Research Article of Research Journal of Mathematics and Computer Science
Expert System Model for Diagnosing Legume Diseases
Rahmon,I.A,Adebola_Akinsanya and Eze M.,.
Computer Science Department Babcock University, Ilishan, Ogun State.
There is a current surge in designing expert systems to solve problems like humans in different domains of research but there presently exist dearth of models that can guide designers of expert systems that can be used to accurately diagnose Soya beans diseases. Soya bean is a leguminous plant that is commonly grown in sub Saharan region and it is currently one of the legumes with high Gross Domestic Product (GDP) in the Nigerian economy, but farmers of this product are faced with serious challenges from legume ravaging diseases that are usually similar in symptoms and very difficult to diagnose by the few practising botanist available. Most models available cannot be used to guide designers who are interested in developing secured and reliable expert systems that can accurately classify, identify, diagnose and recommend treatments for Soya beans diseases. The main aim of this paper is to review existing models that are currently being used in designing expert systems and identify their limitations in other to come up with a better, more reliable and secured model. In other to identify inherent gaps from other models, research review method was adopted to critically investigate the weak points in few related and current expert models for classifying and diagnosing legumes diseases.
Keywords: expert system model, diagnosing legume diseases
How to cite this article:
Kasali et al., A Conceptual Design and Evaluation Framework for Mobile Persuasive Health Technologies (Usability Approach). Research Journal of Mathematics and Computer Science, 2017; 1:4.
1 Adewole, K.S, Hambali, M.A, & Jimoh, M.K (2015). Rule-Based Expert System for Diseases Diagnosis: Book of Proceedings, International Science Technology, Engineering, Arts, Management and Social Sciences (ISTREAMS), vol 7, pp. 183-190
2 Akerkar, R., (2014). Introduction to Artificial Intelligence: PHI Learning Pvt. Ltd., Delhi
3 Amarathunga, A.A, Ellawala, E.P, Abeysekara, G.R, & Amalraj, C.R (2015). Expert System for Diagnosis of Skin Diseases: International Journal of Scientific and Technology Research, vol 4, issue 01, pp. 174-175
4 Barr, A. , & Feigenbaum, E. (1981). Handbook of Artificial Intelligence: vol 1, LOS Altos, LA : Milliam Kaufmann
5 Farahani, F.V, Zarandi, M.H, Amadi, A, (2015). Fuzzy Rule-Based Expert System for Diagnosis of Lung Cancer: IEEE Explore Digital Library, Fuzzy Information Processing Society (NAFIPS), World Conference on Soft Computing (WCONSC), pp.1
6 Flores, V., Hadfes, Y., Beckos, J. & Claudio, M. (2016). Generation of Explanations From A Rule-Based Expert System And a Domain Ontology: Researchgate Publication, pp.2
7 Hossain, M.S, Ahmed, F., Tuj-johora, F., & Anderson, K. (2017). A Belief Rule-Based Expert System to Access Tuberculosis Under Uncertainty: Journal of Medical Systems, vol 41, issue 3
8 Hossain, M.S, Hassan, M.A, Uddim, M., Islam, M.M, & Mustafa, R (2015). A Belief Rule-Based Expert System to Access Lung Cancer Under Uncertainty: 18th International Conference on Computer and Information Technology (ICCIT), pp. 1-2
9 Kadhim, A.K, Afshar, M., & Kaur, H. (2016). A Multi-Intelligent Agent System For Automatic Construction of Rule-Based Expert System: Intelligent Systems and Applications, vol 9, pp 62-68
10 Kaur, R., & Din, S. (2016): Web Based Expert System to Detect and Diagnose the Leaf Diseases of Cereals in Punjabi Language: International Journal of Computer Science and Information Technologies (IJCSIT), vol 7, issue 4, pp. 1771-1773
11 Kaur, R, Din., S. & Panru P.S. (2013). Expert System to Detect and Diagnose the Leaf Disease of Cereal: International Journal of Current Engineering and Technology, vol 3. issue 4
12 Mlakic, D., & Majdandzic, L. (2016). Fuzzy Rule Based Expert System for SCADA Cyber Security: retrieved at https://www.researchgate.net/publication/307607103
13 Patra, P.S, Sahi, D.P, & Mandral, I. (2010). An Expert System for Diagnosis of Human Diseases International Journal of Computer Application: vol 1, issue 13
14 Rani, P.M, Rajesh T., & Saravana, R. (2011). Expert system in Agriculture :A review, Journal of Computer And Application, vol 3. issue 1, pp. 59-71
15 Rizzo,L., Dondio,P., Delany, S.J, & Lungo,L. (2016). Modeling Mental Workload Via Rule Based Expert System: A Comparison with NASA-TLX and Workload Profile, Retrieved at https://www.researchgate.net/publication/307585558
16 Sikchi, S.S., & Sitchi, A.M. (2013). Generic Medical Fuzzy Expert System For Diagnosis of Cardiac Disease: International Journal of Computer Application, vol 66, issue 13
17 Singla, J., Grover, D., & Bhandari. A. (2014). Medical Expert System for Diagnosis of Various Diseases: International Journal of Computer Application, vol 93, issue 7
18 Tunmibi, S., Aregbesola, A., & Dasylva, A. (2013). A Rule Based Expert System for Diagnosis of Fever: Retrieved at https://www.researchgate.net/publication/257655660
19 Zeki, T.S., Malakoti, M.V., Ataapoor, Y., & Tabibi, S.T. (2012). An Expert System for Diabetes Diagnosis: America Academic & Scholarly Research Journal, vol 4, issue 5