HYBRID LEARNING FOR BOTTLENOSE DOLPHIN MOTION APPROXIMATION USING WEIGHTED POLLING KNN AND BAYES MECHANISM
DOI:
https://doi.org/10.15282/Keywords:
Probability classification, Supervised learning, Clustering, KNN, Machine LearningAbstract
Artificial intelligence has been extensively employed to examine social animal behaviours, such as feeding habits, reproduction, swarm structures, and hibernation. For marine biologists, grasping essential habitat characteristics like swimming depth and temperature preferences is vital for evaluating ecological boundaries, habitat deterioration, and conservation initiatives. Machine learning has been used to help identify bottlenose dolphins and their cohabitation patterns, as computer vision and deep learning are often used in studies of fish habitats involving predation, feeding habits, breeding, and other fish. Existing techniques do not provide sufficient variety in estimating classification characteristics for assigning unique animals to their corresponding subclasses, particularly for differentiating various marine species along with their respective groups. The objective for this proposed research framework is aimed at integrating supervised clustering and probabilistic classifiers such as K-Nearest Neighbors (KNN) or Bayes classifying with weighted voting to classify dolphin types, apart from consolidated behavioral patterns. The study further integrated the use of oxygen usage into the dataset for monitoring movement activity after pup pre-feeding intervals in addition to metabolic rates of other animals. Data was cross verified, encompassing trial laps, dolphin identifiers, timing, segment length, and carbon dioxide emission rates, for the purpose of classification. Findings showed an average classification accuracy of 97% for weighted KNN types and 91% for weighted Bayes classifiers, emphasizing their effectiveness in differentiating dolphin classes in swarming settings. These machine learning methods provide essential tools for enhancing marine research and aiding conservation and habitat preservation through improved identification of ecological traits.
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