ISSN: 0975-3826(online); 0975-4660 (Print)
Citation Count – 5
Olugbenga Adejo and Thomas Connolly
School of Engineering and Computing, University of the West of Scotland, Paisley, United Kingdom
Accurate prediction and early identification of student at-risk of attrition are of high concern for higher educational institutions (HEIs). It is of a great importance not only to the students but also to the educational administrators and the institutions in the areas of improving academic quality and efficient utilisation of the available resources for effective intervention. However, despite the different frameworks and various models that researchers have used across institutions for predicting performance, only negligible success has been recorded in terms of accuracy, efficiency and reduction of student attrition. This has been attributed to the inadequate and selective use of variables for the predictive models. This paper presents a multi-dimensional and an integrated system framework that involves considerable learners’ input and engagement in predicting their academic performance and intervention in HEIs. The purpose and functionality of the framework are to produce a comprehensive, unbiased and efficient way of predicting student performance that its implementation is based upon multi-sources data and database system. It makes use of student demographic and learning management system (LMS) data from the institutional databases as well as the student psychosocial-personality (SPP) data from the survey collected from the student to predict performance. The proposed approach will be robust, generalizable, and possibly give a prediction at a higher level of accuracy that educational administrators can rely on for providing timely intervention to students
Prediction, Student performance, Higher education, integrated system, framework.
For More Details : http://aircconline.com/ijcsit/V9N3/9317ijcsit13.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 Braunstein, Andrew W., Mary Lesser, & Donn R. Pescatrice (2006) “The business of freshmen student retention: Financial, institutional, and external factors.” The Journal of Business and Economic Studies Vol.12, No. 2, pp.33.
 Yadav, S.K., Bharadwaj, B. K. & Pal, S. (2012). “Mining Educational Data to Predict Student’s Retention :A Comparative Study”, International Journal of Computer Science and Information Security (IJCSIS), Vol.10, No.2
 Bekele, R. & McPherson, M., (2011). “A Bayesian performance prediction model for mathematicseducation: A prototypical approach for effective group composition”. British Journal of Educational Technology, Vol.4, No.3, pp.395-416.
 Aljohani, O., (2016). “A comprehensive review of the major studies and theoretical models of student retention in higher education. Higher Education Studies,Vol. 6, No.2, p.1.
 Summerskill, J. (1962) Dropout from college. In N.Sanford(ed). The American college, New York, Wiley
 Spady W.G., ( 1971 ). Dropouts from higher education: Toward an empirical model. Interchange, Vol.2, No.3, pp.38-62
 Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of educational research, Vol.45, No.1, pp.89-125
 Bean, J. (1980). “Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education”, Vol.12, No.2, pp.155-187. http://dx.doi.org/10.1007/BF00976194
 Pascarella, E. T., & Terenzini, P.T (1980). “Predicting freshman persistence and voluntary dropout decisions from a theoretical model”. The Journal of Higher Education, Vol.51, No.1, pp.60-75,1980.
 Astin, A.W., (1984). “ Student involvement: A developmental theory for higher education”. Journal of college student personnel, Vol.25, No. 4, pp.297-308.
 Cabrera, A. F., Castaneda, M. B., Nora, A., & Hengstler, D. (1992). “The convergence between two theories of college persistence”. The Journal of Higher Education, Vol.63, No.2, pp.143-164.
 Kotsiantis, S.B. & Pintelas, P.E., (2005), July. “Predicting students marks in Hellenic open university”. In Advanced learning technologies, 2005. ICALT 2005. fifth IEEE international Conference on (pp. 664-668). IEEE.
 Oladokun, V. O., Adebanjo, A. T. & Charles-Owaba, O. E. (2008). “Predicting students’ academic performance using artificial neural network: A case study of an engineering course”. The Pacific Journal of Science and Technology, Vol.9, No.1, pp.72-79.
 Hoe, A.C.K., Ahmad, M.S., Hooi, T.C., Shanmugam, M., Gunasekaran, S.S., Cob, Z.C.& Ramasamy, A., (2013) “Analyzing students records to identify patterns of students’ performance”. In Research and Innovation in Information Systems (ICRIIS), 2013 International Conference on (pp. 544-547). IEEE
 Ikbal, S., Tamhane, A., Sengupta, B., Chetlur, M., Ghosh, S. & Appleton, J. (2015). “On early prediction of risks in academic performance for students. IBM Journal of Research and Development, Vol.59, No.6, pp.5-1.
 Minaei-Bidgoli, B., Kashy, D.A., Kortemeyer, G. & Punch, W., (2003). “Predicting student performance: an application of data mining methods with an educational web-based system”. In Frontiers in education, 2003. FIE 2003 33rd annual (Vol. 1, pp. T2A-13). IEEE
 Romero, C., López, M.I., Luna, J.M. & Ventura, S., (2013). “Predicting students’ final performance from participation in on-line discussion forums”.Computers & Education, Vol.68, pp.458-472
 Agudo-Peregrina A.F., Iglesias-Pradas S., Conde-Gonzalez M.A., and Hernandez-Garcia A. (2014). “Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning” Computers in Human Behavior, Vol.31, No.1, pp. 542-550.
 Cerezoa, R., Sánchez-Santillánb, M., Paule-Ruizb,M.P., & Núñeza J. (2016). “Students’ LMS interaction patterns and their relationship with achievement: A case study in higher education”, Computer and Education Vol. 96, May 2016, pp. 42–54
 Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., & Wani, E.(2011). Prediction of student academic performance by an application of data mining techniques. In International Conference on Management and Artificial Intelligence IPEDR, Vol.6, pp.110-114.
 Fariba, T.B. (2013). “Academic performance of virtual students based on their personality traits, learning styles and psychological well-being: A prediction”. Procedia-Social and Behavioral Sciences, Vol.84, pp.112-116.
 Gray, Geraldine, Colm Mcguinness, and Philip Owende. (2016) “Non-Cognitive Factors of Learning as Early Indicators of Students-at-Risk of Failing in Tertiary Education.” In Non-cognitive Skills and Factors in Educational Attainment, pp. 199-237. SensePublishers.
 Sarker, Farhana, Thanassis Tiropanis, and Hugh C. Davis.( 2013) “Exploring student predictive model that relies on institutional databases and open data instead of traditional questionnaires.” In Proceedings of the 22nd International Conference on World Wide Web, pp. 413-418. ACM.
 Romero, C., Romero, J.R., Luna, J.M. and Ventura, S., (2010). “Mining rare association rules from elearning data”. In Educational Data Mining 2010
 Rubiano, S.M.M. and Garcia, J.A.D., (2015). “Formulation of a predictive model for academic performance based on students’ academic and demographic data”. In Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE (pp. 1-7). IEEE
 Rusli, N.M., Ibrahim, Z. and Janor, R.M., (2008). “Predicting students’ academic achievement: Comparison between logistic regression, artificial neural network, and Neuro-fuzzy”. In Information Technology, 2008. ITSim 2008. International Symposium on (Vol. 1, pp.1-6). IEEE.
Citation Count – 5
Sonia Ordoñez Salinas and Alba Consuelo Nieto Lemus
Faculty of Engineering, Distrial F.J.C University, Bogotá, Colombia
Big Data triggered furthered an influx of research and prospective on concepts and processes pertaining previously to the Data Warehouse field. Some conclude that Data Warehouse as such will disappear; others present Big Data as the natural Data Warehouse evolution (perhaps without identifying a clear division between the two); and finally, some others pose a future of convergence, partially exploring the possible integration of both. In this paper, we revise the underlying technological features of Big Data and Data Warehouse, highlighting their differences and areas of convergence. Even when some differences exist, both technologies could (and should) be integrated because they both aim at the same purpose: data exploration and decision making support. We explore some convergence strategies, based on the common elements in both technologies. We present a revision of the state-of-the-art in integration proposals from the point of view of the purpose, methodology, architecture and underlying technology, highlighting the common elements that support both technologies that may serve as a starting point for full integration and we propose a proposal of integration between the two technologies.
Big Data, Data Warehouse, Integration, Hadoop, NoSql, MapReduce, 7V’s, 3C’s, M&G
For More Details : http://aircconline.com/ijcsit/V9N2/9217ijcsit01.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 P. Bedi, V. Jindal, and A. Gautam, “Beginning with Big Data Simplified,” 2014.
 R. Kimball, M. Ross, W. Thorthwaite, B. Becker, and M. J, The Data Warehouse Lifecycle Toolkit, 2nd Edition. 2008.
 C. Todman, Designing A Data Warehouse: Supporting Customer Relationship Management. 2001.
 W. H. Inmon, Building the Data Warehouse, 4th Edition. 2005.
 “Oracle Database 12c for Data Warehousing and Big Data .” [Online]. Available:http://www.oracle.com/technetwork/database/bi-datawarehousing/data-warehousing-wp-12c1896097.pdf. [Accessed: 09-Sep-2015].
 M. Cox and D. Ellsworth, “Application-Controlled Demand Paging for Out-of-Core Visualization,” 1997. [Online]. Available: http://www.nas.nasa.gov/assets/pdf/techreports/1997/nas-97-010.pdf.[Accessed: 09-Apr-2015].
 S. Chaudhuri and U. Dayal, “An overview of data warehousing and OLAP technology,” ACM SIGMOD Rec., vol. 26, no. 1, pp. 65–74, 1997.
 T. Maiorescu, “General Information on Business Intelligence,” pp. 294–297, 2010.
 “Data Warehouses and OLAP: Concepts, Architectures and Solutions: 9781599043647: Library and Information Science Books | IGI Global.” .
 Y. Demchenko, C. De Laat, and P. Membrey, “Defining Architecture Components of the Big Data Ecosystem,” Collab. Technol. Syst. (CTS), 2014 Int. Conf., pp. 104–112, 2014.
 G. NBD-PWG, “ISO/IEC JTC 1 Study Group on Big Data,” 2013. [Online]. Available: http://bigdatawg.nist.gov/cochairs.php. [Accessed: 24-Oct-2015].
 D. L. W.H. Inmon, Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault. Amsterdam,Boston: Elsevier, 2014.
 G. N. W.H. Inmon, Derek Strauss, DW 2.0: The Architecture for the Next Generation of Data Warehousing (Morgan Kaufman Series in Data Management Systems) (): : Books. Burlington, USA: Morgan Kaufmann Publishers Inc., 2008.
 R. Kimball, “The Evolving Role of the Enterprise Data Warehouse in the Era of Big Data Analytics,” Kimball Gr., 2011
 M. Muntean and T. Surcel, “Agile BI – The Future of BI,” Inform. Econ., vol. 17, no. 3, pp. 114–124, 2013.
 D. Agrawal, “The Reality of Real-Time Business Intelligence,” in Business Intelligence for the RealTime Enterprise, vol. 27 , M. Castellanos, U. Dayal, and T. Sellis, Eds. Springer Berlin Heidelberg , 2009, pp. 75–88.
 R. Castillo, J. Morata, and L. del Arbol, “Operational Data Store (ODS) – 933.pdf,” Actas del III taller nacional de minería de datos y aprendizaje, pp. 359–365, 2005.
 S. YiChuan and X. Yao, “Research of Real-time Data Warehouse Storage Strategy Based on Multilevel Caches,” Phys. Procedia, vol. 25, no. 0, pp. 2315–2321, 2012.
 A. Ma. P. Díaz-zorita, “Evaluación de la herramienta de código libre Apache Hadoop,” Universidad Carlos III de Madrid Escuela Politécnica Superior, 2011.
 R. Kimball, “Newly Emerging Best Practices for Big Data,” Kimball Group, p. 14, 2012.
 M. Maier, “Towards a Big Data Reference Architecture,” no. October, pp. 1–144, 2013.
 O. Corporation, “ORACLE ENTERPRISE ARCHITECTURE WHITE PAPER. An Enterprise Architect ’ s Guide to Big Data,” no. February, 2015.
 F. Kramer, H. Muller, and K. Turowski, “Acceleration of Single Inserts for Columnar Databases — An Experiment on Data Import Performance Using SAP HANA,” in Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on, 2014, pp. 672–676.
 M. R. Patil and F. Thia, Pentaho for Big Data Analytics, vol. 2013. PACKT PUBLISHING, 2013.
 S. G. Manikandan and S. Ravi, “Big Data Analysis Using Apache Hadoop,” in IT Convergence and Security (ICITCS), 2014 International Conference on , 2014, pp. 1–4.
 J. Nandimath, E. Banerjee, A. Patil, P. Kakade, and S. Vaidya, “Big data analysis using Apache Hadoop,” 2013 IEEE 14th Int. Conf. Inf. Reuse Integr., pp. 700–703, 2013.
 A. Katal, M. Wazid, and R. H. Goudar, “Big data: Issues, challenges, tools and Good practices,” in Contemporary Computing (IC3), 2013 Sixth International Conference on , 2013, pp. 404–409.
 A. Pal and S. Agrawal, “An Experimental Approach Towards Big Data for Analyzing Memory Utilization on a Hadoop cluster using HDFS and MapReduce .,” pp. 442–447, 2014.
 R. Zhang, D. Hildebrand, and R. Tewari, “In unity there is strength: Showcasing a unified big data platform with MapReduce Over both object and file storage,” in Big Data (Big Data), 2014 IEEE International Conference on , 2014, pp. 960–966.
 “Welcome to ApacheTM Hadoop®!” [Online]. Available: https://hadoop.apache.org/. [Accessed: 26- Mar-2015].
 “HDFS Architecture Guide.” [Online]. Available:
http://hadoop.apache.org/docs/r1.2.1/hdfs_design.html. [Accessed: 26-Mar-2015].
 S. Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” in Computer Networks and ISDN Systems, 1998, pp. 107–117.
 D. Garlasu, V. Sandulescu, I. Halcu, G. Neculoiu, O. Grigoriu, M. Marinescu, and V. Marinescu, “A big data implementation based on Grid computing,” in Roedunet International Conference (RoEduNet), 2013 11th, 2013, pp. 1–4.
 A. Jorgensen, C. Price, B. Mitchell, and J. Rowlan, Microsoft Big Data Solutions. John Wiley & Sons, Inc., 2014.
 R. T. Kaushik, M. Bhandarkar, and K. Nahrstedt, “Evaluation and Analysis of GreenHDFS: A SelfAdaptive, Energy-Conserving Variant of the Hadoop Distributed File System,” in Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on, 2010, pp. 274–287.
 J. G. Shanahan and L. Dai, “Large Scale Distributed Data Science Using Apache Spark,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 2323–2324.
 R. S. Xin, J. Rosen, M. Zaharia, M. J. Franklin, S. Shenker, and I. Stoica, “Shark: SQL and Rich Analytics at Scale,” in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 2013, pp. 13–24.
 J. Li, J. Wu, X. Yang, and S. Zhong, “Optimizing MapReduce Based on Locality of K-V Pairs and Overlap between Shuffle and Local Reduce,” in Parallel Processing (ICPP), 2015 44th International Conference on, 2015, pp. 939–948.
 E. Brewer, “CAP Twelve Years Later: How the ‘Rules’ Have Changed,” InfoQ, 2012. [Online]. Available: http://www.infoq.com/articles/cap-twelve-years-later-how-the-rules-have-changed. [Accessed: 26-Mar-2015].
 G. Vaish, Getting started with NoSQL. 2013.
 V. N. Gudivada, D. Rao, and V. V. Raghavan, “NoSQL Systems for Big Data Management,” 2014 IEEE World Congr. Serv., pp. 190–197, 2014.
 Cassandra, “The Apache Cassandra Project,” httpcassandraapacheorg, 2010. [Online]. Available: http://cassandra.apache.org/.
 D. Borthakur, “Petabyte Scale Databases and Storage Systems at Facebook,” in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 2013, pp. 1267–1268.
 J. Huang, X. Ouyang, J. Jose, M. Wasi-ur-Rahman, H. Wang, M. Luo, H. Subramoni, C. Murthy, and D. K. Panda, “High-Performance Design of HBase with RDMA over InfiniBand,” in Parallel Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International, 2012, pp. 774–785.
 G. Weintraub, “Dynamo and BigTable – Review and comparison,” Electr. Electron. Eng. Isr. (IEEEI), 2014 IEEE 28th Conv., pp. 1–5, 2014.
 D. Pereira, P. Oliveira, and F. Rodrigues, “Data warehouses in MongoDB vs SQL Server: A comparative analysis of the querie performance,” in Information Systems and Technologies (CISTI), 2015 10th Iberian Conference on, 2015, pp. 1–7
. K. Dehdouh, F. Bentayeb, O. Boussaid, and N. Kabachi, “Columnar NoSQL CUBE: Agregation operator for columnar NoSQL data warehouse,” in Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on, 2014, pp. 3828–3833.
 Y. Liu and T. M. Vitolo, “Graph Data Warehouse: Steps to Integrating Graph Databases Into the Traditional Conceptual Structure of a Data Warehouse,” in Big Data (BigData Congress), 2013 IEEE International Congress on, 2013, pp. 433–434.
 M. Chevalier, M. El Malki, A. Kopliku, O. Teste, and R. Tournier, “Benchmark for OLAP on NoSQL technologies comparing NoSQL multidimensional data warehousing solutions,” in Research Challenges in Information Science (RCIS), 2015 IEEE 9th International Conference on, 2015, pp. 480–485.
 F. Färber, S. K. Cha, J. Primsch, C. Bornhövd, S. Sigg, and W. Lehner, “SAP HANA Database: Data Management for Modern Business Applications,” SIGMOD Rec., vol. 40, no. 4, pp. 45–51, 2012.
 K. M. A. Hasan, M. T. Omar, S. M. M. Ahsan, and N. Nahar, “Chunking implementation of extendible array to handle address space overflow for large multidimensional data sets,” in Electrical Information and Communication Technology (EICT), 2013 International Conference on, 2014, pp. 1– 6.
 S. Müller and H. Plattner, “An In-depth Analysis of Data Aggregation Cost Factors in a Columnar Inmemory Database,” in Proceedings of the Fifteenth International Workshop on Data Warehousing and OLAP, 2012, pp. 65–72.
 H. Plattner, “A Common Database Approach for OLTP and OLAP Using an In-memory Column Database,” in Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, 2009, pp. 1–2.
 J. Schaffner, A. Bog, J. Krüger, and A. Zeier, “A Hybrid Row-Column OLTP Database Architecture for Operational Reporting,” in Business Intelligence for the Real-Time Enterprise SE – 5, vol. 27, M. Castellanos, U. Dayal, and T. Sellis, Eds. Springer Berlin Heidelberg, 2009, pp. 61–74.
 V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth, B. Saha, C. Curino, O. O’Malley, S. Radia, B. Reed, and E. Baldeschwieler, “Apache Hadoop YARN: Yet Another Resource Negotiator,” in Proceedings of the 4th Annual Symposium on Cloud Computing, 2013, pp. 5:1–5:16.
 “Apache Pig Philosophy.” [Online]. Available: http://pig.apache.org/philosophy.html. [Accessed: 26- Mar-2015].
 “Architecture – Apache Drill.” [Online]. Available: http://drill.apache.org/architecture/. [Accessed: 26-Mar-2015].
 “Storm, distributed and fault-tolerant realtime computation.” [Online]. Available:https://storm.apache.org/. [Accessed: 26-Mar-2015].
 “Apache Hive TM.” [Online]. Available: https://hive.apache.org/. [Accessed: 26-Mar-2015].
 “Sqoop -.” [Online]. Available: http://sqoop.apache.org/. [Accessed: 26-Mar-2015].
“ Impala.” [Online]. Available: http://www.cloudera.com/content/cloudera/en/products-andservices/cdh/impala.html [Accessed: 26-Mar-2015].
 “Apache Thrift – Home.” [Online]. Available: https://thrift.apache.org/. [Accessed: 26-Mar-2015].
 “Apache ZooKeeper – Home.” [Online]. Available: https://zookeeper.apache.org/ [Accessed: 26- Mar-2015].
 D. Borthakur, J. Gray, J. Sen Sarma, K. Muthukkaruppan, N. Spiegelberg, H. Kuang, K. Ranganathan, D. Molkov, A. Menon, S. Rash, R. Schmidt, and A. Aiyer, “Apache Hadoop Goes Realtime at Facebook,” in Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, 2011, pp. 1071–1080.
 B. Ghit, A. Iosup, and D. Epema, “Towards an Optimized Big Data Processing System,” in Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on, 2013, pp. 83–86.
 P. Agarwal, G. Shroff, and P. Malhotra, “Approximate Incremental Big-Data Harmonization,” in Big Data (BigData Congress), 2013 IEEE International Congress on, 2013, pp. 118–125.
 Y. Elshater, P. Martin, D. Rope, M. McRoberts, and C. Statchuk, “A Study of Data Locality in YARN,” 2015 IEEE Int. Congr. Big Data, pp. 174–181, 2015.
 A. H. B. James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, “Big data: The next frontier for innovation, competition, and productivity,” McKinsey Glob. Inst., no. June, p. 156, 2011.
 J. S. Marron, “Big Data in context and robustness against heterogeneity,” Econom. Stat., vol. 2, pp. 73–80, 2017.
 L. Kugler, “What Happens When Big Data Blunders?,” Commun. ACM, vol. 59, no. 6, pp. 15–16, 2016.
 S. Sagiroglu, R. Terzi, Y. Canbay, and I. Colak, “Big data issues in smart grid systems,” in 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), 2016, pp. 1007–1012.
 A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J. Inf. Manage., vol. 35, no. 2, pp. 137–144, 2015.
 Jameela Al-Jaroodi, Brandon Hollein, Nader Mohamed, “Applying software engineering processes for big data analytics applications development“, Computing and Communication Workshop and Conference (CCWC) 2017 IEEE 7th Annual, pp. 1-7,2017
Citation Count – 03
José Rafael Cortés León 1, Ricardo Francisco Martínez-González 2 Anilú Miranda Medinay 3 and Luis Alberto Peralta-Pelaez3
1,2Department of Electric-Electronic Engineering, Instituto Tecnologico de Veracruz (TecNM), Veracruz, Mexico
3Department of Chemistry-Biochemistry Engineering, Instituto Tecnologico deVeracruz (TecNM), Veracruz, Mexico
The present paper describes a novel Raspberry Pi and Arduino UNO architecture used as a meteorological station. One of the advantages of the proposed architecture is the huge quantity of sensors developed for its usage; practically one can find them for any application, and weather sensing is not an exception. The principle followed is to configure Raspberry as a collector for measures obtained from Arduino, transmitting occurs via USB; meanwhile, Raspberry broadcasts them via a web page. For such activity is possible thanks to Raspbian, a Linux-based operating system. It has a lot of libraries and resources available, among them Apache Web Server, that gives the possibility to host a web-page. On it, the user can observe temperature, humidity, solar radiance, and wind speed and direction. Information on the web-page is refreshed each five minute; however, measurements arrive at Raspberry every ten seconds. This low refreshment rate was determined because weather variables normally do not abruptly change. As an additional feature, system stores all information on the log file, this gives the possibility for future analysis and processing.
Raspberry Pi, Arduino UNO, Meteorological station, Novel architecture.
For More Details : http://aircconline.com/ijcsit/V9N5/9517ijcsit08.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 Manzano-Agugliaro, F., et al. Scientific production of renewable energies worldwide: an overview. Renewable and Sustainable Energy Reviews, 2013, vol. 18, p. 134-143.
 Hong-Yan, K. A. N. G. Design and realization of internet of things based on embedded system used in intelligent campus. IJACT: International Journal of Advancements in Computing Technology, 2011, vol. 3, no 11, p. 291-298
 Gheorghita, Stefan Valentin; Basten, Twan; Corporaal, Henk. Application scenarios in streamingoriented embedded-system design. IEEE Design & Test of Computers, 2008, vol. 25, no 6.
 Malinowski, Aleksander; Yu, Hao. Comparison of embedded system design for industrial applications. IEEE transactions on industrial informatics, 2011, vol. 7, no 2, p. 244-254.
 Langheinrich, Marc, et al. First steps towards an event-based infrastructure for smart things. In Ubiquitous Computing Workshop (PACT 2000). 2000. p. 34.
 Jin, Jiong, et al. An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal, 2014, vol. 1, no 2, p. 112-121.
 Macii, Alberto. Low-power embedded systems. Journal of Embedded Computing, 2005, vol. 1, no 3, p. 303-304.
 Reza, Sm Khaled; Tariq, Shah Ahsanuzzaman Md; Reza, Sm Mohsin. Microcontroller based automated water level sensing and controlling: design and implementation issue. En Proceedings of the World Congress on Engineering and Computer Science. 2010. p. 20-22.
 Vujovic, Vladimir; Maksimovic, Mirjana. Raspberry Pi as a Sensor Web node for home automation. Computers & Electrical Engineering, 2015, vol. 44, p. 153-171.
 Raspberry Pi: Raspberry pi. Raspberry Pi 1 HDMI 13 Secure Digital 34 Universal Serial Bus 56 Python (programming language), vol. 84, p. 1 (2013)
 Kasim, Mohammad; Khan, Firoz. Home Automation using Raspberry Pi-3.
 Junior, Luiz A., et al. A low-cost and simple arduino-based educational robotics kit. Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Robotics and Control (JSRC), December edition, 2013, vol. 3, no 12, p. 1-7.
 Pan, Tianhong; Zhu, Yi. Designing Embedded Systems with Arduino.
 Banzi, Massimo; Shiloh, Michael. Getting started with Arduino: the open source electronics prototyping platform. Maker Media, Inc., 2014
 Irmak, S., et al. Standardized ASCE Penman-Monteith: Impact of sum-of-hourly vs. 24-hour timestep computations at reference weather station sites. Transactions of the ASAE, 2005, vol. 48, no 3, p. 1063-1077.
 Chemisana, D.; Lamnatou, Chr. Photovoltaic-green roofs: An experimental evaluation of system performance. Applied Energy, 2014, vol. 119, p. 246-256.
 Cadenas, Erasmo; Rivera, Wilfrido. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renewable Energy, 2010, vol. 35, no 12, p. 2732-2738.
 Instituto Nacional de Estadística y Geografía, Conociendo Veracruz de Ignacio de la Llave (Trans. Meeting Veracruz de Ignacio de la Llave), 2013, p.10.
Citation Count – 3
P.K. Sharma1 and Gagandeep Kaur2
1P.G. Department of Mathematics, D.A.V. College, Jalandhar City, Punjab, India
2Research Scholar, IKG PT University, Jalandhar , Punjab
In this paper, we introduce the concept of residual quotient of intuitionistic fuzzy subsets of ring and module and then define the notion of residual quotient intuitionistic fuzzy submodules , residual quotient intuitionistic fuzzy ideals. We study many properties of residual quotient relating to union, intersection, sum of intuitionistic fuzzy sub modules (ideals). Using the concept of residual quotient, we investigate some important characterization of intuitionistic fuzzy annihilator of subsets of ring and module. We also study intuitionistic fuzzy prime sub modules with the help of intuitionistic fuzzy annihilators. Many related properties are defined and discussed.
Intuitionistic fuzzy (prime) sub module (ideal), residual quotient intuitionistic fuzzy sub modules (ideal), intuitionistic fuzzy annihilator, semi prime ring.
For More Details : http://aircconline.com/ijcsit/V9N4/9417ijcsit01.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 K. T. Atanassov,(1986) , Intuitionistic fuzzy sets, Fuzzy Sets and Systems, Vol. 20, No. 1, pp., 87- 96.
 K. T. Atanassov, (1999) ,Fuzzy sets, Theory and Applications, Studies in fuzziness and soft computing, 35, Physica-Verlag, Heidelberg.
 I. Bakhadach , S. Melliani, M. Oukessou and S.L. Chadli,(2016), Intuitionistic fuzzy ideal and intuitionistic fuzzy prime ideal in a ring, Notes on Intuitionistic Fuzzy Sets, Vol. 22, no. 2 pp 59-63.
 D.K. Basnet,(2011) ,Topic in intuitionistic fuzzy algebra, Lambert Academic Publishing, ISBN : 978- 3-8443-9147-3.
 R. Biswas, (1989) , Intuitionistic fuzzy subgroups, Math. Forum, Vol. 10, pp 37–46.
 E. Bland Paul, (2011), Rings and their modules, published by the Deutsche National Bibliothek, Germany ISBN: 978-3-11-025022-0.
 B. Davvaz, W.A. Dudek, Y.B. Jun,(2006), Intuitionistic fuzzy Hv-submodules, Information Sciences, Vol. 176, pp 285-300.
 K. Hur, H.K. Kang and H.K. Song, (2003), Intuitionistic fuzzy subgroup and subrings, Honam Math J. Vol. 25, No. 1, pp 19-41.
 P. Isaac, P.P. John, (2011), On intuitionistic fuzzy submodules of a module, Int. J. of Mathematical Sciences and Applications, Vol. 1, No. 3, pp 1447-1454.
 D. S. Malik and J. N. Mordeson, (1998), Fuzzy Commutative Algebra, World Scientific Publishing Co-Pvt. Ltd.
 K. Meena and K. V. Thomas, (2011), Intuitionistic L-Fuzzy Subrings, International Mathematical Forum, Vol. 6, No. 52, pp 2561 – 2572.
 P.K. Sharma, (2011), (α, β)-Cut of Intuitionistic fuzzy modules- II, International Journal of Mathematical Sciences and Applications, Vol. 3 , No. 1, pp. 11-17.
 P. K. Sharma and Gagandeep Kaur, (2016) , Intuitionistic fuzzy superfluous modules, Notes on Intuitionistic Fuzzy Sets, Vol. 22, No. 3, pp 34-46.
 S. Rahman, H.K. Sailia, (2012) , Some aspects of Atanassov’s intuitionistic fuzzy submodules, Int. J. Pure and Appl. Mathematics, Vol. 77, No. 3, pp 369-383.
 H.K. Saikia and M.C. Kalita, (2009) , On Annihilator of fuzzy subsets of modules, Internal Journal of Algebra Vol. 3, No. 10, pp. 483- 488.
 L. A. Zadeh, (1965), Fuzzy sets , Information and Control, Vol. 8, pp 338–353.
Citation Count – 3
A Meliorated Kashida-Based Approach For Arabic Text Steganography
Ala’a M. Alhusban and Jehad Q. Odeh Alnihoud
Computer Science Dept, Al al-Bayt University, Mafraq, Jordan
Steganography is an art of hiding a secret message within some cover media such as: images, audios, videos, and texts. Many algorithms have been proposed for Arabic text steganography exploiting some features of Arabic language such as: diacritics, extension character (kashida), and pointed letters. In this research we propose a new method to enhance a kashida-based methods for text steganography. In which each existing kashida can hide two bits instead of only one bit. In addition, security measures is increased through embedding the secret bits into the cover text by two different ways since the cover text is divided into two blocks; each one of them is being treated in different way. Moreover, the original kashida in the cover text is ignored by the extractor. A system is designed to achieve the embedding as well as the extracting with high degree of security through authentication operation used in its interface. The proposed approach is tested and compared with the most related kashida-based approaches in terms of capacity and the results are promising. Furthermore, it overcomes the limitations of other approaches, maintain a reasonable increases in the files size, and enhances security measures.
Arabic text, Steganography, Kashida, Pointed characters, Zero-width character.
For More Details : http://aircconline.com/ijcsit/V9N2/9217ijcsit09.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 G. Kipper, (2003) Invistigator’s Guide to Steganography, Auerbach Publications, ISBN 9780849324338, October 27.
 Gutub, A. and M. M. Fattani, (2007) “A Novel Arabic Text Steganography Method Using Letter Points and Extensions“, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol: 1, No: 3.
 M. S. Shahreza and M. H. Shahreza, (2008) “An Improved Version of Persian/Arabic Text Steganography Using “La” Word”, Proceedings of IEEE 2008 6th National Conference on Telecommunication Technologies and IEEE 2008 2nd Malaysian conference on Photonics, 26-27 August, Putrajaya, Malaysia, pp: 372-376.
 A. Al-Nazer, A. Gutub, (2009) “Exploit Kashida Adding to Arabic E-Text for High Capacity Steganography”, Proceedings of Third IEEE International Conference on Network and System Security, Pages: 447-451. DOI: 10.1109/NSS.2009.21.
 F. Al-Haidari, A. Gutub, K. Al-Kahsah, and J. Hamodi, (2009) “Improving Security and Capacity for Arabic Text Steganography Using ‘Kashida’ Extensions”, Proceedings of IEEE/ACS International Conference on Computer Systems and Applications.pp: 396-399.DOI: 10.1109/AICCSA.2009.5069355
 A. Gutub, F. Al-Haidari, K. Al-Kahsah, and J. Hamodi, (2010) “E-Text Watermarking: Utilizing ‘Kashida’ Extensions in Arabic Language Electronic Writing“, Journal of Emerging Technologies in Web Intelligence, Vol. 2, No. 1, Pages: 48-55. DOI: 10.4304/jetwi.2.1.48-55.
 A. Gutub, W. Al-Alwani, and A. Bin Mahfoodh, (2010) “Improved Method of Arabic Text Steganography Using the Extension ‘Kashida’ Character“, Bahria University Journal of Information & Communication Technology Vol. 3, Issue 1, pp: 68-72.
 A. Odeh and K. Elleithy, (2012) “Steganography in Arabic Text Using Zero Width and Kashidha Letters“, International Journal of Computer Science & Information Technology (IJCSIT), Vol. 4, Pages: 1-11. DOI: 10.5121/ijcsit.2012.4301.
 Y. M. Alginahi, M. N. Kabir and O. Tayan, (2013) “An Enhanced Kashida-Based Watermarking Approach for Increased Protection in Arabic Text-Documents“, Proceedings of International Conference on Electronics, Computer and Computation (ICECCO). DOI: 10.1109/ICECCO.2013.6718288
 A. Odeh, K. Elleithy and M. Faezipour, (2013) “Steganography in Arabic Text Using Kashida Variation Algorithm (KVA)”, Proceedings of the 2013 IEEE Systems, Applications and Technology Conference (LISAT). DOI: 10.1109/LISAT.2013.6578239.
 Mersal, S., Alhazmi S., R. Alamoudi and N. Almuzaini, (2014) “Arabic Text Steganography in Smartphone“, International Journal of Computer and Information Technology (ISSN: 2279 – 0764), Vol. 03, No. 02, pp. 441-445. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017 112
Y. Alginahi, M. Kabir and O. Tayan, (2014) “An Enhanced Kashida-Based Watermarking Approach for Increased Protection in Arabic Text- Documents Based on Frequency Recurrence of Characters“, International Journal of Computer and Electrical Engineering, Vol.6, No. 5. DOI: 10.177706/ijcee.2014.v6.857.
 B. Osman, R. Din and M. R. Idrus, (2015) “Capacity Performance of Steganography Method in Text Based Domain“, ARPN Journal of Engineering and Applied Sciences, Vol. 10, No.3.http://repo.uum.edu.my/id/eprint/14832.
 R. Saluja, K. Kanwal and S. Dahyia, (2014) “Review on Steganography for Hiding Data”, International Journal of Computer Science and Mobile Computing, Vol.3 No. 4, pp: 225-229. ISSN 2320–088X.
 R. Jabri and B. Ibrahim, (2016) “Capacity Improved Arabic Text Steganography Technique Utilizing ‘Kashida’ with Whitespaces“, Proceedings of the Third International Conference on Mathematical Sciences and Computer Engineering (ICMSCE2016), pp: 38-44, Langkawi, Malaysia.
Citation Count – 3
1Department of Computer Applications, JSS Academy of Technical Education, Noida, India
An increasing demand of secure data transmission over internet leads to the challenge of implementing a consistent cryptosystem. In 2004, USA navy published the patent which highlights the importance of fractal as an encryption/decryption key in a cryptosystem . Fractal possess butterfly effect i.e. sensitivity to initial condition, due to which small change in input produces a major change in output. This paper summarizes the various recent image encryption techniques in which fractal key is used to encrypt/decrypt followed by substitution, scrambling and diffusion techniques to provide strong cryptosystem. The algorithms covered both private key encryption as well as public key encryption technique in the paper. The analysed algorithms include a set of fractal function such as Mandelbrot set, Julia set, Hilbert curve, 3D fractal, multi-fractal, IFS and chaotic function to generate a complex key used in the encryption process. Corresponding performance of each algorithm is analysed by PSNR test, key space, sensitivity analysis and correlation coefficient value between the adjacent pixels of both images (Original image and encrypted image) which shows significant improvement in performance over the traditional encryption methods.
Image Encryption, fractal, chaotic function, Scrambling, NIST test suite
For More Details : http://aircconline.com/ijcsit/V9N2/9217ijcsit05.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 Huntress G. B., 2004“Encryption using Fractal Key”, United States Patent 6782101.
 Khan M. & Shah T., 2014“A Literature Review on Image Encryption Techniques”, © 3D Research Centre Kwangwoon University and Springer-Verlag Berlin Heidelberg, 5(4), DOI 10.1007/s13319- 014-0029-0, Page 1.
 Abed F. S., 2011 “A New Approach to Encoding and Hiding Information in an Image”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, ISSN (Online): 1694-0814.
 Sun Y, Chen L, Xu R, Kong R, 2014“An Image Encryption Algorithm Utilizing Julia Sets and Hilbert Curves”. PLoS ONE, 9(1): e84655. doi:10.1371/journal.pone.0084655
. Zhang Q., Zhou S. and Wei X.,2011 “An Efficient Approach for DNA Fractal-based Image Encryption”, Applied Mathematics & Information Sciences, 5(3), pp 445-459.
 SomarajS. and Hussain M. A., 2015 “Performance and Security Analysis for Image Encryption using Key Image”, Indian Journal of Science and Technology, Vol 8(35), DOI: 10.17485/ijst/2015/v8i35/73141.
 Stallings W., 1999 “Cryptography and Network Security: Principles and Practice”. Upper Saddle River, N.J: Prentice Hall, ISBN:0136097049 9780136097044.
 Rivest R. L., Shamir A. and Adleman L., 1978“A method for obtaining digital signatures and public key cryptosystems”, Communication of the ACM, 21: pp 120-126.
 Boneh D., 1999 “Twenty years of attacks on the RSA cryptosystem”, American Mathematical Society (AMS), Vol. 46, No. 2, pp. 203-213.
 Diffie W., Hellman M., 1976 “New Directions in Cryptography”, IEEE Transactions on Information Theory, 22(6): 644-654 doi-10.1109/TIT, 1055638.
 Pickover C. “Computers, Pattern, Chaos, and Beauty”, St. Martin’s Press, NewYork, 1990.
 Julia G., 1918 “Mémoire sur l’itération des fonctions rationnelles.” Journal de MathématiquesPures et Appliquées 1: 47-246 (Translated in English by Alessandro Rosa in 2001).
 Mandelbrot B. B. “The Fractal Geometry of Nature”, W. H. Freeman, New York, 1983.
 Devaney R. L., 1992 “A First Course in Chaotic Dynamical Systems: Theory and Experiment”, Addison-Wesley, MR1202237 Zbl 0768.58001.
 Barnsley M. “Fractals everywhere”, Academic Press Professional, Inc., San Diego, CA, 1988.
 Crownover, R. M., “Introduction to Fractals and Chaos”, Jones &Barlett Publishers, 1995.
 Jonathan F. “An Introduction to Julia sets”,2009.
 Zakeri, S., 2006 “On biaccessible points of the Mandelbrot set”. Proceedings of the American Mathematical Society, 134(8), pp 2239-2250.
 Negi D., Negi A., Agarwal S., 2016 “The Complex Key Cryptosystem”, International Journal of Applied Engineering Research, ISSN 0973-4562 Volume 11, Number 1, pp 681-684.
 Fractal Cryptology , New Mexico High School, Supercomputing Challenge Final Report April 2, 2003 , Team Members: Brandi Howell Anna Reese Michael Basile Team Sponsor: Paula Avery Project Mentor: Garth Reese.
 Motýl I., Jašek R., Vařacha P., 2012 “Analysis of the Fractal Structures for the Information Encrypting Process”, International Journal of Computers, Issue 4, Volume 6, pp 224-231.
 Abd-El-Hafiz1 S. K., Radwan A. G.Haleem S. H. A., Barakat M. L., 2014 “A fractal-based image encryption system”IET Image Processing, Vol. 8, Issue 12, pp. 742–752 doi:10.1049/ietipr.2013.0570
 Sun YY, Xu R., Chen L., Hu X., 2014 “Image Compression and Encryption Scheme Using Fractal Dictionary and Julia Set”, IET Image Processing, Vol. 9, Issue. 3, pp. 173–183 doi:10.1049/ietipr.2014.0224
 Alia, M. A. and Samsudin A. B., 2007 “New Key Exchange Protocol Based on Mandelbrot and Julia Fractal Sets”, International Journal of Computer Science and Network Security, VOL.7 No.2, pp 302- 307.
 Alia, M. A. and Samsudin A. B., 2007“A new public-key cryptosystem based on mandelbrot and Julia fractal sets”. Asian Journal of Information Technology, 6(5): pp 567-575.
 Rozouvan V., 2009 “Modulo image encryption with fractal keys”, Optics and Lasers in Engineering, 47(1), pp.1-6.
 Nadia M. G. AL-Saidi and Said M. R. M., 2009“A New Approach in Cryptographic Systems Using Fractal Image Coding”, Journal of Mathematics and Statistics, 5 (3):ISSN 1549-3644, pp183-189.
 Nadia M. G. AL-Saidi and Said M. R. M., 2010 “A New Public Key Cryptosystem Based on IFS”, International Journal of Cryptology Research, 2(1): pp 1-13.
 Nadia M. G. AL-Saidi and Said M. R. M., et al., 2011“Efficiency Analysis for Public Key Systems Based on Fractal Functions”, Journal of Computer Science, 7 (4): pp 526-532, ISSN 1549-3636.
 Sun YY, Kong RQ, Wang XY,et al.,2010 “An Image Encryption Algorithm Utilizing Mandelbrot Set”. International Workshop on Chaos-Fractal Theories and Applications, pp170–173.
 Shaw J., Saha O., Chaudhuri A.,2012 “An Approach for Secured Transmission of Data using Fractal based Chaos” IJCA Proceedings on National Conference on Communication Technologies & its impact on Next Generation Computing, CTNGC(4): pp 13-17.
 Hala B. Wahab A., Sarab S. A., 2013 “Modify Symmetric Block Cipher Algorithm Using Generated Digital 3D Fractal Image”, Iraqi Journal of Science, Vol 54, No.4, pp: 955-964.
 Negi A., Agarwal S., 2014 “A Key Agreement Protocol Based on Superior Fractal Sets”, Journal of Mathematical and Computational Science, Vol 4, No 2, pp 471-478, ISSN: 1927-5307.
 Mann W. R., 1953“Mean value methods in iterations”, Proc. Amer. Math. Soc., 4, pp 506-510.
 Sattari S., Akkasi A., Lari R. A., et al., 2015“Cryptography in social networks using wavelet transform, fractals and chaotic functions”, International Research Journal of Applied and Basic Sciences, Science Explorer Publications, ISSN 2251-838X / Vol, 9 (9): 1627-1635.
 Feasibility Study on Random Number Generators for Symmetric Key Cryptography, Chapter 6, pp 156-204.
 Ali M. Meligy, HossamDiab, Marwa S. El-Danaf,2016“Chaos Encryption Algorithm using Key Generation from Biometric Images”, International Journal of Computer Applications (0975 – 8887) Volume 149 – No.11.
 Wang W., Tan H., Pang Y., Li Z., Ran P. and Wu J.,2016 “A Novel Encryption Algorithm Based on DWT and Multichaos Mapping”, Hindawi Publishing Corporation Journal of Sensors Volume Article ID 2646205, 7 pages,http://dx.doi.org/10.1155/2016/2646205.
 Kashanian H., Davoudi M. and Khorramfar H., 2016 “Image Encryption using chaos functions and fractal key”, International Journal of Advanced Biotechnology and Research (IJBR) ISSN 0976-2612, Online ISSN 2278–599X, Vol-7, Special Issue-Number4, pp1075-1082.
Citation Count – 2
Ryan Alturki and Valerie Gay
School of Electrical and Data Engineering, University of Technology Sydney, Sydney City, Australia
Obesity is a major health problem around the world. Saudi Arabia is a nation where obesity is increasing at an alarming rate. Mobile apps could help obese individuals but they need to be usable and personalized to be adopted by those users. This paper aims at testing the usability of a fitness mobile app” Aded Surat”, an app in Arabic language. This paper presents an extensive literature review on the attributes that improve the usability of fitness apps. Then, it explains our methodology and our set up of a trial to test the usability of Aded Surat app that is popular in Saudi Arabia. The usability attributes tested are effectiveness, efficiency, satisfaction, memorability, errors, learnability and cognitive load. The trial is done in collaboration with participants from the Armed Forces Hospitals – Taif Region in Saudi Arabia. The results highlight that the app failed to meet with the usability attributes.
Usability, Mobile Application, Obesity, User Experience
For More Details : http://aircconline.com/ijcsit/V9N5/9517ijcsit09.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 A. P. SIMOPOULOS and T. B. VAN ITALLIE, “Body weight, health, and longevity,” Annals of internal medicine, vol. 100, pp. 285-295, 1984.
 W. H. Organization. (2016, 2 October). Obesity and overweight. Available: http://www.who.int/mediacentre/factsheets/fs311/en/
 O. R. Center. (2016, 10 October). Obesity in Saudi Arabia. Available:
 A. Afshin, M. H. Forouzanfar, M. B. Reitsma, P. Sur, K. Estep, A. Lee, et al., “Health Effects of Overweight and Obesity in 195 Countries over 25 Years,” The New England journal of medicine, vol. 377, pp. 13-27, 2017.
 K. Singer and C. N. Lumeng, “The initiation of metabolic inflammation in childhood obesity,” The Journal of clinical investigation, vol. 127, pp. 65-73, 2017.
 K. R. Fontaine, D. T. Redden, C. Wang, A. O. Westfall, and D. B. Allison, “Years of life lost due to obesity,” Jama, vol. 289, pp. 187-193, 2003.
 J. Stevens, J. Cai, E. R. Pamuk, D. F. Williamson, M. J. Thun, and J. L. Wood, “The effect of age on the association between body-mass index and mortality,” New England Journal of Medicine, vol. 338, pp. 1-7, 1998.
 E. E. Calle, M. J. Thun, J. M. Petrelli, C. Rodriguez, and C. W. Heath Jr, “Body-mass index and mortality in a prospective cohort of US adults,” New England Journal of Medicine, vol. 341, pp. 1097-1105, 1999.
 C. Summerbell, E. Waters, L. Edmunds, S. Kelly, T. Brown, and K. Campbell, “Interventions for preventing obesity in children (Review),” Cochrane library, vol. 3, pp. 1-71, 2005.
 W. Saris, S. Blair, M. Van Baak, S. Eaton, P. Davies, L. Di Pietro, et al., “How much physical activity is enough to prevent unhealthy weight gain? Outcome of the IASO 1st Stock Conference and consensus statement,” Obesity reviews, vol. 4, pp. 101-114, 2003.
 O. Bar-Or, “Juvenile obesity, physical activity, and lifestyle changes: Cornerstones for prevention and management,” The physician and sportsmedicine, vol. 28, pp. 51-58, 2000.
 J. L. Anderson, E. M. Antman, S. R. Bailey, E. R. Bates, J. C. Blankenship, D. E. Casey Jr, et al., “AHA Scientific Statement,” Circulation, vol. 120, pp. 2271-2306, 2009.
 J. O. Hill and H. R. Wyatt, “Role of physical activity in preventing and treating obesity,” Journal of Applied Physiology, vol. 99, pp. 765-770, 2005.
 J. O. Hill and J. C. Peters, “Environmental contributions to the obesity epidemic,” Science, vol. 280, pp. 1371-1374, 1998.
 I. Contento, G. I. Balch, Y. L. Bronner, L. Lytle, S. Maloney, C. Olson, et al., “The effectiveness of nutrition education and implications for nutrition education policy, programs, and research: a review of research,” Journal of nutrition education (USA), 1995.
 G. D. Foster, A. P. Makris, and B. A. Bailer, “Behavioral treatment of obesity,” The American journal of clinical nutrition, vol. 82, pp. 230S-235S, 2005.
 T. A. Wadden and A. J. Stunkard, Handbook of obesity treatment: Guilford Press, 2002.
 K. D. Brownell, LEARN program for weight management 2000: American Health, 2000.
 J. Yang, “Toward physical activity diary: motion recognition using simple acceleration features with mobile phones,” in Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics, 2009, pp. 1-10.
 T. Denning, A. Andrew, R. Chaudhri, C. Hartung, J. Lester, G. Borriello, et al., “BALANCE: towards a usable pervasive wellness application with accurate activity inference,” in Proceedings of the 10th workshop on Mobile Computing Systems and Applications, 2009, p. 5.
 S. M. Arteaga, M. Kudeki, A. Woodworth, and S. Kurniawan, “Mobile system to motivate teenagers’ physical activity,” in Proceedings of the 9th International Conference on Interaction Design and Children, 2010, pp. 1-10.
 D. E. Conroy, C.-H. Yang, and J. P. Maher, “Behavior change techniques in top-ranked mobile apps for physical activity,” American journal of preventive medicine, vol. 46, pp. 649-652, 2014.
 F. Raben and E. Snip, “The MENAP region is developing, but can it keep its promise?,” Research World, vol. 2014, pp. 6-11, 2014.
 Statista. (2017, 8 March). Number of smartphone users in Saudi Arabia from 2014 to 2021 (in millions)*. Available: https://www.statista.com/statistics/494616/smartphone-users-in-saudi-arabia/
 D. LLP. (2012, 2 February). So Many Apps — So Little To Download. Available: http://www.mondaq.com/x/192692/IT+internet/So+Many+Apps+So+Little+To+Dow%20nload
 S. Dredge, “Most branded apps are a flop says Deloitte. But why,” ed, 2011.
 M. Bhuiyan, A. Zaman, and M. H. Miraz, “Usability Evaluation of a Mobile Application in Extraordinary Environment for Extraordinary People,” arXiv preprint arXiv:1708.04653, 2017.
 R. Youens. (2011, 2 February). 7 Habits of Highly Effective Apps. Available: https://gigaom.com/2011/07/16/7-habits-of-highly-effective-apps/
 I. Nascimento, W. Silva, A. Lopes, L. Rivero, B. Gadelha, E. Oliveira, et al., “An Empirical Study to Evaluate the Feasibility of a UX and Usability Inspection Technique for Mobile Applications,” in 28th International Conference on Software Engineering & Knowledge Engineering, California, USA, 2016.
 H. Hoehle, R. Aljafari, and V. Venkatesh, “Leveraging Microsoft׳ s mobile usability guidelines: Conceptualizing and developing scales for mobile application usability,” International Journal of Human-Computer Studies, vol. 89, pp. 35-53, 2016.
 S. Pagoto, K. Schneider, M. Jojic, M. DeBiasse, and D. Mann, “Evidence-based strategies in weightloss mobile apps,” American journal of preventive medicine, vol. 45, pp. 576-582, 2013.
 A. C. King, E. B. Hekler, L. A. Grieco, S. J. Winter, J. L. Sheats, M. P. Buman, et al., “Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults,” PloS one, vol. 8, p. e62613, 2013.
 A. A. Alnasser, R. E. Amalraj, A. Sathiaseelan, A. S. Al-Khalifa, and D. Marais, “Do Arabic weightloss apps adhere to evidence-informed practices?,” Translational behavioral medicine, vol. 6, pp. 396- 402, 2016.
 E. R. Breton, B. F. Fuemmeler, and L. C. Abroms, “Weight loss—there is an app for that! But does it adhere to evidence-informed practices?,” Translational behavioral medicine, vol. 1, pp. 523-529, 2011.
 A. M. Content. (2016, 5 January). Adad Alsorat from Hmiate Available:
 Hmiate. (2016, 5 January). Adad Alsorat from Hmiate. Available: http://m-diet.com/Home/Tracker
 G. Play. (2016, 5 January). Adad Alsorat from Hmiate Available: https://play.google.com/store/apps/details?id=com.mdiet.tracker&hl=ar
 R. Baharuddin, D. Singh, and R. Razali, “Usability dimensions for mobile applications—A review,” Res. J. Appl. Sci. Eng. Technol, vol. 5, pp. 2225-2231, 2013.
 W. ISO, “9241-11. Ergonomic requirements for office work with visual display terminals (VDTs),” The international organization for standardization, vol. 45, 1998.
 S. Ben and C. Plaisant, “Designing the user interface 4 th edition,” ed: Pearson Addison Wesley, USA, 2005.
 E. Folmer and J. Bosch, “Architecting for usability: a survey,” Journal of systems and software, vol. 70, pp. 61-78, 2004.
 D. Saffer, “Designing for Interaction: Creating Smart Applications and Clever Devices,” New Riders Press,< http://www. designingforinteraction.com, vol. 2, p. 2.1, 2007.
 W. Albert and T. Tullis, Measuring the user experience: collecting, analyzing, and presenting usability metrics: Newnes, 2013.
 K. D. Eason, “Towards the experimental study of usability,” Behaviour & Information Technology, vol. 3, pp. 133-143, 1984.
 A. Dix, S. Cairncross, G. Cockton, R. Beale, R. St Amant, and M. Hause, “Human-Computer Interaction,” 1993.
 J. Nielsen, Usability engineering: Elsevier, 1994.
 A. Abran, A. Khelifi, W. Suryn, and A. Seffah, “Usability meanings and interpretations in ISO standards,” Software Quality Journal, vol. 11, pp. 325-338, 2003.
 D. Norman, “The Design of Everyday Things Basic Books New York,” 2002.
 S. Ben and C. Plaisant, “Designing the user interface 4 th edition,” ed: Pearson Addison Wesley, USA, 2005.
 C. Yeh, “The principles of interaction design in the post-digital age,” Taipei: ARTIST-MAGAZINE, 2010.
 C.-M. Wang and C.-H. Huang, “A study of usability principles and interface design for mobile ebooks,” Ergonomics, vol. 58, pp. 1253-1265, 2015.
 R. Alturki and V. Gay, “USABILITY TESTING OF FITNESS MOBILE APPLICATION: METHODOLOGY AND QUANTITATIVE RESULTS,” presented at the 7th International Conference on Computer Science, Engineering & Applications (ICCSEA 2017), Copenhagen, Denmark, 2017.
 J. Mifsud. (2015, 3 November). Usability Metrics – A Guide To Quantify The Usability Of Any System. Available: http://usabilitygeek.com/usability-metrics-a-guide-to-quantify-system-usability/
 J. Sauro. (2010, 9 December). IF YOU COULD ONLY ASK ONE QUESTION, USE THIS ONE. Available: https://measuringu.com/single-question/
 A. Garcia. (2013, 18 October). UX Research | Standardized Usability Questionnaire. Available: https://chaione.com/blog/ux-research-standardizing-usability-questionnaires/
 J. P. Tracy and M. J. Albers, “Measuring cognitive load to test the usability of web sites,” in Annual Conference-society for technical communication, 2006, p. 256.
 S. G. Hart and L. E. Staveland, “Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research,” Advances in psychology, vol. 52, pp. 139-183, 1988.
 J. Sauro. (2013, 1 December). HOW TO MEASURE LEARNABILITY. Available:
 J. Sauro. (2011, 4 December ). 10 ESSENTIAL USABILITY METRICS. Available:
 R. Harrison, D. Flood, and D. Duce, “Usability of mobile applications: literature review and rationale for a new usability model,” Journal of Interaction Science, vol. 1, pp. 1-16, 2013.
 C. Walsh. (2015, 6 November). A Guide To Simple And Painless Mobile User Testing. Available: https://www.smashingmagazine.com/2015/12/simple-and-painless-mobile-user-testing/
 J. Mifsud. (2016, 3 November). Usability Testing Of Mobile Applications: A Step-By-Step Guide. Available: http://usabilitygeek.com/usability-testing-mobile-applications/
Citation Count – 2
Department of Software Engineering, Yaşar University, Izmir, Turkey
Cinnamons are a new computation model intended to form a theoretical foundation for Control Network Programming (CNP). CNP has established itself as a programming approach combining declarative and imperative features. It supports powerful tools for control of the computation process; in particular, these tools allow easy, intuitive, visual development of heuristic, nondeterministic, or randomized solutions. The paper providesrigorous definitions of the syntax and semantics of the new model of computation, at the same time trying to keep the intuition behind clear. The purposely simplified theoretical model is then compared to both WHILE-programs (thus demonstrating its Turing completeness), and the “real” CNP. Finally, future research possibilities are mentioned that would eventually extend the cinnamon programming and its theoretical foundation into the directions of nondeterminism, randomness and fuzziness.
Control network programming, CNP, Programming languages, Programming paradigms, Computation models, While programs, Theoretical computer science, Recursive automata, Non-determinism, Semantics.
For More Details : http://aircconline.com/ijcsit/V9N5/9517ijcsit10.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 K. Kratchanov (2017), Cinnamons: A Computational Model Underlying Control Network Programming. In: Computer Science and Information Technology, v. 73, 7th Intl Conf. on Computer Science, Engineering & Applications (ICCSEA 2017), Copenhagen, Denmark (N. Meghanathan & D. Wyld – eds.), AIRCC Publ. Co., pp. 1-20. (http://airccj.org/CSCP/vol7/csit77301.pdf)
 K. Kratchanov, E.Golemanova and T. Golemanov (2008),“Control Network Programming Illustrated: Solving Problems With Inherent Graph-Like Structure”, In: Proc. 7th IEEE/ACIS Int. Conf. on Computer and Information Science (ICIS 2008), May 2008, Portland, Oregon, USA, 453-459.
 K. Kratchanov, E. Golemanova and T. Golemanov (2009),“Control Network Programs and Their Execution”, In: Proc. 8thWSEAS Int. Conf. on AI, Knowledge Engineering & Data Bases (AIKED ’09), Feb 2009, Cambridge, UK, 417-422.
 K. Kratchanov, E. Golemanova, T. Golemanov and Y. Gökçen (2012),“Implementing Search Strategies in Winspider II: Declarative, Procedural, and Hybrid Approaches”, In: I. Stanev and K. Grigorova (eds.): Knowledge-Based Automated Software Engineering, Cambridge Scholars Publ., 115-135.
 E. Golemanova (2013),“Declarative Implementations of Search Strategies for Solving CSPs in Control Network Programming”, WSEAS Transactions on Computers, 12 (4), 174-183.
 K. Kratchanov, T. Golemanov, B. Yüksel and E. Golemanova (2014),“Control network programming development environments”,WSEAS Transactions on Computers, 13, 645-659.
 T. Golemanov (2012), “SpiderSNP: An Integrated Environment for Visual Control Network Programming”, Annals of Ruse University, 51, ser. 3.2, 123-127 (in Bulgarian).
 T. Golemanov (2014), Development and Study of an Integrated Development Environment for Control Network Programming, Ph.D. Dissertation, Ruse Univ.
 K. Kratchanov, B. Yüksel, T. Golemanov, and E. Golemanova (2014), Learning Control Network Programming withthe Bouquet Cloud Compiler. In: Recent Advances in Educational Technologies and Education, Proc. 2014 Intl. Conf. on Educational Technologies and Education (ETE 2014), Interlaken, Switzerland, February 22-24, 2014, 29-36. Also: http://www.inase.org/library/2014/interlaken/bypaper/EDU/EDU-02.pdf
 K.Kratchanov, T.Golemanov and E.Golemanova (2009). “Control Network Programming: Static Search Control with System Options”, In: Proc. 8thWSEAS Int. Conf. on AI, Knowledge Engineering & Data Bases (AIKED ’09), Feb 2009, Cambridge, UK, 423-428.
 K.Kratchanov, T.GolemanovE.Golemanova and T.Ercan (2010),“Control Network Programming with SPIDER: Dynamic Search Control”, In: Knowledge-Based and Intelligent Information and Engineering Systems, Proc. 14thIntl. Conf. (KES 2010), Cardiff, UK, Sep 2010, Part II, Lect. Notes in Computer Science (Lect. Notes in Artificial Intelligence), v.6277, Springer, 253-262.
 N. Jones (1997), Computability and Complexity from a Programming Perspective, MIT Press.
 A. Kfoury, R. Moll and M. Arbib (1982, reprints 2011, 2013), A Programming Approach to Computability, Springer.
 M. Fitting (1987), Computability Theory, Semantics, and Logic Programming, Oxford Univ. Press.
 C. Moore and S. Martens (2011), The Nature of Computation, Oxford Univ. Press.
 K. Slonneger and B. Kurtz (1995), Formal Syntax and Semantics of Programming Languages: A Laboratory Based Approach, Addison-Wesley.
 O. Goldreich (2010), P, NP, and NP-Completeness: The Basics of Computational Complexity, Cambridge Univ. Press.
 D. Kozen (2006), Theory of Computation, Springer.
 W. Woods (1970), “Transition Network Grammars of Natural Language Analysis”, Comm. Of the ACM, 13, 591-606.
 E. Popov and G. Firdman (1976), Algorithmic Foundations of Intelligent Robots and Artificial Intelligence, Nauka (in Russian).
 A. Barr and E. Feigenbaum (eds.) (1981), The Handbook of Artificial Intelligence, v. 1, Pitman.
 K. Kratchanov (1985), On the Foundations of Rule-Based Systems, Dpt. Comp. Sci. Techn. Report CSTR 34/85, Brunel Univ., Uxbridge, UK.
 K. Gough (1988), Syntax Analysis and Software Tools, Addison-Wesley.
 R. Alur, M. Benedikt, K. Etessami, P. Godefroid, T. Reps, and M. Yannakakis (2005), “Analysis of recursive state machines”,ACM Trans. on Programming Languages and Systems, 27(4):786–818.
 I. Tellier (2006), “Learning Recursive Automata from Positive Examples”, RSTI – RIA – 20(2006) New Methods in Machine Learning, 775-804.
 S. Chaudhuri (2008), “Subcubic Algorithms for Recursive State Machines”,https://www.cs.rice.edu/~sc40/pubs/popl08.pdf.
 S. LaValle (2009), “Recursive Automata”, https://courses.engr.illinois.edu/cs373/fa2009/recaut.pdf.
 K.Kratchanov, E.Golemanova, T.Golemanov and T,Ercan (2010), “Non-Procedural Implementation of Local Heuristic Search in Control Network Programming”, In: Knowledge-Based and Intelligent Information and Engineering Systems, Proc. 14thIntl. Conf. (KES 2010), Cardiff, UK, Sep 2010, PartII, Lect. Notes in Computer Science (Lect. Notes in Artificial Intelligence), v.6277, Springer, 263- 272.
 K. Kratchanov, E. Golemanova, T. Golemanov and B. Külahçıoğlu (2012), “Using Control Network Programming in Teaching Nondeterminism”, In: Proc. 13thInt. Conf. on Computer Systems and Technologies (CompSysTech’12), Ruse, (B. Rachev, A. Smrikarov – eds.), ACM Press, New York, 391-398.
 K. Kratchanov, E. Golemanova, T. Golemanov and B. Külahçıoğlu (2012),“Using Control Network Programming in Teaching Randomization”, In: Proc. Int. Conf. Electronics, Information and Communication Engineering, Macau (EICE 2012), ASME, 67-71.
 E. Dijkstra (1975),“Guarded Commands, Nondeterminacy and Formal Derivations of Programs”, Comm. of the ACM, 18, 453-457. Also: E. Dijkstra. “Guarded Commands. Non-Determinacy and a Calculus for the Derivation of Programs” (EWD418), 1974
 G. Mascari and M. Zilli (1985),“While Programs with Nondeterministic Assignments and the Logic ALNA”,Theoretical Computer Science, 40, 211-235.
 J. van Leeuwen (Ed.) (1990, 1992), Handbook of Theoretical Computer Science, v. B: Formal Models and Semantics. Elsevier and MIT Press.
 K. Apt, F, de Boer, E. Olderog (2010), Verification of Sequential and Concurrent Programs, 3rd ed., Springer.
 K. Mamouras (2015),“Synthesis of Strategies and the Hoare Logic of Angelic Nondeterminism”, In: Foundations of Software Science and Computation Structures. 18thInt. Conf. FOSSACS 2015 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2015, London, 11-18 Apr 2015, Proc. LNCS 9034, Springer 2015 (A. Pitts – ed.), 25-40.
 R. Motwani, P. Raghavan. Randomized Algorithms. Cambridge Univ. Press, 1995.
 S. Arora, B. Barak. Computational Complexity: A Modern Approach, Cambridge Uni. Press, 2009.
 D. Antonova, D. Kunkle. Theory of Randomized Computation. 2005. http://www.ccs.neu.edu/home/kunkle/papers/techreports/randAlgo.pdf.
 K. Kratchanov (1985), Towards the Fundamentals of Fuzzy Rule-Based Systems, CSTR 35/85, Dept. Comp. Sci., Brunel Univ., Uxbridge, UK
Citation Count – 2
Maher Alharby1,2 and Aad van Moorsel1
1School of Computing Science, Newcastle University, Newcastle, UK
2College of Computer Science and Engineering, Taibah University, Medina, KSA
An appealing feature of blockchain technology is smart contracts. A smart contract is executable code that runs on top of the blockchain to facilitate, execute and enforce an agreement between untrusted parties without the involvement of a trusted third party. In this paper, we conduct a systematic mapping study to collect all research that is relevant to smart contracts from a technical perspective. The aim of doing so is to identify current research topics and open challenges for future studies in smart contract research. We extract 24 papers from different scientific databases. The results show that about two thirds of the papers focus on identifying and tackling smart contract issues. Four key issues are identified, namely, codifying, security, privacy and performance issues. The rest of the papers focuses on smart contract applications or other smart contract related topics. Research gaps that need to be addressed in future studies are provided.
Blockchain, Smart contracts, Systematic mapping study, Survey
For More Details : http://aircconline.com/ijcsit/V9N5/9517ijcsit11.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 V. Buterin, “A next-generation smart contract and decentralized application platform.,” Available online at: https://github.com/ethereum/wiki/wiki/White-Paper/[Accessed 19/02/2017].
 K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering,” in Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE’08, pp. 68-77, BCS Learning & Development Ltd., 2008.
 S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008.
 X. Xu, C. Pautasso, L. Zhu, V. Gramoli, A. Ponomarev, A. B. Tran, and S. Chen, “The blockchain as a software connector,” in 2016 13th Working IEEE/IFIP Conference on Software Architecture (WICSA), pp. 182-191, IEEE, 2016.
 V. Buterin, “On public and private blockchains,” Available online at: https://blog.ethereum.org/2015/08/07/on-public-and-private-blockchains/ [Accessed 01/03/2017].
 N.Szabo, “Formalizing and securing relationships on public networks.,” Available online at: http://rstmonday.org/ojs/index.php/fm/article/view/548/4691[Accessed 15/02/2017].
 J. Stark, “Making sense of blockchain smart contracts,” Available online at:
http://www.coindesk.com/making-sense-smart-contracts/ [Ac-cessed 06/03/2017].
 K. Delmolino, M. Arnett, A. Kosba, A. Miller, and E. Shi, “Step by step towards creating a safe smart contract: Lessons and insights from a cryptocurrency lab,” in International Conference on Financial Cryptography and Data Security, pp. 79-94, Springer, 2016.
 V. Morabito, “Smart contracts and licensing,” in Business Innovation Through Blockchain, pp. 101- 124, Springer, 2017.
 A. Lewis, ”A gentle introduction to smart contracts,” Available online at:
 G. Wood, “Ethereum: A secure decentralised generalised transaction ledger,” Ethereum Project Yellow Paper, 2014.
 K. Christidis and M. Devetsikiotis, “Blockchains and smart contracts for the internet of things,” IEEE Access, vol. 4, pp. 2292-2303, 2016.
 W. Egbertsen, G. Hardeman, M. van den Hoven, G. van der Kolk, and A. van Rijsewijk, “Replacing paper contracts with ethereum smart contracts,” 2016.
 W. Banasik, S. Dziembowski, and D. Malinowski, “Efficient zero-knowledge contingent payments in cryptocurrencies without scripts,” in European Symposium on Research in Computer Security, pp. 261-280, Springer, 2016.
 J. Yli-Huumo, D. Ko, S. Choi, S. Park, and K. Smolander, “Where is current research on blockchain technology?|a systematic review,” PloS one, vol. 11, no. 10, p. e0163477, 2016.
 K. Bhargavan, A. Delignat-Lavaud, C. Fournet, A. Gollamudi, G. Gonthier, N. Kobeissi, N. Kulatova, A. Rastogi, T. Sibut-Pinote, N. Swamy, et al., “Formal verification of smart contracts: Short paper,” in Proceedings of the 2016 ACM Workshop on Programming Languages and Analysis for Security, pp. 91-96, ACM, 2016.
 G. Bigi, A. Bracciali, G. Meacci, and E. Tuosto, “Validation of decentralised smart contracts through game theory and formal methods,” in Programming Languages with Applications to Biology and Security, pp. 142-161, Springer, 2015.
 C. K. Frantz and M. Nowostawski, “From institutions to code: Towards automated generation of smart contracts,” in 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W), pp. 210-215, IEEE, 2016.
 B. Marino and A. Juels, “Setting standards for altering and undoing smart contracts,” in International Symposium on Rules and Rule Markup Languages for the Semantic Web, pp. 151-166, Springer, 2016.
 T. Chen, X. Li, X. Luo, and X. Zhang, “Under-optimized smart contracts devour your money,” in 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 442-446, IEEE, 2017.
 F. Idelberger, G. Governatori, R. Riveret, and G. Sartor, “Evaluation of logic-based smart contracts for blockchain systems,” in International Symposium on Rules and Rule Markup Languages for the Semantic Web,167-183, Springer, 2016.
 C. Natoli and V. Gramoli, “The blockchain anomaly,” in 15th International Symposium on Network Computing and Applications (NCA), 310-317, IEEE, 2016.
 L. Luu, D.-H. Chu, H. Olickel, P. Saxena, and A. Hobor, “Making smart contracts smarter,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, pp. 254-269, ACM, 2016.
 A. Juels, A. Kosba, and E. Shi, “The ring of gyges: Investigating the future of criminal smart contracts,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, pp. 283-295, ACM, 2016.
 F. Zhang, E. Cecchetti, K. Croman, A. Juels, and E. Shi, “Town crier: An authenticated data feed for smart contracts,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, pp. 270-282, ACM, 2016.
 A. Kosba, A. Miller, E. Shi, Z. Wen, and C. Papamanthou, “Hawk: The blockchain model of cryptography and privacy-preserving smart contracts,” in 2016 IEEE Symposium on Security and Privacy (SP),839-858, IEEE, 2016.
 H. Watanabe, S. Fujimura, A. Nakadaira, Y. Miyazaki, A. Akutsu, and J. J. Kishigami, “Blockchain contract: A complete consensus using blockchain,” in 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), pp. 577-578, IEEE, 2015.
 M. Vukolić, “Rethinking permissioned blockchains,” in Proceedings of the ACM Workshop on Blockchain, Cryptocurrencies and Contracts, BCC ’17, pp. 3-7, ACM, 2017.
 N. Atzei, M. Bartoletti, and T. Cimoli, “A survey of attacks on ethereum smart contracts (sok),” in International Conference on Principles of Security and Trust, pp. 164-186, Springer, 2017.
 A. Bogner, M. Chanson, and A. Meeuw, “A decentralised sharing app running a smart contract on the ethereum blockchain,” in Proceedings of the 6th International Conference on the Internet of Things, pp. 177-178, ACM, 2016.
 M. Al-Bassam, “Scpki: A smart contract-based pki and identity system,” in Proceedings of the ACM Workshop on Blockchain, Cryptocurrencies and Contracts, BCC ’17, pp. 35-40, ACM, 2017.
 S. Huh, S. Cho, and S. Kim, “Managing IoT devices using blockchain platform,” in 2017 19th International Conference on Advanced Communication Technology (ICACT), pp. 464-467, IEEE, 2017.
 P. N. Carrillo, C. I. Peña, and J. L. d. L. Rosa, “Eurakos next: a cryptocurrency based on smart contracts,” in Ebook: Artificial Intelligence Research and Development, vol. 288 of Frontiers in Artificial Intelligence and Applications, pp. 221-226, 2016.
 H. Watanabe, S. Fujimura, A. Nakadaira, Y. Miyazaki, A. Akutsu, and J. Kishigami, “Blockchain contract: Securing a blockchain applied to smart contracts,” in 2016 IEEE International Conference on Consumer Electronics (ICCE), pp. 467-468, IEEE, 2016.
 A. Third and J. Domingue, “Linked data indexing of distributed ledgers,” in Proceedings of the 26th International Conference on World Wide Web Companion, WWW ’17 Companion, pp. 1431-1436, 2017.
Citation Count – 12
Arjuna Marzuki and Soh Yan Ying
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia
Agriculture sensors play an important role in modern agriculture. The use of sensors in various agriculture sectors minimizes the environmental impact on crops, helps in increasing yield and saving cost of operation. Among all agriculture industries in Malaysia, the mushroom industry is a comparatively new and small. As most of the mushroom farms in Malaysia are small-scaled, their production capability is limited by inadequate environmental control system and the lack of financial resources to upgrade the systems. This paper presents an environmental monitoring and controlling system to monitor and control the environmental conditions in a mushroom farm. It enables user to monitor temperature, humidity, carbon dioxide concentration and light intensity in a mushroom farm on an android device by using Thing Speak online platform. The control algorithm is able to control devices in a mushroom farm automatically based on feedback from the sensors to maintain the environment in an optimum condition for mushroom growth. The measured percentage error of temperature, humidity, carbon dioxide and the light using the developed system was as low as 0.4%, 1.5%, 2.2% and 1.34% respectively.
Agriculture, Interface Circuit, Internet of Things, Monitoring and Control, Sensor, Wireless.
For More Details : http://aircconline.com/ijcsit/V9N4/9417ijcsit02.pdf
Volume Link: http://airccse.org/journal/ijcsit2017_curr.html
 Unit Pengurusan Prestasi dan Pelaksanaan (2010) Economic Transformation Programme: A Roadmap for Malaysia (1 Malaysia). Performance Management and Delivery Unit, Jabatan Perdana Menteri.
 Istikoma Qurat-ul-Ain., & Dahlan A. R. A, (2015) “The Transformation of Agriculture Based Economy to an Industrial Sector through Crowd Sourcing In Malaysia”, Int. J. Comput. Sci. Inf. Technol. Res., Vol. 3, No. 1, pp.34–41.
 Bakar B.B., (2009) “The Malaysian Agricultural Industry in the New Millennium – Issues and Challenges,” pp. 337–356.
 Rosmiza M., Davies W., Aznie R. C., Jabil M., & Mazdi M, (2016) “Prospects for Increasing Commercial Mushroom Production in Malaysia: Challenges and Opportunities”, Mediterr. J. Soc. Sci., Vol. 7, No. 1, pp. 406–415.
 Haimid M. T., Rahim H., & Dardak R. A, (2013) “Understanding the mushroom industry and its marketing strategies for fresh produce in Malaysia”, Econ. Technol. Manag. Rev., Vol. 8, pp. 27– 37.
 Mat Amin M. Z., & Harun A, (2015) “Competitiveness of the Mushroom Industry in Malaysia” [Online]. Available: http://ap.fftc.agnet.org/ap_db.php?id=481&print=1. [Accessed: 18-Oct-2016].
 Australian Mushroom Growers Association, “Introduction to Mushroom Growing,” AMGA, pp. 1- 16.
 Van Nieuwenhuijzen, Bram., & Oei, P (2005) Small-scale mushroom cultivation oyster, shiitake and wood ear mushrooms, Agrodok;40. Agromisa/CTA, Wageningen, The Netherlands.
 Stamets P., & Chilton, J. S, (1983) “The Mushroom Cultivator: A Practical Guide to Growing Mushrooms at Home”, S. Cal. L. Rev., p. 416
. Grant, J.J (2002) An investigation of the airflow in mushroom growing structures, the development of an improved, three-dimensional solution technique for fluid flow and its evaluation for the modelling of mushroom growing structures. PhD thesis, Dublin City University.
 Kwon H., & Kim, B. S (2004) Mushroom Grow. Handb. 1, pp. 192–196.
 Tisdale T. E (2004) Cultivation of the Oyster Mushroom (Pleurotussp.) on Wood Substrates in Hawaii. MSc thesis, University of Hawai’i.
 Wang X., (2014) “Temperature and Humidity Monitoring System Based on GSM Module”, International Journal of Computer, Consumer and Control., Vol. 3, No. 1, pp. 41–49.
 Rahali A., Guerbaoui M., Ed-dahhak A., El Afou Y., Tannouche A., Lachhab A., & Bouchikhi, B, (2011) “Development of a data acquisition and greenhouse control system based on GSM”, Int. J.Eng. Sci. Technol., Vol. 3, No. 8, pp. 297–306.
 Kalinin Y. S., Velikov E. K., & Markova, V. I, (2015) “Design of Indoor Environment Monitoring System Using Arduino”, Int. J. Innov. Sci. Mod. Eng., Vol. 3, No. 7, pp. 46–49, 20.
 Lokesh Krishna K., Madhuri J., & Anuradha K, (2016) “A ZigBee based Energy Efficient Environmental Monitoring Alerting and Controlling System”, in International Conference On Information Communication And Embedded Systems (ICICES2016).