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Questions: When and how Elm should be used for business applications? When Elm should be selected or not selected for software projects? How to program business applications in Elm? Answers: Introduction This project is focused on gaining an in-depth knowledge in Elm which is a functional programming language. This study will further dive into determining when Elm should be used or not used for software projects. In this project, the discussions will be carried out to become professionals in in using Elm for business applications. Further, this project will help to design and build an application by using Elm. This project will investigate upon the features of functional programming especially Elm and how viable it is context to production (Chakravorty and Hales 2017). The architecture of Elm is simple and easy to use for designing web applications also there is modularity and scope of reusing codes as well as testing. The Elm architecture is easy however it is useful for front-end projects only and has poor integration with the existing applications. The programming industry have to be aware of the context in which Elm can be used or not used for business applications. Hence, this p roject aims to deliver a guide for businesses to determine the when m should and should not be used for business applications. Finally, the research project will help to design and simple business application with the help of Elm. Research Question/s and intended outcomes The aim of this project is determining the use of Elm for business applications and hence the major research question is presented as below: The major aim of the research questions is to investigate Elm as a functional programming language and role played by it for designing business applications. The questions have been prepared for gaining in-depth knowledge in Elm and how it can be associated with software projects. The intended outcome of this project is a simple business application that will be designed by using Elm. This project will help to assess when and how Elm should be used for business applications (Bettinazzi and Zollo 2016). This project will also help the professionals in their future research on utilizing Elm for designing of business applications. Methodology The primary and secondary sources of information together forms the base of data collection tools in almost any research project. The primary sources of information for this project will be collected from online survey questionnaires and direct face to face interviews with professionals in the field of business application programming (Zhang et al. 2016). Favorable position of using primary data is that experts are gathering information for the specific inspirations driving their examination. Surveys routinely contain diverse choice inquiries, manner scales, close request and open-completed request. The online questionnaires will help to gather accurate information. The secondary source of information will comprise of meetings that are generally conducted very close however it can be controlled by telephone or using more innovative technology, for instance, Skype (Mayer 2014). At times they are held in the interviewee's home, as a rule at a more objective place. The secondary data will also be collected from various case studies that relate to the subject topic. Feasibility and Scope of the Study This project is feasible in context to the scope of the study as it involves investigation of functional programming and that has a significant role to play designing of business applications. This project will help to achieve in-depth knowledge of Elm and how it can be used for business applications. The research project will also eventually help to design a simple business application with the help of Elm and produce a guide for businesses on when Elm should and should not be used for business applications (Komi et al. 2017). The professionals in programming industry will find this research project to be beneficial as it will open doors for them to gain in-depth knowledge of Elm. Report Structure (proposed) The report structure being proposed for completion of the research project is presented as below: Activities for the research 1st Week 2nd Week 3rd Week 4th Week 5th Week 6th Week 7th Week Completion dates Gathering information from secondary sources 25/08/2017 Preparing layout for the project 30/08/2017 Literature review on available journals 12/09/2017 Planning for the project 15/09/2017 Collection of information from primary sources 20/09/2017 Findings and Analysis 30/09/2017 Discussion and interpretation 05/10/2017 Drawing conclusion to the study 15/10/2017 Completing the final paper 20/10/2017 References Akusok, A., Veganzones, D., Bjrk, K.M., Sverin, E., du Jardin, P., Lendasse, A. and Miche, Y., 2014. ELM ClusteringApplication to Bankruptcy Prediction. In International work conference on TIme SEries (pp. 711-723). Bettinazzi, E.L.M. and Zollo, M., 2016, January. Stakeholders and Organizational Learning: Theory and Evidence from Corporate Acquisitions. In Academy of Management Proceedings (Vol. 2016, No. 1, p. 13573). Academy of Management. Bgel, P.M., 2015. Processing of CSR communication: insights from the ELM. Corporate Communications: An International Journal, 20(2), pp.128-143. Bowen, P.G., 2014. The Strategy Wagon Wheel and Its Application For Teaching Strategy. Business Education Innovation Journal, 6(2). Chakravorty, S.S. and Hales, D.N., 2017. Sustainability of process improvements: an application of the experiential learning model (ELM).International Journal of Production Research, pp.1-17. della Porta, G., Principi, E., Ferroni, G., Squartini, S., Hussain, A. and Piazza, F., 2016. ELM based algorithms for acoustic template matching in home automation scenarios: advancements and performance analysis. In Recent Advances in Nonlinear Speech Processing (pp. 159-168). Springer International Publishing. Ding, S., Zhao, H., Zhang, Y., Xu, X. and Nie, R., 2015. Extreme learning machine: algorithm, theory and applications. Artificial Intelligence Review, 44(1), pp.103-115. Elm, C., Knight, T., Martin, M.M., Michel, K.J., Boctor, M. and Loberg, S.J., Disney Enterprises, Inc., 2016. Content orchestration for assembly of customized content streams. U.S. Patent 9,503,770. Elm, J.P. and Goldenson, D., 2014. The Business Case for Systems Engineering: Comparison of Defense-Domain and Non-Defense Projects. Ismaeel, S., Miri, A., Sadeghian, A. and Chourishi, D., 2015, November. An Extreme Learning Machine (ELM) Predictor for Electric Arc Furnaces' vi Characteristics. In Cyber Security and Cloud Computing (CSCloud), 2015 IEEE 2nd International Conference on (pp. 329-334). IEEE. Khalid, H.M., Liew, W.S., Helander, M.G. and Loo, C.K., 2016, December. Prediction of trust in scripted dialogs using neuro-fuzzy method. In Industrial Engineering and Engineering Management (IEEM), 2016 IEEE International Conference on (pp. 1558-1562). IEEE. Komi, M., Li, J., Zhai, Y. and Zhang, X., 2017, June. Application of data mining methods in diabetes prediction. In Image, Vision and Computing (ICIVC), 2017 2nd International Conference on (pp. 1006-1010). IEEE. Mayer, B., 2014. Designing the Business Strategy Game to Promote Strategic Thinking and Student Engagement: An Application of the Four Disciplines of Execution. Business Education Innovation Journal VOLUME 6 NUMBER 2 December 2014, p.86. Mila?i?, L., Jovi?, S., Vujovi?, T. and Miljkovi?, J., 2017. Application of artificial neural network with extreme learning machine for economic growth estimation. Physica A: Statistical Mechanics and its Applications, 465, pp.285-288. Razzaghi, T., Otero, A. and Xanthopoulos, P., 2017. Imbalanced classification for business analytics. In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 660-670). IGI Global. Teng, S., Khong, K.W. and Goh, W.W., 2014. Conceptualizing persuasive messages using ELM in social media. Journal of Internet Commerce, 13(1), pp.65-87. Wang, X.Z. and Wang, H., 2014. Guest editorial: learning from uncertainty and its application to intelligent systems of web information. World Wide Web, 17(5), p.1027 Zhang, N., Chen, H., Chen, X. and Chen, J., 2016. ELM meets urban computing: ensemble urban data for smart city application. In Proceedings of ELM-2015 Volume 1 (pp. 51-63). Springer, Cham. Zhang, N., Chen, H., Chen, X. and Chen, J., 2016. ELM Meets Urban Big Data Analysis: Case Studies. Computational intelligence and neuroscience, 2016. ZHANG, Y. and LI, M., 2016. A Novel Evaluation Model of Water Quality Based on PSO-ELM Method. Environmental Science Technology, 5, p.026. ZHENG, H.C., QI, Z.H., WU, J., WANG, T. and WAN, N., 2015. Antecedents of Award-Based Crowdfunding Performance: From the ELM Perspective. Journal of University of Electronic Science and Technology of China (Social Sciences Edition), 1, p.009.