Fuzzy mobile expert system for academic advising

Academic advising of students is a task that requires a lot of time, expertise, and intellectual investments from the academic advisor. In order to help students to find a suitable decision in short time and effort, this project implements an Intelligent Algorithm in order to design an expert application on smart phone. The proposed system was implemented and tested for validation with real data that collected from valid students. The experimental results showed that a system have an average root mean square error of 6.64 % and thus can be used successfully to identify the possibility that registering for the course is a correct decision.

[1]  Yan Chen,et al.  Intelligent Fashion Recommender System: Fuzzy Logic in Personalized Garment Design , 2015, IEEE Transactions on Human-Machine Systems.

[2]  Coskun Bayrak,et al.  Automatic mobile segmentation of dermoscopy images using density based and fuzzy c-means clustering , 2014, 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[3]  Chih-Hung Hsieh,et al.  Convergence or divergence?: A comparison of acceptance and use of technology for smart phones and tablets , 2014, Proceedings of PICMET '14 Conference: Portland International Center for Management of Engineering and Technology; Infrastructure and Service Integration.

[4]  Liya Ding,et al.  Handling Knowledge Imperfection in Hybrid Logic Inference , 2016, KES.

[5]  Hao Chen,et al.  A decision making approach based on bipolar multi-fuzzy soft set theory , 2014, J. Intell. Fuzzy Syst..

[6]  A. R. Abdelaziz,et al.  Development of evolutionary models for long-term load of power plant systems , 2003 .

[7]  Shivani Goel,et al.  Expert system and it's requirement engineering process , 2014, International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014).

[8]  Witold Pedrycz,et al.  Foundations of Fuzzy Logic and Soft Computing, 12th International Fuzzy Systems Association World Congress, IFSA 2007, Cancun, Mexico, June 18-21, 2007, Proceedings , 2007, IFSA.

[9]  Chris Cox,et al.  Multiple-criteria genetic algorithms for feature selection in neuro-fuzzy modeling , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[10]  Ramakanta Mohanty,et al.  An expert system approach for legal reasoning in acquire immovable property , 2014, 2014 First International Conference on Networks & Soft Computing (ICNSC2014).

[11]  A. F. Sheta,et al.  A business mobile application: Estimating financial time series models , 2012, 2012 International Conference on Multimedia Computing and Systems.

[12]  M. Virvou,et al.  A mobile expert system for tutoring multiple languages using machine learning , 2012, 2012 International Conference on E-Learning and E-Technologies in Education (ICEEE).

[13]  Jui-Chung Hung,et al.  Combining Fuzzy Systems and Social Networking Sites Design to Alarm Clocks Using the Android System , 2012, 2012 International Symposium on Computer, Consumer and Control.

[14]  Widodo Budiharto,et al.  The Development of Expert Mood Identifier System using fuzzy Logic on BlackBerry Platform , 2013, J. Comput. Sci..

[15]  Jorge García Duque,et al.  SPELTA-Miner: An expert system based on data mining and multilabel classification to design therapy plans for communication disorders , 2016, 2016 International Conference on Control, Decision and Information Technologies (CoDIT).

[16]  Khaled Eskaf,et al.  Aggregated Activity Recognition Using Smart Devices , 2016, 2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI).

[17]  Suresh Sankaranarayanan,et al.  Intelligent agent based job search system in Android environment , 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON ELECTRO/INFORMATION TECHNOLOGY.

[18]  K. Eskaf,et al.  Predicting blood glucose levels in diabetics using feature extraction and Artificial Neural Networks , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.