Papers by Md Miskat Hossain
Fish-exporting countries must meet international standards and customer expectations to avoid tra... more Fish-exporting countries must meet international standards and customer expectations to avoid trade disruption and economic repercussions. Ensuring the quality of fish products is therefore not just a matter of reputation but also of economic stability. Bangladesh, a major fish exporter, maintaining the quality of exported fish products is crucial. A single faulty product can have serious consequences, potentially causing harm. For this reason, we develop a deep learning-based approach capable of detecting and dividing rotten fish. We have used the Mask R-CNN method for our model. A device captures images of fish eyes and sends them to a computer system. The condition of a fish, whether fresh or rotten, can be determined by its eyes. A collection of 5000 image datasets is developed in this research work. The input images start matching the dataset through the Mask R-CNN and give us the result. Based on the result, if the fish is found fresh, it will proceed through the conveyor system; however, if it is identified as rotten, a robotic arm will separate it. To test its efficiency and reliability, we have tested it with 5000 images. And the test satisfied us with the results of 96.5% accuracy. With the assistance of our model, fish-exporting nations can efficiently distinguish between fresh and rotten fish, enhancing their ability to export more significant quantities of high-quality fish.
Bulletin of Electrical Engineering and Informatics, 2025
Image captioning has emerged as a vital research area in computer vision, aiming to enhance how h... more Image captioning has emerged as a vital research area in computer vision, aiming to enhance how humans interact with visual content. While progress has been made, challenges like improving caption diversity and accuracy remain. This study proposes transfer learning models and RNN algorithms trained on the microsoft common objects in context (MS COCO) dataset to improve image captioning quality. The models combine image and text features, utilizing ResNet50, VGG16, and InceptionV3 with LSTM, and BiLSTM. Performance is measured using metrics such as BLEU, ROUGE, and METEOR for greedy and beam search. The InceptionV3+BiLSTM model outperformed others, achieving a BLEU score of over 60%, a METEOR score of 28.6%, and a ROUGE score of 57.2%. This research contributes to building a simple yet effective image captioning model, providing accurate descriptions with human-like understanding. The error was analyzed to improve results while discussing ongoing research aimed at enhancing the diversity, fluency, and accuracy of generated captions, with significant implications for improving the accessibility and searchability of visual media and informing future research in this area.
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Papers by Md Miskat Hossain