iosr-JCE   Volume-19 ~ Issue-2 ~ March-April 2017

Abstract: Many transportation modes are available in our country but, still transportation is a major issue nowadays in metropolitan cities and urban areas. Increase in number of cars on road leads to many problems such as traffic congestion, health problems, air pollution, environmental degradation etc. This problem can be reduced by using a mechanism called as "Bus sharing or pooling". The aim of the project is to "get Buses into passenger's houses as Call taxi is functioning". This system will avoid traditional route of the buses. Bus pooling is more efficient because of reduces each travelling cost, fuel cost, stress of driving and security to passengers etc.

Keyword: Autonomous Vehicle,Dynamic Bus Navigation, Zigbee

[1]. Peter H. Dana. "Global Positioning System Overview." Internet: http://www.colorado.edu/geography/gcraft/notes/gps/gps_f.html, 1994 [Sep. 15,2013].
[2]. Arduino, "Arduino Microcontrollers," arduinoBoardUno datasheet, [Oct. 14.2013].
[3]. Aziz, Mochamad Vicky Ghani, Rifki Wijaya, Ary Setijadi Prihatmanto, and Diotra Henriyan. "HASH MD5 function implementation at 8-bit microcontroller", 2013 Joint International Conference on Rural Information & Communication Technologyand Electric-Vehicle Technology (rICT &ICeV-T), [2013].
[4]. Arduino, "Arduino GSM Shield Schematic," GSM shield documentation. Oct. 15.2013].
[5]. "GSM".Internet:http://en.wikipedia.org/wiki/GSM, [Oct. 15.2013].

Abstract: Directing at the locating problem of node location in wireless sensor network, a hybrid optimized method using Dynamic weight partical swarm optimization, Linearization method and Differential Evolution algorithms are proposed in this paper to enhance localization in Wireless sensor Network. Reducing the square error of estimated and measured distance between its adjacent anchor node and unknown node can guarantee a better localization accuracy in DWPSO compared to LM. This paper proposes DE algorithm is used to along with DWPSO to obtain the better localization accuracy. Simulation results indicate that this method provides smaller localization error, higher localization accuracy and better stability performance in DWPSOcompared to LM.

Keyword: Localization, Wireless sensor Network,Dynamic weight Particle swarm optimization, Differential Evolution and Linearization method.

[1]. J. Kennedy,and R.C. Eberhart, "Particle warm 0ptimization", In:Proceedings of IEEE International Conference on Neural Net-works1995,1942-1948.
[2]. J. Kennedy and R. Mendes, "Population structure and particle swarm performance," Proceedings of the IEEE Congress on Evolutionary Computation, 2002.
[3]. Anderson, B. D. O.,Mao, G. and Fidan, B.. Wireless sensor network localization techniques, Computer Networks(2007),51(10): 2529–2553.
[4]. W. Ren,and C. Zhao, A localization algorithm based on SFLA and PSO for wireless sensor network ",Information Technology Journal , 2013,vol. 12, no.3,. 502-505.
[5]. Lieping Zhang1,2,*,WenjunJi 2 and Yu Zhang1,2 "Node Localization Method for Wireless Sensor Networks Based on Hybrid Optimization of Differential Evolution and Particle Swarm Algorithm" The Open Automation and Control Systems Journal ,2014,6, 621-628.

Abstract: With the advent of new technology and requirement of data storage cloud computing has become a scalable services in consumption and delivery in the field of Computing. The technical foundations of Cloud Computing include Service-Oriented Architecture (SOA) and Virtualizations of hardware and software. The goal of Cloud Computing is to share resources among the cloud service consumers, cloud partners, and cloud vendors in the cloud value chain. The resource sharing at various levels results in various cloud offerings such as infrastructure cloud (e.g. hardware, IT infrastructure management), software cloud (e.g. SaaS focusing on middleware as a service, or traditional CRM as a service), application cloud (e.g., Application as a Service, UML modeling tools as a service, social network as a service), and business cloud (e.g., business process as a service).

Keyword: Cloud computing, Cloud Service Models, Service Model Architectures, Cloud Deployment Models, challenges & issues.

[1]. P. Mell and T. Grance, "Draft nist working definition of cloud computing - v15," 21. Aug 2009, 2009.
[2]. Z. Chengyun, "Cloud Security: The security risks of cloud computing, models and strategies", Programmer, May.2010, pp.71-73.
[3]. B. Hayes, "Cloud computing," Comm. Acm, vol. 51, no. 7, pp. 9– 11, 2008.
[4]. Effectively and Securely Using the Cloud Computing Paradigm - Peter Mell, Tim GranceNIST, Information Technology Laboratory 10-7-2009
[5]. Cloud computing fundamentals - A different way to deliver computer resources Skill Level: Introductory Grace Walker IT Consultant Walker Automated Services.

Abstract: In present era, Data Mining is becoming popular in healthcare field because there is a need of efficient analytical methodology for detecting unknown and valuable information in health data. Many of the ARM (Association rule Mining) approaches are well investigated in the literature, but it generates large number of association rules. If the dataset size is larger, then huge rules may occur, often it is a critical situation where decision making is difficult or unattainable because knowledge is not directly present in frequent patterns. This paper presents a hybrid model where post-analysis based domain ontology concept has been adopted to discover actionable knowledge from frequent patterns...............

Keyword: Actionable knowledge, Domain Ontology, Prior knowledge, Semantic distance,User belief

[1]. H. C. Koh and G. Tan, "Data Mining Application in Healthcare", Journal of Healthcare Information Management, vol. 19, no. 2, (2005).
[2]. J. Yanqing, H. Ying, J. Tran, P. Dews, A. Mansour and R. Michael Massanari, "Mining Infrequent Causal Associations in Electronic Health Databases", 11th IEEE International Conference on Data Mining Workshops, (2011).
[3]. M. Shaharanee, I. Nizal, H. Fedja et D. Tharam, «Interestingness measures for association rules based on statistical validity,» Knowledge-Based Systems, vol. 24, n° 13, pp. 386-392, 2011.
[4]. P. Manda, F. McCarthy, B. Nanduri et M. Bridges, «Information Theoretic Interestingness Measures for Cross-Ontology Data Mining in the Mouse Anatomy Ontology and the Gene Ontology,» Computational Engineering, Finance, and Science (cs.CE), pp. 116, 2015.
[5]. D. Franke, «System and method for efficiently generating association rules,» U.S. Patent 8,401,986,, n° %18, 2013.

Abstract: The healthcare sector is one of the most important sectors in all countries where it spends an average of about 9.5% of its domestic product. Diabetes is a chronic disease that demands a long term healthcare; it consumes medical resources, requires regular set of checkups and continual requirements for physicians and medical equipment. In addition, the patient should control blood-glucose levels with a normal range through medication, diet and exercise. In consequence, the majority of patients suffer from their incapability to manage their treatments without difficulty and the boring follow-up process.............

Keyword: Context-aware System, E-health, Diabetes, Diabetes Management System, Mobile Cloud Computing, Software Product Lines

[1] Diagnosis and Classification of Diabetes Mellitus, Diabetes Care, vol. 34, no. 1, 2011, pp. S62–S69; doi:10.2337/dc11-S062.
[2] Hamdan, S.: Rapid Increase of Diabetes Strains Middle East's Health Agencies. New York Times, The International Herald Tribune Section, 2011.
[3] Motala, AA.: Diabetes trends in Africa. Diabetes Metab Res Rev. 2002;18:S14–20
[4] Rheeder, P. : Type 2 diabetes: The emerging epidemic. South Afr Fam Pract. 2006;48:20.
[5] Sobngwi, E. , Mauvais-Jarvis, F., Vexiau, P., Mbanya, JC., Gautier, JF. : Diabetes in Africans: Part 1: Epidemiology and clinical specificities. Diabetes Metab. 2001;27:628–34.

Abstract: In past few decades Online Signature Verification have been employed in many applications such as security control, banking, law enforcement etc. A number of techniques have been proposed in the realization of reliable signature verification system such as Dynamic Time Wrapping (DTW), Hidden Markov Model (HMM), Support Vector Machine (SVM) and Neural Networks (NN)In this paper we have presented a review of research carried out in recent field of online signature verification and made a qualitative analysis of these state-of-the-art approaches.

Keyword: Signatures, Verification, fraudulence, feature Extraction, training, Forgery

[1]. D.Muramatsu, M. Kondo, M. Sasaki, S. Tachibana, and T. Matsumoto, "A markov chain monte carlo algorithm for bayesian dynamic signature verification". IEEE Transactions on Information Forensics and Security, March, 2006, 1(1):22–34
[2]. K. Yasuda, D. Muramatsu, and T. Matsumoto, "Visual-based online signature verification by pen tip tracking", Proc. CIMCA 2008, pp. 175–180
[3]. Satoshi Shirato, D. Muramatsu, and T. Matsumoto, "camera-based online signature verification: Effects of camera positions", World Automation congress 2010 TSI press
[4]. D. Muramatsu, K. Yasuda, S. Shirato, and T. Matsumoto. "Visual-based online signature verification using features extracted from video", Journal of Network and Computer Applications Volume 33, Issue 3, May 2010, Pages 333-341
[5]. R. Plamondon and G. Lorette. "Automatic signature verification and writer identification - the state of the art". Pattern Recognition, 22(2):107–131, 1989

Abstract: Data and information are important assets to any organization. However it is more important for some organization than others because this data and information in its core and heart, that means the work of such organizations focused on data. In library specifically, and owing to its un-profit nature works, the information about the number of books, researches, thesis, articles, periodicals, and papers and the move of these assets between 'Borrow and Return' or the way to archive it along with the electronic services nowadays which is consider one of the essential service that any library should has. All this reasons make the database system is the critical factor to determine the level of library's performance.............

Keyword: Quality, Data, Maturity, Symphony.

[1]. Adelman, S., Moss, L., and Abai, M. (2005) "Data Strategy", Addison-Wesley.
[2]. Huang, K.T., Lee, Y., Wang, R. (1999) Quality Information and Knowledge. Prentice Hall, Upper Saddle River.
[3]. Kyung-Seok Ryu, Joo-Seok Park, & Jae-Hong Park. (2005). A Study on Data Quality Management
[4]. Maturity Model. Electronics & Telecommunications Research institute (ETRI).
[5]. lsmael Caballero and Muiioz-Reja Mario Piattini Velthuis. (2003). Data Quality Management Improvement

Abstract: In this information age, searching for information from the internet becomes wide spread, but some of the valuable information are hidden behind dynamically generated page which the search engines never crawled. It becomes imperative to find a way of making them available. This research work provides a scalable framework for locating forms that serves as the entry point for information in web databases. The methodology focuses on the link classifier by using Path-Ascending algorithm which has the advantage of searching as many resources as possible from a particular site, since forms are sparsely distributed, After implementing this framework, it was observed that the framework searches more than 90% of the links in each site tested which is comparatively higher than using search algorithm in the link classifier. This framework suggest using path ascending algorithm in the link classifier which make the crawler to search almost all the links on each page of a site providing a better opportunity to locate a searchable form............

[1] Bergman, M (2001). The deep web: Surfacing Hidden Value. Retrieved September 18, 2015 from http://www.brightplanet.com/technology/deepweb.asp
[2] Onifade, O.F.W& Fagbenro, M.S (2011). Extending the functionality of web crawler to include File Transfer Protocol, Africa Journal of Computing & ICT, Vol. 9, No.2, pp. 17 – 24.
[3] Khandelwal, B, Abbas, S. Q. (2014). Confluent Analysis of Challenges and Scalability of Efforts for Deep Web Data Retrieval, International Journal of Emerging Trends & Technology in Computer Science, Vol. 3, Issue 1,pp.223-227.
[4] Soumen C, Van den Berg M, Byron D (1999). Focused crawling: a new approach to topic-specific Web resource discovery, Computer Science and Engineering, Indian Institute of Technology, Bombay, pp. 545 – 562.
[5] Raghavan S and Garcia-Molina H (2001). Crawling the Hidden Web, In 27th International Conference on Very Large Data Bases (VLDB 2001)

Abstract: Selection of an appropriate Database Management System (DBMS) to sustain and support a particular database system or project is considered as crucial stage in the allied DB development lifecycle. The selection process supposes undertaken prior physical design stage and based on numerous DBMS evaluation features and criteria, which in line with the given system requirements. Recently, security features raise and become a foremost selection criterion as well as an elementary system requirement. Therefore, this paper contributes to such context by conducting a comparative study intended for the security criteria and indicators of the most three famed and widely used Relational DBMSs, namely Oracle, MS SQL Server, and MySQL............

Keyword: Database security, DBMSs, RDBMSs, Oracle Security, MS SQL Server Security, MySQL Security

[1]. Connolly, T. and Begg, C. 2015. Database Systems: A Practical Approach to Design, Implementation and Management. 6th ed. Harlow, UK: © Pearson Education Limited.
[2]. DISA, 2004. Database Security Technical Implementation Guide. Version 7, Release 1. DISA "Defense Information System Agency", 29 October 2004.
[3]. "Database Security". 2016. en.wikipedia.org Retrieved 2016-07-07.
[4]. Matt Bishop. "What is Computer Security", Pages 67-69 IEEE Security & Privacy Vol. 99(1) 2003.
[5]. Paul Beynon-Davies, 2004. Database Systems. 3rd ed. London, UK, © Palgrave Macmillan