iosr-JCE   Volume-18 ~ Issue-5 ~ Sep-Oct 2016

Abstract: The paper describes the future or trends of Cloud for Big Data Analytics . Cloud for Big Data Analytics is also known as Data Analytics as a Service. Enterprise of all types use cloud service to reduce capital cost as they provide resources required to run their applications. Cloud can provide infrastructure, platform, and software resources as services to big data. Big Data gives substantial value to organizations who implement cloud in their infrastructure..........

Keyword: Big Data, Cloud Computing, Analytics, Big data Analytics, Data Analytics as a Service.

[1]. Kanchan A. Khedikar,.Role of Cloud Computing in Big Data Analytics,Using MapReduce Component of Hadoop, Maharashtra, India., International Journal of Innovations in Engineering and Technology (IJIET),Vol. 4 Issue 1 June 2014 87 ISSN: 2319 – 1058
[2]. Han hu1, Yonggang Wen. Tat-Seng Chua1 and xuelong ,Toward Scalable Systems for Big Data Analytics:A Technology Tutorial Komal V. Kumawat
[3]. Marcos D. Assunção a,*, Rodrigo N. Calheiros b, Silvia Bianchi c, Marco A.S. Netto c, Big Data computing and clouds: Trends and future directions, Elsivier,M.D. Assunção et al. / J. Parallel Distrib. Comput. 79–80 (2015) 3–15
[4]. Celestino Güemes , Jordan Janeczko, Thierry Caminel and Matthew Roberts bill, Atos Data Analytics as a Service:unleashing the power of Cloud and Big Data
[5]. booz, allen and Hamilton, cloud analytic playbook

Abstract: Cancer is one of the most commonly affected diseases in the progressing countries. Early diagnosis of cancer plays a significant role in curing cancer patients. Thousands of people every year die due to lung cancer, so there a need for accurate prediction of this disease. In this paper, a Genetic Candidate Group search algorithm approach is proposed. This optimization algorithm allows assistant doctors to identify the nodules present in the lungs at the early stages..........

Keyword: Data Clustering, Genetic Candidate Group search Algorithm(GCGS), Naïve Bayes Classification, Optimization.

[1] P. Bhuvaneswari, Dr. A. Brintha Therese, "Detection of Cancer in Lung With K-NN Classification Using Genetic Algorithm", Science Direct 2nd International Conference on Nanomaterials and Technologies. (CNT 2014)
[2] Anisha P, R, Kishor Kumar Reddy C, " A Pragmatic approach for Detecting Liver Cancer using Image Processing and Data Mining Technique" SPACES-2015.
[3] Mohd Nizam Saad, Noraidah Sahari@ Ashaari, Hamzaini Abdul Hamid, "Image Segmentation for Lung Region in Chest X-ray Images using Edge Detection and Morphology", 2014 IEEE International Conference on Control System, Computing and Engineering, 28 – 30 November 2014, Penang, Malaysia.
[4] Mark´us Gud�mundsson, Essam A. El-Kwae, and Mansur R. Kabuka*, "Edge Detection in Medical Images Using a Genetic Algorithm", IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 17, NO. 3, JUNE 1998 .

Abstract: Kalman Filter is the most popular method for state estimation when the system is linear. State estimation is the typical issue in every part of engineering and science. But, for non linear systems, different extensions of Kalman Filter are used. Extended Kalman Filter is famous to discard the non linearity which uses First order Taylor series expansion. But for these estimation techniques, the tuning of process noise covariance and measurement noise covariance matrices is required. There are different optimization techniques used to tune the parameters of Extended Kalman Filter. In this paper, Particle Swarm Optimization has been proposed to tune the EKF parameters and then the simulations are implemented for permanent magnet synchronous motor

Keyword: Extended Kalman Filter, Particle Swarm Optimization, non-linear, state estimation, tuning of EKF.

[1]. F. Abdallah, A. Gning and P. Bonnifait, Box particle filtering for nonlinear state estimation using interval analysis, Automatica, 44(3), 2008, 807-815.
[2]. DM. Asmar and Greg J. Eslinger, Nonlinear programming approach to filter tuning, 2012.
[3]. K. György, A. Kelemen and L. Dávid, Unscented kalman filters and particle filter methods for nonlinear state estimation, Procedia Technology,12, 2014, 65-74.
[4]. M.S. Arulampalam, S. Maskell, N. Gordon and T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on signal processing, 50(2), 2002, 174-188.
[5]. K. Xiong, H. Zhang and L. Liu, Adaptive robust extended Kalman filter for nonlinear stochastic systems, IET Control Theory & Applications,2(3), 2008, 239-250.

Abstract: India is an agriculture based country and farmer community is the backbone of the agriculture sector. Agribusiness is one of the important segments of agriculture sector. This paper aims to minimize agribusiness risk by deploying the agribusiness knowledge to the farmer community. In this context, this paper intend at predicting agriculture commodity market price using soft computing technique. Such generated knowledge helps farmer community in their decision making and thereby achieving their ultimate goal of profit making.

Keyword: Risk Minimization in Agribusiness, Soft computing in Agriculture, Market Price Prediction

[1] Twelfth five year plan (2012-2017): Planning Commission, Govt. of India, Vol.2 (1), 2013, pp.1-50.
[2] Final Report of Committee of State Ministers: In-charge of Agriculture Marketing to Promote Reforms. Ministry of Agriculture Department of Agriculture and Co-operation Government of India, 2013, pp.1-43.
[3] Ghadiyali, Tejas; Lad, Kalpesh and Patel, Bankim.: Agriculture Intelligence: An emerging tool for farmer community., Proceedings of second international conference on "Emerging Application of Information technology, EAIT 2011; Vol.1(1), pp.313-316. IEEE DOI 10.1108\9/EAIT 2011.36.
[4] Govardhana Rao G., Solmonrajupaul K., Vishnu Sankarrao D., Dayakar G.: Seasonal Variations and Forecasting in Wholesale Prices of Rice (Paddy) In Guntur District of Andhra Pradesh In: International Journal of Development Research, Vol.4(11), Andhra Pradesh, 2014, 2418-2422.

Abstract: In distant learning environments, most learners need feedback from their lecturers and administration. The feedback supports them to achieve their learning objectives. However, the feedback that the most distant learners receive through the student's portal system is not adequate. This leads to learners using a lot of the scarce resources travelling to college to seek guidance on academic and administrative issues. This study proposes a multi-agent system that can provide personalized feedback to learners, lectures and administration in an efficient and effective way...........

Keyword: Classifiers, Distant learners, Feedback, Learner needs, Multi-agent system

[1]. P. Muchiri (2012), Construction and evaluation of the effectiveness of a needs-based learner support system: the case of the school of continuing and distant education, University of Nairobi-Kenya GRL LC 5800 M&3
[2]. Ji –Yeon, (2000), The current status of learner support in distance education. Emerging issues and future research agenda.
[3]. Van Dusen, (1997), The virtual campus: Technology and reform in higher education. ASHE-ERIC Higher Education Report Vol. 25 No.5 Washington, DC: US Department of Education.

Abstract: Feature selection is an effective tool to improve the performance of content based image retrieval systems. This paper presents an effective moment weighting method according to image reconstruction and retrieval accuracyto reduce the dimensionality of moment-based features. Weighting algorithms are important group of feature selection schemes. Among features employed in content based image retrieval systems, orthogonal moments are shape features which have been used to distinguish objects. But in current applications, selecting effective features among momentshas been less considered. The proposed novel weighting algorithm..........

Keyword: Content Based Image Retrieval, Feature Selection, Feature Weighting, Exact Legendre Moments.

[1] G. Chandrashekar, F. Sahin, A survey on feature selection methods, Computers & Electrical Engineering, 40 (2014) 16-28.
[2] A. Chiang, S. Liao, Image analysis with Legendre moment descriptors, Journal of Computer Science, 11 (2014) 127-136.
[3] X. Dai, H. Zhang, T. Liu, H. Shu, L. Luo, Legendre moment invariants to blur and affine transformation and their use in image recognition, Pattern Analysis and Applications, 17 (2014) 311-326.
[4] S.J. Hosaini, S. Alirezaee, M. Ahmadi, S.V.-A.D. Makki, Comparison of the Legendre, Zernike and Pseudo-Zernike moments for feature extraction in iris recognition, in: Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on, IEEE, 2013, pp. 225-228.
[5] K.M. Hosny, Exact Legendre moment computation for gray level images, Pattern Recognition, 40 (2007) 3597-3605.

Abstract: This work aims to propose a framework for the phase space representation of quantum coherence in matter. The central thesis of this work is that quantum coherence could result from weak time-periodic forcing in the absence of other types of interactions (or measurements) from the environment. Therefore the Floquet formalism is employed to depict the coherent quantum system...........

Keyword: Quantum coherence; phase space; Floquet formalism; uncertainty principles; quantum coherent transport; quantum computing

[1]. Zwierlein, M.W., Stan, C. A., Schunck, C. H., Raupach, S. M. F., Gupta, S., Hadzibabic, Z., and Ketterle, W., Observation of
Bose-Einstein Condensation of Molecules, Physical Review Letters, 91, 250401, (2003).
[2]. Cooper, L.N. and Feldman, D., BCS: 50 Years (book). World Scientific. ISBN 978-981-4304-64-1, (2010).

[3]. Sudarshan, E.C.G., Equivalence of Semiclassical and Quantum Mechanical Descriptions of Statistical Light Beams, Physical
Review Letters, 10, 277, (1963).
[4]. Glauber, R.J., Coherent and Incoherent States of the Radiation Field, Physical Review Letters, 131, 2766, (1963).
[5]. Gilchrist, A., Gardiner, C.W. and Drummond, P.D., Positive P representation: Application and validity, Physical Review A, 55,
3014, (1997).

Abstract: Immunization plays a vital role in the lives of children by protecting them against infectious diseases such as Measles, Polio, Tuberculosis, Hepatitis B, Diphtheria, whopping cough, Tetanus etc. There are different programmes and facilities for newborn and child health under National Health Mission (NHM). However, despite these schemes and programmes, India fares poorly when compared to other countries. In this research paper, we present a critical review of the various programmes, schemes and research currently being undergoing in child immunization.

Keyword: Immunization, Measles, Polio, Tuberculosis, Hepatitis B, Diphtheria, Tetanus etc.

[1]. The UNICEF website. [Online]. Available: http://www.unicef.org/immunization/
[2]. Mohitulameen Ahmed Mustafi and Dr. Mir Mohammad Azad, "Factor Influencing of Child Immunization in Bangladesh," International Journal of Mathematics and Statistics Studies, vol. 1, no. 3, pp. 55-65, September 2013.
[3]. The WHO website. [Online]. Available: http://www.who.int/mediacentre/factsheets/fs378/en/ The UNICEF website. [Online].
[4]. Available: http:/www.unicef.org/immunization/files/Immunization_brochure.pdf
[5]. Suresh Sharma, "Immunization Coverage in India," Working Paper Series No. E/283/2007,Institute of Economic Growth, Delhi, India.

Abstract: with the proliferation of scattered new energy sources, there becomes a need to manage this growing phenomenon. The tracking gives a comprehensive view of the availability of the energy sources and aids in the usability of the produced energy. In this paper, we propose a new network operation center concept that provides technical support for the renewable energy sources. First, a comprehensive database of all the vital information of each energy site is designed...........

Keyword: Renewable energy; network operation center; network connectivity; operational scenarios; immersive environment

[1] J. C.Foreman, R.K. Ragade, J. H. Graham; An Immersive Visualization Tool for Teaching and Simulation of Smart Grid Technologies; CoRR abs/1509.06293, 2015
[2] Continuum Managed Services Transformed; Retreived from http://www.continuum.net/msp-resources/mspedia/what-is-a-network-operations-center-noc
[3] ManageEngine - Managed Service Provider; Retreived from https://www.manageengine.com/products/support-center
[4] NOC wall unit example; Retreived fromhttp://9xmedia.com/new/resources/applications.php
[5] Priya Mahadevan, Puneet Sharma, Sujata Banerjee, Parthasarathy Ranganathan; Energy Aware Network Operations; INFOCOM Workshops, IEEE, 2009

Abstract: Agile scrum projects are gaining popularity for software development and delivery, due to its inherent attributes of flexibility in execution and quicker time to market. However, the critics of Agile have been raising questions on the predictability of success of Agile execution as the emphasis given to quantitative management using predictive techniques for an Agile project may not be to the extent of large mission critical projects which uses in-process leading indicators to predict outcomes. One possible solution to this challenge is to use the best practices from industry adopted models like the Capability Maturity Model Integration® for planning and managing Agile software delivery...........

Keyword: Process performancemodel;agile;software development; software metrics; scrum

1]. Abrahamsson, P., Moser, R., Pedrycz, W., Sillitti, A., &Succi, G. (2007, September). Effort prediction in iterative software development processes--incremental versus global prediction models. In Empirical Software Engineering and Measurement, 2007. ESEM 2007. First International Symposium on (pp. 344-353). IEEE.
[2]. Buglione, L., &Abran, A. (2007). Improving Estimations in Agile Projects: issues and avenues. In Proceedings of the 4th Software Measurement European Forum (SMEF 2007), Rome (Italy) (pp. 265-274).
[3]. Cavezza, D. G., Pietrantuono, R., & Russo, S. (2015). Performance of Defect Prediction in Rapidly Evolving Software.
[4]. Cohan, S., & Glazer, H. (2009). An agile development team's quest for CMMI® maturity level 5. In Agile Conference, 2009. AGILE'09. (pp. 201-206). IEEE.
[5]. Cohen, D., Lindvall, M., & Costa, P. (2003). Agile software development. DACS SOAR Report, (11).

Abstract: Lung Cancer is dangerous disease that cause most human to death at early age and it is an uncontrolled cell growth in tissues on the lung. Many algorithms and technologies are introduced for identifying the lung tumors and it does not provide efficient accuracy. Our proposed system introduces a new identification process for Lung tumor. Here, we introduced a novel algorithm named FA-FCM which is a combination of firefly algorithm and Fuzzy C-means clustering. The segmented output image is compared with expert segmented image in terms of Jaccard and Dice similarity values. Our proposed method produced better result on all the tested images compared with the gold/expert segmented images.

Keyword: Fuzzy C-means algorithm, Firefly algorithm, Lung Tumors, Segmentation

[1]. P. Kalavathi and V.B. Surya Prasath, Method on skull stripping of MRI Head Scan Images-a Review, Journal of Digital Imaging, Springer, , Vol. 29, No. 3, pp. 365-379, 2016.
[2]. K. Somasundaram and P.Kalavathi, Brain Segmentation in magnetic Resonance Human Head Scan using multi-seeded Region Growing, Imaging Science Journal, Vol 62, no.5, pp. 273-284, 2014.
[3]. K. Somasundaram and P. Kalavthi, Contour-based brain segmentation method for magnetic Resonance Imaging Human Head Scans, Journal of Computer Assisted Tomography, vol.37, no.3, pp.353-368, 2013.
[4]. K.Somasundaram and P.Kalavthi, A Noval Skull Stripping Technique for T1-weighted MRI Human Head Scans, Acm digital library, pp.1-8, 2012.
[5]. K.Somasundaram and P.Kalavathi, Skull Stripping of MRI Head Scans Based on chan-vese Active Contour model, International Journal of Knowledge Management & e-learning, vol.3, No.1, pp.7-14, 2011.

Abstract: We propose to extend the learning of programming basics in secondary school, creating an educational environment based on using virtual modelling of educational robot task and model's transformation into real environment. The control principles of a real robot are developed using robot's behavior modelling results received from virtual system. The main contribution of the paper is providing an opportunity for students to explore and manage the complexity and scope of the robot task using various abstraction level models and modelling techniques.

Keyword: Educational robotics, meta-program, robot control program, virtual modelling environment

[1]. Ericson, B., Armoni, M., Gal-Ezer, J., Seehorn, D., Stephenson, C., Trees, F.: Ensuring exemplary teaching in an essential discipline: Addressing the crisis in computer science teacher certification. Final Report of the CSTA Teacher Certification Task Force. ACM, (2008)
[2]. Fletcher, G.H., Lu, J.J.: Education Human computing skills: rethinking the K-12 experience. Communications of the ACM 52 2, 23-25 (2009)
[3]. Hazzan, O., Lapidot, T., Ragonis, N.: Guide to teaching computer science: an activity-based approach. Springer, (2015)
[4]. Barr, V., Stephenson, C.: Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? Acm Inroads 2 1, 48-54 (2011)
[5]. Pan, Z., Polden, J., Larkin, N., Van Duin, S., Norrish, J.: Recent progress on programming methods for industrial robots. Robotics and Computer-Integrated Manufacturing 28 2, 87-94 (2012)

Abstract: Customer churn refers to customers terminating the service contract with the company or turning to services provided by the other company. Churn analysis is the calculation of the rate of attrition in the customer base of any company. It involves identifying those consumers who are most likely to discontinue using your service or product. Churn rate is a measure of customer or employee attrition, and is defined as the number of customers who discontinue a service or employees who leave a company during a specified time period divided by the average total number of customers or employees over that same time period and the customer who discontinue a service or leave are simply called Churners

Keyword: Churn, LDA, PDA, Churn Prediction, Modeling, Prediction Algorithms

[1] A New Prediction Model of Customer Churn Based on PCA Analysis By Zhao Xin, Wang Yi and chahongwang
[2] Introduction to pattern analysis , Lecture Texas A&M university
[3] Data Mining Techniques , By Michael J.A. Berry and Gordon S. Linoff
[4] Classification of Churn and non-Churn Customers for Telecommunication Companies, by Tarik Rashid (Computing Faculty/Research and Development Department College of Computer Training (CCT), Ireland.)

Abstract: Intelligent agents such as robots can form ad hoc networks and replace human being in many dangerous scenarios such as a complicated disaster relief site. This project prototypes and builds a computer simulator to simulate robot kinetics,unsupervised learning using Kohonen networks, as well as group intelligence when an ad hoc network is formed.Each robot is modeled using an object with a simple set of attributes and methods that define its internal states and possible actions it may take under certain circumstances...........

Keyword: Robot Simulation,Mobile Ad Hoc Networks, Swarm Intelligence, Unsupervised Learning, Self-Organizing Maps, Kohonen Networks

[1]. P. Bose, P. Morin, I. Stojmenovic, and J. Urrutia, Routing with Guaranteed Delivery in Ad Hoc Wireless Networks, Wireless Networks, (7) 2001, 609-616.
[2]. B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, SPAN: An Energy-efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks. Proc. MobiCom, July 2001, 85-96.
[3]. I. Kang and R. Poovendran, On the Lifetime Extension of Energy-efficient Multihop Broadcast Networks, Proc. IJCNN, May 2002, 365-370.
[4]. http://www.rethinkrobotics.com.
[5]. K. Teuvo, Self-Organized Formation of Topologically Correct Feature Maps, Biological Cybernetics. 43 (1), 1982, 59–69.
[6]. T. Heskes, Energy Functions for Self-Organizing Maps, Kohonen Maps, Elsevier, 1999.

Abstract: Wireless Body Area Network (WBAN) is required to provide quality of service (QoS) guarantees for transporting and processing life threatening gathered data. One important aspect of providing QoS guarantees understands the traffic dynamics of the WBAN. Towards this end, the objective of this project is to study the traffic dynamics of WBAN and attempt to equip a traffic model of WBAN over ZigBee. In this project, we implemented three types of network topologies to study traffic dynamics..........

Keyword: WBAN, traffic modeling, QoS, end-to-end delay.

[1]. Latré, B., Moerman, I. & Dhoedt, B. (2011, April 26). Networking in Wireless Body Area Networks, p.1.
[2]. Marinkovic, S., Spagnol, C. & Popovici, E. (2010, March 26). Energy-Efficient TDMA-based MAC Protocol for Wireless Body Area Networks
[3]. Orhan, I., Gonga, A. & Lindh, T. (2010, April 10). An End-to-End Performance Meter for Applications in Wireless Body Sensor Networks
[4]. Khan, J., Yuce, M. & Karami, F. (2010, March 16). Performance Evaluation of a Wireless Body Area Sensor Network for Remote Patient Monitoring
[5]. Yafeng, W., Keally, M. & Zhou, G. (2011, February 20). Traffic-Aware Channel Assignment in Wireless Sensor Networks