Mosquito-related diseases pose a significant threat to global public health, necessitating efficient and accurate mosquito classification for effective surveillance and control. This work presents an innovative approach to mosquito classification by leveraging state-of-the-art vision transformers and openset learning techniques. A novel framework has been introduced that integrates Transformer-based deep learning models with comprehensive data augmentation and preprocessing methods, enabling robust and precise identification of ten mosquito species. The Swin Transformer model achieves the best performance for traditional closed-set learning with 99.80% accuracy and 0.998 F1 score. The lightweight MobileViT technique attains an almost similar accuracy of 98.90% with significantly reduced parameters and model complexities. Next, the applied deep learning models’ adaptability and generalizability in a static environment have been enhanced by using new classes of data samples during the inference stage that have not been included in the training set. The proposed framework’s ability to handle unseen classes like insects similar to mosquitoes, even humans, through open-set learning further enhances its practical applicability employing the OpenMax technique and Weibull distribution. The traditional CNN model, Xception, outperforms the latest transformer with higher accuracy and F1 score for open-set learning. The study’s findings highlight the transformative potential of advanced deep-learning architectures in entomology, providing a strong groundwork for future research and development in mosquito surveillance and vector control. The implications of this work extend beyond mosquito classification, offering valuable insights for broader ecological and environmental monitoring applications. 

In the course titled Programming Language II (CSE215), I was instructed to make a course Registration and billing system for a private university. Each course has a specific fee depending on its credit and tuition fee per credit. Students can register for the course(s) if that specific course(s) is/are offered in a semester. Students can add or drop a course of their choice on their own. A student with a CGPA ≥ 3.5 can register for a maximum of 18 credits, while for others, the highest limit is 12 credits. Each course has a default seat capacity when it is offered. If the number of students enrolled in that course exceeds that capacity, then the university admin can increase the seating capacity of that course. Once the registration for the courses is completed, the university authority wants to provide a facility to generate a bill for that specific student. As the university is in its earliest stages of development, it charges some extra fees for its development purposes. They also charge BD Tax @15% of the total amount during the Summer semester and Development Fee @10% of the total amount for the other semesters. The university authorities also want to have the feature of imposing extra fees (Development Fee or Govt. Tax) on students’ bills each semester. The university not only charges extra fees on students’ bills but also gives some discounts based on Academic Excellence, Freedom Fighters, and Minority Groups. So, the students’ billing system software must incorporate the above criteria (Extra Fee and Discount) when generating a bill for a student.