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Muhammad Muqsit Islam

Bycvlabadmin

Research Associate | Full Stack Developer | Web Scraping and Automation Expert | Certified DL & ML Specialist

Muhammad Muqsit Islam is a full-stack developer proficient in Web Development, Computer Vision, and Deep Learning. They are skilled in Django, Django Rest Framework, React, Flask, TensorFlow, Docker and other technologies, effectively bridging the gap between frontend and backend development. Beyond coding, Muqsit has a strong foundation in databases, server management, and deployment which make them a valuable asset for overseeing deployment tasks as well.

In their role, they have been intricately involved in the development and management of Early Warning Wildlife Preservation and Forest Fire Detection projects, demonstrating expertise in constructing the backend infrastructure, designing frontend dashboards, crafting APIs, and orchestrating application deployments, alongside ensuring the robust management of servers. A pivotal aspect of their contributions lies in the seamless integration of AI technologies into these systems, serving as the cornerstone of their functionality. Presently, their focus is directed towards enhancing the AI capabilities within the wildlife cam trap project, a venture poised to revolutionize ecosystem monitoring and conservation efforts backed by the World Wildlife Foundation.

They are working on refining the AI by fine-tuning existing models and exploring more efficient architectures to enhance accuracy and speed and trying to leverage pre-trained models and adapting them to the specific requirements of wildlife detection expedites development. Currently, they are exploring the integration of multimodal Large Language Models (LLMs) like CLIP and OWL etc. to detect wildlife using text prompts. This innovative approach leverages both textual and visual information to enhance the accuracy and efficiency of wildlife detection, promising new improvements for conservation efforts.

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Parts of this have been written using generative AI.

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