Nayak Basudev, Khuntia Bonomali, Murmu Laxman Kumar, Sahu Bijayalaxmi, Pandit Rabi Sankar, Barik Tapan Kumar
P.G. Department of Zoology, Berhampur University, Bhanjabihar-760007, Odisha, India.
PG Department of Computer Science, Berhampur University, Bhanjabihar-760007, Odisha, India.
Parasitol Res. 2023 Feb;122(2):369-379. doi: 10.1007/s00436-022-07752-9. Epub 2022 Dec 14.
Artificial intelligence (AI) facilitates scientists to devise intelligent machines that work and behave like humans to resolve difficulties and problems by utilizing minimal resources. The Healthcare sector has benefited due to this. Mosquito-transmitted diseases pose a significant health risk. Despite all advances, present strategies for curbing these diseases still depend largely on controlling the mosquito vectors. This strategy demands an army of entomology experts for thorough monitoring, determining, and finally eradicating the targeted mosquito population. Deep learning (DL) algorithms may substitute such unmanageable processes. The current review focuses on how AI, with particular emphasis on deep learning, demonstrates effectiveness in quick detection, identification, monitoring, and finally controlling the target mosquito populations with minimal resources. It accelerates the pace of operation and data exploration on ongoing evolutionary status, tendency to feed blood, and age grading of mosquitoes. The successful combination of computer and biological sciences will provide practical insight and generate a new research niche in this study area.
人工智能（AI）帮助科学家设计出能像人类一样工作和行动的智能机器，以便利用最少的资源解决困难和问题。医疗保健行业因此受益。蚊媒传播疾病对健康构成重大风险。尽管取得了所有进展，但目前控制这些疾病的策略在很大程度上仍依赖于控制蚊虫媒介。这一策略需要大量昆虫学专家进行全面监测、确定并最终根除目标蚊虫种群。深度学习（DL）算法可能会替代这种难以管理的过程。当前的综述重点关注人工智能，尤其是深度学习，如何在以最少资源快速检测、识别、监测并最终控制目标蚊虫种群方面展现出有效性。它加快了对蚊子当前进化状态、吸血倾向和年龄分级的操作和数据探索速度。计算机科学与生物科学的成功结合将为该研究领域提供切实的见解并开创一个新的研究领域。
