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Cloud computing refers to many different types of services and applications being delivered over the internet cloud. Cloud load balancing is the process of distributing workloads across multiple computing resources. Load balancing is an optimization problem and goal of any optimization is to either minimize effort or to maximize benefit. The effort or the benefit can be usually expressed as a function of certain design variables. Hence, optimization is the process of finding the conditions that give the maximum or the minimum value of a function. Load balancing is a problem where you try to minimize value of parameters like Makespan time, Response Time, etc. and increase the utilization of cloud resources. Metaheuristic algorithms are a natural solution to the problem of load balancing in cloud. But these algorithms as such do not provide a complete solution. This paper proposes a hybrid of Particle Swarm optimization and Ant Colony optimization for load balancing of tasks on cloud resources.

ACO, PSO, VM, SJF, IAAS, PAAS, SAAS, Data Centre, Cloud Computing, DI

[1] Karger D, Stein C, Wein J. Scheduling Algorithms. Algorithms and Theory of Computation Handbook: special topics and techniques. Chapman & Hall/CRC; 2010.
[2] Medhat Tawfeek, Ashraf El-Sisi, Arabi Keshk , Fawzy Torkey, “Cloud Task Scheduling Based on Ant Colony Optimization” , The International Arab Journal of Information Technology, Vol. 12, No. 2, March 2015.
[3] Jyoti Thaman, Manpreet Singh, “Current Perspective in Task Scheduling Techniques in cloud Computing: A Review”, International Journal in Foundations of Computer Science & Technology (IJFCST) Vol.6, No.1, January 2016.
[4] Mala Kalra, Sarbjeet singh, “a review of metaheuristic scheduling techniques in cloud computing”, available online 18 august 2015.
[5] Gurtej Singh, Amritpal kaur, “Bio Inspired Algorithms: An Efficient Approach for Resource Scheduling in Cloud Computing”, International Journal of Computer Applications (0975 – 8887) Volume 116 – No. 10, April 2015.
[6] Nazmul Siddique, Hojjat Adeli, “Nature Inspired Computing: An Overview and Some Future Directions”, Published online: 30 November 2015.
[7] Pratima Dhuldhule, J. Lakshmi, S. K. Nandy, “High Performance Computing Cloud - a Platform-as-a-Service Perspective” , 2015 International Conference on Cloud Computing and Big Data.
[8] Elaheh Hallaj, Elaheh Hallaj, “Study and Analysis of Task Scheduling Algorithms in Clouds Based on Artificial Bee Colony”, Second International Congress on Technology, Communication and Knowledge (ICTCK 2015) November, 11-12, 2015 - Mashhad Branch, Islamic Azad University, Mashhad, Iran.
[9] Aarti Singh, Dimple Juneja, Manisha Malhotra, “Autonomous Agent Based Load Balancing Algorithm in Cloud Computing”, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015), pp. 832-841.
[10] F. Rothlauf, “Design of Modern Heuristics Principles and Application”, Springer. Verlag Berlin Heidelberg, pp. 07-36, 2011. ISBN 978-3-540-72961.
[11] Dorigo M, Stu¨ tzle T. Ant colony optimization. MIT Press; 2004.
[12] J. Kennedy and R. Eberhart. Particle swarm optimization In IEEE International Conference on Neural Networks, volume 4, pages 1942–1948, 1995.
[13] V. Kale, “Big Data Computing: A Guide for Business and Technology”, CRC Press, US, pp. 177-203, ISBN 9781498715331.

D. Gupta, H.J.S. Sidhu, "Hybrid Task Scheduling Algorithm Based on ANT Colony Optimization and Particle Swarm Optimization for Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.324-328, 2018.

Today, students (class) attendance is become more important part for any organizations/institutions. Recording and monitoring of class attendance is an area of administration that requires significant amounts of time and effort in a school/university environment. To solve this problem we are using RFID technology. RFID technology is a powerful tool to manage student‘s attendance throughout the working school day and it will also enhance classroom security. RFID is a technology that allows for a tag affixed on identity card of student to communicate wirelessly with a reader. Reader will read the tag number and send to raspberry pi. Program running in raspberry pi will mark the corresponding student to present. At the end, SMS notification is send to all students’ parents those who are absent.

Student, Internet of Things (IoT), Radio Frequency Identification (RFID), Attendance, Raspberry Pi, SMS Gateway, Arduino Uno

[1] Rajan Patel, Nimisha Patel and Mona Gajjar, “Online Students Attendance Monitoring System in Classroom Using Radio Frequency Identification Technology: A Proposed System Framework”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459,Volume 2, Issue 2, pp. 61-66 February 2012
[2] Ch.S.R.Gowri V.Kiran and G.Rama Krishna, “Automated Intelligence Systemfor Attendance Monitoring With Open CVBased on Internet of Things (IOT)”, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 5, Issue 4, pp. 905-913 April 2016
[3] Tabassam Nawaz, Amber Assad and Zunaira Khalil, “Fully Automated Attendance Record Systemusing Template Matching Technique”, International Journal of Engineering & Technology IJET-IJENS Vol.10 Issue.03 pp.57-64, 2010
[4] Tabassam Nawaz, Saim Pervaiz, Arash Korrani and Azhar-UD-Din, “Development of Academic Attendence Monitoring System Using Fingerprint Identification”, IJCSNS International Journal of Computer Science and Network Security, VOL.9, No.5, pp.164-168 May 2009
[5] Muhammad Fuzail, Hafiz Muhammad Fahad Nouman, Muhammad Omer Mushtaq, Binish Raza, Awais Tayyab and Muhammad Waqas Talib, “ Face Detection System for Attendance of Class Students”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 3, pp.6-10 March 2017
[6] B.Mani Kumar, M.Praveen Kumar and Rangareddy, “RFID based Attendance monitoring system Using IOT with TI CC3200 Launchpad”, International Journal & Magazine of Engineering, Technology, Management and Research, Volume No.2, Issue No. 7,pp.1465-1467, 2015
[7] Nirmalya Kar, Mrinal Kanti Debbarma, Ashim Saha, and Dwijen Rudra Pal “Study of Implementing Automated Attendance System Using Face Recognition Technique”, International Journal of Computer and Communication Engineering, Vol. 1, No. 2, PP.100-103, July 2012.
[8] Yongqiang Zhang and Ji Liu, “Wireless Fingerprint Attendance Management System”, in Proceedings of the 2007 WSEAS International Conference on Computer Engineering and Applications, Gold Coast, Australia, pp.451-455, January 17-19, 2007
[9] Muhammad Faiz Mokhtar1, Che Wan Shamsul Bahri C.W.Ahmad and Khirulnizam Abd Rahman, “ E-AttendanceSsystem (EAS) using Bluetooth”, in Proceeding of the 3rd International Conference on Artificial Intelligence and Computer Science (AICS2015), MALAYSIA, pp.252-261, 12 -13 October 2015
[10] Chandrappa S, Dharmanna L, Shyama Srivatsa Bhatta U V, Sudeeksha Chiploonkar M, Suraksha M N, Thrupthi S,"Design and Development of IoT Device to Measure Quality of Water", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.4, pp.50-56, 2017.DOI: 10.5815/ijmecs.2017.04.06
[11] Chandrappa S, Dharmanna Lamani, Shubhada Vital Poojaryb, Meghana N U,"Automatic Control of Railway Gates and Destination Notification System using Internet of Things (IoT)", International Journal of Education and Management Engineering(IJEME), Vol.7, No.5, pp.45-55, 2017.DOI: 10.5815/ijeme.2017.05.05
[12] Chandrappa, Dharmanna Lamani “Segmentation of Retinal Nerve Fiber Layer in Optical Coherence Tomography (OCT) Images using Statistical Region Merging Technique for Glaucoma Screening” International Journal of Computer Applications (0975 –8887) Volume 128 , Issue No.10, pp-32-35, October 2015
[13] Dharmanna L, Chandrappa S, T. C. Manjunath, Pavithra G,"A Novel Approach for Diagnosis of Glaucoma through Optic Nerve Head (ONH) Analysis using Fractal Dimension Technique", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.1, pp.55-61, 2016.DOI: 10.5815/ijmecs.2016.01.08
[14] Dharmanna L, Chandrappa S, T. C. Manjunath, Pavithra G," Automated Diagnose of Neovascular Glaucoma Disease using advance Image Analysis Technique", International Journal of Applied Information Systems (IJAIS), Volume 9 Isuue No.6, September 2015
[15] Dharmanna L, Chandrappa S, T. C. Manjunath, Pavithra G," Different Clinical Parameters to Diagnose Glaucoma Disease: A Review", International Journal of Computer Applications (0975 –8887), Volume 116 –No.23, pp-43-46, April 2015

Chandrappa S, Dharmanna L, Deekshith K, Jagadeesha S, "IoT Based Automatic Student Attendance Monitoring System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.329-336, 2018.

Edge detection is play important in image processing. Find accurate and correctly edge is very important. Many algorithms used to detect boundary of an object. Compare edge detection algorithms on the base of accuracy, Sharpe edge detection and time. Most of the image shape information of an image object is enclosed in edges. Detect edge of in image using some filter, sharpness and celerity of images can be increase. In this paper we are comparing edge detection methods on the basis of time.

Information security, digital watermarking, copyright protection, graphics contents, 3D polygon model

[1] Ohbuchi R, Masuda H, Aono M. “Watermarking three-dimensional polygonal models through geometric and topological modifications”, IEEE Journal on Selected Areas in Communications, Vol 16, Issue: 4, May 1998.
[2] Siddharth Manay, Byung-Woo Hong, Anthony J. Yezzi, Stefano Soatto, “Integral invariant signatures”, European Conference on Computer Vision, pages 87-99. Springer, 2004.
[3] Helmut Pottmann, Qi-xing Huang, Yong-liang Yang, and Johannes Wallner, “Integral invariants for robust geometry processing”, Elsevier Computer Aided Geometric Design Vol 26, Issue 1, Pages 37-60, January 2009.
[4] Ryutarou Ohbuchia, Hiroshi Masudab, Masaki Aonoa “Data embedding algorithms for geometrical and on geometrical targets in three-dimensional polygonal models”, Elsvier Computer Communications, vol 21, issue 15, pp 1344-1354, Oct. 1998.
[5] Benedens O, Busch C. “Towards blind detection of robust watermarks in polygonal models”, Proceedings of Eurographics, Interlaken, Vol 19, No 3 pp199-208, 2000.
[6] Benedens O. “Affine invariant watermarks for 3D polygonal and NURBS based models”, Proceedings of the 3rd International Workshop Information Security,pp 15-29, March 2000.
[7] Kanai S, Date H, Kishinami T. “Digital watermarking for 3D polygons using multiresolution wavelet decomposition”, Proceedings of International Workshop on Geometric Modeling, Tokyo, 296-307, 1998.
[8] Uccheddu F, Corsini M, Barni M. “Wavelet-based blind watermarking of 3D models”, Proceedings of the 2004 Multimedia and Security Workshop, Magdeburg, pp. 143-154, 2004.
[9] Ohbuchi R, Mukaiyama A, Takahashi S. “A frequency-domain approach to watermarking 3D shapes”, Computer Graphics Forum, Vol 21, Issue 3, Pp. 373–382, Sep 2002.
[10] Wolfgang, R.B. and Delp, E.J.: “A watermark for digital images, A method for signature casting on digital images”, Proc. IEEE Int. Conf. on Image Processing, Vol.3, pp.219-222, 1996.
[11] Igor Guskov , Wim Sweldens , Peter Schröder, “Multiresolution signal processing for meshes”, Proceedings of the 26th annual conference on Computer graphics and interactive techniques, p.325-334, July 1999.
[12] Sagi Katz , Ayellet Tal, “Hierarchical mesh decomposition using fuzzy clustering and cuts”, ACM Transactions on Graphics (TOG), v.22 n.3, July 2003

S. Pahadiya, R. Khatri, "Compare Modify Canny Edge Detection Method with Existing Edge Detection Methods," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.337-340, 2018.

Medicinal leaves carry a huge value and importance in the medical field which can be directly used or medicines are made for medicinal purposes to cure patients. With the variety of leaves present, the proper identification of the leaves is very difficult without prior knowledge and experience. Computer Vision can bring the accurate identification of such leaves using the various feature extraction techniques using leaf images. The aim is to build a methodology using various feature extraction techniques to extract features, clustering algorithm to cluster the features and decision trees as a classifier. Feature extraction techniques like SIFT key descriptors which are robust and provide matching in spite of the change in intensity, size or rotation of the object in the images. Effective corner points are chosen from the image from which magnitude and orientation of surrounding are used to build descriptor that is the vector of feature for each corner points. For clustering the data, various partitional, hierarchical, density based methods are used to cluster the data which cluster the data with respect to inter-connectivity, similarity, closeness, etc. The clusters data is used to build the decision tree like C4.5 and CART which uses entropy and Gini index as the splitting criteria. All these methodologies put together to form an effective method to efficiently recognize the unknown leaf image using trained model.

SIFT Corner points, Chameleon Clustering, Decision Tree Classifier

[1 ] D. Lowe, “Object recognition from local scale-invariant features”, Proceedings of the International Conference on Computer Vision. pp. 1150-1157. 1999.
[2] D. G. Lowe, “Distinctive image features from scale-invariant key points”, International Journal of Computer Vision, November 2004, Vol 60, Issue 2, pp 91–110
[3] Karypis, George, Eui-Hong Han, and Vipin Kumar, "Chameleon: Hierarchical clustering using dynamic modeling." Computer, Vol: 32, Issue. 8, pp 68-75, Aug 1999
[4] Hendrickson, Bruce, and Robert W. Leland, "A Multi-Level Algorithm For Partitioning Graphs”, 95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing, Article No. 28, San Diego, California, USA — December 04 - 08, 1995
[5] Karypis, George, Kirk Schloegel, and Vipin Kumar, "Parmetis: Parallel graph partitioning and sparse matrix ordering library", Version 1.0, Dept. of Computer Science, University of Minnesota (1997)
[6] Karypis, George, and Vipin Kumar, "A Coarse-Grain Parallel Formulation of Multilevel k-way Graph Partitioning Algorithm", Eighth SIAM Conference on Parallel Processing for Scientific Computing PPSC 1997
[7] Quinlan, J. Ross, "Bagging, boosting, and C4. 5", Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI 96, IAAI 96, Portland, Oregon, August 4-8, 1996, Volume 1.
[8] Quinlan, J. Ross, "Improved use of continuous attributes in C4. 5" Journal of Artificial Intelligence Research archive, Volume 4 Issue 1, January 1996, pp 77-90
[9] Peng, Wei, Juhua Chen, and Haiping Zhou, "An implementation of ID3-decision tree learning algorithm" From web. arch. usyd. edu. au/wpeng/DecisionTree2. pdf Retrieved date: May 13 (2009).
[10] Hssina, Badr, et al, "A comparative study of decision tree ID3 and C4. 5", International Journal of Advanced Computer Science and Applications 4.2 (2014): pp13-19.
[11] Bradski, Gary, and Adrian Kaehler, “ Learning OpenCV: Computer vision with the OpenCV library", O’Reilly Media, Inc.", 2008.
[12] Quinlan, J. Ross, "Induction of decision trees", Machine learning 1.1 (1986):pp 81-106.
[13] Dashora, Rajnish, Harsh Bajaj, and Akshat Dube, "Parallel Algorithm for the Chameleon Clustering Algorithm using Dynamic Modeling",International Journal of Computer Applications (0975 – 8887),Volume 79 – No8, October 2013
[14] Agrawal, Gaurav L., and Hitesh Gupta,"Optimization of C4. 5 decision tree algorithm for data mining application", International Journal of Emerging Technology and Advanced Engineering, Volume 3, Issue 3, March 2013, pp: 341-345.
[15] Singh, Sonia, and Priyanka Gupta, "Comparative study ID3, cart and C4. 5 decision tree algorithm: a survey", International Journal of Advanced Information Science and Technology (IJAIST),ISSN: 2319:2682 Vol.27, No.27, July 2014, pp: 97-103
[16] Wu, Xindong, et al, "Top 10 algorithms in data mining", Knowledge and information systems 14.1 (2008), pp:1-37.
[17] Kernighan, Brian W., and Shen Lin, "An efficient heuristic procedure for partitioning graphs", The Bell system technical journal 49.2 (1970), pp:291-307
[18] Quinlan, J. Ross, “C4. 5: programs for machine learning”, Morgan Kaufmann Publishers, Inc., 1993

Jharna Majumdar, Anand Mahato, "Identification of Commonly used Medicinal Leaves using Machine Learning Techniques with SIFT Corner Detector as Features," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.341-346, 2018.

Abstract— This project presents the development of a firmware for a Smart Switch, which can control the on-off of any electrical device at home by using internet. The Smart Switch is connected to internet via Wi-Fi, through a computer, smartphone, tablet or any device with internet access. In order to perform this connection it is necessary to write the IP pre-programmed into the Smart Switch in a web browser (Internet Explorer, Chrome,Firefox, etc.) with the purpose to load the Smart Switch server, which will open a configuration page to write the data of the user’s network. Then, the user will select in automatic mode the network, the security type, and the user must have written a passphrase. Once these information is uploaded and saved, it is necessary to restart the Smart Switch in order to get access to internet, from which the user can control the Smart Switch simply sending a number one or a number zero to switch the electrical device, this process is done in principle via the internet.

Home automation, internet of things, embeddedswitch

[1] Ajah, G, David, N, Abioye, A, Web Based Security System, Sch. J. Eng. Tech, 1(3):112-116, 2013.
[2] Mahmood, S M, Abdulsattar, M, Firas, A Y; Home Automation Management with WLAN (802.11g) and RF Remote Control, Raf. J. of Comp. & Math’s, 6(1), 2009.
[3] Aru O E ,Ihekweaba G, Opara F K, Design Exploration of a Microcontroller Based RF Remote Control 13amps Wall Socket, IOSRJCE, 11(1), 56-60, 2013.
[4] David, N, Design of an Internet Based Security System, NIJOTECH, 29(2) 118-129, 2010.
[5] Diaa, M F, Mahmood, B M, Data Acquisition of Greenhouse Using Arduino, Journal of Babylon University/Pure and Applied Sciences/ No.(7)/ Vol.(22), 1908-1916, 2014.
[6] Asif, O, Hossain, B, Hasan M, Rahman, T, Chowdhury, M, Fire-Detectors Review and Design of an Automated, Quick Responsive Fire-Alarm, 2014

A. Sukanya Rani, S. Umamaheshwar, "A Smart Switch to Connect and Disconnect Electrical Devices At Home by Using Internet," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.347-351, 2018.

Security of Multimedia is very challenging task in today because data move over Internet. Anyone can easily hack or misuse the data specially images. So hide data such as text document, images, and audio, video in to digital images. In this paper, we proposed random significant bit (RSB) method for text or image watermarking. Random significant bit method and least significant method implemented using java. Compare random significant bit method and Least Significant method on the basis of time. Found that random significant bit method take less time compare to Least Significant method.

Information security, Image watermarking, Digital watermarking, LSB, RSB

[1] C.Y. Chang and S.J. Su, “The Application of a Full Counter propagation Neural Network to Image Watermarking”, Conference on Networking, Sensing and Control IEEE, March 2005.
[2] Wai C. Chu, “DCT-Based Image Watermarking Using Subsampling”, IEEE Trans. on multimedia, vol. 5, no. 1, pp.34-38, March 2003.
[3] Gurpreet Kaur and Kamaljeet kaur, “Image Watermarking Using LSB(Least Significant Bit)”, in Proc. of International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 4, April 2013, pp. 858-861.
[4] Maher EL’ARBI, Chokri BEN AMAR and Henri NICOLAS, “Video Watermarking Based On Neural Networks”, IEEE Proc. of ICME, pp. 1577-1580, 2006.
[5] F.M. Boland , J.J.K. O`Ruanaidh , C. Dautzenberg, “Watermarking digital images for copyright protection”, Fifth International Conference on Image Processing and its Applications, pp. 250 – 256, August 1996.
[6] Martin F. H. Schuurmans., “Keynote Speech III Digital Watermarking”, 15th International Conference on VLSI Design. Proceedings, August 2002.
[7] Yogesh Jadav, “Comparison of LSB and Sub band DCT Technique for Image Watermarking,” in Proc. of Conference on Advances in Communication and Control Systems (CAC2S 2013), pp. 398-401, 2013.
[8] M. D. Swanson, M. Kobayashi and A. H. Tewfik, “Multimedia data-embedding and watermarking techniques”, Proc. of IEEE, vol. 86, no. 6, pp. 1064-1087, 1998.
[9] Zhang Zhi-Ming, Li Rang-Yan, Wang Lei, “Adaptive Watermark Scheme with RBF Neural Networks,”, International Conference on Neural Networks and Signal Processing, pp. 1517-1520, Dec 2003.
[10] J.W Bae, S.H. Lee and J.S. Yoo “an efficient wavelet based motion estimation algorithm”, IEEE Trans. INF & SYST, vol. E88-D, no. 1,January 2005.
[11] Puneet Kr Sharma and Rajni, "Information security through Image Watermarking using Least Significant Bit Algorithm," Computer Science & Information Technology, vol. 2, no. 2, pp. 61-67, May 2012.
[12] Malihe Soleimani, Faezeh Sanaei Nezhad, Hadi Mahdipour and Morteza Khademi, "A Robust Digital Blind Image Watermarking Based on Spread Spectrum in DCT Domain," Science Academy Transactions on Computer and Communication Network, vol. 2, no. 2, pp. 122-126, June 2012.
[13] Thanuja T C, P Nagaraju, Vinay J, Kavya N Bhushan and Naren S Vasanad, "Hardware Implementation of a Robust Modulo Watermarking Algorithm," ME Journal of Technology and Management, vol. 2, no. 1, pp. 51-56, 2011.
[14] S. Craver, N. Memon, “Resolving Rightful Ownership with Invisible Watermarking Techniques: Limitations, Attacks and Implications”, IEEE Journal on Selected Areas in Communications, vol 16, no. 4, pp. 573-586, 1998.

Y. Pahadiya, R. Khatri, "Text and Image Watermarking Using Random Significant Bit," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.352-355, 2018.