 Vol.4, No. 12, pp. 1145-1153, 2014.
[6] Jyoti, Sanjeev Dhawan and Kulvinder Singh, “Analysing user ratings for classifying online movie data using various classifiers to generate recommendations”, in proceedings of “IEEE International Conference on Futuristic Trends on Computational Analysis and Knowledge Management(ABLAZE)”, pp: 295-300, Noida, India, 2015.
[7] Sanjeev Dhawan, Kulvinder Singh and Jyoti, “High Rating Recent Preferences Based Recommendation System”, in proceedings of “4th International Conference on Eco-friendly Computing and Communication Systems”, pp: 259-264, Kurukshetra, India, 2015.
[8] Anand Bhave, Himanshu Kulkarni, Vinay Biramane, PranaliKosamkar, “Role of Different Factors in Predicting Movie Success”,in proceedings of “International Conference on Pervasive Computing (ICPC)”, pp: 1-4, Pune, India, 2015.
[9] Yashar Deldjoo, Mehdi Elahi and Paolo Cremonesi, “Using Visual Features and Latent Factors for Movie Recommendation”, in proceedings of “CBRecSys”, pp: 1-4, Boston, MA, USA, 2016.
[10] Khyati Aggarwal and Yashowardhan Soni, “Movie Recommendations using Hybrid Recommendation Systems”,“International Journal on Recent and Innovation Trends in Computing and Communication” ,Vol. 4 No. 12, pp: 206-209, 2016.
[11] Jiaxin Zhu, Yijun Guo, Jianjun Hao and Jianfeng Li, “Gaussian Mixture Model Based Prediction Method of Movie Rating”, in proceedings of “ 2nd IEEE International Conference on Computer and Communications”, pp: 2114-2118, Chengdu, China, 2016.
[12]A. Sieg, B. Mobasher, and R. Burke, “Improving the effectiveness of collaborative recommendation with ontology-based user profiles,” in Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, ser. HetRec ’10. New York, NY, USA: ACM, 2010, pp. 39–46.

Sanjeev Dhawan, Kulvinder Singh and Neha Singh, "Movie Recommendation Framework Based on Users Interests for Online Social Networks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.88-91, 2018.

Using Mobile Agents (MAs) every conventional distributed system work can be performed efficiently, robustly and easily within a single and general framework. Despite many benefits, Mobile Agents have a number of issues like fault tolerance, security , routing etc. Among these issues this paper emphasizes on routing of MAs. This paper defines types of itineraries based on their knowledge and based on number of Mobile Agents used to perform optimum itinerary. It describes disadvantages of single mobile agent itinerary planning(SIPs) and different challenges faced by multi mobile Agent itinerary planning(MIPs). The objective of this paper is to bring out a comparative analysis of the existing Itinerary planning algorithms.

Itinerary planning, Mobile Agent, Mobile Agent routing

[1]. M. Mitchell, “An Introduction to Genetic Algorithms”, MIT Press, 1998.
[2]. Qi, H., Wang, F.: Optimal Itinerary Analysis for Mobile Agents in Ad Hoc Wireless Sensor Networks, Proceedings of the13th International Conference on Wireless Communications (Wireless’2001), pp. 147-153, (2001).
[3]. Wu,Q.,Rao,N.,Barhen,J.,Iyengar,S.,Vaishnavi,V.,Qi,H.,C hakrabarty,K.:OnComputing Mobile Agent Routes for Data Fusion in Distributed Sensor Networks. IEEE Transactions on Knowledge and Data Engineering, 16(6), pp. 740-753, June 2004.
[4]. Bean, N., & Costa, A. (2005). An analytic modelling approach for network routing algorithms that use “ant-like” mobile agents. Computer Networks, 49(2), 243-268.
[5]. Chen, M., Kwon, T., Yuan, Y., Leung, V.C.: ‘Mobile agent based wireless sensor networks’, J. Comput., 2006, 1, (1), pp. 14 – 21
[6]. Gavalas,D.,Pantziou,G.,Konstantopoulos,C.,Mamalis,B.: NewTechniquesforIncremen- tal Data Fusion in Distributed Sensor Networks, In Proceedings of the 11th Panhellenic Con- ference on Informatics (PCI’2007), pp. 599-608, (2007).
[7]. Chen, M., Kwon, T., Yuan, Y., Choi, Y., Leug, V.C.M.: Mobile Agent-Based Directed Diffusion in Wireless Sensor Networks, EURASIP Journal on Advances in Signal Processing Volume 2007, Hindawi Publishing Corporation (2007).
[8]. Chen, M., Gonzlez, S., Zhang, Y., Leung, V.C.: ‘Multi-agent itinerary planning for sensor networks’. Proc. IEEE 2009 Int. Conf. Heterogeneous Networking for Quality, Reliability, Security and Robustness (QShine 2009), Las Palmas de Gran Canaria, Spain, 2009
[9]. Chen, M., Leung, V., Mao, S., Kwon, T., Li, M.: ‘Energy-efficient itinerary planning for mobile agents in wireless sensor networks’. Proc. IEEE 2009 Int. Conf. Communications (ICC 2009), Bresden, Germany, 2009
[10]. Gavalas, D., Mpitziopoulos, A., Pantziou, G., Konstantopoulos, C.: ‘An approach for near-optimal distributed data fusion in wireless sensor networks’, Springer Wirel. Netw., 2009, 16, (5), pp. 1407–1425
[11]. Mpitziopoulos, A., Gavalas, D., Konstantopoulos, C., Pantziou, G.: ‘CBID: a scalable method for distributed data aggregation in WSNs’, Hindawi Int. J. Distrib. Sens. Netw., 2010, 2010, Article ID 206517, DOI: 10.1155/2010/206517
[12]. Chen, M., Cai, W., Gonzalez, S., Leung, V.C.: ‘Balanced itinerary planning for multiple mobile agents in wireless sensor networks’. Proc. Second Int. Conf. Ad Hoc Networks (ADHOCNETS2010), Victoria, BC, Canada, 2010
[13]. Konstantopoulos, C., Mpitziopoulos, A., Gavalas, D., Pantziou, G.: ‘Effective determination of mobile agent itineraries for data aggregation on sensor networks’, IEEE Trans. Knowl. Data Eng., 2010, 22, (12), pp. 1679– 1693
[14]. Jia, Y.X., Mickelson, J., Simpson, T., Ye, Q.: ‘Routing with localized clusters for wireless sensor networks’, J. Internet Technol., 2010, 11, (4), pp. 483 – 490
[15]. Cai, W., Chen, M., Hara, T., Shu, L.: ‘GA-MIP: genetic algorithm based multiple mobile agents itinerary planning in wireless sensor network’. Proc. Fifth Int. Wireless Internet Conf. (WICON), Singapore, 2010 

[16]. Chen, M., Gonzalez, S., Leung, V.: ‘Directional source grouping for multi-agent itinerary planning in wireless sensor networks’. Proc. Int. Conf. ICT Convergence (ICTC), Jeju Isaland, Korea, 2010.
[17]. Cai, W., Chen, M., Hara, T., Shu, L., & Kwon, T. (2011). A genetic algorithm approach to multi-agent itinerary planning in wireless sensor networks. Mobile Networks and Applications, 16(6), 782-793.
[18]. K. Lingaraj, Rajashree. V. Biradar, V. C. Patil. "A survey on mobile-agent middleware design principals and itinerary planning algorithms", 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2015.
[19]. Prapulla, S. B., Chandra, J., Mudakavi, M. B., Shobha, G., & Thanuja, T. C. (2016, October). Multi Mobile Agent itinerary planning using Farthest Node First Nearest Node Next (FNFNNN) technique. In Computation System and Information Technology for Sustainable Solutions (CSITSS), International Conference on (pp. 105-111). IEEE

Nidhi, Shuchita Upadhyaya, "A Comparative Analysis of Itinerary Planning Algorithms for Single Mobile Agent and Multi Mobile Agent", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.92-96, 2018.

The usual method of health decision support system through regular database provides less efficient prediction. The analysis accuracy is reduced when the quality of medical data is incomplete. It is replaced by a health decision support system which uses big data and a framework called hadoop. The decision support system is used for implementing the healthcare with the help of Hadoop as it contains large amount of data. Hadoop is used to predict the disease based upon the symptoms. The patients are provided with the unique ID. The Patient’s Health Record (PHR’s) of the patient is stored in the public cloud and is encrypted by homomorphic encryption. When the PHR is needed, they are retrieved from the cloud by decrypting it with the key so, this results in providing the confidentiality to the data. This proposed system provides accurate information and is handy for doctors to diagnose the patients quickly.

Disease prediction, Machine learning, big data, Naïve Bayes, Hadoop, Health care, diagnosis

[1] Marc Pi˜nol, Rui Alves, Ivan Teixid´o, Jordi Mateo, Francesc Solsona, Ester Vilapriny´ o.” Rare Disease Discovery: an optimized disease ranking system” IEEE Transactions on Industrial Informatics vol no1551-3203 (c) 2016 IEEE.
[2] Daniele Apiletti, Elena Baralis, Giulia Bruno, and Tania Cerquitelli.” REAL-TIME ANALYSIS OF PHYSIOLOGICAL DATA TO SUPPORT MEDICAL APPLICATIONS”, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 13, NO. 3, MAY 2009
[3] Urs An liker, Jamie A. Ward, “AMON: A WEARABLE MULTIPARAMETERMEDICAL MONITORING AND ALERT SYSTEM”. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 8, NO. 4, DECEMBER 2004
[4] Ren-Guey Lee, Kuei-Chien Chen, Chun-Chieh Hsiao, and Chwan-Lu Tseng, “A MOBILE CARE SYSTEM WITH ALERT MECHANISM”. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 11, NO. 5, SEPTEMBER 2007
[5] Gregorio Lopez, Vıctor Custodio, and Jose Ignacio Moreno, “LOBIN: E-TEXTILE AND WIRELESS-SENSOR-NETWORK-BASED PLATFORM FOR HEALTHCARE MONITORINGIN FUTURE HOSPITAL ENVIRONMENTS”, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 14, NO. 6, NOVEMBER 2010
[6] Arsalan Mohsen Nia, Mehran Mozaffari-Kermani, Susmita Sur-Kolay, Anand Raghunathan, and Niraj K. Jha, “ENERGY-EFFICIENT LONG-TERM CONTINUOUS PERSONALHEALTH MONITORING”, IEEE Transactions on Multi−Scale Computing Systems, 2332−7766 (c) 2015.
[7] https://github.com/dhimmel/hsdn/blob/gh-pages/data/symptoms-DO.tsv
[8] https://www.tutorialspoint.com/hadoop/hadoop_introduction.htm
[9] Yunjing An, Shutao Sun, Shujuan Wang,” Naive Bayes Classifiers for Music Emotion Classification Based on Lyrics” IEEE ICIS 2017, May 24-26, 2017, Wuhan, China
[10] https://en.wikipedia.org/wiki/Naive_Bayes_classifier

S.Subbalakshmi, M.Sumithra, "A Health Decision Support System for Disease Diagnosis based on Machine Learning via Big Data", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.97-103, 2018.

Intelligence exhibited by machines is known as artificial intelligence. We are all aware of the advantages of AI and how it has proved to be helpful in fields like agriculture, medical diagnosis, electronic training, robotic control and remote sensing. At the same time we may also have to analyze the negative side of artificial intelligence. Artificial Intelligence exceeding humans in reasoning and intelligence can replace humans and take away their jobs. This paper discusses about Ellon Musk’s Neuralink which is centered on creating artificially intelligent devices like brain chips that can be implanted in human brain with purpose of helping humans merge with software and to keep pace with the advancements in artificial intelligence with the help of the Neural Lace Technology. This paper has attempted to give the basic idea of Neural Lace and how this can be helpful in overcoming brain disorders like chronic pain disorder permanently and help the patients get rid of the side effects of the medication of chronic pain disorder that has got several side effects.

artificial intelligence, brain chips, chronic pain disorder and neural lace

[1]"Neuralink", En.wikipedia.org, 2018. [Online]. Available: https://en.wikipedia.org/wiki/Neuralink. [Accessed: 03- Feb- 2018]
[2]"Everything you need to know about Neuralink: Elon Musk’s brainy new venture", Digital Trends, 2018. [Online]. Available: https://www.digitaltrends.com/cool-tech/neuralink-elon-musk/. [Accessed: 02- Feb- 2018]
[3]Y. Gao and N. Japkowicz, Advances in artificial intelligence. Berlin: Springer, 2009.
[4]M. Molloy, "Scientists discover how to `upload knowledge to your brain`", The Telegraph, 2018. [Online]. Available: http://www.telegraph.co.uk/technology/2016/03/01/scientists-discover-how-to-download-knowledge-to-your-brain/. [Accessed: 01- Feb- 2018]
[5]f. Madeleine Acey, "Brain implants: Restoring memory with a microchip - CNN", CNN, 2018. [Online]. Available: https://edition.cnn.com/2013/05/07/tech/brain-memory-implants-humans/index.html. [Accessed: 30th- Jan- 2018]
[6]R. Sepponen, "Low-field magnetic resonance imaging—Clinical experiences", Magnetic Resonance Imaging, vol. 3, no. 3, p. 309, 1985.
[7]T. Sharp and A. Harvey, "Chronic pain and posttraumatic stress disorder: mutual maintenance?", 2018. .
[8]A. Vallabhaneni, T. Wang and B. He, "Brain—Computer Interface", 2018. .
[9]A. Bell, "Levels and loops: the future of artificial intelligence and neuroscience", 2018. .
[10]"Deep brain stimulation", En.wikipedia.org, 2018. [Online]. Available: https://en.wikipedia.org/wiki/Deep_brain_stimulation. [Accessed: 02- Feb- 2018]

M.P. Anjana, "“NEURALINK” Implantation of Artificial Intelligence in Humanbeings", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.104-107, 2018.