I am a highly skilled Fisheries Expert with a Fisheries from Jagannath University and a proven track record at Jagannath University. In my current role, I lead impactful research initiatives, collaborating with interdisciplinary teams to implement sustainable fisheries management plans. With expertise in fisheries biology, environmental impact assessment, and stakeholder engagement, I am passionate about contributing to Jagannath University's commitment to academic excellence and research. My dedication to sustainable practices aligns seamlessly with the institution's values, making me an ideal candidate for the Fisher
Working as a Research Assistant to the ‘DNA Barcoding of Marine water Fishes of Bangladesh’ project funded by the grants for Advanced Research in Education (Grants , Ministry of Education, Government of the People’s Republic of Bangladesh from October 2017 to 2019 at the Advanced Fisheries and DNA Barcoding Lab, Department of Zoology, University of Dhaka.
EDUCATION
M.Sc. in Zoology (Specialization in Fisheries) from Jagannath University, Dhaka
B.Sc. in Zoology from Jagannath University, Dhaka
HSC in Science from Ullapara Science College
SSC in Science from Dhunat N.U. Pilot High School
RESEARCH, TEACHING, or OTHER INTERESTS
Animal Science and Zoology, Aquatic Science, Molecular Biology, Computer Engineering
5
Scopus Publications
Scopus Publications
FetalDenseNet: multi-scale deep learning for enhanced early detection of fetal anatomical planes in prenatal ultrasound Samrat Kumar Dey, Arpita Howlader, Md. Shabukta Haider, Tonmoy Saha, Deblina Mazumder Setu, Tania Islam, Umme Raihan Siddiqi, Md. Mahbubur Rahman Journal of Perinatal Medicine, 2025 Objectives The study aims to improve the classification of fetal anatomical planes using Deep Learning (DL) methods to enhance the accuracy of fetal ultrasound interpretation. Methods Five Convolutional Neural Network (CNN) architectures, such as VGG16, ResNet50, InceptionV3, DenseNet169, and MobileNetV2, are evaluated on a large-scale, clinically validated dataset of 12,400 ultrasound images from 1,792 patients. Preprocessing methods, including scaling, normalization, label encoding, and augmentation, are applied to the dataset, and the dataset is split into 80 % for training and 20 % for testing. Each model was fine-tuned and evaluated based on its classification accuracy for comparison. Results DenseNet169 achieved the highest classification accuracy of 92 % among all the tested models. Conclusions The study shows that CNN-based models, particularly DenseNet169, significantly improve diagnostic accuracy in fetal ultrasound interpretation. This advancement reduces error rates and provides support for clinical decision-making in prenatal care.
Characterization of spiny lobsters from Bangladesh waters using morphology, COI and 16S rRNA sequences Md. Sagir Ahmed, Anindita Barua, Sujan Kumar Datta, Tonmoy Saha, Durjoy Raha Antu, Sumaiya Ahmed Heliyon, 2022 This study aims to taxonomically identify and characterise the phylogenetic relationships of spiny lobsters based on mitochondrial cytochrome c oxidase I (COI) and 16S rRNA genes from Bangladesh waters. A total of 19 barcode sequences (10 partial COI sequences and 9 partial 16S rRNA) were successfully generated from 12 collected spiny lobster samples representing four species belonging to the family Palinuridae. The average genetic distances within and between species were 0.834 ± 0.427 and 17.810 ± 0.830, respectively, in COI and 0.107 ± 0.255 and 8.401 ± 2.547, respectively, in 16S rRNA genes. The successful amplification rate of 16S rRNA was higher than that of the COI marker. In the maximum likelihood (ML) tree, the sequences of the same species were clustered together under a single clade for both COI and 16S rRNA, which supports the efficacy of both marker genes in differentiating lobster species.
Molecular characterization of marine and coastal fishes of Bangladesh through DNA barcodes Md. Sagir Ahmed, Sujan Kumar Datta, Tonmoy Saha, Zarif Hossain Ecology and Evolution, 2021 This study describes the molecular characterization of marine and coastal fishes of Bangladesh based on the mitochondrial cytochrome c oxidase subunit I (COI) gene as a marker. A total of 376 mitochondrial COI barcode sequences were obtained from 185 species belonging to 146 genera, 74 families, 21 orders, and two classes of fishes. The mean length of the sequences was 652 base pairs. In Elasmobranchii (Sharks and rays), the average Kimura two parameter (K2P) distances within species, genera, families, and orders were 1.20%, 6.07%, 11.08%, and 14.68%, respectively, and for Actinopterygii, the average K2P distances within species, genera, families, and orders were 0.40%, 6.36%, 14.10%, and 24.07%, respectively. The mean interspecies distance was 16‐fold higher than the mean intraspecies distance. The K2P neighbor‐joining (NJ) trees based on the sequences generally clustered species in accordance with their taxonomic position. A total of 21 species were newly recorded in Bangladesh. High efficiency and fidelity in species identification and discrimination were demonstrated in the present study by DNA barcoding, and we conclude that COI sequencing can be used as an authentic identification marker for Bangladesh marine fish species.
