An intelligent nocturnal animal identification system is proposed in this work to assist in the recognition of different kinds of nocturnal animals. Firstly, each sound sample underwent the steps of the noise reduction and the syllable segmentation. Next, the segmentation was converted into Mel Frequencies Cepstral Coefficients (MFCCs) as the main identification feature of the proposed work. To increase the recognition accuracy and lower the computation overhead, the system can guide a user to input environmental information as the parameters of the decision tree classification module. It then proceeds with the clustering, and the results are classified with a composite classifier. A series of simulations were conducted to demonstrate the feasibility of the proposed algorithm. Drawing from 18 species, 408 sound samples, and 4477 feature segmentations were utilized to verify the accuracy rate of our work. Our simulations showed that the proposed approach can effectively increase the recognition rate and decrease the training time. In particular, the collected sound sample of the Apus affinis is only one that results in a lower accuracy rate. We expect to expand the database of sound samples, and thus we expect to be able to dramatically improve on the above-mentioned problem, thereby increasing the accuracy rate for each species.
Abstract-In this work, an asynchronous learning platform that detects whether the learners address the expected discussion issues on asynchronous discussion boards is proposed. Concept maps related to the learning topics are first outlined by the instructor. After each learner presents a post on the asynchronous learning platform, a term weighting method is adopted to derive input parameters of a Support Vector Machines (SVMs) classifier. The classifier then determines if the learners' posts are related to the discussion topics. Notably, a peer review mechanism based on group intelligence is established in this work to improve the quality of the classifier. At the same time, a feedback module is used to issue feedback messages to the learners in cases where the proposed asynchronous discussion board detects that the learners have gone off on a tangent. The experimental results revealed that the 31 students in a junior high school participating in asynchronous online discussion activities related to natural science were benefited by the proposed learning assistance platform.
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