Submissions/Implementations of artificial intelligence techniques in ecosystems applications
After careful consideration, the Programme Committee has decided not to accept the below submission at this time. Thank you to the author(s) for participating in the Wikimania 2014 programme submission, we hope to still see you at Wikimania this August.
- Submission no. 6006
- Title of the submission
- IMPLEMENTATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN ECOSYSTEMS APPLICATIONS
- Type of submission (discussion, hot seat, panel, presentation, tutorial, workshop)
- Author of the submission
- Khalda F.Ali
- E-mail address
- Country of origin
- Affiliation, if any (organisation, company etc.)
- PhD Student at UKM Malaysia university
- Personal homepage or blog
- Abstract (at least 300 words to describe your proposal)
- Artificial Neural Networks (ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is in solving problems which are too complex for conventional technologies, that do not have an algorithmic solutions or their algorithmic Solutions is too complex to be found. In general, because of their abstraction from the biological brain, ANNs are developed from concept that evolved in the late twentieth century neuro-physiological experiments on the cells of the human brain. To overcome the perceived inadequacies with conventional ecological data analysis methods. ANNs have gained increasing attention in ecosystems applications, because of ANN’s capacity to detect patterns in data through non-linear relationships, this characteristic confers them a superior predictive ability. In this research, we apply ANNs in an ecological system analysis. The neural networks use the well known Back propagation (BP) Algorithm with the Delta Rule for adaptation of the system. The Back Propagation (BP) training Algorithm is an effective analytical method for adaptation of the ecosystems applications, the main reason because of their capacity to detect patterns in data through non-linear relationships. This characteristic confers them a superior predicting ability. The(Bp) algorithm uses supervised learning, which means that we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error is calculated. The idea of the back propagation algorithm is to reduce this error, until the ANNs learns the training data. The training begins with random weights, and the goal is to adjust them so that the error will be minimal. This research evaluated the use of artificial neural networks (ANNs) techniques in an ecological system analysis and modeling The experimental results from this research demonstrate that an artificial neural network system can be trained to act as an expert ecosystem analyzer for many applications in ecological fields. The pilot ecosystem analyzer shows promising ability for generalization and requires further tuning and refinement of the basis neural network system for optimal performance.
- Open Scholarship
- Length of session (if other than 30 minutes, specify how long)
- 30 Minutes
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- Slides or further information (optional)
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