Asia-Pacific Forum on Science Learning and Teaching, Volume 19, Issue 1, Article 8 (Jun., 2018)
Ananta Kumar JENA
Predicting learning outputs and retention through neural network artificial intelligence in photosynthesis, transpiration and translocation

Previous Contents Next


References

Adel, M., Soteris A., Kalogirou, S. A. (2008). Artificial intelligence techniques for photovoltaic applications. Progress in Energy and Combustion Science, 34(5), 574-632.

Agung ,A & Gaol, F.L  (2012).Game Artificial Intelligence Based Using Reinforcement Learning. Procedia Engineering, 50, 555-565.

Almeida,J.S.(2002). Predictive non-linear modeling of complex data by artificial neural networks. Current Opinion in Biotechnology, 13(1), 72-76.

Barto,A.G., Sutton,R.S.(1997). Chapter 19 reinforcement learning in artificial intelligence. Advances in psychology,121, 358-386. DOI:10.1016/So166-4115(97)80105-7.

Beltramett,L., Fiorentin,R.,Marengo, L.& Tamborini ,R.(1997). A learning-to-forecast experiment on the foreign exchange market with a classifier system. Journal of Economic Dynamics and Control, 21(8–9), 1543-1575.

Benaroch, M. (1996). Artificial intelligence in economics Truth and dare. Journal of Economic Dynamics and Control, 20(4), 601-605.

Bennett,C. C. & Hauser , K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial Intelligence in Medicine, 57(1), 9-19.

Blandford, A. (1990). Engineering design education: the potential offered by artificial intelligence techniques. Design Studies, 11 (4) pp. 212-222. 10.1016/0142-694X(90)90040-J. 

Boden,M.A.(1998).Creativity And Artificial Intelligence. Artificial Intelligence, 103 (1-2), 347-356.

Brady, M. (1985). Artificial intelligence and robotics. Artificial Intelligence, 26(1), 79-121.

Bratko, I.(1993). Machine learning in artificial intelligence. Artificial Intelligence in engineering ,8(3),159-164.

Callow,D., Blumenstein,J.L.M.,Guan,H.,Loo,Y.C.(2013). Development of hybrid optimization method for  Artificial Intelligence based bridge deterioration model-Feasibility study. Automation in Construction31 (2013), 83-91.

Cantu- Ortiz F. J. (2013). Advancing artificial intelligence research and dissemination through conference series: Benchmark, scientific impact and the MICAI experience. Expert systems with applications, In press Corrected Proof, Available online.

Chan, C.W. & Huang, G.H. (2003). Artificial intelligence for management and control of pollution minimization and mitigation processes. Engineering Applications of Artificial Intelligence, 16(2), 75-90.

Chen, S.H. , Jakeman, A.J. & Norton J.P. (2008). Artificial Intelligence techniques: An introduction to their use for modelling environmental systems. Mathematics and computers in simulation, 78 (2-3), 379-400.

Conrad, M (1987). Rapprochement of artificial intelligence and dynamics. European Journal of Operational Research, 30(3), 280-290.

Davies, C.R. (2011). An evolutionary step in intellectual property rights – Artificial intelligence and intellectual property. Computer Law & Security Review, 27(6), 601-619.

Dzeroski,S.,Grbovic,J.,Walley,W. J., Kompare,B.(1997). Using machine-learning techniques in the construction of models. Ecological Modelling, 95(1),95-111.

Elofson, G.S. & Konsynski, B. R.(1993). Performing organizational learning with machine apprentices. Decision Support Systems, 10(2),109-119.

Feldman , J. A. & Yakimovsky ,Y.(1974). Decision theory and artificial intelligence: I. A semantics-based region analyzer. Artificial Intelligence, 5 (4), 349-371.

Fethi, M.D. & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204(2), 189-198.

Frantz, R. Herbert Simon, H. (2003). Artificial intelligence as a framework for understanding intuition. Journal of Economic Psychology, 24(2), 265-277.

Froese ,T .& Ziemkegion, T.(2009). Enactive artificial intelligence: Investigating the systemic organization of life and mind. Journal of Artificial Intelligence, 173 (3-4),349-371.

Gascue,I.O. (1993). Inductive learning and biological sequence analysis. The PLAGE program Original. Biochimie, 75 (5), 363-370.

Girosi, F. (1992). Some of radial basis extensions functions and their applications in artificial intelligence. Computers  and Mathematics with application, 24 (12), 61-80.

Glover, F. & Greenberg, H. J. (1989). New approaches for heuristic search: A bilateral linkage with artificial intelligence. European Journal of Operational Research, 39(2), 119-130.

Goyache,F., Bahamonde,A., Alonso, J., Lopez, S., Del Coz, J.J. , Quevedo, J.R. , Ranilla, J., Luaces, O. Alvarez, I. , Royo,L.J. & Diez, J  ( 2001). The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science & Technology, 12(10), 370-381.

Gray, N.A.B. (1988).   Artificial intelligence in chemistry. Analytica Chimica Acta, 210, 9-32.

Guastello, S.J. & Rieke, M.L. (1994).Computer-based test interpretations as expert systems: Validity and viewpoints from artificial intelligence theory. Computers in Human Behavior, 10(4), 435-455.

Hendry ,L.C. (1987). The potential impact of artificial intelligence on the practice. European Journal of Operational Research, 28(2), 218-225.

Horvitz, E.J., Breese, J.S. & Henrion, M (1988). Decision Theory In Expert System And Artificial Intelligence. International Journal of Approximate Reasoning, 2(3), 247-302.

Hunniford, T.J.C. & Hickey ,R.J.(1999). A simulated annealing technique for generating artificial data to assess concept learning algorithms. Intelligent Data Analysis, 3(3),177-189.

Iglesias, G., Castro, A. & Fraguela, J.A. (2010).  Artificial intelligence applied to floating boom behavior under waves and currents. Ocean Engineering, 37(17-18), 1513-1521.

Jena, A.K. (2012). Does constructivist approach applicable through concept maps to achieve meaningful learning in Science? Asia-Pacific Forum on Science Learning and Teaching, 13(1), Article 7 (Jun., 2012).

Jena, A.K. (2014). Effects of collaborative and individual modes of concept maps on plant science: a comparative analysis. Int. J. Innovation and Learning, 15(2), 167–191.

Jena, A.K. (2015). Effects of web reading, online animation models, online flash models, and online youtube learning in digestive system. The Online Journal of Distance Education and e-Learning, 3(4), 28-43.

Jena, A.K. (2015). Science Achievement Test, Assam University,Silchar.

Jena, A.K., Gogoi, S.S., Deka, M. (2016). Cell (biology)-wikipedia learning performance in relation to cognitive styles, learning styles, and science ability of students: a hierarchical multiple regression analysis. The Online Journal of Distance Education and e-Learning, 4(2),1-19.

Jolly, K.G., Ravindran, K.P., Vijayakumar, R. & Sreerama Kumar, R. (2007). Intelligent decision-making in multi-agent robot soccer system through compounded artificial neural networks. Robotics and Autonomous Systems, 55(7),589-596.

Kadkhoda ,M., & Jahani ,H.(2012). Problem-solving capacities of spiritual intelligence for artificial intelligence. Procedia - Social and Behavioral Sciences, 32, 170-175.

Kaiser ,W. & Faber ,T. S. &  Martin F. (1996)Automatic learning of rules: A practical example of using artificial intelligence to improve computer-based detection of myocardial infarction and left ventricular hypertrophy in the 12-lead ECG. Journal of Electrocardiology, 29(1), 17-20.

Khosrowshahi,F.(2011). Innovation in artificial neural network learning: Learn-On-Demand methodology. Automation in Construction, 20(8), 1204-1210.

Kompare ,B. (1998).Estimating environmental pollution by xenobiotic chemicals using QSAR (QSBR) models based on artificial intelligence. Water Science and Technology, 37(8), 9-18.

Kumar, G.P. & Venkataram, P.(1997). Artificial intelligence approaches to network management:  recent advances and a survey. Computer Communications, 20 (15), 1313-1322.

Laghaee,A.,Malcolm,C.,Hallam,J.& Ghazal,P.(2005). Artificial intelligence and robotics in high throughput post-genomics. Drug Discovery Today,10(18),1253-1259.

Larranaga, P. & Moral, S. (2011). Probabilistic graphical models in artificial intelligence. Applied Soft Computing, 11 (2), 1511-1528.

Lu, S.& Ham, I. (1989) .Machine Learning Techniques for Group Technology Applications. CIRP Annals - Manufacturing Technology, 38(1),455-459.

Madan, S. &  Bollinger,K.E.(1997). Applications of artificial intelligence in power system. Electric Power Systems Research, 41(2) ,117-131.

Madani, K. & Sabourin,C(2011). Multi-level cognitive machine learning based concept for human-like “artificial” walking: Application to autonomous stroll of humanoid robots. Neurocomputing, 74(8), 1213-1228.

Meservy,R.D., Denna, E.L. & Hansen J.V. (1992). Application of artificial intelligence to accounting, tax, and audit services: Research at Brigham Young University. Expert Systems with Applications, 4(2), 213-218.

Mira,J.M.(2008).Symbols versus connections: 50 years of artificial intelligence. Neuro computting , 71(4-6), 671-680.

Monostori ,L.(2003). AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing.Engineering Applications of Artificial Intelligence, 16(4), 277-291.

Mulholland, M.,Hibbert ,D.B., Haddad, P.R. & Parslov ,P.(1995). A comparison of classification in artificial intelligence, induction versus self-organising neural networks. Chemometrics and Intelligent Laboratory Systems, 30(1),117-128.

Nandhakumar, N & Aggarwal, J.K.(1985). The artificial intelligence approach to pattern recognition—a perspective and an overview. Pattern Recognition, 18(6), 383-389.                                       

Nongjian,Z.(1999). A core of ego and a new system of artificial intelligence. Computers  in Human Behavior, 15(5), 625-652.

Noroozi,A., Mokhtari,H. & Abadi, I. N. K. (2013). Research on computational intelligence algorithms with adaptive learning approach for scheduling problems with batch processing machines.  Neurocomputing, 101(4), 190-203.

Olley, P & Kochhar,A.K. (1996). Case simulation to assess learning systems. Engineering Applications of Artificial Intelligence, 9(3), 285-300.

Olmo, F.H., Llanes,F.H & Gaudioso,E.(2012). An emergent approach for the control of wastewater treatment plants by means of reinforcement learning techniques. Expert Systems with Applications, 39(3), 2355-2360.

Orallo, J. H. & Dowe D. L. (2010). Measuring universal intelligence: Towards an anytime intelligence test.  Artificial Intelligence, 174 (18) 1508-1539.

Partridge, D. (1988). Artificial intelligence and software engineering: a survey of possibilities. Information and Software Technology, 30 (3), 146-152.

Pau, L.F. & Tan P.Y. (1996). Chapter 8 Artificial intelligence in economics and finance: A state of the art — 1994: The real estate price, assets, and liability analysis case.  Handbook of Computational Economics. 1, 405-439.

Pham, D.T. & Pham, P.T.N. (1999). Artificial intelligence in engineering. International Journal of Machine Tools and Manufacture, 39(6), 937-949.

Pierre, S.(1993). Application of artificial intelligence techniques to computer network topology design. Application of Artificial Intelligence Techniques to Computer,6(5),465-472.

Place, J.F., Truchaud, A., Ozawa, K., Pardue, H., & Schnipelsky, P. (1995). Use of artificial intelligence in analytical systems for the clinical laboratory. Clinical Biochemistry, 28(4), 373-389.

Pomeral, J.C. (1997). Artificial intelligence and human decision-making. European Journal of Operational Research, 99(1), 3-25.

Prieto, A. & Atencia, M. & Sandoval, F. (2013). Advances in artificial neural networks and machine learning. Neurocomputing, 121(9), 1-4.

Roch, C., Pun, T., Hochstrasser D.F., &  Pellegrini, C.(1989). Atomatic' learning strategies, and their application to electrophoresis analysis. Computerized Medical Imaging and Graphics, 13 (5), 383-391.

Rosner, M. & Baj, F. (1993). Portable AI lab for teaching artificial intelligence. Education and Computing, 8(4), 347-355.

Rowe, W. B., Yan, L. Inasaki, I. & Malkin, S. (1994). Applications of Artificial Intelligence in Grinding. CIRP Annals - Manufacturing Technology, 43 (2), 521-531.

Rowe,R.C. & Roberts,R.J (1998. Artificial intelligence in pharmaceutical product formulation: knowledge-based and expert systems. Pharmaceutical Science & Technology  Today, 1(4), 153-159.

Shaw, M. J.  & Fox, M. S. (1993). Distributed artificial intelligence for group decision support: Integration of problem solving, coordination, and learning. Decision Support Systems, 9(4), 349-367.

Smithers, T.,Tang M.X., Ross, P & Tomes, N. (1993). Supporting drug design using an incremental learning approach. Artificial Intelligence in Engineering, 8(3), 201-216.

Spackman  K.A. (1985). A program for machine learning of counting criteria: empirical induction of logic-based classification rules. Computer Methods and Programs in Biomedicine, 21(3), 221-226.

Stjepanovič,Z. &  Jezernik, A. ( 1991). The prediction of cotton yarn properties using artificial intelligence. Computers in Industry, 17(2–3), 217-223.

Stojanov, G., Trajkovski, G & Kulakov, A. (2006). Interactivism in artificial intelligence (AI) and intelligent robotics. New Ideas in Psychology, 24(2), 163-185.

Sun, R. (2001). Artificial Intelligence: Connectionist and Symbolic Approache. International Encyclopedia of the Social & Behavioral Sciences, 783-789.

Torasso, P. (1991). Supervising the heuristic learning in a diagnostic expert system. Fuzzy Sets and Systems, 44(3), 357-372.

Uraikul,V., Chan,C.W.,& Tontiwachwuthikul, P.(2007). Artificial intelligence for monitoring and supervisory control of process systems. Engineering Applications of Artificial Intelligence, 20( 2), 115-131.

Williams, N. (1992). The artificial intelligence applications to learning programme. Computers & Education, 18(1-3), 101-107.

 

 


Copyright (C) 2018 EdUHK APFSLT. Volume 19, Issue 1, Article 8 (Jun., 2018). All Rights Reserved.