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Handwritten document image segmentation is key step for OCR (Optical Character Recognition) System. It is an important step because inaccurately segmented text lines will cause errors in the recognition stage. The selection of segmentation algorithm being used is the essential factor in deciding the accuracy of the OCR system. Devnagari is the most popular script in India. Devnagari is the script for Sanskrit, Hindi, Marathi, Kashmiri, Sindhi, Bihari, Bhili, Konkani, Bhojpuri and Nepali languages. It has vowels, consonants, vowel modifiers and compound characters, numerals. Optical Character Recognition for Devanagari is highly complex due to its rich set of conjuncts. The nature of handwriting makes the process of text line segmentation very challenging. Several techniques to segment handwriting text line have been proposed in the past. Our purpose is to provide a learning-based approach for segmentation of handwritten document images. This paper presents a quantitative comparison of three algorithms for page segmentation: Projection Profile, Run-length Smearing and Bounding Box along with some morphological operations like erosion, dilation etc. We have implemented these algorithms on our own dataset of handwritten documents. We have experimented and compare the accuracy and results of these methods.

OCR, Line and Word Segmentation, Projection Profile, Bounding Box, Run length Smearing

[1] L. L. Sulem, A. Zahour, B. Taconet, “Text line segmentation of historical documents: a survey”, IJDAR, Vol. 9, No. 2-4, pp. 123-138 , 2007.
[2] S. Nicolas, T. Paquet, L. Heutte, ``Text Line Segmentation in Handwritten Document Using a Production System``, Proceedings of the 9th IWFHR,Tokyo, Japan, pp. 245-250, 2004.
[3] A. Zahour, B. Taconet, P. Mercy, and S. Ramdane, “Arabic Hand-written Text-line Extraction”, in Proceedings of the Sixth International. Conference on Document Analysis and Recognition, ICDAR 2001, Seattle, USA, pp. 281–285, September 10-13 2001.
[4] O.Okun, M. Pietikainen, and J. Sauvola, "Document skew estimation without angle range restriction," IJDAR 2, pp. 132 - 144, 1999.
[5] N. Tripathy and U. Pal. ,“Handwriting Segmentation of Unconstrained Oriya Text,” in International Workshop on Frontiers in Handwriting Recognition, pp. 306–311 , 2004.
[6] Pal U., Datta S. ,” Segmentation of Bangla unconstrained handwritten text”,Proceedings of Seventh International Conference on Document Analysis and Recognition, pp 1128 – 1132,2003.
[7] Arivazhagan, M. ." A statistical approach to
line segmentation in handwritten documents. Document Recognition and Retrieval" XIV, Proceedings of SPIE, San Jose, CA, USA, 6500,2007.
[8] Ha, J., Haralick, R. M., & Phillips, I. T.," Recursive X-Y Cut using Bounding Boxes of Connected Components ", 952–955,1995.
[9] He, S., Samara, P., Burgers, J., & Schomaker, L. "Image-based historical manuscript dating using contour and stroke fragments. Pattern Recognition " , 58.,2016
[10] Le, V. P., Nayef, N., Visani, M., Ogier, J. M., & Tran, C. De.,"Text and non-text segmentation based on connected component features ". In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR (Vol. 2015–November).
[11] Louloudis, G., Gatos, B., Pratikakis, I., & Halatsis, K. (n.d.). "A Block-Based Hough Transform Mapping for Text Line Detection in Handwritten Documents", Department of Informatics , Elsevire Pattern Recognition Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006.
[12] Papavassiliou, V., Stafylakis, T., Katsouros, V., & Carayannis, G.."Handwritten document image segmentation into text lines and words ". Pattern Recognition, 43(1), 369–377, 2010.
[13] A.N. Rajath. "An Adaptive Approach : Text Line Extraction from Multi-Skewed Hand Written Documents", 5(6), 158–161,2015.
[14] Yin, F. E. I, & Liu, C.." Handwritten text line extraction based on minimum spanning tree clustering ". International Conference on Wavelet Analysis and Pattern Recognition, 1123–1128,2007.
[15] H. R. Mamatha and k. Srikantamurthy, “Morphological Operations and Projection Profiles based Segmentation of Handwritten Kannada Document”,International Journal of Applied Information Systems (IJAIS)–ISSN:2249-0868 Foundation of Computer Science FCS,2012
[16] Chethana, H. T., & Mamatha, H. R.. “Comparative Study of Text Line Segmentation on Handwritten Kannada Documents”, 7(1), 26–33,2016.
[17] Kinhekar, S..”Comparative Study of Segmentation and Recognition Methods for Handwritten Devnagari Script “, 105(9), 34–39,2014.
[18] Santos, R. P., Clemente, G. S., Ren, T. I., & Calvalcanti, G. D. C..” Text Line Segmentation Based on Morphology and Histogram Projection “,2009 .

Ms. S. A. Bhopi, Mr. M. P. Singh, "A Comparative Study of Segmentation Techniques used in Handwritten Documents," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.200-205, 2018.

Our research goal is to develop an advance web portal system which is efficient in the evolution of recruitment system. This system is effectual for the Recruiter for Recruiting the job aspirants which are worthy for the respective job and the job aspirants can also apply for job position posted on the portal as well as the job aspirant can Search for the specific Job Position as their preferences. Thus, only the specific mail on the basis of their preferences should be sent, no other job position mail should be sent. This system maintains a trustworthy relation with the job aspirants and the recruiter by verifying the company is actually exist or not. It’s also check whether the job aspirants have all the skills mention in his resume. A survey conducted for measuring the problem faced by the students on existing Job Portal System and after that the idea for the Advance Recruitment System is enacted.

Ranking Algorithm, Recruitment System, Scalable Keyword Search Technique, Information Retrieval

[1] Vivek Kumar Sehga, Akshay Jagtiani, Meha Shah. “Job Portal-A Web Application for Geographically Distributed Multiple Clients”.
[2] Alexander Richter, Michael Koch "Functions of Social Networking Services ", 8TH International Conference on the Design of Cooperative Systems.
[3] L. Dabbish, C. Stuart, J. Tsay, and J. Herbsleb. Social coding in GitHub: transparency and collaboration in an open software repository.In Proceedings of the ACM 2012 conference on Computer Supported Co-operative Work, ACM, 2012.
[4] AmyGreenwald, JohnWicks. “QuickRank: A Recursive Ranking Algorithm”.
[5] Wangchao Le, Feifei Li. “Scalable Keyword Search on Large RDF Data”. IEEE Transactions on Knowledge and Data Engineering (Volume: 26, Issue: 11, Nov. 2014)
[6] C. Hauff and G. Gousios. Matching GitHub developer profiles to job advertisements. In Proceedings of the 12th Working Conference on Mining Software Repositories, 2015.
[7] M. A. Russell. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub and More " ’O’ Reilly Media, Inc.", 2013.
[8] Liang Zhang; Beihong Jin; Jiannong Cao. “AMBP: An Adaptive Mailbox Based Protocol for Mobile Agent Communication”. 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing.
[9] Calì, A., Calvanese, D., Colucci, S., Di Noia, T. D. & Donini, F.M. (2004). A logic-based approach for matching user profiles. In KES 2004, Lecture Notes in Artificial Intelligence, 187-195.
[10] Saat, N.M.; Singh, D. "Assessing suitability of candidates for selection using candidates` profiling report", Electrical Engineering and Informatics (ICEEI), 2011 International Conference.
[11] Rafter, R., Bradley, K., & Smyth, B. (2000). “Personalized retrieval for online recruitment Services, In Proceedings of the 22nd Annual Colloquium on Information Retrieval”.
[12] Stanley Wasserman and Katherine Faust: “Social Network Analysis: Methods and Applications”.
[13] Koch, M.; Richter, A.; Schlosser, “A.: Services and applications for IT-supported social networking in companies, Wirtschaftsinformatik”.
[14] Soonhee, K.; Hyangsoo, L.: The impact of organizational context and information technology on employee knowledge –sharing capabilities. Public Administration Review, May-June, 370-385 (2006)

H.U Joshi, G.R. Patil, Y. B. Singanjude, A. N. Dhote, M. S. Adhav, "Advance Recruitment System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.206-210, 2018.

Today Recommender system predicts the future preferences of the user based on the user’s profile. A number of approaches have been taken to address the issue of recommendations, be it user based filtering methods, item-based filtering methods etc. The popular is Collaborative filtering technique used by some renowned companies like Amazon, YouTube and others. But the problem that still holds is the cold start problem and the amount of time and accuracy that is associated with these algorithms. A recent improvement suggested is the Reverse Collaborative filtering for the accuracy and pre-processing time. This paper implements and compares collaborative and reverse collaborative filtering solutions to address the cold start problem.

Personalization, Profiles, Recommendation Systems, Cold Start Problem

[1] Konstan JA, Riedl J. Recommender systems: from algorithms to the user experience. User Model User-Adapt Interact 2012;22:101–23.
[2] Pan C, Li W. Research paper recommendation with topic analysis. In Computer Design and Applications IEEE 2010;4,pp. V4-264.
[3] Pathak B, Garfinkel R, Gopal R, Venkatesan R, Yin F. Empirical analysis of the impact of recommender systems on sales. JManage, Inform Syst 2010;27(2):159–88.
[4] Z. Huang, H. Chen, and D. Zeng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst., 22(1):116–142, 2004.
[5] J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI ’98, 1998.
[6] R. Jin, J. Y. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. In Proc. of SIGIR ’04, pages 337–344, Sheffield, United Kingdom, 2004.
[7] P. Bedi, H. Kaur, and S. Marwaha. Trust-based recommender system for the semantic web. In Proc. Of IJCAI’07, pages 2677–2682, 2007.
[8] P. Massa and P. Avesani. Trust-aware collaborative filtering for recommender systems. In Proceedings of CoopIS/DOA/ODBASE, pages 492–508, 2004.
[9] J. O’Donovan and B. Smyth. Trust in recommender systems. In Proc. of IUI ’05, pages 167–174, San Diego, California, USA, 2005.
[10] T. Hofmann. Collaborative filtering via Gaussian probabilistic latent semantic analysis. In Proc. Of SIGIR ’03, pages 259–266, Toronto, Canada, 2003.
[11] Y. Lu, P. Tsaparas, A. Ntoulas, and L. Polanyi. Exploiting social context for review quality prediction. In Proc. of WWW ’10, pages 691–700, Raleigh, North Carolina, USA, 2010.
[12] Q. Mei, D. Cai, D. Zhang, and C. Zhai. Topic modeling with network regularization. In Proc. Of WWW ’08, pages 101–110, Beijing, China, 2008.
[13] Shardanand, U. and Maes, P., 1995, May. Social information filtering: algorithms for automating “word of mouth”. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 210-217). ACM Press/Addison-Wesley Publishing Co.

Saniya Zahoor , "Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.211-214, 2018.

With the emergence of Internet technology, users have started exploring, connecting and socializing themselves on the social media anywhere and anytime. Social networks have reformed the means we communicate. Online social networks are gaining importance due to the generation of large metadata that was never possible before. With this metadata from social networks, recommender systems gain benefit to determine rating preferences of users. Nowadays, social networks are also becoming useful in academics. They promote collaborative learning between students. This paper inspects the role of social networks in recommending projects to students. We propose a system that uses social network information of students to generate recommendations. We use several factors which play essential role in project recommendations. The contextual information from user profiles and the tags that are used by projects for reviewing, rating, tagging or contributing are employed. These tags are then used to extract the most relevant tags on the basis of the factors considered.

Recommender system, Social networks, Collaborative learning

[1] P. Dillenbourg, “Collaborative learning: Cognitive and computational approaches, ” Elsevier, 1999.
[2] F. Ricci, L. Rokach, B. Shapira, “Recommender Systems: Introduction and Challenges,” Springer, pp. 1–34, 2015.
[3] P. Melville, V. Sindhwani, “Recommender systems,” in Encyclopedia of Machine Learning, Springer US, pp. 829–838, 2010.
[4] M. Kohar, C. Rana, “Survey Paper on Recommendation System,” International Journa of Computer Science and Information Technology, Vol.3, Issue.2, pp. 3460–3462, 2012.
[5] N.H.M. Alwi, N. A. Mahir, S. Ismail, “Infusing Social Media in Teaching and Learning (TnL) at Tertiary Institutions: A Case of Effective Communication in Universiti Sains Islam Malaysia (USIM),” Procedia - Social and Behavioral Sciences., Vol.155, Issue.October, pp. 265–270, 2014.
[6] J. Shokeen, P. Yadav, Meenakshi, “Community detection in social networks,” Journal of Emerging Technologies and Innovative Research, Vol.3, Issue.8, pp. 32–34, 2016.
[7] I. Guy, D. Carmel, “Social recommender systems,” in Recommender Systems Handbook, Springer, pp. 511–543, 2015.
[8] P. Rani, J. Shokeen, “Issues and Challenges in Link Prediction for Social Networks,” In the Proceedings of the 11th INDIACom and 4th International Conference on Computing for Sustainable Global Development, India, pp. 6889–6895, 2017.
[9] M. Ali, R. A.I.B.R. Yaacob, M.N.A.B. Endut, N.U. Langove, “Strengthening the academic usage of social media: An exploratory study,” Journal of King Saud University - Computer and Information Sciences, Vol. 29, Issue.4, pp. 553–561, 2017.
[10] W.M. Al-Rahmi, M.S. Othman, M.A. Musa, “The Improvement of Students’ Academic Performance by Using Social Media through Collaborative Learning in Malaysian Higher Education,” Asian Social Science, Vol.10, Issue.8, pp. 210–221, 2014.
[11] C. Rana, S.K. Jain, “Building a book recommender system using time based content filtering,” WSEAS Trans. Comput., Vol. 11, Issue.2, pp. 27–33, 2012.
[12] S. Garcia-Martinez, A. Hamou-Lhadj, “Educational Recommender Systems: A Pedagogical-Focused Perspective,” in Multimedia Services in Intelligent Environments. Smart Innovation, Systems and Technologies, Vol.25, G. A. Tsihrintzis, M. Virvou, and L. C. Jain, Eds. Heiderlberg: Springer, 2013, pp. 113–124.
[13] T.Y. Tang, G. McCalla, “A multidimensional paper recommender: Experiments and evaluations,” IEEE Internet Comput.ing, Vol.13, Issue.4, pp. 34–41, 2009.
[14] M.M. Recker, A. Walker, K. Lawless, “What do you recommend? Implementation and analyses of collaborative information filtering of web resources for education,” Instructional Science, Vol.31, Issue.4–5, pp. 299–316, 2003.
[15] E. Seralidou, C. Douligeris, “Identification and Classification of Educational Collaborative Learning Environments,” Procedia of Computer Science, Vol.65, no. Iccmit, pp. 249–258, 2015.
[16] J. Shokeen, C. Rana, “A study on Trust-aware Social Recommender Systems,” In the Proceedings of the 12th INDIACom and 5th International Conference on Computing for Sustainable Global Development, India, pp. 4268–4272, 2018.
[17] P. Sharma, R.K. Gupta, “A Novel Web Usage Mining Technique Analyzing User Behaviour Using Dynamic Web Log,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.106-111, 2017.
[18] C. Rana, S.K. Jain, “A study of the dynamic features of recommender systems,” Artificial Intelligence Review, Vol.43, Issue.1, pp. 141–153, 2012.
[19] S. Guha, R. Rastogi, K. Shim, “Rock: A robust clustering algorithm for categorical attributes,” Information Systems, Vol. 25, Issue.5, pp. 345–366, 2000.

Jyoti Shokeen, "On Measuring the Role of Social Networks in Project Recommendation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.215-219, 2018.

In the present occupied world time is an imperative issue which can`t be overseen by seeing every single marvel with our tight timetable. So now daily`s Automatic frameworks are being favored over manual framework to make life less complex and less demanding in all angles. Increasing populace rate, has continually being reason for destruction in the situation of cleanliness regarding waste administration framework. The flood of waste in canisters in community territories produces unhygienic condition in the neighboring zones. It might bother various extreme infections for the close-by individuals. This will mortify the evaluation of the influenced territory. For disposing of or moderating the refuse`s and look after cleanness, there is requiring of `Keen waste checking and gathering framework`. This paper proposes IOT based junk gathering framework which checks the waste level in the junk containers by utilizing Sensor frameworks. At the point when the junk container is full as SMS will be produced from the framework which will then send to a specialist observing it as a notice status. This framework utilizes Microcontroller for legitimate information procurement, preparing and its transmission. For runtime monitorization and joining website page is produced to gain wanted data identified with levels of waste in the dustbin at various areas. This followed the greenish in nature and support swachh bharat for cleanness.

Internet of Things; Big Data, Ultrasonic, ATmega8 microcontroller, Wi-Fi, L.E.D.

[1] N. Sharma, N. Singha, T. Dutta, “Smart Bin Implementation for Smart Cities”, International Journal of Scientific & Engineering Research, Volume 6, Issue 9,pp 1-5,2015
[2] R.B Tapase,A. Mohite,T. Kadam,P. Deshmukh,” Intelligent Monitoring System For Garbage Waste Using Arduino”, International Journal of Research in Engineering and Technology, Volume 05,Issue 12,pp 1-3,2017
[3] D.D. Guinard and V.M. Trifa ,” Building the Web of Things”, Manning, United States,pp 1-28,2016, ISBN 9781617292682
[4] R. Gorli, “Interlinking of IoT, Big data, Smart Mobile app with Smart Garbage Monitoring”, IJCSE Transaction, Vol.5,Issue.1, pp.31-01, 2017, E-ISSN: 2347-2693.
[5] A. Medvedev, P. Fedchenkov, and A.A. Zaslavsky “ Waste Management as an IoT Enabled Service in Smart Cities”, Balandin, S., Andreev, S., Koucheryavy, Y. (eds.) ruSMART 2015. LNCS, Springer, Cham, vol. 9247, pp. 104–115, 2015
[6] J. Joshi, J. Reddy, P. Reddy, A. Agarwal, R. Agarwal, A. Bagga, and A. Bhargava, “Cloud Computing Based Smart Garbage Monitoring System”,3rd International Conference on Electronic Design, pp 70-75,2016.
[7] S.V. Kumar, T.S. Kumaran, A.K. Kumar and M. Mathapati, “Smart Garbage Monitoring and Clearance System using Internet of Things”, IEEE International Conference on Smart Technologies And Management For Computing,Communication,Controls,Energy & Material , pp- 184 – 189, 2017
[8] S. Shukla and N. Shukla,"Smart Waste Collection System based on IoT: A survey", International Journal of Computer Applications, vol. 162,Issue 3, pp 1-3,2017
[9] M. Schwartz,"Internet of Things with ESP8266",packt publishing,United Kingdom,pp 1-47,2016,ISBN-10: 1786468026

C.S. Khandait, P.P. Rane, A.S.Jaiswal, N.K.Bais, "IoT Based Garbage Collection System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.220-225, 2018.

Cognitive radio network (CRN) is the wide and most popular emerging technology for a smart wireless communication environment rapidly in the recent years. Cognitive radio (CR) is an intelligent technology which provides eminent solution to the problem of spectrum scarcity and to allow the competent distribution of the available radio spectrum without affecting any destructive interference to the primary user (PU) in wireless networks. The utility of the spectrum sensing is to recognizing the offered and organizing through other users for spectrum access, increasing the channel utility, sinking collision rates, and growing detecting overhead are the primary aspects in cognitive radio medium access control protocols. The smart usage of radio spectrum widely depends on precise sensing of spectrum, mobility of spectrum and spectrum decisions. Therefore, the CR technology can considerably provide a smart solution to solve the spectrum scarcity problem by exploiting idle radio spectrum by licensed users. Though, several technical problems still essential to be solved for the suitable functioning of CRNs. The MAC protocols for CRNs should provide a comprehensive description of common control channels (CCC), sensing of spectrum, and harmful interference to PUs, the spectrum availability in distinction rates, the infrastructure support, required time management, and the quantity of radio transceivers. In this paper we studied the distinction MAC technology used for channel allocation and presented the literature survey on MAC protocols with their different usages and also highlight and explore some essential research issues and challenges that might drive further research in this field.

CR, MAC, CCC, DSA

[1] Dina Tarek Mohamed, Amira M. Kotb, S.H.Ahmed, “A Medium Access Protocol for Cognitive Radio Networks Based on Packet`s Collision and Channels` Usage”, International Journal of Digital Information and Wireless Communications (IJDIWC) 4(3), 2014, pp. 314-332.
[2] Z.Htike, C.S.Hong, S.Lee, Iikwon Cho, “DYN-MAC: A MAC protocol for cognitive radio networks with dynamic controlchannel assignment”, IEICE Trans Commun, Vol. E97-B, No. 8, 2014, pp. 1577-1585.
[3] M.Chodhury, Asaduzzaman, Md.Fazlul Kader, M.O.Rahman, “Design of an Efficient MAC Protocol for Opportunistic Cognitive Radio Network”, IJCSIT, Vol. 4, No. 5, 2012, pp.233-242.
[4] Le Thanh Tan, Long Bao Le, “Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis, and Optimization”, IEEE Transactions on Vehicular Technology, VOL. 60, NO. 8, 2011, pp. 3990-4003.
[5] S.M.Kamruzzaman, “An Energy Efficient Multichannel MAC Protocol for Cognitive Radio Ad Hoc Networks”, IJCNIS, Vol. 2, No. 2, 2010, pp. 112-119.
[6] Le Thanh Tan, Long Bao Le, “Joint cooperative spectrum sensing and MAC protocol design for multi-channel cognitive radio networks”, EURASIP Journal on Wireless Communications and Networking, 2014, pp. 1-21.
[7] M.M.A.Priton, M.M.Hassan, S.Sarker, Md.A.Razzaque, M.A.Hossain, A.Alelaiwi, “A Multiconstrained QoS Aware MAC Protocol for Cluster-BasebCognitive Radio Sensor Networks”, International Journal of Distributed Sensor Networks, Vol. 2015, pp. 1-13.
[8] S.Debroy, S.De, M.Chatterjee, “Contention Based Multichannel MAC Protocol for Distributed Cognitive Radio Networks” IEEE Transactions On Mobile Computing, VOL. 13, NO. 12, 2014, pp. 2749-2762,
[9] Paulo.M.R.dos Santos, M.A.Kalil, O.Artmenko, A.Lavrenko, A.M.Thiel, “Self-Organized Common Control Channel Design for Cognitive Radio Ad Hoc Networks”, 2013 IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks, 2013, pp. 2419-2423.
[10] Peng Hu, Mohamed Ibnkahla, “A MAC protocol with mobility support in cognitive radio ad hoc networks: Protocol design and analysis”, Ad Hoc Network 17(2014), 2014, pp. 114-128.
[11] M.Zareer, A.K.M.M.Islam, N.Mansoor, S.Baharun, E.M.Mohamed, S.Sampei, “CMCS: a cross-layer mobility-aware MAC protocol for cognitive radio sensor networks”, EURASIP Journal on Wireless Communications and Networking (2016), pp. 1-15.
[12] S.Anamalamudi, Minglu JIN, J.M.Kim, “Interference-Aware Hybrid MAC protocol for Cognitive Radio Ad-Hoc Networks with Directional Antennas”, EAI Endorsed Transactions on Wireless Spectrum, Vol.1, No. 3, 2015, pp. 1-10.
[13] Aohan Li, Guangjie Han, “A fairness-based MAC protocol for 5G Cognitive Radio Ad Hoc Networks”, Journal of Network and Computer Applications, 111(2018), pp. 28-34.
[14] Md Akbar Hossain, Nurul I Sarkar, “A distributed multichannel MAC protocol for rendezvous establishment in cognitive radio ad hoc networks”, AdHocNetworks70(2018), pp. 44–60
[15] Irfan Latif Khan, Riaz Hussain, Adeel Iqbal, Atif Shakeel, Shakeel Alvi, Waseem Abbas, adeer ul Hasan, Shahzad A. Malik, “Design and Evaluation of Self Organizing, Collision Free MAC Protocol for Distributed Cognitive Radio Networks”, 2017.
[16] Claudia Cormio, Kaushik R. Chowdhury, “A survey on MAC protocols for cognitive radio networks”, Ad Hoc Networks 7 (2009), pp. 1315–1329.
[17] Ajmery Sultana, Xavier Fernando, Lian Zhao, “An overview of medium access control strategies for opportunistic spectrum access in cognitive radio networks”, Peer-to-Peer Netw. Appl. 2016,
[18] S.Tamilarasan, P.Kumar, “A Survey on Dynamic Resource Allocation in Cognitive Radio Networks”, International Journal of Computer Sciences and Engineering, Vol.-4(7), 2016, pp. 86-93.
[19] Bang Chul Jung, Woongsup Lee, “Performance analysis of opportunistic CSMA schemes in cognitive radio networks”, Wireless Netw, springer publication, 2016,
[20] Zong-Heng Wei, Bin-jie Hu, “A Fair Multi-channel Assignment Algorithm with Practical Implementation in Distributed Cognitive Radio Networks”, IEEE ACCESS, 2018, pp. 1-13.
[21] S. Senthilmurugan, T. G. Venkatesh, “Analysis of Quiet Period Scheduling in QP-CSMA-CA Cognitive Radio MAC Protocol”, Wireless Pers Commun (2017) 92, pp. 1625–1637.
[22] Juncheng Jia, Qian Zhang, Xuemin, “HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management”, IEEE Journal on Selected Areas in Communications, VOL. 26, NO. 1, January 2008, pp. 106-117.
[23] S. M. KAMRUZZAMAN, Md. Abdul HAMID, M. Abdullah-Al-WADUD, “An Energy-Efficient MAC Protocol for QoS Provisioning in Cognitive Radio Ad Hoc Networks”, Radio engineering, VOL. 19, NO. 4, December 2010, pp. 567-578.
[24] Long Le, Ekram Hossain, “OSA-MAC: A MAC Protocol for Opportunistic Spectrum Access in Cognitive Radio Networks”, Proc. IEEE WCNC, 2008, pp. 1426–30.
[25] Yi Song, Jiang Xie, “ProSpect: a proactive spectrum handoff framework for cognitive radio ad hoc networks without common control channel”, IEEE Transactions on Mobile Computing, VOL. 11, NO. 7, pp. 1127-1139, July 2012
[26] Y. Zhao, M. Song, C. Xin, “FMAC: A fair MAC protocol for coexisting cognitive radio networks,” in Proceedings of IEEE INFOCOM, 2013, pp.1474–1482.
[27] Mohamed A. Kalil, Andre Puschmann, Andreas Mitschele-Thiel, “Switch: A multichannel mac protocol for cognitive radio ad hoc network”, in Proceedings of IEEE 76th vehicular technology conference (VTC2012-Fall), Qubec City, Canada, 2012.

S.Tamilarasan, P.Kumar, "Medium Access Control (MAC) Protocol for Resource Allocation in Cognitive radio Networks – A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.226-230, 2018.

This paper deals with analysis implementation of open loop and closed loop speed control for a Brushless dc (BLDC) motor drive using Hysteresis and MRAC. Mostly BLDC drives are used for electrical vehicle applications. Generally control algorithms which are developed for the motor drive might show good simulation results during steady state and transient conditions. Model reference adaptive system is a control strategy to drive electrical machine. Conventional cascade PI controllers are often used to control speed, torque and current. Adaptive PI controller for speed and current of a low inertia machine, change in speed set point should be slowly applied in order avoid stability problem. Hysteresis current control is used to eliminate voltage stability problem. In order to improve the performance compare to hysteresis current control the new model reference adaptive system used in speed control block. So, here initially open loop and closed loop system of BLDC drive using hysteresis current control is simulated and results are verified, after that MRAC is introduced to analysis improvement and dynamic behaviour of system and performance are analysed using MATLAB/ SIMULINK software. Hardware implementation of open loop BLDC drive system performance is validated by using DSP processor. In this project detailed procedure of effectively controlled the BLDC drive real time is presented.

BLDC motors, PI controller, Model Reference Adaptive Control (MRAC

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[3] Domenico Arrigo, “L6235 Three Phase Motor Driver,” STAN1088, 2001.
[4] Feyzi M.R, Ebadpour M, Mozaffari Niapour S.A.KH, Arshya Feizi, Mousavi.R Aghdam “A New Single Current Strategy for HighPerformance Brushless DC Motor Drives”, International Conference on Electrical and Computer Engineering, IEEE.
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[7] Muhammad Mubeen, “Brushless DC Motor Primer,” Motion Tech Trends, July, 2008.
[8] Padmaraja Yedamale,“Hands-on Workshop: Motor Control Part 4 -Brushless DC (BLDC) Motor Fundamentals,” Microchip AN885, 2003.
[9] Samitha Ransara H.K, Madawala U.K “A Low Cost Drive for Three Phase Operation of Brushless DC Motors”, International conference on IEEE Industrial Electronics Society, IECON 2011, pp. 1692 - 1697.
[10] “Sensorless BLDC Motor Control and BEMF Sampling Methods with ST7MC,” ST AN1946, July, 2007.
[11] Sundararju, K., and R. Senthil Kumar. "Modelling and analysis of relative power system with cascaded multilevel inverter STATCOM using fuzzy controller." Journal of advances in chemistry 12.10 (2016).

M. Lincy Luciana, R. Senthil kumar, "Performance Analysis of Open Loop and Closed Loop Control in BLDC Drives for Electrical Vehicle Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.231-235, 2018.

Cloud computing is allowing business to focus on core businesses in spite of pay additional amount. Cloud environment has been considered as a model that is enabling access to a shared pool of configurable remote resources on demand. In this research the multi layer security for the cloud servers have been provided. It has been found that cloud services are offering flexible & scalable services. But there is always issue of security during information transmission from centrally located server storage to another location. Thus there is need to enhance the security of traditional cloud systems. Here in this paper need of cloud computing has been discussed along with its limitations. Focus of research is to provide security to cloud server, Security issue with existing system, cloud Server Model and programming module to perform encryption decryption and IP verification has been discussed.

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Neetu Rani, Sandeep Dalal, "Implementation of Multiple layer security of Cloud server," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.236-241, 2018.

Suggestion system plays vital role in WWW world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in Suggestion system. Suggestion system is divided into three main types Collaborative Filtering, Content based and hybrid-based Method. The work in our categories collaborative filtering as Memory based type and Model based type. The paper discusses in detail the methods, their pros and cons. The paper proves to be a milestone in the research field of suggestion system.

Suggestion, Collaborative filtering, Model based, Memory based, Content based, Hybrid.

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Ankit Sharma, Ekta, "Review on Machine Learning Based Suggestion System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.242-244, 2018.

The central tenets of modern cryptography are data confidentiality, data integrity, authentication, and non-repudiation. There are a number of algorithms being used for confidential transmission of messages from one end to the other. The PlayFair Cipher is a substitution cipher. The classic PlayFair cipher uses 5 * 5 matrix to encrypt pairs of letters (diagrams).The frequency analysis is harder than simple substitution ciphers as there are 25 * 25 = 625 diagrams. But with increase in computing power, classical ciphers have become easy to break. The classical substitution ciphers can be broken by cipher text-only attacks. This paper presents a modified version of the classical PlayFair Cipher. The improvement is done to the original 5 * 5 cipher to modify it to 10 * 9 matrix. The matrix uses uppercase and lowercase English characters, numbers, punctuation marks and some special characters. The security aspect is enhanced by the use of Chaotic Genetic Algorithm to encrypt the cipher text again using Genetic crossover and mutation operations with a Chaotic Pseudo Random Sequence.

Component, Chaos Theory, Cryptography, Genetic Algorithm, PlayFair Algorithm, Pseudo Random Sequence

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Archi Seth, Siddhartha Sankar Biswas, "Chaotic Genetic Enhancements to the Modified PlayFair Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.245-250, 2018.