Educational Platform SOLL with the IoT
Andreia Magalhães, António Andrade, José Matias Alves
J INFORM SYSTEMS ENG, Volume 4, Issue 4, Article No: em0101
https://doi.org/10.29333/jisem/6345
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
The Internet of Things (IoT) is a network composed of various objects and devices connected to the Internet, which emerge with great potential for education.
Thus, in order to verify the potential of IoT in an interdisciplinary approach of the science curriculum in the 3rd Cycle of Basic Education emerges project SOLL: Intelligent Objects Linked to Learning, which is an interactive, dynamic and interdisciplinary learning platform, supported by a set of technologies that collect and store data from a greenhouse for later interdisciplinary analysis.
In this article, the platform’s architecture is exposed and, from a mixed methodology - student questionnaires, teacher focus group interviews and continuous observation of participants recorded in the researcher’s diary - the data obtained show that this platform respond to the new learning community structure, by adopting a different learning model, with exploration of interests and enrichment of educational experiences.
Keywords: education, technology, Internet of Things
REFERENCES
- Adrião, D. (2018). Um novo paradigma educativo para Portugal no século XXI. Educanology.
- Aldowah, I., Ghazal, H., Rehman, S. and Umar, S. (2017). Internet of Things in Higher Education: A Study on Future Learning. J. Phys. Conf. Ser. https://doi.org/10.1088/1742-6596/892/1/012017
- Atzori, L., Iera, A. and Morabito, G. (2010). The Internet of Things : A survey. Comput. Networks, 54(15), 2787-2805. https://doi.org/10.1016/j.comnet.2010.05.010
- Barbosa, N. V. S. (2009). A horta escolar dinamizando o currículo da escola. Brasília MEC.
- Bruno, S., Schuchter, A. and Junior, L. (2019). Formação docente e uso dos laboratórios de informática na educação básica: divergências entre os contextos do discurso oficial e da prática. In Educação e Tecnologias na Sociedade Digital; Whitebooks.
- Comissão Europeia (2012). Comunicação da Comissão ao Parlamento Europeu, ao Conselho, ao Comité Económico e Social Europeu e ao Comité das Regiões, Repensar a Educação - Investir nas Competências para melhores resultados socioeconómicos.
- Costa, H. (2014). Inovação Pedagógica: A tecnologia ao serviço da educação. Chiado Ed.
- Cribb, S. L. S. P. (2010). Contribuições Da Educação Ambiental E Horta Escolar Na Promoção De Melhorias Ao Ensino, À Saúde E Ao Ambiente. Rev. Eletrônica do Mestr. Prof. em Ensino Ciências da Saúde e do Ambient., 3(1), 42-60. https://doi.org/10.22409/resa2010.v3i1.a21103
- Fullan, A. and Hargreaves, M. (2001). Por que é que vale a pena lutar?. Porto Ed.
- Gubbi, M., Buyya, J., Marusic, R. and Palaniswami, S. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Futur. Gener. Comput. Syst., 29(7), 1645-1660. https://doi.org/10.1016/j.future.2013.01.010
- Johnson, L., Adams Becker, S., Estrada, V., and Freeman, A. (2015). Horizon Report: 2015 Higher Education Edition.
- Kranenburg, R., et al. (2016). The Internet of Things. Pap. 1st Belin Symp. Internet Soc. Oct. 25-27.
- Magalhães, A., Andrade, A. and Alves, J. (2019). SOLL: Smart Objects Linked to Learning - Educational platform with the Internet of Thingsitle. In 2019, 14a Conferência Ibérica Sist. e Tecnol. Informação (CISTI), IEEE. https://doi.org/10.23919/CISTI.2019.8760921
- Martins, S., Gomes, G., Brocardo, C., Pedroso, J., Carrillo, J., Silva, J., Encarnação, L., Horta, M., Calçada, M., Nery, M. and Rodrigues, R. (2017). Perfil dos Alunos à Saída da Escolaridade Obrigatória. Ministério da Educ. - Direção Geral da Educ.
- Moreira, J. A. (2012). Novos cenários e modelos de aprendizagens construtivistas em plataformas digitais. In Educação online - Pedagogia e a prendizagem em plataformas digitais, 27-44.
- Morgado, J. (2006). A Horta Escolar na Educação Ambientar e Alimentar: experiências do projeto horta viva nas escolas municipais de Florianópolis. Cent. Ciências Agrárias. Univ. Fed. St. Catarina, Florianópolis.
- Morgado, L. (2015). INGRESS: Potencialidades Pedagógicas de um jogo Georreferenciado de Realidade Alternativa em Rede. In Inovação e Formação na Sociedade Digital. Ambientes Virtuais, Tecnologias e Serious Games.
- O’Brien, H. M. (2016). The Internet of Things. J. Internet Law, 19(12), 1-20. https://doi.org/10.1007/978-1-4842-2108-2_1
- Osborne, J. and Dillon, J. (2008). Science Education in Europe: Critical Reflexions. London Nuff. Found.
- Patrício, M. (2019). Educação e formação em TIC intergeracional. In Educação e Tecnologias na Sociedade Digital; Whitebooks.
- Reid-Martinez, L. D. and Grooms, K. (2018). Online Learning Propelled by Constructivism. Encycl. Inf. Sci. Technol. (4th Ed., pp. 2588-2598). IGI Glob. https://doi.org/10.4018/978-1-5225-2255-3.ch226
- S. I. de 2018-08-03 Diário da República n.o 149/2018, 1o Suplemento, “Decreto-Lei n.o 55/2018, de 6 de julho.”
- Singer, T. (2012). Tudo conectado: conceitos e representações da internet das coisas. Simpósio em Tecnol. Digit. e Sociabilidade – Práticas Interacionais em Rede.
- Slimp, R. and Bartels, M. (2019). How the Internet of Things is Changing our Colleges, our Classroom, and our Students. Foreword by Fred Lokken. Br. Libr. Publ. Inf.
- Souza, L. (2010). Estratégias de aprendizagem e fatores motivacionais relacionados. Educ. rev., Curitiba, (36). https://doi.org/10.1590/S0104-40602010000100008
- Stošić, L. (2015). The Importance of Educational Tecnology in Teaching. Int. J. Cogn. Res. Sci. Eng. Educ., 3(1).
- Sultan, A., Woods, W. and Koo, P. (2011). A constructivist approach for digital learning: Malaysian schools case study. Educ. Technol. Soc., 14(4), 149-163.
- Xia, F., Yang, L. T., Wang, L. and Vinel, A. (2012). Internet of Things. Int. J. Comun. Syst., 25(9), 1101-1102. https://doi.org/10.1002/dac.2417
Spectral Enhancement of Imagery for Small Inland Water Bodies Monitoring: Utilization of UAV-Based Data
Jitka Komarkova, Ivana Cermakova, Pavel Sedlak, Jakub Jech
J INFORM SYSTEMS ENG, Volume 4, Issue 4, Article No: em0102
https://doi.org/10.29333/jisem/6346
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
The article describes a way for identification of land cover types and consequently land cover changes around a small water body, which is based on spectral enhancement of RGB UAV-based data. A middle-class unmanned aerial vehicle (UAV) – DJI Phantom 3 Pro, was used for data collection. UAV represents a cheap and on-demand available solution for remote data sensing. Its utilization is limited by weather conditions and particular legal regulations must be followed. The article is focused on a monitoring of a small water body and its surrounding by spectral enhancement. Spectral indices, which are calculated only from the visible bands, are used to identify particular land cover types: Color Index of Vegetation Extraction (CIVE), Excess Green (ExG), Excess Red (ExR), Green Leaf Index (GLI), Normalized Green-Red Difference Index (NGRDI), Red-Green-Blue Vegetation Index (RGBVI), Visible Atmospherically Resistant Index (VARI), and ExG – ExR difference. Low pass filtering was used for post-processing and results were simply visualised in a form of classified raster (by natural breaks – Jenks). Even this simple spectral enhancement of imagery supports its visual interpretation. Visible spectral indices highlight particular land cover types, namely green vegetation and water surface but other types of land cover can be distinguished as well.
Keywords: small water bodies, UAV, spectral enhancement, visible indices, spectral indices
REFERENCES
- Abdelkader, M., et al. (2013). A UAV based system for real time flash flood monitoring in desert environments using Lagrangian microsensors. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 25–34. https://doi.org/10.1109/ICUAS.2013.6564670
- Baker, P. and Kamgar-Parsi, B. (2010). Using shorelines for autonomous air vehicle guidance. Computer Vision and Image Understanding. Information Technology Division, Naval Research Laboratory, Washington, DC 20375, United States, 114(6), 723–729. https://doi.org/10.1016/j.cviu.2010.01.009
- Bendig, J., et al. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation. Institute of Geography, GIS and RS Group, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany, 39, pp. 79–87. https://doi.org/10.1016/j.jag.2015.02.012
- Bhagat, V. S. and Sonawane, K. R. (2011). Use of Landsat ETM+ data for delineation of water bodies in hilly zones. Journal of Hydroinformatics, 13(4), 661–671. https://doi.org/10.2166/hydro.2010.018
- Bukata, R. P., Harris, G. P. and Bruton, J. E. (1974). The detection of suspended solids and chlorophyll-a utilizing digital multispectral ERTS-1 data. In Second Canadian Symposium on Remote Sensing, pp. 551–564.
- Casado, M. R., et al. (2018). The use of unmanned aerial vehicles to estimate direct tangible losses to residential properties from flood events: A case study of Cockermouth Following the Desmond Storm. Remote Sensing. School ofWater, Energy and Environment, Cranfield University, College Road, Cranfield, MK430AL, United Kingdom, 10(10). https://doi.org/10.3390/rs10101548
- Cermakova, I., Komarkova, J. and Sedlak, P. (2019). Calculation of Visible Spectral Indices from UAV-Based Data: Small Water Bodies Monitoring. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–5. https://doi.org/10.23919/CISTI.2019.8760609
- Czech Fishing Union (2018). Available at: www.rybsvaz.cz (Accessed: 28 August 2018).
- DJI (2018). Available at: https://www.dji.com (Accessed: 28 August 2018).
- Feng, Q., Liu, J. and Gong, J. (2015). UAV Remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sensing. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No.20, Datun Road, Chaoyang District, Beijing, 100101, China, 7(1), 1074–1094. https://doi.org/10.3390/rs70101074
- Gallop, S. L., et al. (2015). The impact of temperate reefs on 34 years of shoreline and vegetation line stability at Yanchep, southwestern Australia and implications for coastal setback. Marine Geology, 369, 224–232. https://doi.org/10.1016/j.margeo.2015.09.001
- Gitelson, A. A., et al. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87. https://doi.org/10.1016/S0034-4257(01)00289-9
- Haas, E. M., Bartholomé, E. and Combal, B. (2009). Time series analysis of optical remote sensing data for the mapping of temporary surface water bodies in sub-Saharan western Africa. Journal of Hydrology, 370(1–4), 52–63. https://doi.org/10.1016/j.jhydrol.2009.02.052
- Iizuka, K., et al. (2018). Advantages of unmanned aerial vehicle (UAV) photogrammetry for landscape analysis compared with satellite data: A case study of postmining sites in Indonesia. Cogent Geoscience, 4(1). https://doi.org/10.1080/23312041.2018.1498180
- Jiang, H., et al. (2014). An automated method for extracting rivers and lakes from Landsat imagery. Remote Sensing, 6(6), 5067–5089. https://doi.org/10.3390/rs6065067
- Jones, S. K., et al. (2017). Big data and multiple methods for mapping small reservoirs: Comparing accuracies for applications in agricultural landscapes. Remote Sensing, 9(12). https://doi.org/10.3390/rs9121307
- Kataoka, T., et al. (2003). Crop growth estimation system using machine vision. In Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), Japan, pp. 1079–1083. https://doi.org/10.1109/AIM.2003.1225492
- Lejot, J., et al. (2007). Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surface Processes and Landforms, 32(11), 1705–1725. https://doi.org/10.1002/esp.1595
- Li, H., et al. (2015). Flood monitoring in Hainan Island based on HJ-CCD data. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 31(17), 191–198. https://doi.org/10.11975/j.issn.1002-6819.2015.17.025
- Lin, Y., et al. (2014). Correlating analysis on spatio-temporal variation of LUCC and water resources based on remote sensing data. In Proceedings of SPIE - The International Society for Optical Engineering, China. https://doi.org/10.1117/12.2063843
- Louhaichi, M., Borman, M. M. and Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65–70. https://doi.org/10.1080/10106040108542184
- Maleki, S., et al. (2018). Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland. Journal of the Indian Society of Remote Sensing, 46(11), 1871–1879. https://doi.org/10.1007/s12524-018-0842-7
- Meyer, G. E., et al. (2004). Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Computers and Electronics in Agriculture, 42(3), 161–180. https://doi.org/10.1016/j.compag.2003.08.002
- Meyer, G. E., Hindman, T. and Laksmi, K. (1999). Machine vision detection parameters for plant species identification. Proceedings of SPIE - The International Society for Optical Engineering, United States, 3543, pp. 327–335. Available at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032636094&partnerID=40&md5=34630f1675107f6dc64809d2e2e26d08
- Nandi, S., et al. (2016). Shoreline shifting and its prediction using remote sensing and GIS techniques: a case study of Sagar Island, West Bengal (India). Journal of Coastal Conservation, 20(1), 61–80. https://doi.org/10.1007/s11852-015-0418-4
- Ogilvie, A., et al. (2018). Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes. Journal of Hydrology, 566, 109–121. https://doi.org/10.1016/j.jhydrol.2018.08.076
- Pásler, M., Komárková, J. and Sedlák, P. (2015). Comparison of possibilities of UAV and Landsat in observation of small inland water bodies. In International Conference on Information Society, i-Society 2015, Czech Republic, pp. 45–49. https://doi.org/10.1109/i-Society.2015.7366855
- Pechanec, V., et al. (2014). Analyses of moisture parameters and biomass of vegetation cover in southeast Moravia. International Journal of Remote Sensing, 35(3), 967–987. https://doi.org/10.1080/01431161.2013.875236
- Pechanec, V., et al. (2015). Decision support tool for the evaluation of landscapes. Ecological Informatics, 30, 305–308. https://doi.org/10.1016/j.ecoinf.2015.06.006
- Saharan, M. A., et al. (2018). Classification and assessment of the land use - Land cover changes in Jodhpur city using remote sensing technologies. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, India, pp. 767–771. Available at: https://www.scopus.com/inward/record. uri?eid=2-s2.0-85057629755&partnerID=40&md5=df8448a4596bd5fc26c45a796c605382
- Salamí, E., Barrado, C. and Pastor, E. (2014). UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing, 6(11), 11051–11081. https://doi.org/10.3390/rs61111051
- Srikudkao, B., et al. (2015). Flood warning and management schemes with drone emulator using ultrasonic and image processing. Advances in Intelligent Systems and Computing, 107–116. https://doi.org/10.1007/978-3-319-19024-2_11
- Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
- Woebbecke, D. M., et al. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the American Society of Agricultural Engineers, 38(1), 259–269. https://doi.org/10.13031/2013.27838
Applications (Ideas) in Linear Algebra with Digital Image Processing. Can we Do, Teach, Motivate and Evaluate?
C. M. R. Caridade
J INFORM SYSTEMS ENG, Volume 4, Issue 4, Article No: em0103
https://doi.org/10.29333/jisem/6347
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
The application of new methodologies in higher education brings with it questions such as: “Can we do it?”, “Can we teach?”, “Can we motivate students?”, “Can we evaluate?”. During the last 8 years, in the Linear Algebra course of the Mechanical and Electromechanical Engineering of the Polytechnic of Coimbra-Institute of Engineering, it has been trying to answer these questions. The teaching methodology presented consists in the approach of Linear Algebra contents, using digital Image Processing applications. This new environment is intended to encourage students to learn the concepts and their manipulation in a productive and dynamic way. From the analysis of the studies and the student’s opinions (students’ adherence and motivation, questionnaires), it has been found that the use of this new teaching methodology helps the ‘learning to learn’ as long as it is well defined, with clear objectives for those who implements (teacher) and who interacts (students) with it.
Keywords: teaching mathematics, linear algebra, image processing
REFERENCES
- Berriochoa, E., Cachafeiro, A. and Illán, J. (2009). An approach for teaching the linear algebra for students of engineering. Proc. of International Conference of Education, Research and Innovation, pp. 3756-3763.
- Bovik, A. (2000). Handbook of Image and Video processing. San Diego, CA: Academic Press. https://doi.org/10.1016/B978-0-12-119792-6.X5062-1
- Caridade, C. M. R. (2011). Applying image processing techniques to motivate students in linear algebra classes. Proc. of the 1st World Engineering Flash Week, Lisbon 2011, 114-121.
- Caridade, C. M. R. (2019). Linear Algebra and Image Processing: a new teaching approach. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), IEEE, 1-6. https://doi.org/10.23919/CISTI.2019.8760909
- Caridade, C. M. R., Encinas, A. H., Martín-Vaquero, J. and Queiruga-Dios, A. (2014). CAS and real life problems to learn basic concepts in Linear Algebra courses. Computer Apllications in Engineering Education, 23(4), 567-577. https://doi.org/10.1002/cae.21627
- Costicã, L. (2014). The contribution of the new technologies to learning mathematics. Procedia – Social and Behavioral Sciences, 128, 240-245. https://doi.org/10.1016/j.sbspro.2014.03.150
- Donevska-Todorova, A. (2018) Fostering Students’ Competencies in Linear Algebra with Digital Resources. In: S. Stewart, C. Andrews-Larson, A. Berman and M. Zandieh (eds.) Challenges and Strategies in Teaching Linear Algebra. ICME-13 Monographs. Springer, Cham. https://doi.org/10.1007/978-3-319-66811-6_12
- Garg, A. and Kaundal, K. (2017). A study of Linear Algebra for Computer Vision. International Journal of Innovative Research in Compute and Communication Engineerin, 3, 4169-4176. https://doi.org/10.15680/IJIRCCE.2017.0503081
- Garía, I. and Cano, E. (2018). A computer game for teaching and learning algebra topics at undergraduate level. Comput. Appl. Eng. Educ, 26(2), 326-340. https://doi.org/10.1002/cae.21887
- Gomes, M. J. (2009). Problemas da avaliação em educação online. Educação online: cenário, formação e questões didático-metodológicas. Rio de Janeiro: WAK, 309-336.
- Gonzalez, R. C., Woods, R. E. and Eddins, S. L. (2009). Digital Image Processing Using MATLAB (2nd Ed.). Gatesmark Publishing.
- Ibrahim, R., Bakri, N., Salleh, T. S. A. and Zin, Z. M. (2012). Incorporating mathematics in teaching and learning of image processing. 4th International Congress on Engineering Education. https://doi.org/10.1109/ICEED.2012.6779260
- Izquierdo, J., Benítez, J., Berenguer, A. and Lago-Alonso, C. (2016). I decide, therefore I am (relevant!): A project‐based learning experience in linear algebra. Computer Apllications in Engineering Education, 24(3), 481-492. https://doi.org/10.1002/cae.21725
- Machado, C. and Gomes, M. J. (2013). Avaliação de cursos b-learning: uma proposta. Atas da VIII Conferência Internacional de Tecnologias da Informação e Comunicação – Challenges, 1635-1642.
- Mathsisfun.com (n.d.). Sam Loyd Puzzles. Available at: https://www.mathsisfun.com/puzzles/sam-loyd-puzzles-index.html/
- Notícias ao Minuto (n.d.). Palestras sobre a canção de Coimbra terminam com homenagem à guitarra. Available at: https://www.noticiasaominuto.com/amp/705258
- Ohrstrom, L., Svensson, G., Larsson, S., Christie, M. and Niklasson, C. (2005). The pedagogical implications of using MATLAB in integrated chimestry and mathematics courses. International Journal of Engineering Education, 21(4), 683-691.
- Pinterest (n.d.). Ballerina dancing on the beach | The best wallpapers collection ... | Dance, Ballerina dancing, Dance poses. Available at: https://www.pinterest.de/pin/247135098280420209/
- Produto.mercadolivre.com.br (n.d.). Mercado Livre Brasil - Onde comprar e vender de Tudo. Available at: https://produto.mercadolivre.com.br/MLB-740979663-azulo-treine-seu-passaro-de-maneira-profissional-150-cantos-_JM?quantity=1
- Silverman, J. and Rosen, G. (2010). Supporting students interest in mathematics throught applications from digital image processing. Journal of the Research Center for Education Technology, 6(2), 63-77.
- Thetapestryhouse.com (n.d.). van Gogh Starry Night - French tapestry wallhanging. Available at: https://www.thetapestryhouse.com/tapestries/view/1026/van-gogh-starry-night
- wikipedia.org (n.d.a). The Division Bell. Available at: https://en.wikipedia.org/wiki/The_Division_Bell
- wikipedia.org (n.d.b). Animals (Pink Floyd album). Available at: https://en.wikipedia.org/wiki/Animals_(Pink_Floyd_album)
- wikipedia.org (n.d.c). Available at: https://pt.wikipedia.org/wiki/Universidade_de_Coimbra__Alta_e_Sofia#/media/Ficheiro:Coimbra_December_2011-19a.jpg
- Zin, Z. M., Sallehb, T. S. and Bakrib, N. (2015). Transforming Teaching and Learning Approach of Mathematics and Image Processing. Journal of Science and Engineering Technology, 2(2).
A Percentile Methodology Applied to Binarization of Swarm Intelligence Metaheuristics
Matias Valenzuela, Hernan Pinto, Paola Moraga, Francisco Altimiras, Gabriel Villavicencio
J INFORM SYSTEMS ENG, Volume 4, Issue 4, Article No: em0104
https://doi.org/10.29333/jisem/6348
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
The binarization mechanisms of continuous metaheuristics are of interest in operational research. This is mainly due to the fact that there are a lot of combinatorial problems that are NP-hard. In this article, we exploit the concept of percentile as a mechanism of binarization of swarm intelligence continuous metaheuristics. To evaluate the behavior of our binary operator, the Multi-verse metaheuristic is used and applied to solve the combinatorial problem of the knapsack. The binary algorithm obtained, the binary multi-verse Optimizer (BMVO) shows good performance in solving the most difficult problems of the knapsack.
Keywords: metaheuristics, multidimensional knapsack problem, binarization, percentile
REFERENCES
- Astorga, G., Crawford, B., Soto, R., Monfroy, E., García, J. and Cortes, E. (2018). A meta-optimization approach to solve the set covering problem. Ingeniería, 23(3), 1-14.
- Bansal, J. C. and Deep, K. (2012). A modified binary particle swarm optimization for knapsack problems. Applied Mathematics and Computation, 218(22), 11042-11061. https://doi.org/10.1016/j.amc.2012.05.001
- Barman, S. and Kwon, Y. K. (2017). A novel mutual information-based Boolean network inference method from time-series gene expression data. PloS one, 12(2), e0171097. https://doi.org/10.1371/journal.pone.0171097
- Crawford, B., Soto, R., Astorga, G., García, J., Castro, C. and Paredes, F. (2017). Putting continuous metaheuristics to work in binary search spaces. Complexity. https://doi.org/10.1155/2017/8404231
- Crawford, B., Soto, R., Monfroy, E., Astorga, G., García, J. and Cortes, E. (2018). A meta-optimization approach to solve the set covering problem. Ingeniería, 23(3), 274-288. https://doi.org/10.14483/23448393.13247
- Crawford, B., Soto, R., Monfroy, E., Astorga, G., García, J. and Cortes, E. (2017, September). A meta-optimization approach for covering problems in facility location. In Workshop on Engineering Applications (pp. 565-578). Springer, Cham. https://doi.org/10.1007/978-3-319-66963-2_50
- Garcia, J. and Măntoiu, M. (2014). Localization results for zero order pseudodifferential operators. Journal of Pseudo-Differential Operators and Applications, 5(2), 255-276. https://doi.org/10.1007/s11868-013-0084-y
- García, J. and Peña, A. (2018). Robust optimization: concepts and applications. Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization, 7. https://doi.org/10.5772/intechopen.75381
- García, J., Altimiras, F., Peña, A., Astorga, G. and Peredo, O. (2018a). A binary cuckoo search big data algorithm applied to large-scale crew scheduling problems. Complexity. https://doi.org/10.1155/2018/8395193
- García, J., Crawford, B., Soto, R. and Astorga, G. (2017, September). A percentile transition ranking algorithm applied to knapsack problem. In Proceedings of the Computational Methods in Systems and Software (pp. 126-138). Springer, Cham. https://doi.org/10.1007/978-3-319-67621-0_11
- García, J., Crawford, B., Soto, R. and Astorga, G. (2019a). A clustering algorithm applied to the binarization of swarm intelligence continuous metaheuristics. Swarm and evolutionary computation, 44, 646-664. https://doi.org/10.1016/j.swevo.2018.08.006
- García, J., Crawford, B., Soto, R. and García, P. (2017, February). A multi dynamic binary black hole algorithm applied to set covering problem. In International Conference on Harmony Search Algorithm (pp. 42-51). Springer, Singapore. https://doi.org/10.1007/978-981-10-3728-3_6
- García, J., Crawford, B., Soto, R., Castro, C. and Paredes, F. (2018b). A k-means binarization framework applied to multidimensional knapsack problem. Applied Intelligence, 48(2), 357-380. https://doi.org/10.1007/s10489-017-0972-6
- García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., ... Astorga, G. (2019b). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. Computational intelligence and neuroscience. https://doi.org/10.1155/2019/3238574
- García, J., Pope, C. and Altimiras, F. (2017). A Distributed-Means Segmentation Algorithm Applied to Lobesia botrana Recognition. Complexity. https://doi.org/10.1155/2017/5137317
- Graells-Garrido, E. and García, J. (2015, December). Visual exploration of urban dynamics using mobile data. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 480-491). Springer, Cham. https://doi.org/10.1007/978-3-319-26401-1_45
- Graells-Garrido, E., Peredo, O. and García, J. (2016). Sensing urban patterns with antenna mappings: the case of Santiago, Chile. Sensors, 16(7), 1098. https://doi.org/10.3390/s16071098
- Haddar, B., Khemakhem, M., Hanafi, S. and Wilbaut, C. (2016). A hybrid quantum particle swarm optimization for the multidimensional knapsack problem. Engineering Applications of Artificial Intelligence, 55, 1-13. https://doi.org/10.1016/j.engappai.2016.05.006
- Kong, X., Gao, L., Ouyang, H. and Li, S. (2015). Solving large-scale multidimensional knapsack problems with a new binary harmony search algorithm. Computers & Operations Research, 63, 7-22. https://doi.org/10.1016/j.cor.2015.04.018
- Kumar, A. and Suhag, S. (2017). Multiverse optimized fuzzy-PID controller with a derivative filter for load frequency control of multisource hydrothermal power system. Turkish Journal of Electrical Engineering & Computer Sciences, 25(5), 4187-4199. https://doi.org/10.3906/elk-1612-176
- Liu, J., Wu, C., Cao, J., Wang, X. and Teo, K. L. (2016). A binary differential search algorithm for the 0–1 multidimensional knapsack problem. Applied Mathematical Modelling, 40(23-24), 9788-9805. https://doi.org/10.1016/j.apm.2016.06.002
- Meng, T. and Pan, Q. K. (2017). An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem. Applied Soft Computing, 50, 79-93. https://doi.org/10.1016/j.asoc.2016.11.023
- Mirjalili, S., Mirjalili, S. M. and Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513. https://doi.org/10.1007/s00521-015-1870-7
- Peredo, O. F., García, J. A., Stuven, R. and Ortiz, J. M. (2017). Urban dynamic estimation using mobile phone logs and locally varying anisotropy. In Geostatistics Valencia 2016 (pp. 949-964). Springer, Cham. https://doi.org/10.1007/978-3-319-46819-8_66
- Pirkul, H. (1987). A heuristic solution procedure for the multiconstraint zero‐one knapsack problem. Naval Research Logistics (NRL), 34(2), 161-172. https://doi.org/10.1002/1520-6750(198704)34:2<161::AID-NAV3220340203>3.0.CO;2-A
- Trivedi, I. N., Jangir, P., Jangir, N., Parmar, S. A., Bhoye, M. and Kumar, A. (2016, March). Voltage stability enhancement and voltage deviation minimization using multi-verse optimizer algorithm. In 2016 International conference on circuit, power and computing technologies (ICCPCT) (pp. 1-5). IEEE. https://doi.org/10.1109/ICCPCT.2016.7530136
- Valenzuela, M., Valenzuela, P., Cáceres, C., Jorquera, L. and Pinto, H. (2019, June). A Percentile Multi-Verse Optimizer Algorithm applied to the Knapsack problem. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-8). IEEE. https://doi.org/10.23919/CISTI.2019.8760613
- Zhang, X., Wu, C., Li, J., Wang, X., Yang, Z., Lee, J. M. and Jung, K. H. (2016). Binary artificial algae algorithm for multidimensional knapsack problems. Applied Soft Computing, 43, 583-595. https://doi.org/10.1016/j.asoc.2016.02.027
Social CRM Analytics Challenges
Margarida Almeida Marques, Carlos J. Costa
J INFORM SYSTEMS ENG, Volume 4, Issue 4, Article No: em0105
https://doi.org/10.29333/jisem/6349
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
Social Customer Relationship Management (Social CRM) is an emerging concept that integrates traditional CRM and social networks, influenced by Web 2.0, to provide benefits for both organizations and customers. This paper aims to give a review of the existing literature on Social CRM, seeking later to develop a solution that helps to understand all the data that flow in a Social CRM system and that can help us to focus on a better strategy.
Keywords: Social CRM, social networks, CRM, Web 2.0, Business Analytics
REFERENCES
- Aparicio M. and Costa, C. (2012). Collaborative systems: characteristics and features. In Proceedings of the 30th ACM international conference on Design of communication (SIGDOC ‘12). ACM, New York, NY, USA, https://doi.org/141-146 10.1145/2379057.2379087
- Aparicio, M. Costa, C. and Simoes Braga. A. (2012). Proposing a system to support crowdsourcing. In Proceedings of the Workshop on Open Source and Design of Communication, ACM, pp. 13-17. https://doi.org/10.1145/2316936.2316940
- Araujo, H., Costa, C. and Aparicio. M. (2017). Modelo de competitive intelligence (CI) competitive intelligence (CI) model. Information Systems and Technologies (CISTI), 2017 12th Iberian Conference on. IEEE https://doi.org/10.23919/CISTI.2017.7975787
- Askool S. and Nakata, K. (2011) A conceptual model for acceptance of social CRM systems based on a scoping study. AI & Society 26(3), 205-220. https://doi.org/10.1007/s00146-010-0311-5
- Baird, C. H. and Parasnis, G. (2013). From social media to Social CRM - What customers want, IBM.
- Chi, H. (2011). Interactive Digital Advertising VS. Virtual Brand Community: Exploratory Study of User Motivation and Social Media Marketing Responses in Taiwan. Journal of Interactive Advertising, 12(1), 44-61. https://doi.org/10.1080/15252019.2011.10722190
- Choudhury, M. and Harrigan, P. (2014). CRM to social CRM: the integration of new technologies into customer relationship management. Journal of Strategic Marketing, 22(2), 149-176. https://doi.org/10.1080/0965254X.2013.876069
- Costa, C. and Alturas, B. (2010). Social networks and design of communication. In Proceedings of the Workshop on Open Source and Design of Communication (OSDOC ‘10). ACM, New York, NY, USA, 11-14. https://doi.org/10.1145/1936755.1936759
- Costa, C. and Aparicio, M. (2013). Social networks: intentions and usage. In Proceedings of the 2013 International Conference on Information Systems and Design of Communication (ISDOC ‘13). ACM, New York, NY, USA,. 101-107 https://doi.org/10.1145/2503859.2503875
- Costa, C. (1996). Internet e Estratégia Empresarial. Revista Portuguesa de Marketing, 1(3), 88-97.
- Greenberg, P. (2010). The impact of CRM 2.0 on customer insight. Journal of Business & Industrial Marketing, 25(6), 410-419, https://doi.org/10.1108/08858621011066008
- Han, S. (2010). Theorizing New Media: Reflexivity, Knowledge, and the Web 2.0. Sociological Inquiry 80.2, pp 200-213. https://doi.org/10.1111/j.1475-682X.2010.00327.x
- Kaplan, A. and Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53, 59-68. https://doi.org/10.1016/j.bushor.2009.09.003
- Kumar, V. and Reinartz, W. (2012). Customer Relationship Management. Springer Gabler, Berlin, Heidelberg,. https://doi.org/10.1007/978-3-662-55381-7
- Lehmkuhl, T. and Jung, R. (2013). Towards Social CRM - Scoping the concept and guiding research. Proceedings of the 26th Bled eConference.
- Lehmkuhl, T. (2014). Towards Social CRM: A Model for Deploying Web 2.0 in Customer Relationship Management (PhD Dissertation), Bamberg: Difo-Druck GmbH.
- Mangold, W. and Faulds, D. (2009). Social Media: The New Hybrid Element of the Promotion Mix. Business Horizons, 52(4), 357-365.
- O’Reilly, T. (2005). What Is Web 2.0? http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html (Accessed December 2017)
- Sexton, K (2012). Business Must Have: Social CRM Apps. http://www.business2community.com/mobileapps/business-must-have-social-crm-apps-0179264 (Accessed May 2015)
- Sinclaire, J., Jollean, K. and Vogus, C. (2011). Adoption of social networking sites: an exploratory adaptive structuration perspective for global organizations. Information Technology and Management, 12(4), 293-314. https://doi.org/10.1007/s10799-011-0086-5
- Wittwer, M, Reinhold, O. and Alt, R. (2016). Social Media Analytics in Social CRM-Towards a Research Agenda. BLED 2016 Proceedings. 32. https://aisel.aisnet.org/bled2016/32
Web-based System for Decision Support on Surface Irrigation Modernization
Tiago Levita, Diana Gonçalves, Qingfeng Miao, José Manuel Gonçalves
J INFORM SYSTEMS ENG, Volume 4, Issue 4, Article No: em0106
https://doi.org/10.29333/jisem/6350
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
The purpose of this paper is to describe a web-based system to assist on-farm design and management of surface irrigation systems. This application was designed to be an appropriate tool for generating design alternatives associated with attributes of technical, economic, and environmental nature, and handling and evaluating a large number of input and output data. It also allows the evaluation and ranking of design alternatives using multicriteria analysis where criteria are weighted according to the priorities and perception of the designer and users, and provides an appropriate dialogue between the designer and the user, with an effective help support with information about equipments and irrigation practices. The application has tools for the resolution of specific problems, such as land leveling, pipe sizing and economic calculation. Built with a simple user friendly interface, with several optional languages and online help for technical aspects, this tool will contribute to support the dissemination of knowledge, design procedures and field practices of surface irrigation. Tests and demonstrations are being done on Hetao Irrigation District, China.
Keywords: surface irrigation systems, Decision Support Systems (DSS), Web-service, multicriteria analysis, Hetao Irrigation District
REFERENCES
- Antonopoulou, E., Karetsos, S. T., Maliappis, M. and Sideridis, A. B. (2010). Web and mobile technologies in a prototype DSS for major field crops. Computers and Electronics in Agriculture, 70(2), 292-301. https://doi.org/10.1016/j.compag.2009.07.024
- Car, N. J., Christen, E. W., Hornbuckle, J. W. and Moore, G. A. (2008). A Web Services-supported, Calendar-based, Irrigation Decision Support System. Irrigation Australia, pp.10.
- Gonçalves, J. M. and Pereira, L. S. (2009). A decision support system for surface irrigation design. Journal of Irrigation and Drainage Engineering, 135(3), 343-356. https://doi.org/10.1061/ASCEIR.1943-4774.0000004
- Gonçalves, J. M., Muga, A. and Pereira, L. S. (2011). A Web-based Decision Support System for Surface Irrigation Design. In C. Jao (ed.) Efficient Decision Support Systems: Practice and Challenges – From Current to Future / Book 2, InTech – Open Access Publ., 291-318.
- Levita, T., Gonçalves, D., Miao, Q. and Gonçalves, J. M. (2019). Developing a Web-based service to support on-farm irrigation on Hetao Irrigation District, China. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), 1-5. https://doi.org/10.23919/CISTI.2019.8760818
- Miao, Q., Shi, H., Gonçalves, J. M. and Pereira, L. S. (2018). Basin Irrigation Design with Multi-Criteria Analysis Focusing on Water Saving and Economic Returns: Application to Wheat in Hetao, Yellow River Basin. Water, 10(1), 67. https://doi.org/10.3390/w10010067
- Oracle (n.d.). Java. Available at: www.oracle.com/java
- PHP (n.d.). PHP. Available at: www.php.net
- Pomerol, J. C. and Romero, B. (2000). Multicriterion Decision in Management: Principles and Practice. Springer Science+Business Media, LLC. https://doi.org/10.1007/978-1-4615-4459-3
- Reenskaug, T. (n.d.). MVC — XEROX PARC 1978-79. Available at: http://heim.ifi.uio.no/~trygver/themes/mvc/mvc-index.html
- Rinaldi, M. and He, Z. (2014). Decision Support Systems to Manage Irrigation in Agriculture. Advances in Agronomy, 123(6), 229-279. https://doi.org/10.1016/B978-0-12-420225-2.00006-6
- Shao, W.W., Yang, D.W., Hu, H.P., and Sanbongi, K. (2009) Water resources allocation considering the water use flexible limit to water shortage - A case study in the Yellow River Basin of China. Water Resour. Manage., 23, 869-880. https://doi.org/10.1007/s11269-008-9304-2
- Thysen, I. and Detlefsen, N. K. (2006). Online decision support for irrigation for farmers. Agricultural Water Management, 94(1-3), 93-108. https://doi.org/10.1016/j.agwat.2006.05.016
- Wang, L., Liu, T., Ding, Y., Wang, G. and Liu, X. (2016) Characteristics and tendency of climate change in the Hetao irrigation District in the past 50 years. Journal of Beijing Normal University, 52(3), 402-407 (in Chinese).
- Zazueta, F. S., Xin, J., Pereira, L. S. and Musy, A. (2006). Information Technologies in Water Management. In A. Munack, (ed.) CIGR Handbook of Agricultural Engineering, Vol. VI: Information Technologies, ASABE, St. Joseph, MI, 314-324.