Application of Artificial Intelligence for the Development Africa

1. Application of Artificial Intelligence for the Development Africa Dr Osei Adjei

2. Overview • What is Artificial Intelligence? • AI applications • Agriculture • Other significant applications. • AI paradigms • Previous applications. • Current Research and applications • Conclusions • Acknowledgements

3. What is Artificial Intelligence? • Machines that mimic the behaviour of human beings. • Machines that can think for themselves. • These machines can be robots, a product inspection machine on assembly line, machine for diagnosing diseases, software etc.

4. AI Applications • Forest land mapping and classification. • Forest growth and dynamics modeling, • Spatial data analysis and modeling. • Plant disease dynamics modeling. • Climate change research. • Agriculture.

5. Agriculture • An expert geographical information system for land evaluation. • Artificial neural networks for plant classification with image processing. • Simulation: Biomass growth model. • Control of green house. • Control of biomass.

6. Other significant applications • Transportation: Planning and logistics • Medicine: Diagnosis of cancerous cells, X-ray and NMR images. • Military: Detection of enemy aircrafts, remote sensing. • National and airport security: Face recognition, fingerprint recognition, fast DNA data analysis.

7. Modelling • Complex systems can be non-linear, difficult to formulate any mathematical expresssion and are multi-variate. • Traditional methods (usually statistical methods) are not adequate to model complex system. • AI is a useful tool for modeling such systems. • AI produces results that even surpasses those derived from traditional methods.

8. AI Paradigms • Neural Computation (also known as Neural networks) • Fuzzy Logic • Genetic algorithms • Expert Systems • Swarm Intelligence

9. Neural Computation • Neural computation is carried out by a number basic elements. • The elements are modelled on real neurons in the mammalian brain. • The elements put together function as parallel distributed processors. • Thus, they have the ability to model any complex system.

10. Neural network Courtesy: http://interests.caes.uga.edu/

11. Fuzzy Logic • Fuzzy Logic is a multi-valued logic that allows intermediate values to be defined between Aristotelian logic evaluations, where only true/false, yes/no or 1/0 is used. • Notions such as hot or very comfortable can be formulated mathematically and processed by computers. • It provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information.

12. Fuzzy Logic (cont.) • Fuzzy Logic has been used in control applications more often because the controllers are easy to design, often requiring few hours to develop a very sophisticated system. • Although the Fuzzy Logic designs are simple their performances are usually higher than those using traditional methods.

13. Genetic Algorithms • Genetic Algorithms are a means by which machines can emulate the mechanisms of natural selection. • This involves searching high-dimensional spaces for superior or optimal solutions. • The algorithms are simple, robust, and general. • GAs assume no knowledge of the search space and can be computationally intensive. • GAs are inherently parallel in nature and so their execution rate increases with the number of processors.

14. Expert Systems • Expert systems represent knowledge in the form of if-then-else rules. • Initial stage of building an expert system is to acquire the rules. • This is also known as the knowledge acquisition stage. • Knowledge acquisition requires the interaction between two specialists: A knowledge engineer and a domain expert. • The outcome of such a meeting is that a preliminary rule base is produced.

15. Evaluating rules in Expert Systems • The rules are evaluated on trial data with errors noted. • Following this, subsequent meetings are held between the two specialists to resolve all the erroneous issues. • The main problem associated with the construction of an expert system is that sometimes the domain expert may not be fully cooperative or he/she might find it difficult to create rules that he/she uses in his work. • Hence, the system may not be fully realized.

16. Swarm Intelligence • Swarm intelligence refers to the property of a system in which the collective behaviours of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. • It provides the basis which makes it possible to explore a collective (or distributed) problem solving without centralized control or the provision of a global model. • Examples of such a system are ants and bees colonies

17. Applications of AI • Security applications: • Fingerprint applications • Facial identification. • Inspection of manufactured parts. • Bin picking. • Visual Inspection of objects (e.g. fruits). • Medical Intelligent Tutoring Systems. • Transportation. • Medical Applications: • Diagnosis • NMR/X-Ray Slides Diagnosis • Determination of mineral resources.

18. Current Research and Applications • A Data-driven Adaptive Tutoring System for teaching Modern Standard Arabic language • Intelligent Multimedia Medical Assessment Based on Hidden Markov Model. • Detection of Drowsy Behaviour in restrictive environment. • Trust: Intelligent Agents for policing the Internet.

19. Conclusions • Applications range from all areas of both the industrial and commercial infrastructure of countries. • It has been applied in agriculture, health, medicine and as a teaching tool in the form of intelligent tutoring system. • AI is not just a scientist tool but a very significant tool that will help Africa and the rest of the developing world to solve a vast number of their problems. • Africa must be more involved in AI technology because it has been successful in the developed countries. • It is now a matured technology that can be applied anywhere with confidence.

20. Acknowledgement Many thanks to the UN, IRAC, CATS Faculty, University of Bedfordshire for partially sponsoring my trip to Addis Ababa to present this paper.