Rivers are dynamic geomorphological systems experiencing continuous transformation through erosion and sediment transport. In Bangladesh, these processes displace 50,000-200,000 people annually, causing severe losses along the Padma River according to Natural Resources Defense Council (NRDC). Traditional monitoring via field surveys and manual remote sensing proves costly, spatially limited, and unsuitable for multi-year pre-diction. This study presents a comprehensive framework, termed SpaTempNet, for forecasting river morphological evolution from freely available satellite data using spatiotemporal deep learning. A 38-year time-series (1987–2025) of binary water masks was constructed from Landsat (5 TM, 7 ETM+, 8 OLI) and Sentinel (Sentinel-2 MSI, Sentinel-1 SAR) imagery via Google Earth Engine. Water extraction used Modified Normalised Difference Water Index (MNDWI). The proposed Bidirectional ConvLSTM gap-filling model achieved IoU = 0.7366, outperforming classical methods. Five spatiotemporal architectures (ConvLSTM, U-Net+LSTM, Attention U-Net+ConvLSTM, Swin Transformer, ViT-based model) were evaluated across yearly, quarterly, and bi-monthly resolutions. Hybrid CNN-LSTM models consistently outperformed pure transformers. Attention U-Net+ConvLSTM achieved best yearly performance (IoU = 0.7005); U-Net+LSTM led bi-monthly prediction (IoU = 0.7791). Statistical analysis quantified mean annual migration of 255.4 m yr−1 with 1998 extreme of 1,485.5 m yr−1. An expansion of about 290 km2 was projected, and a spatially explicit risk map was generated through long-term forecasting (2026-2040). Results demonstrate that freely available satellite imagery with deep learning can provide practical, scalable framework for riverbank hazard monitoring and disaster management.
S. S. Mahmud Turza, Mustafa Anik, Md. Shadmim Hasan Sifat et al.· International Journal of Adv...· 0 citations
Deep fake technology has significantly advanced the creation of synthetic images and videos, sparking widespread concerns about its potential misuse in spreading misinformation, violating privacy, and enabling identity theft. As these manipulations be-come increasingly sophisticated, the development of reliable detection methods has become a pressing necessity. This research tackles this challenge by proposing a robust deep fake detection pipeline, leveraging a custom dataset created using Roboflow. The dataset is divided into two primary classes: real and fake, with the fake class further categorized into three subtypes based on complexity: easy fake, mid fake, and hard fake. Easy fake images involve basic manipulations that are easily identifiable by the human eye, while mid fake images combine AI-generated and human-generated elements, and hard fake images are entirely AI-generated, posing significant challenges for detection. To ensure authenticity and diversity, real images were collected from personal networks and online repositories. We trained and evaluated four YOLO-based models YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for the detection task. YOLOv8 emerged as the top-performing model, achieving an accuracy of 96.2% in distinguishing between real and fake images. Finally, addressing ethical considerations and developing countermeasures to mitigate the societal impact of deep fakes should remain a priority for future research.
A. Al Noman, Abdullah Al Afiq, Md. Humayun Kabir et al.· Discover Computing· 0 citations
The rapid emergence of multidrug-resistant Klebsiella pneumoniae has significantly reduced the effectiveness of conventional antibiotics, highlighting the need for alternative therapeutic strategies. This study employed a comprehensive in silico pipeline to identify antimicrobial peptides (AMPs) targeting the essential DNA replication initiator protein DnaA. A total of 28,361 peptide sequences were collected from publicly available AMP databases and sequentially filtered based on peptide length, net charge, GRAVY score, instability index, antimicrobial activity, toxicity, hemolytic potential, aggregation propensity, sequence similarity and favorable amphipathic properties. Four peptides satisfied all selection criteria and were subjected to structural prediction, membrane-binding analysis, protein–DNA docking, protein–peptide docking, and Normal Mode Analysis. Protein–DNA docking identified the functional DNA-binding residues of DnaA, while peptide docking demonstrated that all four peptides interacted within this region. Peptide 3 exhibited the strongest predicted interaction, with a binding energy of −61.8±5.1, a buried surface area of 1151.7±30.8 Å2, and seven hydrogen bonds with key DnaA residues. Normal Mode Analysis further supported the structural stability of the peptide–protein complexes. These findings identify four promising AMP candidates targeting DnaA and provide a computational framework for peptide prioritization against multidrug-resistant K. pneumoniae. However, the proposed interactions remain computational predictions and require experimental validation.
Pranab Dev Sharma, A. Al Noman, Md Al Amin Talukder et al.· Bioinformatics and Biology I...· 0 citations
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