Efficient Elephant Identification: Integrating Extreme Gradient Boosting (XGBoost) with Genetic Algorithms for Wildlife Conservation

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@article{1705661,
      author = {Prashant Mahendra Yadav, Mithilesh Vishwakarma, Sonal Rajkumar Mourya, Aman Mishra},
      title = {Efficient Elephant Identification: Integrating Extreme Gradient Boosting (XGBoost) with Genetic Algorithms for Wildlife Conservation},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {10},
      pages = {40-43},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705661.pdf},
      abstract = {Our research delves into the multifaceted realm of elephant recognition, employing diverse methodologies to address the challenges posed by image classification and feature extraction. We explore Extreme Gradient Boosting (XGBoost) for image classification, achieving an impressive 87.50% accuracy in discerning elephant subtypes. Furthermore, we investigate Genetic Algorithms for feature extraction, providing an alternative approach with a commendable 76% accuracy. Our project leverages these techniques to distinguish between various elephant species, such as African bush elephants, Sumatran elephants, and Asian elephants, contributing to wildlife conservation efforts. Through extensive experimentation, we showcase the strengths and limitations of each approach, offering valuable insights for researchers and practitioners in the field.},
      keywords = {Elephant Recognition, XGBoost, Genetic Algorithms, Wildlife Conservation, Image Classification},
      month = {April},
  }