Browsing by Author "Abdullah, S. M."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    First report on the occurrence of Microphallus indicus in the Sartoriana spinigera freshwater crab in Bangladesh
    (Паразитология, 2024-09) Wasef, Al; Adhikary, Anup Kumar; Abdullah, S. M.; Mohanta, Uday Kumar
    Sartoriana spinigera is an edible freshwater crab species that plays an important role as an intermediate host of Microphallus indicus and Paragonimus westermani. P. westermani, the lung fluke, has been reported from northeastern India bordering Bangladesh and other parts of India. Nonetheless the existence of P. westermani in Bangladesh is yet to investigate. Therefore, the objective of the study was to estimate the prevalence of lung flukes in Bangladesh. During the survey, the metacercariae of food-borne trematodes were investigated from a total of 261 fresh-water crabs collected from different locations in Bangladesh. Only the metacercariae of M. indicus were recovered. The overall prevalence of M. indicus metacercariae was 26% with intensity of 1–63 per crab. The highest prevalence was recorded from Naogaon district (83.33%), followed by Sirajgonj (57.5%), Rajshahi (40%), Brahmanbaria (16%), Narsingdi (15%), Faridpur (10%), and Sunamgonj (7%). None of the crabs from Rangpur, Thakurgoan, Jashore, Meherpur, Magura, Barguna, Sylhet, Kishorgonj, Cox’sBazar, and Bandarban were infected. Among the water bodies, the highest number of infected crabs was found in the rivers (56%) followed by haor, canal, and ditches (5%). To the best of our knowledge, this study reports M. indicus for the first time from Bangladesh.
  • No Thumbnail Available
    Item
    Pothole detection using lightweight network models
    (BRAC University, 2024-01) Abdullah, S. M.; Hasan, Shakib Al; Parsa, Antara Firoz; Kabbya, MD. Asif Shahidullah; Talukder, Anika Hasan; Noor, Jannatun
    Potholes are defective cavities found on road surfaces. Potholes can lead to serious accidents and vehicle damage if not properly detected. Thus, we are proposing the use of neural network models for pothole classification. The study involves a comprehensive performance analysis of existing lightweight neural network models in pothole classification, compared against the traditional heavyweight models. Lightweight models are emphasized in the thesis due to their low computational requirements, faster prediction times and better compatibility with real-time detection. We have tested six lightweight models (CCT, CNN, INN, Swin Transformer, EANet and ConvMixer) and four heavyweight models (VGG16. ResNet50, DenseNet201 and Xception). A custom dataset of 900 images containing image samples from roads of Dhaka and Bogura was created by the authors to run the models. The dataset was further augmented into 10,000 images by applying various augmentation methods. Separate tests for each model were conducted in the augmented dataset to compare performance against the original dataset. Augmentation enhanced the performance of 9 out of the 10 models. CNN achieved the highest accuracy of 96.55% and the highest F1 score of 0.96 in our testing. Furthermore, CCT exhibited accuracy of 94.6% and F1 score of 0.9. The lightweight models overall performed better than the heavyweight models in both datasets.