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Nowadays Question answering services have gained very popular. It helps all members to post and answer questions as well enables general users to check information from a total set of well answered questions. But, existing QA applications mainly provides only textual answers, which are not enough understandable for many questions. In this paper, we are going to create a scheme that is able to provide textual answers in Question answering with particular media data. This approach get easily checks which type of media information should be added for answers. It then collects data from the web to enrich the answer. This web application type service contain three facts: answer medium selection, query generation for multimedia search, and multimedia data selection and presentation. To answer any query this system process a large set of QA pairs and add them to a pool, it can enable a novel multimedia question answering (MMQA) approach as users can find multimedia answers by matching their questions with those in the pool. There are many different QA research efforts that finds directly answer questions with multimedia data i.e. images and videos, but our approach is created based on community contributed textual question answers and thus it is helps to deal with more day to day needed community complex questions. We have conducted much performances on a multisource QA database’s. The results demonstrate the effectiveness of our approach.

Web Information, MMQA, QA, Multimedia Search Reranking

[1]. Liqiang Nie ,meng Wang,Zheng jun Zha,Yue Gao IEEE members”Beyond Text QA:Multimedia Answer Generation by Harvesting Web Information-2013”
[2]. S. A Quarteroni and S. Manandhar, “Designing an interactive open domain question answering system,” J. Natural Lang. Eng., vol. 15, no. 1, pp. 73–95, 2008.
[3]. L. A. Adamic, J. Zhang, E. Bakshy, and M. S. Ackerman, “Knowledge sharing and Yahoo answers: Everyone knows something,” in Proc. Int. World Wide Web Conf., 2008.
[4]. G. Zoltan, K. Georgia, P. Jan, and G.-M. Hector, Questioning Yahoo! Answers, Stanford InfoLab, 2007, Tech. Rep.
[5]. H. Yang, T.-S. Chua, S. Wang, and C.-K. Koh, “Structured use of external knowledge for event-based open domain question answering,” in Proc. ACM Int. SIGIR Conf., 2003.
[6]. Trec: The Text Retrieval Conf. [Online]. Available: http://trec.nist.gov/.

Umesh S. Chaudhari , Shivaji B. Patil,Omprakash B. Bhange and Amin R. Abbas, "Enhanced Answer Generation from Web Information," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.156-159, 2015.

Sentiment analysis is a machine learning approach in which machines analyzes and classifies the sentiments, emotions, opinions about any particular topics or entity which are expressed in the form of text or speech. Due to large volume of textual data increasing on the web so much of the current research is focusing on the area of sentiment analysis. People are interested to develop and design a system that can identify and classify the sentiments as represented in textual form. Sentiment analysis is used to extract the subjective information in source material by applying various techniques such as Natural language Processing (NLP), Computational Linguistics and text analysis and classify the polarity of the opinion. In this paper, we are going to discuss different levels of sentiment analysis, approaches for sentiment classification, Data Source for sentiment analysis and comparative study of approaches for sentiment classification.

Sentiment Analysis, Opinion Extraction, Text Mining, Natural Language Processing, Subjective Analysis, Machine Learning Algorithm

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[3] Raisa Varghese1, Jayasree M2, “A SURVEY ON SENTIMENT ANALYSIS AND OPINION MINING”, IJRET:International Journal of Research in Engineering and Technology ISSN: 2319-1163 | ISSN: 2321-7308.
[4] Arti Buche, Dr. M. B. Chandak, Akshay Zadgaonkar, “OPINION MINING AND ANALYSIS: A SURVEY”, International Journal on Natural Language Computing (IJNLC) Vol. 2, No.3, June 2013.
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[6] Siddhi Patni, Avinash Wadhe, “Review Paper on Sentiment Analysis is – Big Challenge”, International Journal of Advance Research in Computer Science and Management Studies Volume 2, Issue 2, ISSN: 2321-7782 (Online), February 2014 .
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[11] Jiawen Liu, Mantosh Kumar Sarkar and GoutamChakraborty, “Feature-based Sentiment Analysis on Android App Reviews Using SAS® Text Miner and SAS® Sentiment Analysis Studio”, SAS Global Forum 2013.
[12] Bing Liu, “Sentiment Analysis and Opinion Mining”, Morgan and Claypool Publishers, p.18-19, 27-28, 44-45, 47, 90-101, May 2012.
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[16] Ajayi Adebowale, Idowu S.A, Anyaehie Amarachi A., “Comparative Study of Selected Data Mining Algorithms Used For Intrusion Detection”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-3, July 2013.
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[20] Abdullah Dar*, Anurag Jain, “Survey paper on Sentiment Analysis: In General Terms”, International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-3, Issue-11).
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[26] Pimwadee Chaovalit, Lina Zhou, “Extracting Product Features and Opinions from Product Reviews Using Dependency Analysis”, Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, Shandong, pp. 2358-2362, 2010.
[27] Pollach, I., “Automating user reviews using ontologies: an agent-based approach”, Springer Journal on World Wide Web, Vol. 15, No.3, pp. 285-323, 2012.
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Supriya B. Moralwar and Sachin N. Deshmukh, "Different Approaches of Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.160-165, 2015.

In today’s era, service development demand is increasing day by day.Client’s ever changing requirements made agile methodology to come into existence due to fasten production, flexibility and improved quality.Moreover, it can accommodate changes and provide instant feedbacks and transparent communication. Due to geographic distribution of team members and client, leading IT companies thought to strengthen the agile methodology by giving the user access to an environment that can be accessed worldwide i.e. CLOUD. This combination of agile and cloud has a positive impact on IT companies. For achieving Quality, we need to test all functionality so in our work we emphasis on testing assimilation using cloud services in agile environment. Cloud team members can perform testing easily using Test-as-a-Service without increasing the cost of the project and get the results faster. We can also use agile management tools to reduce the problem of communication and for all the updates in the product backlog as well as Sprint backlog lists.

Software Testing, Agile testing, SCRUM, Testing as-a-service

[1] Willie, “ Reinforcing Agile Software Development in the Cloud” , White Paper by Collabnet.
[2] Ahsan Nawaz & Kashif Masood Malik: “Software Testing Process In Agile Development”,
Department of Computer Science School of Engineering Blekinge Institute of Technology Box 520 SE – 372 25 Ronneby Sweden, June 2008..
[3] “Agile Testing In The Cloud”, IBM, SearchSoftwareQuality.com E-Guide.
[4] Jerry Gao , Xiaoying Bai, and Wei-Tek Tsai: “ Cloud Testing- Issues, Challenges, Needs and Practice”, SEIJ, September 2011.
[5] Borland Agile Testing White Paper: “Adopting Agile Testing, Micro Focus Company,UK.
[6] Koray ˙Incki, ˙Ismail Arı , Hasan S¨ozer, “A Survey of Software Testing in the Cloud ”, IEEE Sixth International Conference on Software Security and Reliability Companion, Digital Object Identifier: 10.1109/SERE-C.2012.32, Publication Year: 2012 , Page(s): 18 - 23
[7] dPankaj Nakhat, “A Tester’s Perspective on Agile Projects”, LogicGear Magzine, July 4 2012.
[8] Sheetal Sharma, Darothi Sarkar, Divya Gupta, “Agile Processes and Methodologies: A Conceptual Study”, IJCSE. Vol. 4 Issue 5 2012, ISSN: 0975–3397
[9]d S. Kalem , D. Donko and D. Boskovic “Agile Methods for Cloud Computing”,Information& Communication Technology Electronics and Microelectronics May 20 2013.
[10] Amit Dumbre, Sathya Priya Senthil, Sidharth Subhash Ghag “Practicing Agile software development on the Windows Azure™ platform, White paper. g
[11] Sumit Mehrotra , “ Five Steps to Agile Development in the Cloud ”, June 23 2011
[12] “Agile Software Development In a Nutshell”, www.telerik.com/automated-testing-tools/products/agile-testing-with-test-studio.aspx.
[13] Belatrix White Paper: “Agile Software Testing”
[14] B.J.D Kalyani ,“Challenges in the Cloud Application Development”, International Journal of Advanced Research in Computer Engineering & Technology( IJRACET), 2013
[15] Abhinava Kumar Srivastava , Divya Kant Yadav:“TaaS: An Evolution of Testing Services using Cloud Computing” ,IJARCET ,December 2012.
[16] Scott W. Ambler, “Disciplined Agile Testing”,IBM
[17] Shruti N. Pardeshi1, Vaishali Choure , “ Testing as a Service on Cloud: A Review”, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169. Volume: 2 Issue: 2. 188 – 193. 188. IJRITCC | February 2014
[18] Gaurav Raj, Naga Sri Morampudi, “Evaluating Strengths and Weaknesses of Agile SCRUM Framework using Knowledge Management”, International Journal of Computer Applications , Volume 65– No.23, page no. 01-06, ISSN: (0975 – 8887).
[19] Ibm cp-40 project. http://en.wikipedia.org/wiki/IBM
CP-40, 2012. [On- line; accessed 12-Feb-2012].
[20] Won Kim, Soo Dong Kim, Eunseok Lee, and Sungyoung Lee. Adoption issues for cloud computing. In Proc. of the 7th International Conference on Advances in Mobile Computing and Multimedia , pages 2–5, New York, NY, USA, 2009.
[21] Youssef Ridene and Franck Barbier. A model-driven approach for automating mobile applications testing. In
Proc. of the 5th European Conference on Software Architecture: Companion Volume , pages 9:1– 9:7, New York, NY, USA, 2011.
[22] Peter Mell and Timothy Grance. The nist definition of cloud computing ( draft ) recommendations of the national institute of standards and technology. Nist Special Publication
, 145(6):7, 2011.
[23] Mladen A Vouk. Cloud computing: Issues, research and implementations. ITI 2008 30th International Conference on Information Technology Interfaces , 16(4):31–40, 2008.
[24] Leah Muthoni Riungu, Ossi Taipale, and Kari Smolander. Software testing as an online service: Observations from practice. IEEE International Conference on Software Testing Verification and Validation Workshop,0:418–423, 2010.
[25] Lian Yu, Wei-Tek Tsai, Xiangji Chen, Linqing Liu, Yan Zhao, Liangjie Tang, and Wei Zhao. Testing as a service over cloud. In Service Oriented System Engineering (SOSE), 2010 Fifth IEEE International Symposium on , pages 181 –188, June 2010.
[26] Srikanth Baride and Kamlesh Dutta. “A cloud based software testing paradigm for mobile applications”, ACM SIGSOFT Software Engineering Notes , 36(3):1–4, 2011.
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, pages 183–194, New York, NY, USA, 2010.

Sana Bharti and Shaliendra Narayan Singh, "Improvised Agile SCRUM Using Test-Asa-Service," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.166-171, 2015.

Securing ad hoc network is one of the trending issues that is going on in the networking industry which has lead to the development of various algorithms and methods for identifying intrusions. Intrusions are of various types namely packet forwarding attack, black hole, sequence numbers etc., there are still so many such intrusions that are taking place. The proposed project is to study the impact of a malicious node in the ad hoc network. The proposed system uses MNI-AODV algorithm by using malicious node isolation(MNI) method. In this paper methods against malicious node is implemented by introducing MNI-AODV in the ad hoc network [7]. The impact of nodes in the presence of packet dropping attack is also presented.

Network Security, Malign nodes, ad hoc and network attacks, ns2

[1] C.Siva Ram Murthy and B.S.Manoj, “Ad hoc Wireless Networks”, Pearson Publication ,2005.ISBN 81-297-0945-7
[2] C.K.Toh, “Adhoc Mobile wireless networks: Protocols and Systems”, Prentice Hall ,New Jersy,2002.

[3] Umang, Reddy B.V.R and Hoda M. N., “Enhanced IDS in Adhoc routing protocol using minimal energy Consumption”, ISSN-1751-8628, DOI: 10.1049/iet-com.2009.0616, IEE Journal Communications IET, Volume 4, Issue 17, Nov, 2010 (Impact Factor- 0.963)

[4] Umang, Reddy B.V.R and Hoda M. N., “GNDA: Detecting Good Neighbor nodes in Adhoc Routing Protocol”, ISSN 978-0-7695-4329-1/11/$26.00©IEEE, DOI: 10.1109/EAIT.2011.62, IEEE EAIT 2011, pp no 235-238.Feb 19-20, 2011

[5] Umang, Reddy B.V.R and Hoda M. N., “Vulnerability of Numerous Black Hole Nodes in Mobile Ad hoc Networks-Problem”, IEEE International Advance Computing Conference”, ISBN: 978-981-08-2465-5© IEEE , March, 2009.

[6] Umang, Reddy B. V. R and Hoda M. N; “Impact of malicious nodes in mobile adhoc networks using AODV routing, National Conference on “Advances in Wireless Cellular Telecommunications: Technologies and Services”, organized by Institution of Communication Engineers and Information Technologists (ICEIT) Delhi, 14th -15th April, 2011.
[7] Umang,Dr. B V. R. Reddy and Dr. M .N. Hoda “MNI-AODV: Analytical Model for attack mitigation using AODV routing in ad hoc networks”, 2014 International Conference on Computing for Sustainable Global Development (INDIACom)
[8] Muhammad Zeshan , Shoad A. khan’Adding Security Against packet dropping Attack in Mobile Ad hoc Networks’, Proceedings of ACM International Seminar on Future Information Tech & Mgmt Engg (FITME 2008).
[9] Jaydip Sen , Girish Chandra. P ’A Distributed Protocol for Detection of Packet Dropping Attack in Mobile Ad Hoc Networks’, Proceedings of IEEE International conference on Telecommunication(2007).
[10] Sirisha R. Medidi, Muralidhar Medidi & Sireesh Gavini’Detecting Packet-dropping Faults in Mobile Ad-hoc networks’, IEEE 2003.
[11] S.Marti,T.Giuli,k.Lai,and M.baker,”Mitigating routing misbehaviour in mobile ad hoc networks”,proceedings of international conference on mobile computing and networking,AUG 2000.
[12] Bhalaji .N & Dr. Shanmugam .A,’Reliable Routing Against Selective Packet Drop Attack in DSR Based MANET’, Journal of Software 2009.
[13] K.liu.J.Deng, P.Varshney, K.balakrishnana, ” An acknowledgment based approach for the detection of routing misbehaviour in MANETs”, IEEE Transaction on mobile computing ,2007

S.Madhurikkha, C.Meenu Kumari, S.Revathi and P.Nathiya, "Preventing Packet Dropping Attack in Ad hoc Networks Using Malicious node Isolation Model," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.72-177, 2015.

Number of people who uses internet and websites for various purposes is increasing at an astonishing rate. More and more people rely on online sites for purchasing rented movies, songs, apparels, books etc. The competition between numbers of sites forced the web site owners to provide personalized services to their customers. So the recommender systems came into existence. LARS* is a location-aware recommender system that uses location based ratings to produce recommendations. LARS* supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. The item based collaborative filtering used for generating recommendations in LARS* suffers from cold start problem. In cold start problem, the recommenders cannot draw inferences for users who are new to the system (new user problem) and for items which does not have sufficient ratings (new item problem). New user cold start problem can be resolved by utilizing the demographic data explicitly given by a user. Also the content based filtering does not suffer from new item cold start problem. From the survey carried out, a hybrid recommender system which exploits the demographic and content based filtering features can be used for alleviating cold start problem.

Location Aware Recommender System, Collaborative filtering, cold-start problem, demographic filtering, content based filtering, Hybrid Systems

[1] Mohamed Sarwat, Justin J. Levandoski, Ahmed Eldawy, and Mohamed F. Mokbel, “LARS*: An Effcient and Scalable Location- Aware Recommender System, "IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 6, June 2014
[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. Knowl. Data Eng., vol. 17, no. 6, pp. 734-749, Jun. 2005.
[3] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering," IEEE Internet Comput., vol. 7, no. 1, pp. 76{80, Jan./Feb. 2003.

[4] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,"in Proc. Int. Conf. WWW, Hong Kong, China, 2001.

[5] James Salter and Nick Antonopoulos,”CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering "in IEEE Computer Society, jan/feb ,2006.

[6] Eduardo Castillejo and Aitor Almeida and Diego Lopez-de-Ipina , “Social Network Analysis Applied to Recommendation Systems: Alleviating the Cold-User Problem," in Ubiquitous Computing and Ambient Intelligence,Lecture Notes in Computer Science Volume 7656, 2012, pp 306-313
[7] Pasquale Lops, Marco de Gemmis and Giovanni Semeraro, “Content-based Recommender Systems: State of the Art and Trends, "in Springer Science+Business Media, LLC 2011
[8] M. Pazzani, “A Framework for Collaborative, Content-Based, and Demographic Filtering,"Arti_cial Intelligence Rev., pp. 393-408, Dec. 1999.
[9] Laila Safoury and Akram Salah, “Exploiting User Demographic Attributes for Solving Cold- Start Problem in Recommender System," Lecture Notes on Software Engineering, Vol. 1, No. 3, August 2013
[10] Robin Burke, “Hybrid Recommender Systems: Survey and Experiments,"1997
[11] Jens Grivolla, Toni Badia,Diego Campo, Miquel Sonsona, Jose-Miguel Pulido, “A hybrid recommender combining user, item and interaction data,"European Union's Seventh Framework Programme managed by REA-Research Executive Agency http://ec.europa.eu/research/rea
[12] Chris Anderson, “Recommender systems for e-shops, "Business Mathematics and Informaticspaper,2011

Mili Mohan and Robert.S, "A Survey on Alleviating Cold – Start Problem in Lars* Using Hybrid System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.178-183, 2015.

Nowadays the heart problems are like one of the common things that are happening throughout the world. There are various reasons that lead to heart diseases problems, and the most common among is the change in lifestyle. For doctors it becomes quite tedious task to identify and rectify disease as there are thousands of symptoms that are held responsible for it. Comprehensive study of various machine learning approaches like various supervised and unsupervised algorithm like neural network, Genetic algorithm as well as Data mining approaches are covered in this paper which are helpful in early prediction of heart diseases so that many lives could be saved. Other approaches are also discussed in this paper that help in early prediction of heart disease e.g. with the help of speech analysis and also with the help of Big Data.

Data Mining, Big Data, ECG, Machine Learning

[1] G.Subbalakshmi, “Decision Support in Heart Disease Prediction System using Naive Bayes, Computer ISSN: 0976-5166 Vol. 2, 2011.
[2] T. John Peter K. Somasundaram, “Study and Development of Novel Feature Selection Framework for Heart Disease Prediction”, International Journal of Scientific and Research Publications, Volume 2, Issue 10, 2012.
[3] Benish Fida, Muhammad Nazir, Nawazish Naveed, Sheeraz Akram “Heart Disease Classification Ensemble Optimization Using Genetic Algorithm”, 978-1-4577-0657-8/11 IEEE 2011.
[4] Carlos Ordonez, “Improving Heart Disease Prediction Using Constrained Association Rules”, Seminar Presentation at University of Tokyo, International journal of Computer Applications (IJCA) volume 47-Number 10, 2004.
[5] Soni, J., Ansari, U., Dipesh Sharma, “ Intelligent and Effective Heart Disease Prediction System using Weighted Associative classifiers”, International Journal on Computer Science and Engineering, vol 3, No. 6, pp.2385- 2392, 2011.
[6] Adeli, A., Mehdi.Neshat, “A Fuzzy Expert System for Heart Disease Diagnosis” Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol I, ISSN 2078-0966, 2010.
[7] E.P.Ephzibah, Dr. V. Sundarapandian, “A Neuro fuzzy expert system for Heart disease diagnosis”, Computer Science & Engineering: An International Journal (CSEIJ), Vol.2, No.1, 2012.
[8] B.Ericson (1997) Heart Sounds and Murmurs: A Practical Guide, Mosby Year Book Inc.
[9] D. Kumar, P.Carvalho, M. Antunes, P. Gil, J.Henriques, L.Eugenio, “A New Algorithm for Detection Of S1 and S2 Heart Sounds”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Volume: 2, 2006.
[10] Ishanka S. Perera, Fathima A. Muthalif, Mathuranthagaa Selvarathnam, “Automated Diagnosis of Cardiac Abnormalities using Heart Sounds” IEEE 2013.
[11] ManneI Robert (2011) Speech Acoustics.
[12] Weenink David (2012) Speech Signal Processing with Praat.
[13] Deshpande Nivedita, Thakur Kavita, Zadgaonkar A.S., “First degree heart block system from the speech analysis”, International Conference on Signal Processing, Image Processing and Pattern Recognition [ICSIPRl], 2013.

[14] Sheng Jen Jian, Ruey Kei Chiu, Shin-An-Wang , “A cloud decision support system for the risk assessment of Coronary heart disease”, IEEE, International Conference on Machine Learning and Cybernetics (ICMLC), Volume 4, pp. 1435 – 1440, 2012.

[15] Islam, M. R., Ahmad, S., Hirose, K., & Molla, M. K. I., “Data adaptive analysis of ECG signals for cardiovascular disease diagnosis” Paper presented at the Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on pp-2243-2246.
[16] S. Zhao, H. Chao, L. Jingsheng, and M. Q. H. Meng, “An algorithm of ST segment classification and detection, Automation and Logistics” (ICAL),IEEE International Conference on, pp. 559-564, 2010.
[17] Kiyana Zolfaghar, NarenMeadem, AnkurTeredesai, SenjutiBasu Roy, Si-Chi Chin and Brian Muckian, “ Big Data Solutions for Predicting Risk-of –Readmission for Congestive Heart Failure Patients in conference on Big Data Solution”, IEEE, pp. 64-71, 2013.

Kumari Nirmala, R.M.Singh and Shilpi Gupta, "Analysis for Heart Related Issues using comprehensive Approaches: A Review," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.184-187, 2015.

In this project a solutions has been provided to the problems encountered during parking a vehicle at commercial parking lots. This problems has been resolved using Parking Reservation System. The Parking Reservation System is an access control and automated Reservation system that provides ID based parking slot provision system. This system is designed for Multiple Dwelling Units (MDU), offices, schools, colleges, Malls, Cinema theaters, Airports, railway station and many more where there is a requirement of a systematic parking reservation of vehicles. The unique identification of the Vehicle entering using RFID tags permits tracking of vehicles entering and exiting the parking premises. It helps the system to know whether the vehicle or its owner is registered so as to prioritize allocation of parking spaces to incoming customers.

RFID tag, RFID reader, GSM, Micro-controller, IR sensors

[1] Ekta Soni Karamjeet Kaur Anil Kumar “Design And Development Of RFID Based Automated Car Parking System ”, The International Journal of Mathematics, Science, Technology and Management, Volume-2, Issue-2.
[2]IEEE Emerging Technology portal, 2006 – 2012, “RFID”www.ieee.org/about/technologies/emerging/rfid.pdf

[3] K.Sushma1, P. Raveendra Babu, J. Nageshwara Reddy”, Reservation Based Vehicle Parking System Using GSM and RFID Technology”, Journal of Engineering Research and Applications, Volume-3, Issue-5 ,Sep-Oct 2013.
[4] M. Chen, Tianhai Chang,” A Parking Guidance and Information System based on Wireless Sensor Network”, IEEE International Conference on Information and Automation, June 2011.
[5]. Ms. Rajeshri Prakash Mane,” Intelligent Fleet Management System Using Active RFID”, International Journal of Computer Science Engineering (IJCSE).
[6] P. Vivekanand, Thakare, N. A. Chavan.” Performance Evaluation of Parking Guidance and Management System using Wireless Sensor Network”. International Journal of Recent Technology and Engineering (IJRTE). Volume-1. Issue-2. 2277-3878. June 2012.
[7] P. Parkhi, Snehal Thakur, Sonakshi Chauhan,” RFID-based Parking Management System”, International Journal of Advanced Research in Computer and Communication Engineering, Volume-3, Issue-2, February 2014.
[8]S. Rachid, Omar CHEIKHROUHOU1, Ines KAMMOUN1, Mohamed ABID,” A Parking Management System Using Wireless Sensor Networks”, Tunisia.
[9] S. V.Reve, Sonal Choudhri,” Management of Car Parking System Using Wireless Sensor Network”, International Journal of Emerging Technology and Advanced Engineering. Volume-2. Issue-7, July 2012.
[10] W.S. Vanessa, Tang, Yuan Zheng, Jiannong Cao,” An Intelligent Car Park Management System based on Wireless Sensor Networks”, International Symposium on Pervasive Computing and Applications,2006.

Shrungashri Chaudhary and Mudit Kapoor , "Design and Implementation of Reservation Of Parking Spaces Using RFID and GSM Technology," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.188-191, 2015.

Travelling salesperson problem is a nondeterministic polynomial hard problem in combinatorial optimization studied in Operations Research and theoretical computer science. To solve this problem, we used two popular meta-heuristics techniques-Ant Colony Optimization and Genetic Algorithm. Both techniques are applied to solve a TSP with same dataset. We then compare them. For Ant Colony Optimization, we studied the effect of some parameters (number of ants, evaporation and number of iterations) on the produced results. On the other hand, we studied chromosome population, crossover probability and mutation probability parameters that effect Genetic Algorithm results.

Ant; Colony; Genetic; Algorithm; Travelling; Salesperson

[1] M.Dorigo and T.Stutze, “Research Paper on Ant Colony Optimization”, MIT Press, Cambridge (2004).
[2] E. Lawer and J. Rooney, “Research Paper on The Travelling Salesman Problem”, John Wiley & Sons, New York (1985).
[3] M. Dorigo, “PhD thesis Optimization, Learning and Natural Algorithms”, Politecnico di Milano, Italy (1992).
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Ashwani Chandel and Vikram Jeet Singh, "Relative Investigation of Ant Colony Optimization and Genetic Algorithm based Solution to Travelling Salesperson Problem," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.192-195, 2015.

MapReduce is widely used and popular programming model for huge amount of data processing. Hadoop is open source implementation of MapReduce framework. Performance of Hadoop depends some of the metrics like job execution time and cluster throughput. In MapReduce, Job is divided into multiple map and reduce tasks. Some tasks can be executed slowly due to internal or external reasons. Because of this slow tasks job execution time is prolonged which leads to degradation of Hadoop performance. To overcome this, current MapReduce framework launch speculative execution in which each slow tasks is backed up other node in order to reduce the job execution time. These slow tasks can be called as straggler tasks. However, current MapReduce speculative execution does not estimate the progress of the tasks properly which leads to identifying incorrect slow tasks. Also, they do not consider data skew among the tasks. This paper studies various speculative execution strategy like HAT (History based auto-tuning), Longest Approximate Time to End (LATE) and Maximum Cost Performance (MCP). These strategies overcome the drawbacks of default speculative execution to improve MapReduce performance.

MapReduce, Hadoop, Straggler, speculative execution

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Rahul R. Ghuleand Sachin N. Deshmukh, "Comparative Study on Speculative Execution Strategy to Improve MapReduce Performance," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.197-200, 2015.

Wireless Sensor Network (WSN) collects large amount of information and sends them to the Base Station (BS). WSN contains low-cost nodes with limited battery power and battery replacement is not easy for WSN with thousands of physically embedded nodes, which means energy efficient routing protocol should be employed to offer a long-life work time. To achieve this minimizing total energy consumption and balancing WSN load is required. So, in the existing system, a Tree-Based Energy-Balance routing protocol (TBEB) is presented which builds a routing tree using a process where, for each round, BS assigns a root node and broadcasts this selection to all sensor nodes. Subsequently, each node selects its parent by considering only itself and its neighbours information, thus making TEB a dynamic protocol. But in this method the parent node selection is based on only the residual energy level. The drawback in this method is if the parent node has less communication capacity, high interference and congestion there is less network performance in terms of packet delivery ratio, delay, throughput etc. So, an innovative technique is introduced which is named as Tree Based QoS Balanced Routing Protocol (TQR) in order to improve the performance. Communication capacity deals with the data handling capacity of the nodes. The communication capacity is computed based on the utilization factor. For the interference level, the signal to interference noise ratio is computed for computing the interference level of the node. Since congestion significantly reduces the effective bandwidth of a link, the effective link data-rate depends on the congestion level. So, based on this the parent node is selected. An experimental result shows that the proposed system achieves high data rate, throughput and less end-to-end delay.

Energy-Balance, Network Lifetime, Routing Protocol, Tree Balanced, Wireless Sensor Network

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Sathish Kumar S and Dr. A. Grace Selvarani, "Improving Energy Efficiency by Using Tree-Based Routing Protocol for Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.201-206, 2015.