. To identify the problems with thyroid gland, an algorithm based on blood test results is developed. This algorithm is implemented in a web based application with client-server architecture. Three-tier architecture is used to realize the user interface, business logic and computer data storage and data access. Using the document
Gaitonde, D. Y., Rowley,K. D. and Sweeney, L. B. (2012). Hypothyroidism: An Update. American Family Physician, vol. 86, 244-251.
Spencer, C. A. (1988). Clinical utility and cost-effectiveness of sensitive thyrotropin assays in ambulatory and hospitalized patients. ? In: Mayo Clinic Proceedings, vol. 63, 1214-1222.
Surks, M. I., Ortiz, E., Daniels, G. H., et al. (2004). Subclinical thyroid disease: scientific review and guidelines for diagnosis and management. JAMA, vol. 291, 228-238.
Rastogi, M. V., and LaFranchi, S. H. (2010). Congenital hypothyroidism. ? Orphanet Journal of Rare Diseases, vol. 5, DOI: 10.1186/1750-1172-5-17.
Paes, J. E., Burman, K. D., Cohen, J., Franklyn, J., McHenry, C. R., Shoham, S. and Kloos, R. T. (2010). Acute bacterial suppurative thyroiditis: a clinical review and expert opinion. Thyroid, vol. 20, 247-55.
Schneider, D. F. and Chen, H. (2013). New Developments in the Diagnosis and Treatment of Thyroid Cancer. CA: A Cancer Journal for Clinicians, vol. 63, DOI: 10.3322/caac.21195.
Shelly, G. and Rosenblatt, H. J. (2011). Systems Analysis and Design.
Ying, M. and Miller, J. (2013). Refactoring legacy AJAX applications to improve the efficiency of the data exchange component. Journal of Systems and Software, vol. 86, no 1, 72-88.
Visualizations of algorithms contribute to improving computer science education. The process of teaching and learning of algorithms is often complex and hard to understand problem. Visualization is a useful technique for learning in any computer science course. In this paper an e-learning tool for shortest paths algorithms visualization is described.
Visualizations of algorithms contribute to improving computer science education. The process of teaching and learning of algorithms is often complex and hard to understand problem. Visualization is a useful technique for learning in any computer science course. In this paper an e-learning tool for shortest paths algorithms visualization is described. The developed e-learning tool allows creating, editing and saving graph structure and visualizes the algorithm steps execution. It is intended to be used as a supplement to face-to-face instruction or as a stand-alone application. The conceptual applicability of the described e-learning tool is illustrated by implementation of Dijkstra algorithm. The preliminary test results provide evidence of the usability of the e-learning tool and its potential to support students? development of efficient mental models regarding shortest paths algorithms. This e- learning tool is intended to integrate different algorithms for shortest path determination. Full article
Ardito C., De Marsico, M., Lanzilotti, R., Levialdi, S., Roselli, T., Rossano,V. & Tersigni, M. Usability of E-Learning Tools, Availableonlineat Utili/p80-ardito.pdf
Ahuja R. K., Magnanti, T. L. & Orlin, J. B. (1993). Network Flows:Theory,AlgorithmsandApplications.Englewood Cliffs, NJ: Prentice Hall.
Akoumianakis D. (2011). Learning as 'Knowing': Towards Retaining and Visualizing Use in Virtual Settings. Educational Technology&Society, 14(3), 55-68.
Ozyurt O., Ozyurt H., Baki A., Guven B. & Karal H. (2012). Evaluation of an adaptive and intelligent educational hypermedia for enhanced individual learning of mathematics: A qualitative study. ExpertSystemswithApplications, 39(15),12092-12104.
[Nguyen V.A. & Yamamoto A. (2012). Learning from graph data by putting graphs on the lattice. ExpertSystemswithApplications, 39(12), 11172-11182.
[Karavirta V. (2007). Integrating Algorithm Visualization Systems. ElectronicNotes inTheoreticalComputerScience, 178(4), pp. 79-87.
Seppala O. & Karavirta V. (2009). Work in Progress: Automatic Generation of Algorithm Animations for Lecture Slides. ElectronicNotesin TheoreticalComputerScience, 224, 97-103.
Hundhausen C. D., Douglas S. A. & Stasko J. T. (2002). A meta- study of algorithm visualization effectiveness. JournalofVisual Languages andComputing, 13(3), 259-290.
Fouh E., Akbar M. & Shaffer C. A. (2012). The Role of Visualization in Computer Science Education. Computersinthe Schools, 29(1-2),95-117.
Roles J.A. & ElAarag H. (2013). A Smoothest Path algorithm and its visualization tool. Southeastcon, In Proc.ofIEEE, DOI: 10.1109/SECON.2013.6567453.
Paramythis A., Loidl S., M?hlbacher J. R., & Sonntag M. (2005). A Framework for Uniformly Visualizing and Interacting with Algorithms. In Montgomerie, T.C., & Parker E-Learning, J.R. (Eds.), In Proc. IASTEDConf.onEducationandTechnology (ICET 2005), Calgary, Alberta, Canada, 2-6 July 2005, pp. 28- 33.
Nussbaumer A., Dahrendorf D., Schmitz H.-Ch., Kravcik M., Berthold M. & Albert D. (2014). Recommender and Guidance Strategies for Creating Personal Mashup Learning Environments. Computer Scienceand InformationSystems, 11(1), 321-342.
Gillet D., Law E.L.C. & Chatterjee A. (2010). Personal Learning Environments in a Global Higher. Engineering Education Web 2.0 Realm. In Proc. of IEEEEDUCON2010Conference. pp. 897- 906.
Wilson S., Liber P.O., Johnson M., Beauvoir P. & Sharples P. (2007). Personal Learning Environments: Challenging the Dominant Design of Educational Systems. Journalofe-Learning andKnowledgeSociety, 3(2), pp. 27-28.
Godsk M. (2014). Improving Learning in a Traditional, Large- Scale Science Module with a Simple and Efficient Learning Design. EuropeanJournalofOpen,Distanceande-Learning, 17(2), 142-158.
Guliashki V., Genova K. & Kirilov L. (2013). The Decision Support System WebOptim in an E-Learning Context. In: Proc. of International Conference"AutomaticsandInformatics'2013", 2013, pp. I-117-I-120.
Dijkstra, E. W. (1959). A Note on Two Problems in Connection with Graphs. NumericheMathematik, 1, 269-271.
Dial R., Glover F., Karney D. & Klingman D. (1979). A Computational Analysis of Alternative Algorithms and Labeling Techniques for Finding Shortest Path Trees. Networks, 9(3), 215- 248.
Glover F., Klingman D. & Phillips N. (1985). A New Polynomially Bounded Shortest Paths Algorithm. Operations Research, 33(1),(pp. 65-73).
Gallo G. & Pallottino S. (1988). Shortest Paths Algorithms. Annalsof Operations Research, 13(1), 73-79.
Hung M. H. & Dovoky J. J. (1988). A Computational Study of Efficient Shortest Path Algorithms.Computers&Operations Research, 15(6),567-576.
Mondou J-F, Crainic T. G. & Nguyen S. (1991). Shortest Path Algorithms: A Computational Study with the C Programming Language. Computers&OperationsResearch, 18(8), 767-786.
Cherkassky B. V., Goldberg A. V. & Radzik T. (1996). Shortest Paths Algorithms: Theory and Experimental Evaluation. MathematicalProgramming, Ser.A73(2), 129-174.
Silveira D.S., Melo V.A. & Boaventura Netto. P. O. (2009). AlgoDeGrafos: An Application to Assist in Course Lectures on Graph Theory. CLEI ElectronicJournal, 12(1), paper 2.
Goldberg A. V. & Radzik T. (1993). A Heuristic Improvement of the Bellman-Ford Algorithm. AppliedMathematicsLetter, 6(3), 3-6.
S?nchez-Torrubia M.G., Torres-Blanc C. & L?pez-Mart?nez M. A. (2009). PathFinder: A Visualization eMathTeacher for Actively Learning Dijkstra?s Algorithm. ElectronicNotesin Theoretical Computer Science, 224, pp. 151-158.