SlideShare a Scribd company logo

Nov 2018 Table of contents; current issue -International Journal on Soft Computing, Artificial Intelligence and Applications

•
I
IJSCAI Journal

The document discusses using machine learning algorithms to analyze the relationship between economic freedom and quality of life. It examines the Economic Freedom of the World index and the Human Development Index with modern machine learning methods. The analysis finds that these advanced algorithms achieve a stronger correlation between the indices than statistical means alone. However, there is still some room for non-liberal interpretations of how to design economic policies to improve people's lives. The goal is to objectively evaluate the impact of economic freedom through a data-driven approach.

Engineering
1 of 13
Download to read offline
1
2
3
4
5
6
7
8
9
10
11
12
13
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) ISSN : 2319 - 1015 [Online]; 2319 - 4081 [Print]. http://airccse.org/journal/ijscai/index.html Current Issue : November 2018, Volume 7, Number 4 -- Table of Contents http://airccse.org/journal/ijscai/current2018.html
Paper 01 INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MINING ENGINE ORIENTED TO BUILDING ELECTRICAL MANAGEMENT Alessandro massaro, giacomo meuli and angelo galiano, dyrecta lab, italy ABSTRACT In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization. KEYWORDS Intelligent Electrical Outlets, Energy Management, Load Monitoring and Piloting, Smart Grids, Energy Routing, Data Mining, KNIME, Long Short-Term Memory (LSTM), Neural Network, Correlation Matrix. For more details : http://aircconline.com/ijscai/V7N4/7418ijscai01.pdf Volume link: http://airccse.org/journal/ijscai/current2018.html
REFERENCES [1] Gross, P. et al. (2006) “Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis”, Proceeding IAAI'06 18th conference on Innovative Applications of Artificial Intelligence, Vol. 2, pp 1705-1711. [2] Zipperer, A., Aloise-Young, P. A., Suryanarayanan, S., Zimmerle, Roche, D. R., Earle, L., Christensen, D., Bauleo, P. (2013) “Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior”, Proceeding of the IEEE, Vol. 101, No. 11, pp 2397-2408. [3] Singh, R. P., Gao, P. X., Lizotte, D. J. (2012) “On Hourly Home Peak Load Prediction”, Conference on Smart Grid Communications (SmartGridComm), Proceeding of IEEE Third International Conference on Smart Grid Communications (SmartGridComm). [4] Mohsenian-Rad, A.-H., Alberto Leon-Garcia, A. (2010) “Optimal Residential Load Control with Price Prediction in Real-Time Electricity Pricing Environments,” IEEE Transactions on Smart Grid Vol. 1, No. 2, pp 120 -133. [5] Vazquez, F. I., Kastner, W., Gaceo, S. C., Reinisch, C. (2011) “Electricity Load Management in Smart Home Control”, Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney, 14-16 November 2011, pp 957-964. [6] Miceli, R. (2013) “Energy Management and Smart Grids”, Energies, Vol. 6, pp 2262- 2290. [7] Agyeman, K., A., Han, S., Han, S. (2015) “Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System”, Energies , Vol.2, No. 8, pp 9029-9048. [8] Aman, S., Frincu, M., Chelmis, C., Noor, M. N., Simmhan, Y., Prasanna, V. (2014) “Empirical Comparison of Prediction Methods for Electricity Consumption Forecasting”, University of Southern California, Tech. Rep, pp 14-942. [9] Callaway, D. S., Hiskens, I. A. (2011) “Achieving Controllability of Electric Loads”, Proceedings of the IEEE, Vol. 99, No. 1, pp 184- 199.
[10] Seppala, A. (1996) “Load research and Load Estimation in Electricity Distribution”, VTT Publications 289, ISBN 951-38-4947-3, pp 1-118. [11] Yardi, V. S. (2015) “Design of Smart Home Energy Management System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, No. 3, pp 1851-1857. [12] Barbato, A., Capone, A., Carello, G., Delfanti, M., Falabretti, D., Merlo, M. (2014) “A framework for home energy management and its experimental validation”, Energy Efficiency, Vol. 7, No. 6, pp 1013-1052. [13] Anastasi, G., Corucci, F., Marcelloni, F. (2011) “An Intelligent System for Electrical Energy Management in Buildings”, Proceeding of 11th International Conference on Intelligent Systems Design and Applications (ISDA). [14] Yan, M. (2012) “The Design and Application of Intelligent Electrical Outlet for Campus’s Electricity Saving and Emission Reduction”, Journal of Computers, Vol. 7, No. 7, pp 1696-1703. [15] Jabbar, Z. A., Kawitkar, R. S. (2016) “Implementation of Smart Home Control by Using Low Cost Arduino & Android Design”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, No. 2, pp 248-256. [16] Jing-Min Wang andMing-Ta Yang (2014) “Design a Smart Control Strategy to Implement an Intelligent Energy Safety and Management System”, Hindawi Publishing Corporation: International Journal of Distributed Sensor Networks, Vol. 2014, No. 312392, pp 1-10. [17] Iwayemi, A., Wan, W., Zhou, C. (2011) “Energy Management for Intelligent Buildings”, Energy Management Systems, Edited by Dr Giridhar, Kini, INTECH book 2011. [18] Dent, I., Aickelin, U., Rodden, T. (2011) “The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences using UK Data”, http://dx.doi.org/10.2139/ssrn.2829282 .
[19] Gajowniczek, K., Ząbkowski, T. (2015) “Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data”, Energies, Vol. 8, No. 7, pp 7407-7427. [20] Fernández-Caramés, T. M. (2015) “An Intelligent Power Outlet System for the Smart Home of the Internet of Things”, Hindawi Publishing Corporation: International Journal of Distributed Sensor Networks, Vol. 2015, No. 214805, pp 1-11. [21] Mocanu, E., Nguyen P. H., Gibescu, M., Kling, W. K. (2016) “Deep Learning For Estimating Building Energy Consumption”, Sustainable Energy, Grids and Networks, Vol. 6, pp 91-99. [22] Zheng, J., Xu, C., Zhang, Z., Li, X. (2017) “Electric Load Forecasting in Smart Grids using Long-Short-Term-Memory based Recurrent Neural Network”, 1st Annual Conference on Information Sciences and Systems (CISS), pp 1-6. [23] Okafor, K. C., Ononiwu, G. C., Precious, U., Godis, A. C. (2017) “Development of Arduino Based IoT Metering System for On-Demand Energy Monitoring”, International Journal of Arduino based IoT Metering System for On-Demand Energy Monitoring, Vol. 7, No. 23, pp 3208-3224. [24] Folayan G. B., Idowu, O. O. (2018) “Remote Controlled Advanced Power Strip Using Raspberry Pi for Effective Energy Management”, Innovation Energy & Research, Vol. 7, No. 1, pp 1-4. [25] Massaro, A., Galiano, A., Meuli, G. Massari, S. F. (2018) “Overview and Application of Enabling Technologies oriented on Energy Routing Monitoring, on Network Installation and on Predictive Maintenance” International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, pp 1-20. [26] “Individual household electric power consumption Data Set” 2018. [Online]. Available: http://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consump tion [27] Martino, L. M., Amarasinghe, K., Manic, M. (2016) “Building Energy Load Forecasting using Deep Neural Networks” IEEE Proceeding of the 42nd Annual Conference of the IEEE Industrial Electronics Society (IECON).
[28] “Kaggle” 2018. [Online]. Available: https://www.kaggle.com/ [29] Myers, J. L., Well, A. D. (2003) “Research Design and Statistical Analysis”, (2nd ed.) Lawrence Erlbaum. pp. 508, ISBN 0-8058-4037-0. CorrespondingAuthor Alessandro Massaro: Research & Development Chief of Dyrecta Lab s.r.l.
Paper 02 FORECASTING MACROECONOMICAL INDICES WITH MACHINE LEARNING : IMPARTIAL ANALYSIS OF THE RELATION BETWEEN ECONOMIC FREEDOM AND QUALITY OF LIFE Jonathan Staufer and Patricia Brockmann, Technische Hochschule Nurnberg, Germany ABSTRACT The importance of economic freedom has often been stressed by supporters of liberalism, but can its actual effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberalistic oriented economic policy, an objective recommendation for creating an economic policy that improves people’s everyday lives might be derived by the analysis results. It was found that these more advanced algorithms achieve a considerably stronger correlation between both indices than pure statistical means yet leave a small room for interpretation towards a counter-liberalistic implementation of demand-driven economic policy. KEYWORDS Data Mining, Machine Learning, Neural Networks, Economic Freedom of the World Index, Human Development Index For more details:http://aircconline.com/ijscai/V7N4/7418ijscai02.pdf Volume link: http://airccse.org/journal/ijscai/current2018.html
REFERENCES [1]Derbal, H., Abdelkafi, R., & Chkir, A. (2011). The Effects of Economic Freedom Components on Economic Growth: An Analysis with A Threshold Model. Journal of Politics and Law, 4(2). https://doi.org/10.5539/jpl.v4n2p49. [2]Hall, J. C., & Lawson, R. A. (2014). Economic Freedom of the World: An Accounting of the Literature. Contemporary Economic Policy, 32(1), 1–19. https://doi.org/10.1111/coep.12010. [3]Gropper, D. M., Lawson, R. A., & Throne Jr., J. T. (2011). Economic Freedom and Happiness. Cato Journal. (31), 237–255. Retrieved from https://business.fau.edu/images/business/ourcollege/deans_office/dean_groppers_publications /Economic-Freedom-and-Happiness.pdf. [4]Nikolaev, B. (2014). Economic Freedom and Quality of Life: Evidence from the OECD's Your Better Life Index. (29). Retrieved from http://borisnikolaev.com/wp- content/uploads/2014/08/Economic-Freedom-and-Quality-of-Life.pdf [5]Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Adaptive computation and machine learning. Cambridge, Mass: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN= 307676 [6]Samuel, A. L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. (Volume:3, Issue: 3), 210. [7]Cleve, J. (2016). Data Mining (2nd ed.). De Gruyter Studium. Berlin: De Gruyter. Retrieved from http://ebookcentral.proquest.com/lib/gbv/detail.action?docID=4793920 [8]Rey, G. D., & Wender, K. F. (2011). Neuronale Netze: Eine Einführung in die Grundlagen, Anwendungen und Datenauswertung (2., vollst. überarb. und erw. Aufl.). Bern: Huber. Retrieved from http://sub-hh.ciando.com/book/?bok_id=67571 [9]James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R (Corr. at 4. print). Springer texts in statistics. New York NY u.a.: Springer.
[10] Luhmann, N. (1984). Die Wirtschaft der Gesellschaft als autopoietisches System. Zeitschrift für Soziologie, 13(4), 308–327. Retrieved from http://www.zfs- online.org/index.php/zfs/article/viewFile/2528/2065 [11]Dörner, D. (2000). Die Logik des Mißlingens: Strategisches Denken in komplexen Situationen (95. - 100. Tsd). Rororo: Vol. 19314. Reinbek bei Hamburg: Rowohlt. [12]Starbatty, J. (2017). Marktwirtschaft, Soziale Marktwirtschaft, Konrad-Adenauer-Stiftung. Retrieved from http://www.kas.de/wf/de/71.11494/ [13]Farmer, K. (Ed.). (2006). Marktwirtschaft und Ethik: Vol. 10. Theorie der Wirtschaftspolitik, Entwicklungspolitik und Wirtschaftsethik: Festschrift für Werner Lachmann zum 65. Geburtstag. Wien: Lit-Verl. [14]Erhard, L., & Langer, W. (1964). Wohlstand für alle (8th ed.). Köln: Anaconda Verl. [15]Berger, J. (2012a). Soziale Marktwirtschaft. Retrieved from http://www.bpb.de/politik/grundfragen/deutsche-verhaeltnisse-eine- sozialkunde/138633/soziale-marktwirtschaft [16]Arnold, L. (2010). Unabhängige Wirtschaftspolitik. Wiesbaden: VS Verlag für Sozialwissenschaften. [17]Schüller, A. (2017). Ordoliberalismus Soziale Marktwirtschaft. Retrieved from http://www.kas.de/wf/de/71.10255/ [18]Berger, J. (2012b). Soziale Marktwirtschaft. Retrieved from http://www.bpb.de/politik/grundfragen/deutsche-verhaeltnisse-eine- sozialkunde/138633/soziale-marktwirtschaft. [19]Scherer, J. F. (2016). Impulse - Freiheit, Gleichheit, Inklusivität: Der Ordoliberalismus als Ausgangspunkt einer Neuen Sozialen Ordnungspolitik. Retrieved from http://library.fes.de/pdf- files/managerkreis/12642.pdf [20]Döhrn, R. (2014). Konjunkturdiagnose und - prognose: Eine anwendungsorientierte Einführung. Berlin, Heidelberg: Springer Berlin Heidelberg.
[21]Fraser Institute. Economic Freedom Basics. Retrieved from https://www.fraserinstitute.org/economic-freedom/economic-freedom-basics [22]Gwartney, J., Lawson, R., & Fraser Institute. (2002). Economic Freedom of the World: 2002 Annual Report. [23]Fraser Institute. (2015). The Benefits of Economic Freedom. Retrieved from https://www.fraserinstitute.org/economic-freedom/economic-freedom-basics [24]Gwartney, J., Lawson, R., & Gartzke, E. (2005). Economic Freedom of the World: 2005 Annual Report. [25]Bologna, J., & Hall, J. (2014). Economic Freedom Research: Some Comments and Suggestions. The Annual, 2014, 123–136. Retrieved from https://www.beloit.edu/upton/assets/VOL_VI.Bologna.Hall.pdf [26]United Nations Development Programme. (2013a). Bericht über die menschliche Entwicklung 2013: Der Aufstieg des Südens: Menschlicher Fortschritt in einer ungleichen Welt. Bericht über die menschliche Entwicklung: Vol. 2013. Bonn: UNO-Verl. [27]United Nations Development Programme. (2017). About Human Development | Human Development Reports. Retrieved from http://hdr.undp.org/en/humandev [28]United Nations Development Programme. (2013b). Technical Notes. Retrieved from http://hdr.undp.org/sites/default/files/hdr_2013_en_technotes.pdf [29]United Nations Development Programme. (2015a). The 2010 Human Development Index (HDI). Retrieved from http://hdr.undp.org/sites/default/files/hdi_training.pdf [30]United Nations Development Programme. (2015b). The 2010 Human Development Index (HDI). Retrieved from http://hdr.undp.org/sites/default/files/hdi_training.pdf [31]United Nations Development Programme. (2016). Human Development Index (HDI). Retrieved from http://hdr.undp.org/en/content/human-development-index-hdi
[32]Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine. (17). Retrieved from https://www.aaai.org/ojs/index.php/aimagazine/article/viewFile/1230/1131 [33]Piatetsky, G. (2016). R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results. Retrieved from http://www.kdnuggets.com/2016/06/r-python-top- analytics-data-mining-data-science-software.html/2 [34]RapidMiner GmbH. (2017). Gradient Boosted Trees - RapidMiner Documentation. Retrieved from https://docs.rapidminer.com/studio/operators/modeling/predictive/trees/gradient_boosted_tr ees.html [35]Piatetsky, G. (2013). KDnuggets Annual Software Poll:RapidMiner and R vie for first place. Retrieved from http://www.kdnuggets.com/2013/06/kdnuggets-annual-software-poll- rapidminer-r-vie-for-first-place.html [36]The R Foundation. (2016). R: What is R? Retrieved from https://www.r-project.org/about.html [37]University of Waikato. Data Mining with Open Source Machine Learning Software in Java. Retrieved from http://www.cs.waikato.ac.nz/ml/weka/ [38]Mierswa, I. (2016). Introducing RapidMiner 7.2, Free Versions of Server & Radoop, and New Pricing | RapidMiner. Retrieved from https://rapidminer.com/introducing-new-rapidminer- pricing-free-versions-server-radoop/ [39]Mason, L., Baxter, J. O., Bartlett, P. L., & Frean, M. R. (1999). Boosting Algorithms as Gradient Descent. Retrieved from http://papers.nips.cc/paper/1766-boosting-algorithms-as-gradient- descent.pdf Authors Jonathan Staufer completed his Bachelor of Science and Master of Science Degrees in Information Systems at the Technische Hochschule Nuernberg Patricia Brockmann is a professor for Information Systems at the Technische Hochschule Nuernberg, Germany
Paper 03 DESIGN AND IMPLEMENTATION OF SMART COOKING BASED ON AMAZON ECHO Lin Xiaoguang1,2,3, Yang Yong3 and Zhang Ju1,3, 1University of Chinese Academy of Sciences, China, 2Chinese Academy of Sciences - Chengdu, China and 3Chinese Academy of Sciences - Chongqing, China ABSTRACT Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking food. People may speak to Amazon Echo during the cooking in order to get the information and situation of the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services, and speaks to people the results that cloud services make by querying the embedded cooking knowledge and achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile application are well-designed and implemented to monitor the temperature of cooking food. KEYWORDS Smart Cooking, Things of Internet, Cloud Services, Smart Home. For More details ; http://aircconline.com/ijscai/V7N4/7418ijscai03.pdf Volume Link: http://airccse.org/journal/ijscai/current2018.html
REFERENCES [1] Alam M R, Reaz M B, Ali M A, (2012) “A Review of Smart Homes—Past, Present, and Future”, systems man and cybernetics, Vol 42, No. 6, pp1190-1203. [2] Alif Ahmad Syamsudduha, Dyah Pratiw, etc, (2013) “Future Smart Cooking Machine System Design”, TELKOMNIKA, Vol.11, No.4, pp827~834 [3] Hashimoto Atsushi, Mori Naoyuki, etc, (2008) “Smart Kitchen: A User Centric Cooking Support System”, Proceedings of IPMU'08, pp848-854. [4] Margaret Rouse, “smart speaker”, https://whatis.techtarget.com/definition/smart- speaker, May 2017. [5] “Amazon.com Help: Set Up Your Amazon Echo”, Amazon.com. March 4, 2015. [6] Dieter Bohn, “You can finally say 'Computer' to your Echo to command it”, The Verge, Jan 23, 2017. [7] “Alexa Voice Service Overview”, Amazon.com, Feb 7, 2016. [8] Alex Handy, “Amazon introduces Lambda, Containers at AWS re:Invent”, SD Times, November, 14, 2014 [9] Beth M Sheppard, (2017) “Theological Librarian vs. Machine: Taking on the Amazon Alexa Show (with Some Reflections on the Future of the Profession)”, Theological Librarianship, Vol 10, issue 1, pp8-23. Authors Lin Xiaoguang Lin Xiaoguang is a Ph.D. candidate in University of Chinese Academy of Sciences.

More Related Content

What's hot (20)

PDF
Top Viewed Articles from Academia in 2019- International Journal of Distribu...
ijdpsjournal
 
PDF
Essays on the Smart Grid
Kim Balle
 
PDF
CRITICAL INFRASTRUCTURE CYBERSECURITY CHALLENGES: IOT IN PERSPECTIVE
IJNSA Journal
 
PDF
Design a smart control strategy to implement an intelligent
Anggara Nasution
 
PDF
Fajar J. Ekaputra, Marta Sabou, Estefania Serral and Stefan Biffl | Knowledge...
semanticsconference
 
PDF
Energy efficient clustering using the AMHC (adoptive multi-hop clustering) t...
IJECEIAES
 
PDF
Top 5 most viewed articles from academia in 2021 - International Journal of C...
IJCSEA Journal
 
PDF
Comparison of classification_techniques_on_energy_
naveen700194
 
DOCX
Paper
Sugeng Widodo
 
PDF
Predicting Energy Performance of an Educational Building through Artificial N...
frececco
 
PDF
Jaswant singh cv- 14-may_2018 (1)
Jaswant Singh
 
PDF
resume v 5.0
Ye Xu
 
DOCX
Analysis and Comparison of Energy Efficiency of Android based Indoor/Outdoor ...
vishnuRajan20
 
PDF
Smart Grid Technologies in Power Systems An Overview
Raja Larik
 
PDF
Muhamad Rashid Resume
Muhammad Roussab Rashid, EIT
 
PDF
Technologies used in Smart grids for power distribution
Raja Larik
 
PDF
IRJET- Use of Artificial Neural Network in Construction Management
IRJET Journal
 
PDF
Literature Review: Application of Artificial Neural Network in Civil Engineering
Bid4Papers
 
PDF
AN INVESTIGATION OF THE ENERGY CONSUMPTION BY INFORMATION TECHNOLOGY EQUIPMENTS
ijcsit
 
PPTX
klint_plsim_poster_MJK
Jimmy Cai
 
Top Viewed Articles from Academia in 2019- International Journal of Distribu...
ijdpsjournal
 
Essays on the Smart Grid
Kim Balle
 
CRITICAL INFRASTRUCTURE CYBERSECURITY CHALLENGES: IOT IN PERSPECTIVE
IJNSA Journal
 
Design a smart control strategy to implement an intelligent
Anggara Nasution
 
Fajar J. Ekaputra, Marta Sabou, Estefania Serral and Stefan Biffl | Knowledge...
semanticsconference
 
Energy efficient clustering using the AMHC (adoptive multi-hop clustering) t...
IJECEIAES
 
Top 5 most viewed articles from academia in 2021 - International Journal of C...
IJCSEA Journal
 
Comparison of classification_techniques_on_energy_
naveen700194
 
Paper
Sugeng Widodo
 
Predicting Energy Performance of an Educational Building through Artificial N...
frececco
 
Jaswant singh cv- 14-may_2018 (1)
Jaswant Singh
 
resume v 5.0
Ye Xu
 
Analysis and Comparison of Energy Efficiency of Android based Indoor/Outdoor ...
vishnuRajan20
 
Smart Grid Technologies in Power Systems An Overview
Raja Larik
 
Muhamad Rashid Resume
Muhammad Roussab Rashid, EIT
 
Technologies used in Smart grids for power distribution
Raja Larik
 
IRJET- Use of Artificial Neural Network in Construction Management
IRJET Journal
 
Literature Review: Application of Artificial Neural Network in Civil Engineering
Bid4Papers
 
AN INVESTIGATION OF THE ENERGY CONSUMPTION BY INFORMATION TECHNOLOGY EQUIPMENTS
ijcsit
 
klint_plsim_poster_MJK
Jimmy Cai
 

Similar to Nov 2018 Table of contents; current issue -International Journal on Soft Computing, Artificial Intelligence and Applications (20)

PDF
June 2025 Top 10 Sites -Electrical and Electronics Engineering: An Internatio...
elelijjournal653
 
PDF
January 2024 Top Cited -.pdf
ijscai
 
PDF
Modeling and Analysis of Energy Efficient Cellular Networks
ijtsrd
 
PDF
AI Driven Transformation: Advancing Clean Energy in Contemporary Power Systems
AJHSSR Journal
 
PDF
Analysis of Smart Grid Interoperability
ijtsrd
 
PDF
International Journal of Wireless & Mobile Networks (IJWMN)
ijwmn
 
PDF
Kezunovic project t 37-pserc_final_report_2010
backam78
 
PPTX
Technical seminar ppt ai techniques in smart grid
HeenaKousar53
 
PDF
A Comprehensive Review on Smart Grid Ecosystem.pdf
ssuser793b4e
 
PPTX
1st Review 1st Review1st Review1st Review1st Review1st Review1st Review
chetangavane55
 
PDF
Top 10 Cited Articles in VLSI Design & Communication Systems Research: Januar...
VLSICS Design
 
PDF
Energy-aware strategy for data forwarding in IoT ecosystem
IJECEIAES
 
PDF
Top 10 Read Article - International Journal of Wireless & Mobile Networks (IJ...
ijwmn
 
PDF
IRJET - IoT based Energy Monitoring System for Energy Conservation
IRJET Journal
 
PDF
06011696
Diwakar Joshi
 
PDF
March 2021: Top 10 Cited Article in VLSI Design & Communication Systems
VLSICS Design
 
PDF
Most viewed articles - International Journal of Advanced Smart Sensor Network...
ijassn
 
PDF
Blockchain outlook for deployment of IoT in distribution networks and smart h...
IJECEIAES
 
PPTX
Mega Silfiani_ABS 98 - Copy.pptx
MegaSilfiani2
 
PPTX
Energy Infrastructure theme_20Feb_VH_NCCARF
Veryan Hann
 
June 2025 Top 10 Sites -Electrical and Electronics Engineering: An Internatio...
elelijjournal653
 
January 2024 Top Cited -.pdf
ijscai
 
Modeling and Analysis of Energy Efficient Cellular Networks
ijtsrd
 
AI Driven Transformation: Advancing Clean Energy in Contemporary Power Systems
AJHSSR Journal
 
Analysis of Smart Grid Interoperability
ijtsrd
 
International Journal of Wireless & Mobile Networks (IJWMN)
ijwmn
 
Kezunovic project t 37-pserc_final_report_2010
backam78
 
Technical seminar ppt ai techniques in smart grid
HeenaKousar53
 
A Comprehensive Review on Smart Grid Ecosystem.pdf
ssuser793b4e
 
1st Review 1st Review1st Review1st Review1st Review1st Review1st Review
chetangavane55
 
Top 10 Cited Articles in VLSI Design & Communication Systems Research: Januar...
VLSICS Design
 
Energy-aware strategy for data forwarding in IoT ecosystem
IJECEIAES
 
Top 10 Read Article - International Journal of Wireless & Mobile Networks (IJ...
ijwmn
 
IRJET - IoT based Energy Monitoring System for Energy Conservation
IRJET Journal
 
06011696
Diwakar Joshi
 
March 2021: Top 10 Cited Article in VLSI Design & Communication Systems
VLSICS Design
 
Most viewed articles - International Journal of Advanced Smart Sensor Network...
ijassn
 
Blockchain outlook for deployment of IoT in distribution networks and smart h...
IJECEIAES
 
Mega Silfiani_ABS 98 - Copy.pptx
MegaSilfiani2
 
Energy Infrastructure theme_20Feb_VH_NCCARF
Veryan Hann
 
Ad

More from IJSCAI Journal (20)

PDF
Design and Implementation of Smart Cooking Based on Amazon Echo
IJSCAI Journal
 
PDF
Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysi...
IJSCAI Journal
 
PDF
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...
IJSCAI Journal
 
DOCX
6th international conference on artificial intelligence and applications (aia...
IJSCAI Journal
 
PDF
Image Generation from Caption
IJSCAI Journal
 
PDF
Temporally Extended Actions For Reinforcement Learning Based Schedulers
IJSCAI Journal
 
PDF
Knowledgebase Systems in Neuro Science - A Study
IJSCAI Journal
 
PDF
An Iranian Cash Recognition Assistance System For Visually Impaireds
IJSCAI Journal
 
PDF
An Experimental Study of Feature Extraction Techniques in Opinion Mining
IJSCAI Journal
 
PDF
Monte-Carlo Tree Search For The "Mr Jack" Board Game
IJSCAI Journal
 
PDF
Unsupervised learning models of invariant features in images: Recent developm...
IJSCAI Journal
 
PDF
A Naive Method For Ontology Construction
IJSCAI Journal
 
PDF
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...
IJSCAI Journal
 
PDF
A Study on Graph Storage Database of NOSQL
IJSCAI Journal
 
PDF
A Study on Graph Storage Database of NOSQL
IJSCAI Journal
 
PDF
Estimation Of The Parameters Of Solar Cells From Current-Voltage Characterist...
IJSCAI Journal
 
PDF
Implementation of Folksonomy Based Tag Cloud Model for Information Retrieval ...
IJSCAI Journal
 
PDF
Study of Distance Measurement Techniques in Context to Prediction Model of We...
IJSCAI Journal
 
PDF
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA
IJSCAI Journal
 
PDF
Design of Dual Axis Solar Tracker System Based on Fuzzy Inference Systems
IJSCAI Journal
 
Design and Implementation of Smart Cooking Based on Amazon Echo
IJSCAI Journal
 
Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysi...
IJSCAI Journal
 
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...
IJSCAI Journal
 
6th international conference on artificial intelligence and applications (aia...
IJSCAI Journal
 
Image Generation from Caption
IJSCAI Journal
 
Temporally Extended Actions For Reinforcement Learning Based Schedulers
IJSCAI Journal
 
Knowledgebase Systems in Neuro Science - A Study
IJSCAI Journal
 
An Iranian Cash Recognition Assistance System For Visually Impaireds
IJSCAI Journal
 
An Experimental Study of Feature Extraction Techniques in Opinion Mining
IJSCAI Journal
 
Monte-Carlo Tree Search For The "Mr Jack" Board Game
IJSCAI Journal
 
Unsupervised learning models of invariant features in images: Recent developm...
IJSCAI Journal
 
A Naive Method For Ontology Construction
IJSCAI Journal
 
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...
IJSCAI Journal
 
A Study on Graph Storage Database of NOSQL
IJSCAI Journal
 
A Study on Graph Storage Database of NOSQL
IJSCAI Journal
 
Estimation Of The Parameters Of Solar Cells From Current-Voltage Characterist...
IJSCAI Journal
 
Implementation of Folksonomy Based Tag Cloud Model for Information Retrieval ...
IJSCAI Journal
 
Study of Distance Measurement Techniques in Context to Prediction Model of We...
IJSCAI Journal
 
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA
IJSCAI Journal
 
Design of Dual Axis Solar Tracker System Based on Fuzzy Inference Systems
IJSCAI Journal
 
Ad

Recently uploaded (20)

PDF
All chapters of Strength of materials.ppt
girmabiniyam1234
 
PDF
Introduction to Ship Engine Room Systems.pdf
Mahmoud Moghtaderi
 
PDF
Biodegradable Plastics: Innovations and Market Potential (www.kiu.ac.ug)
publication11
 
PPTX
Basics of Auto Computer Aided Drafting .pptx
Krunal Thanki
 
PPTX
Online Cab Booking and Management System.pptx
diptipaneri80
 
PDF
Air -Powered Car PPT by ER. SHRESTH SUDHIR KOKNE.pdf
SHRESTHKOKNE
 
PDF
Machine Learning All topics Covers In This Single Slides
AmritTiwari19
 
DOCX
SAR - EEEfdfdsdasdsdasdasdasdasdasdasdasda.docx
Kanimozhi676285
 
PDF
Construction of a Thermal Vacuum Chamber for Environment Test of Triple CubeS...
2208441
 
PPTX
ENSA_Module_7.pptx_wide_area_network_concepts
RanaMukherjee24
 
PDF
EVS+PRESENTATIONS EVS+PRESENTATIONS like
saiyedaqib429
 
PDF
Zero Carbon Building Performance standard
BassemOsman1
 
PDF
Jual GPS Geodetik CHCNAV i93 IMU-RTK Lanjutan dengan Survei Visual
Budi Minds
 
PPTX
business incubation centre aaaaaaaaaaaaaa
hodeeesite4
 
PDF
settlement FOR FOUNDATION ENGINEERS.pdf
Endalkazene
 
PDF
CAD-CAM U-1 Combined Notes_57761226_2025_04_22_14_40.pdf
shailendrapratap2002
 
PPTX
Ground improvement techniques-DEWATERING
DivakarSai4
 
PDF
Packaging Tips for Stainless Steel Tubes and Pipes
heavymetalsandtubes
 
PDF
20ME702-Mechatronics-UNIT-1,UNIT-2,UNIT-3,UNIT-4,UNIT-5, 2025-2026
Mohanumar S
 
PDF
Zero carbon Building Design Guidelines V4
BassemOsman1
 
All chapters of Strength of materials.ppt
girmabiniyam1234
 
Introduction to Ship Engine Room Systems.pdf
Mahmoud Moghtaderi
 
Biodegradable Plastics: Innovations and Market Potential (www.kiu.ac.ug)
publication11
 
Basics of Auto Computer Aided Drafting .pptx
Krunal Thanki
 
Online Cab Booking and Management System.pptx
diptipaneri80
 
Air -Powered Car PPT by ER. SHRESTH SUDHIR KOKNE.pdf
SHRESTHKOKNE
 
Machine Learning All topics Covers In This Single Slides
AmritTiwari19
 
SAR - EEEfdfdsdasdsdasdasdasdasdasdasdasda.docx
Kanimozhi676285
 
Construction of a Thermal Vacuum Chamber for Environment Test of Triple CubeS...
2208441
 
ENSA_Module_7.pptx_wide_area_network_concepts
RanaMukherjee24
 
EVS+PRESENTATIONS EVS+PRESENTATIONS like
saiyedaqib429
 
Zero Carbon Building Performance standard
BassemOsman1
 
Jual GPS Geodetik CHCNAV i93 IMU-RTK Lanjutan dengan Survei Visual
Budi Minds
 
business incubation centre aaaaaaaaaaaaaa
hodeeesite4
 
settlement FOR FOUNDATION ENGINEERS.pdf
Endalkazene
 
CAD-CAM U-1 Combined Notes_57761226_2025_04_22_14_40.pdf
shailendrapratap2002
 
Ground improvement techniques-DEWATERING
DivakarSai4
 
Packaging Tips for Stainless Steel Tubes and Pipes
heavymetalsandtubes
 
20ME702-Mechatronics-UNIT-1,UNIT-2,UNIT-3,UNIT-4,UNIT-5, 2025-2026
Mohanumar S
 
Zero carbon Building Design Guidelines V4
BassemOsman1
 

Nov 2018 Table of contents; current issue -International Journal on Soft Computing, Artificial Intelligence and Applications

  • 1. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) ISSN : 2319 - 1015 [Online]; 2319 - 4081 [Print]. http://airccse.org/journal/ijscai/index.html Current Issue : November 2018, Volume 7, Number 4 -- Table of Contents http://airccse.org/journal/ijscai/current2018.html
  • 2. Paper 01 INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MINING ENGINE ORIENTED TO BUILDING ELECTRICAL MANAGEMENT Alessandro massaro, giacomo meuli and angelo galiano, dyrecta lab, italy ABSTRACT In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization. KEYWORDS Intelligent Electrical Outlets, Energy Management, Load Monitoring and Piloting, Smart Grids, Energy Routing, Data Mining, KNIME, Long Short-Term Memory (LSTM), Neural Network, Correlation Matrix. For more details : http://aircconline.com/ijscai/V7N4/7418ijscai01.pdf Volume link: http://airccse.org/journal/ijscai/current2018.html
  • 3. REFERENCES [1] Gross, P. et al. (2006) “Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis”, Proceeding IAAI'06 18th conference on Innovative Applications of Artificial Intelligence, Vol. 2, pp 1705-1711. [2] Zipperer, A., Aloise-Young, P. A., Suryanarayanan, S., Zimmerle, Roche, D. R., Earle, L., Christensen, D., Bauleo, P. (2013) “Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior”, Proceeding of the IEEE, Vol. 101, No. 11, pp 2397-2408. [3] Singh, R. P., Gao, P. X., Lizotte, D. J. (2012) “On Hourly Home Peak Load Prediction”, Conference on Smart Grid Communications (SmartGridComm), Proceeding of IEEE Third International Conference on Smart Grid Communications (SmartGridComm). [4] Mohsenian-Rad, A.-H., Alberto Leon-Garcia, A. (2010) “Optimal Residential Load Control with Price Prediction in Real-Time Electricity Pricing Environments,” IEEE Transactions on Smart Grid Vol. 1, No. 2, pp 120 -133. [5] Vazquez, F. I., Kastner, W., Gaceo, S. C., Reinisch, C. (2011) “Electricity Load Management in Smart Home Control”, Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney, 14-16 November 2011, pp 957-964. [6] Miceli, R. (2013) “Energy Management and Smart Grids”, Energies, Vol. 6, pp 2262- 2290. [7] Agyeman, K., A., Han, S., Han, S. (2015) “Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System”, Energies , Vol.2, No. 8, pp 9029-9048. [8] Aman, S., Frincu, M., Chelmis, C., Noor, M. N., Simmhan, Y., Prasanna, V. (2014) “Empirical Comparison of Prediction Methods for Electricity Consumption Forecasting”, University of Southern California, Tech. Rep, pp 14-942. [9] Callaway, D. S., Hiskens, I. A. (2011) “Achieving Controllability of Electric Loads”, Proceedings of the IEEE, Vol. 99, No. 1, pp 184- 199.
  • 4. [10] Seppala, A. (1996) “Load research and Load Estimation in Electricity Distribution”, VTT Publications 289, ISBN 951-38-4947-3, pp 1-118. [11] Yardi, V. S. (2015) “Design of Smart Home Energy Management System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, No. 3, pp 1851-1857. [12] Barbato, A., Capone, A., Carello, G., Delfanti, M., Falabretti, D., Merlo, M. (2014) “A framework for home energy management and its experimental validation”, Energy Efficiency, Vol. 7, No. 6, pp 1013-1052. [13] Anastasi, G., Corucci, F., Marcelloni, F. (2011) “An Intelligent System for Electrical Energy Management in Buildings”, Proceeding of 11th International Conference on Intelligent Systems Design and Applications (ISDA). [14] Yan, M. (2012) “The Design and Application of Intelligent Electrical Outlet for Campus’s Electricity Saving and Emission Reduction”, Journal of Computers, Vol. 7, No. 7, pp 1696-1703. [15] Jabbar, Z. A., Kawitkar, R. S. (2016) “Implementation of Smart Home Control by Using Low Cost Arduino & Android Design”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, No. 2, pp 248-256. [16] Jing-Min Wang andMing-Ta Yang (2014) “Design a Smart Control Strategy to Implement an Intelligent Energy Safety and Management System”, Hindawi Publishing Corporation: International Journal of Distributed Sensor Networks, Vol. 2014, No. 312392, pp 1-10. [17] Iwayemi, A., Wan, W., Zhou, C. (2011) “Energy Management for Intelligent Buildings”, Energy Management Systems, Edited by Dr Giridhar, Kini, INTECH book 2011. [18] Dent, I., Aickelin, U., Rodden, T. (2011) “The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences using UK Data”, http://dx.doi.org/10.2139/ssrn.2829282 .
  • 5. [19] Gajowniczek, K., Ząbkowski, T. (2015) “Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data”, Energies, Vol. 8, No. 7, pp 7407-7427. [20] Fernández-Caramés, T. M. (2015) “An Intelligent Power Outlet System for the Smart Home of the Internet of Things”, Hindawi Publishing Corporation: International Journal of Distributed Sensor Networks, Vol. 2015, No. 214805, pp 1-11. [21] Mocanu, E., Nguyen P. H., Gibescu, M., Kling, W. K. (2016) “Deep Learning For Estimating Building Energy Consumption”, Sustainable Energy, Grids and Networks, Vol. 6, pp 91-99. [22] Zheng, J., Xu, C., Zhang, Z., Li, X. (2017) “Electric Load Forecasting in Smart Grids using Long-Short-Term-Memory based Recurrent Neural Network”, 1st Annual Conference on Information Sciences and Systems (CISS), pp 1-6. [23] Okafor, K. C., Ononiwu, G. C., Precious, U., Godis, A. C. (2017) “Development of Arduino Based IoT Metering System for On-Demand Energy Monitoring”, International Journal of Arduino based IoT Metering System for On-Demand Energy Monitoring, Vol. 7, No. 23, pp 3208-3224. [24] Folayan G. B., Idowu, O. O. (2018) “Remote Controlled Advanced Power Strip Using Raspberry Pi for Effective Energy Management”, Innovation Energy & Research, Vol. 7, No. 1, pp 1-4. [25] Massaro, A., Galiano, A., Meuli, G. Massari, S. F. (2018) “Overview and Application of Enabling Technologies oriented on Energy Routing Monitoring, on Network Installation and on Predictive Maintenance” International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, pp 1-20. [26] “Individual household electric power consumption Data Set” 2018. [Online]. Available: http://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consump tion [27] Martino, L. M., Amarasinghe, K., Manic, M. (2016) “Building Energy Load Forecasting using Deep Neural Networks” IEEE Proceeding of the 42nd Annual Conference of the IEEE Industrial Electronics Society (IECON).
  • 6. [28] “Kaggle” 2018. [Online]. Available: https://www.kaggle.com/ [29] Myers, J. L., Well, A. D. (2003) “Research Design and Statistical Analysis”, (2nd ed.) Lawrence Erlbaum. pp. 508, ISBN 0-8058-4037-0. CorrespondingAuthor Alessandro Massaro: Research & Development Chief of Dyrecta Lab s.r.l.
  • 7. Paper 02 FORECASTING MACROECONOMICAL INDICES WITH MACHINE LEARNING : IMPARTIAL ANALYSIS OF THE RELATION BETWEEN ECONOMIC FREEDOM AND QUALITY OF LIFE Jonathan Staufer and Patricia Brockmann, Technische Hochschule Nurnberg, Germany ABSTRACT The importance of economic freedom has often been stressed by supporters of liberalism, but can its actual effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberalistic oriented economic policy, an objective recommendation for creating an economic policy that improves people’s everyday lives might be derived by the analysis results. It was found that these more advanced algorithms achieve a considerably stronger correlation between both indices than pure statistical means yet leave a small room for interpretation towards a counter-liberalistic implementation of demand-driven economic policy. KEYWORDS Data Mining, Machine Learning, Neural Networks, Economic Freedom of the World Index, Human Development Index For more details:http://aircconline.com/ijscai/V7N4/7418ijscai02.pdf Volume link: http://airccse.org/journal/ijscai/current2018.html
  • 8. REFERENCES [1]Derbal, H., Abdelkafi, R., & Chkir, A. (2011). The Effects of Economic Freedom Components on Economic Growth: An Analysis with A Threshold Model. Journal of Politics and Law, 4(2). https://doi.org/10.5539/jpl.v4n2p49. [2]Hall, J. C., & Lawson, R. A. (2014). Economic Freedom of the World: An Accounting of the Literature. Contemporary Economic Policy, 32(1), 1–19. https://doi.org/10.1111/coep.12010. [3]Gropper, D. M., Lawson, R. A., & Throne Jr., J. T. (2011). Economic Freedom and Happiness. Cato Journal. (31), 237–255. Retrieved from https://business.fau.edu/images/business/ourcollege/deans_office/dean_groppers_publications /Economic-Freedom-and-Happiness.pdf. [4]Nikolaev, B. (2014). Economic Freedom and Quality of Life: Evidence from the OECD's Your Better Life Index. (29). Retrieved from http://borisnikolaev.com/wp- content/uploads/2014/08/Economic-Freedom-and-Quality-of-Life.pdf [5]Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Adaptive computation and machine learning. Cambridge, Mass: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN= 307676 [6]Samuel, A. L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. (Volume:3, Issue: 3), 210. [7]Cleve, J. (2016). Data Mining (2nd ed.). De Gruyter Studium. Berlin: De Gruyter. Retrieved from http://ebookcentral.proquest.com/lib/gbv/detail.action?docID=4793920 [8]Rey, G. D., & Wender, K. F. (2011). Neuronale Netze: Eine Einführung in die Grundlagen, Anwendungen und Datenauswertung (2., vollst. überarb. und erw. Aufl.). Bern: Huber. Retrieved from http://sub-hh.ciando.com/book/?bok_id=67571 [9]James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R (Corr. at 4. print). Springer texts in statistics. New York NY u.a.: Springer.
  • 9. [10] Luhmann, N. (1984). Die Wirtschaft der Gesellschaft als autopoietisches System. Zeitschrift für Soziologie, 13(4), 308–327. Retrieved from http://www.zfs- online.org/index.php/zfs/article/viewFile/2528/2065 [11]Dörner, D. (2000). Die Logik des Mißlingens: Strategisches Denken in komplexen Situationen (95. - 100. Tsd). Rororo: Vol. 19314. Reinbek bei Hamburg: Rowohlt. [12]Starbatty, J. (2017). Marktwirtschaft, Soziale Marktwirtschaft, Konrad-Adenauer-Stiftung. Retrieved from http://www.kas.de/wf/de/71.11494/ [13]Farmer, K. (Ed.). (2006). Marktwirtschaft und Ethik: Vol. 10. Theorie der Wirtschaftspolitik, Entwicklungspolitik und Wirtschaftsethik: Festschrift für Werner Lachmann zum 65. Geburtstag. Wien: Lit-Verl. [14]Erhard, L., & Langer, W. (1964). Wohlstand für alle (8th ed.). Köln: Anaconda Verl. [15]Berger, J. (2012a). Soziale Marktwirtschaft. Retrieved from http://www.bpb.de/politik/grundfragen/deutsche-verhaeltnisse-eine- sozialkunde/138633/soziale-marktwirtschaft [16]Arnold, L. (2010). Unabhängige Wirtschaftspolitik. Wiesbaden: VS Verlag für Sozialwissenschaften. [17]Schüller, A. (2017). Ordoliberalismus Soziale Marktwirtschaft. Retrieved from http://www.kas.de/wf/de/71.10255/ [18]Berger, J. (2012b). Soziale Marktwirtschaft. Retrieved from http://www.bpb.de/politik/grundfragen/deutsche-verhaeltnisse-eine- sozialkunde/138633/soziale-marktwirtschaft. [19]Scherer, J. F. (2016). Impulse - Freiheit, Gleichheit, Inklusivität: Der Ordoliberalismus als Ausgangspunkt einer Neuen Sozialen Ordnungspolitik. Retrieved from http://library.fes.de/pdf- files/managerkreis/12642.pdf [20]Döhrn, R. (2014). Konjunkturdiagnose und - prognose: Eine anwendungsorientierte Einführung. Berlin, Heidelberg: Springer Berlin Heidelberg.
  • 10. [21]Fraser Institute. Economic Freedom Basics. Retrieved from https://www.fraserinstitute.org/economic-freedom/economic-freedom-basics [22]Gwartney, J., Lawson, R., & Fraser Institute. (2002). Economic Freedom of the World: 2002 Annual Report. [23]Fraser Institute. (2015). The Benefits of Economic Freedom. Retrieved from https://www.fraserinstitute.org/economic-freedom/economic-freedom-basics [24]Gwartney, J., Lawson, R., & Gartzke, E. (2005). Economic Freedom of the World: 2005 Annual Report. [25]Bologna, J., & Hall, J. (2014). Economic Freedom Research: Some Comments and Suggestions. The Annual, 2014, 123–136. Retrieved from https://www.beloit.edu/upton/assets/VOL_VI.Bologna.Hall.pdf [26]United Nations Development Programme. (2013a). Bericht über die menschliche Entwicklung 2013: Der Aufstieg des Südens: Menschlicher Fortschritt in einer ungleichen Welt. Bericht über die menschliche Entwicklung: Vol. 2013. Bonn: UNO-Verl. [27]United Nations Development Programme. (2017). About Human Development | Human Development Reports. Retrieved from http://hdr.undp.org/en/humandev [28]United Nations Development Programme. (2013b). Technical Notes. Retrieved from http://hdr.undp.org/sites/default/files/hdr_2013_en_technotes.pdf [29]United Nations Development Programme. (2015a). The 2010 Human Development Index (HDI). Retrieved from http://hdr.undp.org/sites/default/files/hdi_training.pdf [30]United Nations Development Programme. (2015b). The 2010 Human Development Index (HDI). Retrieved from http://hdr.undp.org/sites/default/files/hdi_training.pdf [31]United Nations Development Programme. (2016). Human Development Index (HDI). Retrieved from http://hdr.undp.org/en/content/human-development-index-hdi
  • 11. [32]Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine. (17). Retrieved from https://www.aaai.org/ojs/index.php/aimagazine/article/viewFile/1230/1131 [33]Piatetsky, G. (2016). R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results. Retrieved from http://www.kdnuggets.com/2016/06/r-python-top- analytics-data-mining-data-science-software.html/2 [34]RapidMiner GmbH. (2017). Gradient Boosted Trees - RapidMiner Documentation. Retrieved from https://docs.rapidminer.com/studio/operators/modeling/predictive/trees/gradient_boosted_tr ees.html [35]Piatetsky, G. (2013). KDnuggets Annual Software Poll:RapidMiner and R vie for first place. Retrieved from http://www.kdnuggets.com/2013/06/kdnuggets-annual-software-poll- rapidminer-r-vie-for-first-place.html [36]The R Foundation. (2016). R: What is R? Retrieved from https://www.r-project.org/about.html [37]University of Waikato. Data Mining with Open Source Machine Learning Software in Java. Retrieved from http://www.cs.waikato.ac.nz/ml/weka/ [38]Mierswa, I. (2016). Introducing RapidMiner 7.2, Free Versions of Server & Radoop, and New Pricing | RapidMiner. Retrieved from https://rapidminer.com/introducing-new-rapidminer- pricing-free-versions-server-radoop/ [39]Mason, L., Baxter, J. O., Bartlett, P. L., & Frean, M. R. (1999). Boosting Algorithms as Gradient Descent. Retrieved from http://papers.nips.cc/paper/1766-boosting-algorithms-as-gradient- descent.pdf Authors Jonathan Staufer completed his Bachelor of Science and Master of Science Degrees in Information Systems at the Technische Hochschule Nuernberg Patricia Brockmann is a professor for Information Systems at the Technische Hochschule Nuernberg, Germany
  • 12. Paper 03 DESIGN AND IMPLEMENTATION OF SMART COOKING BASED ON AMAZON ECHO Lin Xiaoguang1,2,3, Yang Yong3 and Zhang Ju1,3, 1University of Chinese Academy of Sciences, China, 2Chinese Academy of Sciences - Chengdu, China and 3Chinese Academy of Sciences - Chongqing, China ABSTRACT Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking food. People may speak to Amazon Echo during the cooking in order to get the information and situation of the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services, and speaks to people the results that cloud services make by querying the embedded cooking knowledge and achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile application are well-designed and implemented to monitor the temperature of cooking food. KEYWORDS Smart Cooking, Things of Internet, Cloud Services, Smart Home. For More details ; http://aircconline.com/ijscai/V7N4/7418ijscai03.pdf Volume Link: http://airccse.org/journal/ijscai/current2018.html
  • 13. REFERENCES [1] Alam M R, Reaz M B, Ali M A, (2012) “A Review of Smart Homes—Past, Present, and Future”, systems man and cybernetics, Vol 42, No. 6, pp1190-1203. [2] Alif Ahmad Syamsudduha, Dyah Pratiw, etc, (2013) “Future Smart Cooking Machine System Design”, TELKOMNIKA, Vol.11, No.4, pp827~834 [3] Hashimoto Atsushi, Mori Naoyuki, etc, (2008) “Smart Kitchen: A User Centric Cooking Support System”, Proceedings of IPMU'08, pp848-854. [4] Margaret Rouse, “smart speaker”, https://whatis.techtarget.com/definition/smart- speaker, May 2017. [5] “Amazon.com Help: Set Up Your Amazon Echo”, Amazon.com. March 4, 2015. [6] Dieter Bohn, “You can finally say 'Computer' to your Echo to command it”, The Verge, Jan 23, 2017. [7] “Alexa Voice Service Overview”, Amazon.com, Feb 7, 2016. [8] Alex Handy, “Amazon introduces Lambda, Containers at AWS re:Invent”, SD Times, November, 14, 2014 [9] Beth M Sheppard, (2017) “Theological Librarian vs. Machine: Taking on the Amazon Alexa Show (with Some Reflections on the Future of the Profession)”, Theological Librarianship, Vol 10, issue 1, pp8-23. Authors Lin Xiaoguang Lin Xiaoguang is a Ph.D. candidate in University of Chinese Academy of Sciences.